chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
+135
@@ -0,0 +1,135 @@
|
||||
/*
|
||||
**
|
||||
** License Agreement
|
||||
** For chi_table.h
|
||||
**
|
||||
** Copyright (C) 2007 Per-Erik Forssen, all rights reserved.
|
||||
**
|
||||
** Redistribution and use in source and binary forms, with or without modification,
|
||||
** are permitted provided that the following conditions are met:
|
||||
**
|
||||
** * Redistribution's of source code must retain the above copyright notice,
|
||||
** this list of conditions and the following disclaimer.
|
||||
**
|
||||
** * Redistribution's in binary form must reproduce the above copyright notice,
|
||||
** this list of conditions and the following disclaimer in the documentation
|
||||
** and/or other materials provided with the distribution.
|
||||
**
|
||||
** * The name of the copyright holders may not be used to endorse or promote products
|
||||
** derived from this software without specific prior written permission.
|
||||
**
|
||||
** This software is provided by the copyright holders and contributors "as is" and
|
||||
** any express or implied warranties, including, but not limited to, the implied
|
||||
** warranties of merchantability and fitness for a particular purpose are disclaimed.
|
||||
** In no event shall the Intel Corporation or contributors be liable for any direct,
|
||||
** indirect, incidental, special, exemplary, or consequential damages
|
||||
** (including, but not limited to, procurement of substitute goods or services;
|
||||
** loss of use, data, or profits; or business interruption) however caused
|
||||
** and on any theory of liability, whether in contract, strict liability,
|
||||
** or tort (including negligence or otherwise) arising in any way out of
|
||||
** the use of this software, even if advised of the possibility of such damage.
|
||||
**
|
||||
** Content origin: http://users.isy.liu.se/cvl/perfo/software/chi_table.h
|
||||
*/
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||||
#define TABLE_SIZE 400
|
||||
|
||||
static double chitab3[]={0, 0.0150057, 0.0239478, 0.0315227,
|
||||
0.0383427, 0.0446605, 0.0506115, 0.0562786,
|
||||
0.0617174, 0.0669672, 0.0720573, 0.0770099,
|
||||
0.081843, 0.0865705, 0.0912043, 0.0957541,
|
||||
0.100228, 0.104633, 0.108976, 0.113261,
|
||||
0.117493, 0.121676, 0.125814, 0.12991,
|
||||
0.133967, 0.137987, 0.141974, 0.145929,
|
||||
0.149853, 0.15375, 0.15762, 0.161466,
|
||||
0.165287, 0.169087, 0.172866, 0.176625,
|
||||
0.180365, 0.184088, 0.187794, 0.191483,
|
||||
0.195158, 0.198819, 0.202466, 0.2061,
|
||||
0.209722, 0.213332, 0.216932, 0.220521,
|
||||
0.2241, 0.22767, 0.231231, 0.234783,
|
||||
0.238328, 0.241865, 0.245395, 0.248918,
|
||||
0.252435, 0.255947, 0.259452, 0.262952,
|
||||
0.266448, 0.269939, 0.273425, 0.276908,
|
||||
0.280386, 0.283862, 0.287334, 0.290803,
|
||||
0.29427, 0.297734, 0.301197, 0.304657,
|
||||
0.308115, 0.311573, 0.315028, 0.318483,
|
||||
0.321937, 0.32539, 0.328843, 0.332296,
|
||||
0.335749, 0.339201, 0.342654, 0.346108,
|
||||
0.349562, 0.353017, 0.356473, 0.35993,
|
||||
0.363389, 0.366849, 0.37031, 0.373774,
|
||||
0.377239, 0.380706, 0.384176, 0.387648,
|
||||
0.391123, 0.3946, 0.39808, 0.401563,
|
||||
0.405049, 0.408539, 0.412032, 0.415528,
|
||||
0.419028, 0.422531, 0.426039, 0.429551,
|
||||
0.433066, 0.436586, 0.440111, 0.44364,
|
||||
0.447173, 0.450712, 0.454255, 0.457803,
|
||||
0.461356, 0.464915, 0.468479, 0.472049,
|
||||
0.475624, 0.479205, 0.482792, 0.486384,
|
||||
0.489983, 0.493588, 0.4972, 0.500818,
|
||||
0.504442, 0.508073, 0.511711, 0.515356,
|
||||
0.519008, 0.522667, 0.526334, 0.530008,
|
||||
0.533689, 0.537378, 0.541075, 0.54478,
|
||||
0.548492, 0.552213, 0.555942, 0.55968,
|
||||
0.563425, 0.56718, 0.570943, 0.574715,
|
||||
0.578497, 0.582287, 0.586086, 0.589895,
|
||||
0.593713, 0.597541, 0.601379, 0.605227,
|
||||
0.609084, 0.612952, 0.61683, 0.620718,
|
||||
0.624617, 0.628526, 0.632447, 0.636378,
|
||||
0.64032, 0.644274, 0.648239, 0.652215,
|
||||
0.656203, 0.660203, 0.664215, 0.668238,
|
||||
0.672274, 0.676323, 0.680384, 0.684457,
|
||||
0.688543, 0.692643, 0.696755, 0.700881,
|
||||
0.70502, 0.709172, 0.713339, 0.717519,
|
||||
0.721714, 0.725922, 0.730145, 0.734383,
|
||||
0.738636, 0.742903, 0.747185, 0.751483,
|
||||
0.755796, 0.760125, 0.76447, 0.768831,
|
||||
0.773208, 0.777601, 0.782011, 0.786438,
|
||||
0.790882, 0.795343, 0.799821, 0.804318,
|
||||
0.808831, 0.813363, 0.817913, 0.822482,
|
||||
0.827069, 0.831676, 0.836301, 0.840946,
|
||||
0.84561, 0.850295, 0.854999, 0.859724,
|
||||
0.864469, 0.869235, 0.874022, 0.878831,
|
||||
0.883661, 0.888513, 0.893387, 0.898284,
|
||||
0.903204, 0.908146, 0.913112, 0.918101,
|
||||
0.923114, 0.928152, 0.933214, 0.938301,
|
||||
0.943413, 0.94855, 0.953713, 0.958903,
|
||||
0.964119, 0.969361, 0.974631, 0.979929,
|
||||
0.985254, 0.990608, 0.99599, 1.0014,
|
||||
1.00684, 1.01231, 1.01781, 1.02335,
|
||||
1.02891, 1.0345, 1.04013, 1.04579,
|
||||
1.05148, 1.05721, 1.06296, 1.06876,
|
||||
1.07459, 1.08045, 1.08635, 1.09228,
|
||||
1.09826, 1.10427, 1.11032, 1.1164,
|
||||
1.12253, 1.1287, 1.1349, 1.14115,
|
||||
1.14744, 1.15377, 1.16015, 1.16656,
|
||||
1.17303, 1.17954, 1.18609, 1.19269,
|
||||
1.19934, 1.20603, 1.21278, 1.21958,
|
||||
1.22642, 1.23332, 1.24027, 1.24727,
|
||||
1.25433, 1.26144, 1.26861, 1.27584,
|
||||
1.28312, 1.29047, 1.29787, 1.30534,
|
||||
1.31287, 1.32046, 1.32812, 1.33585,
|
||||
1.34364, 1.3515, 1.35943, 1.36744,
|
||||
1.37551, 1.38367, 1.39189, 1.4002,
|
||||
1.40859, 1.41705, 1.42561, 1.43424,
|
||||
1.44296, 1.45177, 1.46068, 1.46967,
|
||||
1.47876, 1.48795, 1.49723, 1.50662,
|
||||
1.51611, 1.52571, 1.53541, 1.54523,
|
||||
1.55517, 1.56522, 1.57539, 1.58568,
|
||||
1.59611, 1.60666, 1.61735, 1.62817,
|
||||
1.63914, 1.65025, 1.66152, 1.67293,
|
||||
1.68451, 1.69625, 1.70815, 1.72023,
|
||||
1.73249, 1.74494, 1.75757, 1.77041,
|
||||
1.78344, 1.79669, 1.81016, 1.82385,
|
||||
1.83777, 1.85194, 1.86635, 1.88103,
|
||||
1.89598, 1.91121, 1.92674, 1.94257,
|
||||
1.95871, 1.97519, 1.99201, 2.0092,
|
||||
2.02676, 2.04471, 2.06309, 2.08189,
|
||||
2.10115, 2.12089, 2.14114, 2.16192,
|
||||
2.18326, 2.2052, 2.22777, 2.25101,
|
||||
2.27496, 2.29966, 2.32518, 2.35156,
|
||||
2.37886, 2.40717, 2.43655, 2.46709,
|
||||
2.49889, 2.53206, 2.56673, 2.60305,
|
||||
2.64117, 2.6813, 2.72367, 2.76854,
|
||||
2.81623, 2.86714, 2.92173, 2.98059,
|
||||
3.04446, 3.1143, 3.19135, 3.27731,
|
||||
3.37455, 3.48653, 3.61862, 3.77982,
|
||||
3.98692, 4.2776, 4.77167, 133.333 };
|
||||
@@ -0,0 +1,28 @@
|
||||
License Agreement
|
||||
For chi_table.h
|
||||
|
||||
Copyright (C) 2007 Per-Erik Forssen, all rights reserved.
|
||||
|
||||
Redistribution and use in source and binary forms, with or without modification,
|
||||
are permitted provided that the following conditions are met:
|
||||
|
||||
* Redistribution's of source code must retain the above copyright notice,
|
||||
this list of conditions and the following disclaimer.
|
||||
|
||||
* Redistribution's in binary form must reproduce the above copyright notice,
|
||||
this list of conditions and the following disclaimer in the documentation
|
||||
and/or other materials provided with the distribution.
|
||||
|
||||
* The name of the copyright holders may not be used to endorse or promote products
|
||||
derived from this software without specific prior written permission.
|
||||
|
||||
This software is provided by the copyright holders and contributors "as is" and
|
||||
any express or implied warranties, including, but not limited to, the implied
|
||||
warranties of merchantability and fitness for a particular purpose are disclaimed.
|
||||
In no event shall the Intel Corporation or contributors be liable for any direct,
|
||||
indirect, incidental, special, exemplary, or consequential damages
|
||||
(including, but not limited to, procurement of substitute goods or services;
|
||||
loss of use, data, or profits; or business interruption) however caused
|
||||
and on any theory of liability, whether in contract, strict liability,
|
||||
or tort (including negligence or otherwise) arising in any way out of
|
||||
the use of this software, even if advised of the possibility of such damage.
|
||||
@@ -0,0 +1,12 @@
|
||||
set(the_description "2D Features Framework")
|
||||
|
||||
ocv_add_dispatched_file(sift SSE4_1 AVX2 AVX512_SKX)
|
||||
|
||||
set(debug_modules "")
|
||||
if(DEBUG_opencv_features2d)
|
||||
list(APPEND debug_modules opencv_highgui)
|
||||
endif()
|
||||
ocv_define_module(features2d opencv_imgproc ${debug_modules} OPTIONAL opencv_flann WRAP java objc python js)
|
||||
|
||||
ocv_install_3rdparty_licenses(mscr "${CMAKE_CURRENT_SOURCE_DIR}/3rdparty/mscr/chi_table_LICENSE.txt")
|
||||
ocv_install_3rdparty_licenses(features2d "${CMAKE_CURRENT_SOURCE_DIR}/src/kaze/LICENSE.KAZE" "${CMAKE_CURRENT_SOURCE_DIR}/src/kaze/LICENSE.AKAZE")
|
||||
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,284 @@
|
||||
#!/usr/bin/perl
|
||||
use strict;
|
||||
use warnings;
|
||||
use autodie; # die if problem reading or writing a file
|
||||
|
||||
my $filein = "./agast.txt";
|
||||
my $fileout = "./agast_new.txt";
|
||||
my $i1=1;
|
||||
my $i2=1;
|
||||
my $i3=1;
|
||||
my $tmp;
|
||||
my $ifcount0=0;
|
||||
my $ifcount1=0;
|
||||
my $ifcount2=0;
|
||||
my $ifcount3=0;
|
||||
my $ifcount4=0;
|
||||
my $elsecount;
|
||||
my $myfirstline = $ARGV[0];
|
||||
my $mylastline = $ARGV[1];
|
||||
my $tablename = $ARGV[2];
|
||||
my @array0 = ();
|
||||
my @array1 = ();
|
||||
my @array2 = ();
|
||||
my @array3 = ();
|
||||
my $homogeneous;
|
||||
my $success_homogeneous;
|
||||
my $structured;
|
||||
my $success_structured;
|
||||
|
||||
open(my $in1, "<", $filein) or die "Can't open $filein: $!";
|
||||
open(my $out, ">", $fileout) or die "Can't open $fileout: $!";
|
||||
|
||||
|
||||
$array0[0] = 0;
|
||||
$i1=1;
|
||||
while (my $line1 = <$in1>)
|
||||
{
|
||||
chomp $line1;
|
||||
$array0[$i1] = 0;
|
||||
if (($i1>=$myfirstline)&&($i1<=$mylastline))
|
||||
{
|
||||
if($line1=~/if\(ptr\[offset(\d+)/)
|
||||
{
|
||||
if($line1=~/if\(ptr\[offset(\d+).*\>.*cb/)
|
||||
{
|
||||
$tmp=$1;
|
||||
}
|
||||
else
|
||||
{
|
||||
if($line1=~/if\(ptr\[offset(\d+).*\<.*c\_b/)
|
||||
{
|
||||
$tmp=$1+128;
|
||||
}
|
||||
else
|
||||
{
|
||||
die "invalid array index!"
|
||||
}
|
||||
}
|
||||
$array1[$ifcount1] = $tmp;
|
||||
$array0[$ifcount1] = $i1;
|
||||
$ifcount1++;
|
||||
}
|
||||
else
|
||||
{
|
||||
}
|
||||
}
|
||||
$i1++;
|
||||
}
|
||||
$homogeneous=$ifcount1;
|
||||
$success_homogeneous=$ifcount1+1;
|
||||
$structured=$ifcount1+2;
|
||||
$success_structured=$ifcount1+3;
|
||||
|
||||
close $in1 or die "Can't close $filein: $!";
|
||||
|
||||
open($in1, "<", $filein) or die "Can't open $filein: $!";
|
||||
|
||||
|
||||
$i1=1;
|
||||
while (my $line1 = <$in1>)
|
||||
{
|
||||
chomp $line1;
|
||||
if (($i1>=$myfirstline)&&($i1<=$mylastline))
|
||||
{
|
||||
if ($array0[$ifcount2] == $i1)
|
||||
{
|
||||
$array2[$ifcount2]=0;
|
||||
$array3[$ifcount2]=0;
|
||||
if ($array0[$ifcount2+1] == ($i1+1))
|
||||
{
|
||||
$array2[$ifcount2]=($ifcount2+1);
|
||||
}
|
||||
else
|
||||
{
|
||||
open(my $in2, "<", $filein) or die "Can't open $filein: $!";
|
||||
$i2=1;
|
||||
while (my $line2 = <$in2>)
|
||||
{
|
||||
chomp $line2;
|
||||
if ($i2 == $i1)
|
||||
{
|
||||
last;
|
||||
}
|
||||
$i2++;
|
||||
}
|
||||
my $line2 = <$in2>;
|
||||
chomp $line2;
|
||||
if ($line2=~/goto (\w+)/)
|
||||
{
|
||||
$tmp=$1;
|
||||
if ($tmp eq "homogeneous")
|
||||
{
|
||||
$array2[$ifcount2]=$homogeneous;
|
||||
}
|
||||
if ($tmp eq "success_homogeneous")
|
||||
{
|
||||
$array2[$ifcount2]=$success_homogeneous;
|
||||
}
|
||||
if ($tmp eq "structured")
|
||||
{
|
||||
$array2[$ifcount2]=$structured;
|
||||
}
|
||||
if ($tmp eq "success_structured")
|
||||
{
|
||||
$array2[$ifcount2]=$success_structured;
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
die "goto expected: $!";
|
||||
}
|
||||
close $in2 or die "Can't close $filein: $!";
|
||||
}
|
||||
#find next else and interpret it
|
||||
open(my $in3, "<", $filein) or die "Can't open $filein: $!";
|
||||
$i3=1;
|
||||
$ifcount3=0;
|
||||
$elsecount=0;
|
||||
while (my $line3 = <$in3>)
|
||||
{
|
||||
chomp $line3;
|
||||
$i3++;
|
||||
if ($i3 == $i1)
|
||||
{
|
||||
last;
|
||||
}
|
||||
}
|
||||
while (my $line3 = <$in3>)
|
||||
{
|
||||
chomp $line3;
|
||||
$ifcount3++;
|
||||
if (($elsecount==0)&&($i3>$i1))
|
||||
{
|
||||
if ($line3=~/goto (\w+)/)
|
||||
{
|
||||
$tmp=$1;
|
||||
if ($tmp eq "homogeneous")
|
||||
{
|
||||
$array3[$ifcount2]=$homogeneous;
|
||||
}
|
||||
if ($tmp eq "success_homogeneous")
|
||||
{
|
||||
$array3[$ifcount2]=$success_homogeneous;
|
||||
}
|
||||
if ($tmp eq "structured")
|
||||
{
|
||||
$array3[$ifcount2]=$structured;
|
||||
}
|
||||
if ($tmp eq "success_structured")
|
||||
{
|
||||
$array3[$ifcount2]=$success_structured;
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
if ($line3=~/if\(ptr\[offset/)
|
||||
{
|
||||
$ifcount4=0;
|
||||
while ($array0[$ifcount4]!=$i3)
|
||||
{
|
||||
$ifcount4++;
|
||||
if ($ifcount4==$ifcount1)
|
||||
{
|
||||
die "if else match expected: $!";
|
||||
}
|
||||
$array3[$ifcount2]=$ifcount4;
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
die "elseif or elsegoto match expected: $!";
|
||||
}
|
||||
}
|
||||
last;
|
||||
}
|
||||
else
|
||||
{
|
||||
if ($line3=~/if\(ptr\[offset/)
|
||||
{
|
||||
$elsecount++;
|
||||
}
|
||||
else
|
||||
{
|
||||
if ($line3=~/else/)
|
||||
{
|
||||
$elsecount--;
|
||||
}
|
||||
}
|
||||
}
|
||||
$i3++;
|
||||
}
|
||||
printf("%3d [%3d][0x%08x]\n", $array0[$ifcount2], $ifcount2, (($array1[$ifcount2]&15)<<28)|($array2[$ifcount2]<<16)|(($array1[$ifcount2]&128)<<5)|($array3[$ifcount2]));
|
||||
close $in3 or die "Can't close $filein: $!";
|
||||
$ifcount2++;
|
||||
}
|
||||
else
|
||||
{
|
||||
}
|
||||
}
|
||||
$i1++;
|
||||
}
|
||||
|
||||
printf(" [%3d][0x%08x]\n", $homogeneous, 252);
|
||||
printf(" [%3d][0x%08x]\n", $success_homogeneous, 253);
|
||||
printf(" [%3d][0x%08x]\n", $structured, 254);
|
||||
printf(" [%3d][0x%08x]\n", $success_structured, 255);
|
||||
|
||||
close $in1 or die "Can't close $filein: $!";
|
||||
|
||||
$ifcount0=0;
|
||||
$ifcount2=0;
|
||||
printf $out " static const unsigned long %s[] = {\n ", $tablename;
|
||||
while ($ifcount0 < $ifcount1)
|
||||
{
|
||||
printf $out "0x%08x, ", (($array1[$ifcount0]&15)<<28)|($array2[$ifcount0]<<16)|(($array1[$ifcount0]&128)<<5)|($array3[$ifcount0]);
|
||||
|
||||
$ifcount0++;
|
||||
$ifcount2++;
|
||||
if ($ifcount2==8)
|
||||
{
|
||||
$ifcount2=0;
|
||||
printf $out "\n";
|
||||
printf $out " ";
|
||||
}
|
||||
|
||||
}
|
||||
printf $out "0x%08x, ", 252;
|
||||
$ifcount0++;
|
||||
$ifcount2++;
|
||||
if ($ifcount2==8)
|
||||
{
|
||||
$ifcount2=0;
|
||||
printf $out "\n";
|
||||
printf $out " ";
|
||||
}
|
||||
printf $out "0x%08x, ", 253;
|
||||
$ifcount0++;
|
||||
$ifcount2++;
|
||||
if ($ifcount2==8)
|
||||
{
|
||||
$ifcount2=0;
|
||||
printf $out "\n";
|
||||
printf $out " ";
|
||||
}
|
||||
printf $out "0x%08x, ", 254;
|
||||
$ifcount0++;
|
||||
$ifcount2++;
|
||||
if ($ifcount2==8)
|
||||
{
|
||||
$ifcount2=0;
|
||||
printf $out "\n";
|
||||
printf $out " ";
|
||||
}
|
||||
printf $out "0x%08x\n", 255;
|
||||
$ifcount0++;
|
||||
$ifcount2++;
|
||||
printf $out " };\n\n";
|
||||
|
||||
$#array0 = -1;
|
||||
$#array1 = -1;
|
||||
$#array2 = -1;
|
||||
$#array3 = -1;
|
||||
|
||||
close $out or die "Can't close $fileout: $!";
|
||||
@@ -0,0 +1,244 @@
|
||||
#!/usr/bin/perl
|
||||
use strict;
|
||||
use warnings;
|
||||
use autodie; # die if problem reading or writing a file
|
||||
|
||||
my $filein = "./agast_score.txt";
|
||||
my $fileout = "./agast_new.txt";
|
||||
my $i1=1;
|
||||
my $i2=1;
|
||||
my $i3=1;
|
||||
my $tmp;
|
||||
my $ifcount0=0;
|
||||
my $ifcount1=0;
|
||||
my $ifcount2=0;
|
||||
my $ifcount3=0;
|
||||
my $ifcount4=0;
|
||||
my $elsecount;
|
||||
my $myfirstline = $ARGV[0];
|
||||
my $mylastline = $ARGV[1];
|
||||
my $tablename = $ARGV[2];
|
||||
my @array0 = ();
|
||||
my @array1 = ();
|
||||
my @array2 = ();
|
||||
my @array3 = ();
|
||||
my $is_not_a_corner;
|
||||
my $is_a_corner;
|
||||
|
||||
open(my $in1, "<", $filein) or die "Can't open $filein: $!";
|
||||
open(my $out, ">", $fileout) or die "Can't open $fileout: $!";
|
||||
|
||||
|
||||
$array0[0] = 0;
|
||||
$i1=1;
|
||||
while (my $line1 = <$in1>)
|
||||
{
|
||||
chomp $line1;
|
||||
$array0[$i1] = 0;
|
||||
if (($i1>=$myfirstline)&&($i1<=$mylastline))
|
||||
{
|
||||
if($line1=~/if\(ptr\[offset(\d+)/)
|
||||
{
|
||||
if($line1=~/if\(ptr\[offset(\d+).*\>.*cb/)
|
||||
{
|
||||
$tmp=$1;
|
||||
}
|
||||
else
|
||||
{
|
||||
if($line1=~/if\(ptr\[offset(\d+).*\<.*c\_b/)
|
||||
{
|
||||
$tmp=$1+128;
|
||||
}
|
||||
else
|
||||
{
|
||||
die "invalid array index!"
|
||||
}
|
||||
}
|
||||
$array1[$ifcount1] = $tmp;
|
||||
$array0[$ifcount1] = $i1;
|
||||
$ifcount1++;
|
||||
}
|
||||
else
|
||||
{
|
||||
}
|
||||
}
|
||||
$i1++;
|
||||
}
|
||||
$is_not_a_corner=$ifcount1;
|
||||
$is_a_corner=$ifcount1+1;
|
||||
|
||||
close $in1 or die "Can't close $filein: $!";
|
||||
|
||||
open($in1, "<", $filein) or die "Can't open $filein: $!";
|
||||
|
||||
|
||||
$i1=1;
|
||||
while (my $line1 = <$in1>)
|
||||
{
|
||||
chomp $line1;
|
||||
if (($i1>=$myfirstline)&&($i1<=$mylastline))
|
||||
{
|
||||
if ($array0[$ifcount2] == $i1)
|
||||
{
|
||||
$array2[$ifcount2]=0;
|
||||
$array3[$ifcount2]=0;
|
||||
if ($array0[$ifcount2+1] == ($i1+1))
|
||||
{
|
||||
$array2[$ifcount2]=($ifcount2+1);
|
||||
}
|
||||
else
|
||||
{
|
||||
open(my $in2, "<", $filein) or die "Can't open $filein: $!";
|
||||
$i2=1;
|
||||
while (my $line2 = <$in2>)
|
||||
{
|
||||
chomp $line2;
|
||||
if ($i2 == $i1)
|
||||
{
|
||||
last;
|
||||
}
|
||||
$i2++;
|
||||
}
|
||||
my $line2 = <$in2>;
|
||||
chomp $line2;
|
||||
if ($line2=~/goto (\w+)/)
|
||||
{
|
||||
$tmp=$1;
|
||||
if ($tmp eq "is_not_a_corner")
|
||||
{
|
||||
$array2[$ifcount2]=$is_not_a_corner;
|
||||
}
|
||||
if ($tmp eq "is_a_corner")
|
||||
{
|
||||
$array2[$ifcount2]=$is_a_corner;
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
die "goto expected: $!";
|
||||
}
|
||||
close $in2 or die "Can't close $filein: $!";
|
||||
}
|
||||
#find next else and interpret it
|
||||
open(my $in3, "<", $filein) or die "Can't open $filein: $!";
|
||||
$i3=1;
|
||||
$ifcount3=0;
|
||||
$elsecount=0;
|
||||
while (my $line3 = <$in3>)
|
||||
{
|
||||
chomp $line3;
|
||||
$i3++;
|
||||
if ($i3 == $i1)
|
||||
{
|
||||
last;
|
||||
}
|
||||
}
|
||||
while (my $line3 = <$in3>)
|
||||
{
|
||||
chomp $line3;
|
||||
$ifcount3++;
|
||||
if (($elsecount==0)&&($i3>$i1))
|
||||
{
|
||||
if ($line3=~/goto (\w+)/)
|
||||
{
|
||||
$tmp=$1;
|
||||
if ($tmp eq "is_not_a_corner")
|
||||
{
|
||||
$array3[$ifcount2]=$is_not_a_corner;
|
||||
}
|
||||
if ($tmp eq "is_a_corner")
|
||||
{
|
||||
$array3[$ifcount2]=$is_a_corner;
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
if ($line3=~/if\(ptr\[offset/)
|
||||
{
|
||||
$ifcount4=0;
|
||||
while ($array0[$ifcount4]!=$i3)
|
||||
{
|
||||
$ifcount4++;
|
||||
if ($ifcount4==$ifcount1)
|
||||
{
|
||||
die "if else match expected: $!";
|
||||
}
|
||||
$array3[$ifcount2]=$ifcount4;
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
die "elseif or elsegoto match expected: $!";
|
||||
}
|
||||
}
|
||||
last;
|
||||
}
|
||||
else
|
||||
{
|
||||
if ($line3=~/if\(ptr\[offset/)
|
||||
{
|
||||
$elsecount++;
|
||||
}
|
||||
else
|
||||
{
|
||||
if ($line3=~/else/)
|
||||
{
|
||||
$elsecount--;
|
||||
}
|
||||
}
|
||||
}
|
||||
$i3++;
|
||||
}
|
||||
printf("%3d [%3d][0x%08x]\n", $array0[$ifcount2], $ifcount2, (($array1[$ifcount2]&15)<<28)|($array2[$ifcount2]<<16)|(($array1[$ifcount2]&128)<<5)|($array3[$ifcount2]));
|
||||
close $in3 or die "Can't close $filein: $!";
|
||||
$ifcount2++;
|
||||
}
|
||||
else
|
||||
{
|
||||
}
|
||||
}
|
||||
$i1++;
|
||||
}
|
||||
|
||||
printf(" [%3d][0x%08x]\n", $is_not_a_corner, 254);
|
||||
printf(" [%3d][0x%08x]\n", $is_a_corner, 255);
|
||||
|
||||
close $in1 or die "Can't close $filein: $!";
|
||||
|
||||
$ifcount0=0;
|
||||
$ifcount2=0;
|
||||
printf $out " static const unsigned long %s[] = {\n ", $tablename;
|
||||
while ($ifcount0 < $ifcount1)
|
||||
{
|
||||
printf $out "0x%08x, ", (($array1[$ifcount0]&15)<<28)|($array2[$ifcount0]<<16)|(($array1[$ifcount0]&128)<<5)|($array3[$ifcount0]);
|
||||
|
||||
$ifcount0++;
|
||||
$ifcount2++;
|
||||
if ($ifcount2==8)
|
||||
{
|
||||
$ifcount2=0;
|
||||
printf $out "\n";
|
||||
printf $out " ";
|
||||
}
|
||||
|
||||
}
|
||||
printf $out "0x%08x, ", 254;
|
||||
$ifcount0++;
|
||||
$ifcount2++;
|
||||
if ($ifcount2==8)
|
||||
{
|
||||
$ifcount2=0;
|
||||
printf $out "\n";
|
||||
printf $out " ";
|
||||
}
|
||||
printf $out "0x%08x\n", 255;
|
||||
$ifcount0++;
|
||||
$ifcount2++;
|
||||
printf $out " };\n\n";
|
||||
|
||||
$#array0 = -1;
|
||||
$#array1 = -1;
|
||||
$#array2 = -1;
|
||||
$#array3 = -1;
|
||||
|
||||
close $out or die "Can't close $fileout: $!";
|
||||
@@ -0,0 +1,32 @@
|
||||
perl read_file_score32.pl 9059 9385 table_5_8_corner_struct
|
||||
move agast_new.txt agast_score_table.txt
|
||||
perl read_file_score32.pl 2215 3387 table_7_12d_corner_struct
|
||||
copy /A agast_score_table.txt + agast_new.txt agast_score_table.txt
|
||||
del agast_new.txt
|
||||
perl read_file_score32.pl 3428 9022 table_7_12s_corner_struct
|
||||
copy /A agast_score_table.txt + agast_new.txt agast_score_table.txt
|
||||
del agast_new.txt
|
||||
perl read_file_score32.pl 118 2174 table_9_16_corner_struct
|
||||
copy /A agast_score_table.txt + agast_new.txt agast_score_table.txt
|
||||
del agast_new.txt
|
||||
|
||||
perl read_file_nondiff32.pl 103 430 table_5_8_struct1
|
||||
move agast_new.txt agast_table.txt
|
||||
perl read_file_nondiff32.pl 440 779 table_5_8_struct2
|
||||
copy /A agast_table.txt + agast_new.txt agast_table.txt
|
||||
del agast_new.txt
|
||||
perl read_file_nondiff32.pl 869 2042 table_7_12d_struct1
|
||||
copy /A agast_table.txt + agast_new.txt agast_table.txt
|
||||
del agast_new.txt
|
||||
perl read_file_nondiff32.pl 2052 3225 table_7_12d_struct2
|
||||
copy /A agast_table.txt + agast_new.txt agast_table.txt
|
||||
del agast_new.txt
|
||||
perl read_file_nondiff32.pl 3315 4344 table_7_12s_struct1
|
||||
copy /A agast_table.txt + agast_new.txt agast_table.txt
|
||||
del agast_new.txt
|
||||
perl read_file_nondiff32.pl 4354 5308 table_7_12s_struct2
|
||||
copy /A agast_table.txt + agast_new.txt agast_table.txt
|
||||
del agast_new.txt
|
||||
perl read_file_nondiff32.pl 5400 7454 table_9_16_struct
|
||||
copy /A agast_table.txt + agast_new.txt agast_table.txt
|
||||
del agast_new.txt
|
||||
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,48 @@
|
||||
/*M///////////////////////////////////////////////////////////////////////////////////////
|
||||
//
|
||||
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
|
||||
//
|
||||
// By downloading, copying, installing or using the software you agree to this license.
|
||||
// If you do not agree to this license, do not download, install,
|
||||
// copy or use the software.
|
||||
//
|
||||
//
|
||||
// License Agreement
|
||||
// For Open Source Computer Vision Library
|
||||
//
|
||||
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
|
||||
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
|
||||
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
|
||||
// Third party copyrights are property of their respective owners.
|
||||
//
|
||||
// Redistribution and use in source and binary forms, with or without modification,
|
||||
// are permitted provided that the following conditions are met:
|
||||
//
|
||||
// * Redistribution's of source code must retain the above copyright notice,
|
||||
// this list of conditions and the following disclaimer.
|
||||
//
|
||||
// * Redistribution's in binary form must reproduce the above copyright notice,
|
||||
// this list of conditions and the following disclaimer in the documentation
|
||||
// and/or other materials provided with the distribution.
|
||||
//
|
||||
// * The name of the copyright holders may not be used to endorse or promote products
|
||||
// derived from this software without specific prior written permission.
|
||||
//
|
||||
// This software is provided by the copyright holders and contributors "as is" and
|
||||
// any express or implied warranties, including, but not limited to, the implied
|
||||
// warranties of merchantability and fitness for a particular purpose are disclaimed.
|
||||
// In no event shall the Intel Corporation or contributors be liable for any direct,
|
||||
// indirect, incidental, special, exemplary, or consequential damages
|
||||
// (including, but not limited to, procurement of substitute goods or services;
|
||||
// loss of use, data, or profits; or business interruption) however caused
|
||||
// and on any theory of liability, whether in contract, strict liability,
|
||||
// or tort (including negligence or otherwise) arising in any way out of
|
||||
// the use of this software, even if advised of the possibility of such damage.
|
||||
//
|
||||
//M*/
|
||||
|
||||
#ifdef __OPENCV_BUILD
|
||||
#error this is a compatibility header which should not be used inside the OpenCV library
|
||||
#endif
|
||||
|
||||
#include "opencv2/features2d.hpp"
|
||||
@@ -0,0 +1,33 @@
|
||||
#ifndef OPENCV_FEATURE2D_HAL_INTERFACE_H
|
||||
#define OPENCV_FEATURE2D_HAL_INTERFACE_H
|
||||
|
||||
#include "opencv2/core/cvdef.h"
|
||||
//! @addtogroup features2d_hal_interface
|
||||
//! @{
|
||||
|
||||
//! @name Fast feature detector types
|
||||
//! @sa cv::FastFeatureDetector
|
||||
//! @{
|
||||
#define CV_HAL_TYPE_5_8 0
|
||||
#define CV_HAL_TYPE_7_12 1
|
||||
#define CV_HAL_TYPE_9_16 2
|
||||
//! @}
|
||||
|
||||
//! @name Key point
|
||||
//! @sa cv::KeyPoint
|
||||
//! @{
|
||||
struct CV_EXPORTS cvhalKeyPoint
|
||||
{
|
||||
float x;
|
||||
float y;
|
||||
float size;
|
||||
float angle;
|
||||
float response;
|
||||
int octave;
|
||||
int class_id;
|
||||
};
|
||||
//! @}
|
||||
|
||||
//! @}
|
||||
|
||||
#endif
|
||||
@@ -0,0 +1 @@
|
||||
include/opencv2/features2d.hpp
|
||||
@@ -0,0 +1 @@
|
||||
misc/java/src/cpp/features2d_converters.hpp
|
||||
@@ -0,0 +1,18 @@
|
||||
{
|
||||
"type_dict" : {
|
||||
"Feature2D": {
|
||||
"j_type": "Feature2D",
|
||||
"jn_type": "long",
|
||||
"jni_type": "jlong",
|
||||
"jni_var": "Feature2D %(n)s",
|
||||
"suffix": "J",
|
||||
"j_import": "org.opencv.features2d.Feature2D"
|
||||
},
|
||||
"uchar": {
|
||||
"j_type": "byte",
|
||||
"jn_type": "byte",
|
||||
"jni_type": "jbyte",
|
||||
"suffix": "B"
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,112 @@
|
||||
#define LOG_TAG "org.opencv.utils.Converters"
|
||||
#include "common.h"
|
||||
#include "features2d_converters.hpp"
|
||||
|
||||
using namespace cv;
|
||||
|
||||
#define CHECK_MAT(cond) if(!(cond)){ LOGD("FAILED: " #cond); return; }
|
||||
|
||||
|
||||
//vector_KeyPoint
|
||||
void Mat_to_vector_KeyPoint(Mat& mat, std::vector<KeyPoint>& v_kp)
|
||||
{
|
||||
v_kp.clear();
|
||||
CHECK_MAT(mat.type()==CV_32FC(7) && mat.cols==1);
|
||||
for(int i=0; i<mat.rows; i++)
|
||||
{
|
||||
Vec<float, 7> v = mat.at< Vec<float, 7> >(i, 0);
|
||||
KeyPoint kp(v[0], v[1], v[2], v[3], v[4], (int)v[5], (int)v[6]);
|
||||
v_kp.push_back(kp);
|
||||
}
|
||||
return;
|
||||
}
|
||||
|
||||
|
||||
void vector_KeyPoint_to_Mat(std::vector<KeyPoint>& v_kp, Mat& mat)
|
||||
{
|
||||
int count = (int)v_kp.size();
|
||||
mat.create(count, 1, CV_32FC(7));
|
||||
for(int i=0; i<count; i++)
|
||||
{
|
||||
KeyPoint kp = v_kp[i];
|
||||
mat.at< Vec<float, 7> >(i, 0) = Vec<float, 7>(kp.pt.x, kp.pt.y, kp.size, kp.angle, kp.response, (float)kp.octave, (float)kp.class_id);
|
||||
}
|
||||
}
|
||||
|
||||
//vector_DMatch
|
||||
void Mat_to_vector_DMatch(Mat& mat, std::vector<DMatch>& v_dm)
|
||||
{
|
||||
v_dm.clear();
|
||||
CHECK_MAT(mat.type()==CV_32FC4 && mat.cols==1);
|
||||
for(int i=0; i<mat.rows; i++)
|
||||
{
|
||||
Vec<float, 4> v = mat.at< Vec<float, 4> >(i, 0);
|
||||
DMatch dm((int)v[0], (int)v[1], (int)v[2], v[3]);
|
||||
v_dm.push_back(dm);
|
||||
}
|
||||
return;
|
||||
}
|
||||
|
||||
|
||||
void vector_DMatch_to_Mat(std::vector<DMatch>& v_dm, Mat& mat)
|
||||
{
|
||||
int count = (int)v_dm.size();
|
||||
mat.create(count, 1, CV_32FC4);
|
||||
for(int i=0; i<count; i++)
|
||||
{
|
||||
DMatch dm = v_dm[i];
|
||||
mat.at< Vec<float, 4> >(i, 0) = Vec<float, 4>((float)dm.queryIdx, (float)dm.trainIdx, (float)dm.imgIdx, dm.distance);
|
||||
}
|
||||
}
|
||||
|
||||
void Mat_to_vector_vector_KeyPoint(Mat& mat, std::vector< std::vector< KeyPoint > >& vv_kp)
|
||||
{
|
||||
std::vector<Mat> vm;
|
||||
vm.reserve( mat.rows );
|
||||
Mat_to_vector_Mat(mat, vm);
|
||||
for(size_t i=0; i<vm.size(); i++)
|
||||
{
|
||||
std::vector<KeyPoint> vkp;
|
||||
Mat_to_vector_KeyPoint(vm[i], vkp);
|
||||
vv_kp.push_back(vkp);
|
||||
}
|
||||
}
|
||||
|
||||
void vector_vector_KeyPoint_to_Mat(std::vector< std::vector< KeyPoint > >& vv_kp, Mat& mat)
|
||||
{
|
||||
std::vector<Mat> vm;
|
||||
vm.reserve( vv_kp.size() );
|
||||
for(size_t i=0; i<vv_kp.size(); i++)
|
||||
{
|
||||
Mat m;
|
||||
vector_KeyPoint_to_Mat(vv_kp[i], m);
|
||||
vm.push_back(m);
|
||||
}
|
||||
vector_Mat_to_Mat(vm, mat);
|
||||
}
|
||||
|
||||
void Mat_to_vector_vector_DMatch(Mat& mat, std::vector< std::vector< DMatch > >& vv_dm)
|
||||
{
|
||||
std::vector<Mat> vm;
|
||||
vm.reserve( mat.rows );
|
||||
Mat_to_vector_Mat(mat, vm);
|
||||
for(size_t i=0; i<vm.size(); i++)
|
||||
{
|
||||
std::vector<DMatch> vdm;
|
||||
Mat_to_vector_DMatch(vm[i], vdm);
|
||||
vv_dm.push_back(vdm);
|
||||
}
|
||||
}
|
||||
|
||||
void vector_vector_DMatch_to_Mat(std::vector< std::vector< DMatch > >& vv_dm, Mat& mat)
|
||||
{
|
||||
std::vector<Mat> vm;
|
||||
vm.reserve( vv_dm.size() );
|
||||
for(size_t i=0; i<vv_dm.size(); i++)
|
||||
{
|
||||
Mat m;
|
||||
vector_DMatch_to_Mat(vv_dm[i], m);
|
||||
vm.push_back(m);
|
||||
}
|
||||
vector_Mat_to_Mat(vm, mat);
|
||||
}
|
||||
@@ -0,0 +1,21 @@
|
||||
#ifndef __FEATURES2D_CONVERTERS_HPP__
|
||||
#define __FEATURES2D_CONVERTERS_HPP__
|
||||
|
||||
#include "opencv2/opencv_modules.hpp"
|
||||
#include "opencv2/core.hpp"
|
||||
#include "opencv2/features2d.hpp"
|
||||
|
||||
void Mat_to_vector_KeyPoint(cv::Mat& mat, std::vector<cv::KeyPoint>& v_kp);
|
||||
void vector_KeyPoint_to_Mat(std::vector<cv::KeyPoint>& v_kp, cv::Mat& mat);
|
||||
|
||||
void Mat_to_vector_DMatch(cv::Mat& mat, std::vector<cv::DMatch>& v_dm);
|
||||
void vector_DMatch_to_Mat(std::vector<cv::DMatch>& v_dm, cv::Mat& mat);
|
||||
|
||||
void Mat_to_vector_vector_KeyPoint(cv::Mat& mat, std::vector< std::vector< cv::KeyPoint > >& vv_kp);
|
||||
void vector_vector_KeyPoint_to_Mat(std::vector< std::vector< cv::KeyPoint > >& vv_kp, cv::Mat& mat);
|
||||
|
||||
void Mat_to_vector_vector_DMatch(cv::Mat& mat, std::vector< std::vector< cv::DMatch > >& vv_dm);
|
||||
void vector_vector_DMatch_to_Mat(std::vector< std::vector< cv::DMatch > >& vv_dm, cv::Mat& mat);
|
||||
|
||||
|
||||
#endif
|
||||
@@ -0,0 +1,85 @@
|
||||
package org.opencv.test.features2d;
|
||||
|
||||
import org.opencv.test.OpenCVTestCase;
|
||||
import org.opencv.test.OpenCVTestRunner;
|
||||
import org.opencv.features2d.AgastFeatureDetector;
|
||||
|
||||
public class AGASTFeatureDetectorTest extends OpenCVTestCase {
|
||||
|
||||
AgastFeatureDetector detector;
|
||||
|
||||
@Override
|
||||
protected void setUp() throws Exception {
|
||||
super.setUp();
|
||||
detector = AgastFeatureDetector.create(); // default (10,true,3)
|
||||
}
|
||||
|
||||
public void testCreate() {
|
||||
assertNotNull(detector);
|
||||
}
|
||||
|
||||
public void testDetectListOfMatListOfListOfKeyPoint() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testDetectListOfMatListOfListOfKeyPointListOfMat() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testDetectMatListOfKeyPoint() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testDetectMatListOfKeyPointMat() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testEmpty() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testRead() {
|
||||
String filename = OpenCVTestRunner.getTempFileName("xml");
|
||||
writeFile(filename, "<?xml version=\"1.0\"?>\n<opencv_storage>\n<name>Feature2D.AgastFeatureDetector</name>\n<threshold>11</threshold>\n<nonmaxSuppression>0</nonmaxSuppression>\n<type>2</type>\n</opencv_storage>\n");
|
||||
|
||||
detector.read(filename);
|
||||
|
||||
assertEquals(11, detector.getThreshold());
|
||||
assertEquals(false, detector.getNonmaxSuppression());
|
||||
assertEquals(2, detector.getType());
|
||||
}
|
||||
|
||||
public void testReadYml() {
|
||||
String filename = OpenCVTestRunner.getTempFileName("yml");
|
||||
writeFile(filename, "%YAML:1.0\n---\nname: \"Feature2D.AgastFeatureDetector\"\nthreshold: 11\nnonmaxSuppression: 0\ntype: 2\n");
|
||||
|
||||
detector.read(filename);
|
||||
|
||||
assertEquals(11, detector.getThreshold());
|
||||
assertEquals(false, detector.getNonmaxSuppression());
|
||||
assertEquals(2, detector.getType());
|
||||
}
|
||||
|
||||
public void testWrite() {
|
||||
String filename = OpenCVTestRunner.getTempFileName("xml");
|
||||
|
||||
detector.write(filename);
|
||||
|
||||
String truth = "<?xml version=\"1.0\"?>\n<opencv_storage>\n<name>Feature2D.AgastFeatureDetector</name>\n<threshold>10</threshold>\n<nonmaxSuppression>1</nonmaxSuppression>\n<type>3</type>\n</opencv_storage>\n";
|
||||
String actual = readFile(filename);
|
||||
actual = actual.replaceAll("e([+-])0(\\d\\d)", "e$1$2"); // NOTE: workaround for different platforms double representation
|
||||
assertEquals(truth, actual);
|
||||
}
|
||||
|
||||
public void testWriteYml() {
|
||||
String filename = OpenCVTestRunner.getTempFileName("yml");
|
||||
|
||||
detector.write(filename);
|
||||
|
||||
String truth = "%YAML:1.0\n---\nname: \"Feature2D.AgastFeatureDetector\"\nthreshold: 10\nnonmaxSuppression: 1\ntype: 3\n";
|
||||
String actual = readFile(filename);
|
||||
actual = actual.replaceAll("e([+-])0(\\d\\d)", "e$1$2"); // NOTE: workaround for different platforms double representation
|
||||
assertEquals(truth, actual);
|
||||
}
|
||||
|
||||
}
|
||||
@@ -0,0 +1,67 @@
|
||||
package org.opencv.test.features2d;
|
||||
|
||||
import org.opencv.test.OpenCVTestCase;
|
||||
import org.opencv.test.OpenCVTestRunner;
|
||||
import org.opencv.features2d.AKAZE;
|
||||
|
||||
public class AKAZEDescriptorExtractorTest extends OpenCVTestCase {
|
||||
|
||||
AKAZE extractor;
|
||||
|
||||
@Override
|
||||
protected void setUp() throws Exception {
|
||||
super.setUp();
|
||||
extractor = AKAZE.create(); // default (5,0,3,0.001f,4,4,1)
|
||||
}
|
||||
|
||||
public void testCreate() {
|
||||
assertNotNull(extractor);
|
||||
}
|
||||
|
||||
public void testDetectListOfMatListOfListOfKeyPoint() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testDetectListOfMatListOfListOfKeyPointListOfMat() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testDetectMatListOfKeyPoint() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testDetectMatListOfKeyPointMat() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testEmpty() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testReadYml() {
|
||||
String filename = OpenCVTestRunner.getTempFileName("yml");
|
||||
writeFile(filename, "%YAML:1.0\n---\nformat: 3\nname: \"Feature2D.AKAZE\"\ndescriptor: 4\ndescriptor_channels: 2\ndescriptor_size: 32\nthreshold: 0.125\noctaves: 3\nsublevels: 5\ndiffusivity: 2\n");
|
||||
|
||||
extractor.read(filename);
|
||||
|
||||
assertEquals(4, extractor.getDescriptorType());
|
||||
assertEquals(2, extractor.getDescriptorChannels());
|
||||
assertEquals(32, extractor.getDescriptorSize());
|
||||
assertEquals(0.125, extractor.getThreshold());
|
||||
assertEquals(3, extractor.getNOctaves());
|
||||
assertEquals(5, extractor.getNOctaveLayers());
|
||||
assertEquals(2, extractor.getDiffusivity());
|
||||
}
|
||||
|
||||
public void testWriteYml() {
|
||||
String filename = OpenCVTestRunner.getTempFileName("yml");
|
||||
|
||||
extractor.write(filename);
|
||||
|
||||
String truth = "%YAML:1.0\n---\nformat: 3\nname: \"Feature2D.AKAZE\"\ndescriptor: 5\ndescriptor_channels: 3\ndescriptor_size: 0\nthreshold: 0.0010000000474974513\noctaves: 4\nsublevels: 4\ndiffusivity: 1\nmax_points: -1\n";
|
||||
String actual = readFile(filename);
|
||||
actual = actual.replaceAll("e([+-])0(\\d\\d)", "e$1$2"); // NOTE: workaround for different platforms double representation
|
||||
assertEquals(truth, actual);
|
||||
}
|
||||
|
||||
}
|
||||
@@ -0,0 +1,48 @@
|
||||
package org.opencv.test.features2d;
|
||||
|
||||
import org.opencv.core.Core;
|
||||
import org.opencv.core.CvType;
|
||||
import org.opencv.core.Mat;
|
||||
import org.opencv.core.MatOfKeyPoint;
|
||||
import org.opencv.core.Point;
|
||||
import org.opencv.core.Scalar;
|
||||
import org.opencv.core.KeyPoint;
|
||||
import org.opencv.features2d.ORB;
|
||||
import org.opencv.features2d.DescriptorMatcher;
|
||||
import org.opencv.features2d.BOWImgDescriptorExtractor;
|
||||
import org.opencv.test.OpenCVTestCase;
|
||||
import org.opencv.test.OpenCVTestRunner;
|
||||
import org.opencv.imgproc.Imgproc;
|
||||
|
||||
public class BOWImgDescriptorExtractorTest extends OpenCVTestCase {
|
||||
|
||||
ORB extractor;
|
||||
DescriptorMatcher matcher;
|
||||
int matSize;
|
||||
|
||||
public static void assertDescriptorsClose(Mat expected, Mat actual, int allowedDistance) {
|
||||
double distance = Core.norm(expected, actual, Core.NORM_HAMMING);
|
||||
assertTrue("expected:<" + allowedDistance + "> but was:<" + distance + ">", distance <= allowedDistance);
|
||||
}
|
||||
|
||||
private Mat getTestImg() {
|
||||
Mat cross = new Mat(matSize, matSize, CvType.CV_8U, new Scalar(255));
|
||||
Imgproc.line(cross, new Point(20, matSize / 2), new Point(matSize - 21, matSize / 2), new Scalar(100), 2);
|
||||
Imgproc.line(cross, new Point(matSize / 2, 20), new Point(matSize / 2, matSize - 21), new Scalar(100), 2);
|
||||
|
||||
return cross;
|
||||
}
|
||||
|
||||
@Override
|
||||
protected void setUp() throws Exception {
|
||||
super.setUp();
|
||||
extractor = ORB.create();
|
||||
matcher = DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE);
|
||||
matSize = 100;
|
||||
}
|
||||
|
||||
public void testCreate() {
|
||||
BOWImgDescriptorExtractor bow = new BOWImgDescriptorExtractor(extractor, matcher);
|
||||
}
|
||||
|
||||
}
|
||||
@@ -0,0 +1,102 @@
|
||||
package org.opencv.test.features2d;
|
||||
|
||||
import org.opencv.core.CvType;
|
||||
import org.opencv.core.Mat;
|
||||
import org.opencv.core.MatOfKeyPoint;
|
||||
import org.opencv.core.Point;
|
||||
import org.opencv.core.Scalar;
|
||||
import org.opencv.core.KeyPoint;
|
||||
import org.opencv.test.OpenCVTestCase;
|
||||
import org.opencv.test.OpenCVTestRunner;
|
||||
import org.opencv.imgproc.Imgproc;
|
||||
import org.opencv.features2d.Feature2D;
|
||||
|
||||
public class BRIEFDescriptorExtractorTest extends OpenCVTestCase {
|
||||
|
||||
Feature2D extractor;
|
||||
int matSize;
|
||||
|
||||
private Mat getTestImg() {
|
||||
Mat cross = new Mat(matSize, matSize, CvType.CV_8U, new Scalar(255));
|
||||
Imgproc.line(cross, new Point(20, matSize / 2), new Point(matSize - 21, matSize / 2), new Scalar(100), 2);
|
||||
Imgproc.line(cross, new Point(matSize / 2, 20), new Point(matSize / 2, matSize - 21), new Scalar(100), 2);
|
||||
|
||||
return cross;
|
||||
}
|
||||
|
||||
@Override
|
||||
protected void setUp() throws Exception {
|
||||
super.setUp();
|
||||
extractor = createClassInstance(XFEATURES2D+"BriefDescriptorExtractor", DEFAULT_FACTORY, null, null);
|
||||
matSize = 100;
|
||||
}
|
||||
|
||||
public void testComputeListOfMatListOfListOfKeyPointListOfMat() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testComputeMatListOfKeyPointMat() {
|
||||
KeyPoint point = new KeyPoint(55.775577545166016f, 44.224422454833984f, 16, 9.754629f, 8617.863f, 1, -1);
|
||||
MatOfKeyPoint keypoints = new MatOfKeyPoint(point);
|
||||
Mat img = getTestImg();
|
||||
Mat descriptors = new Mat();
|
||||
|
||||
extractor.compute(img, keypoints, descriptors);
|
||||
|
||||
Mat truth = new Mat(1, 32, CvType.CV_8UC1) {
|
||||
{
|
||||
put(0, 0, 96, 0, 76, 24, 47, 182, 68, 137,
|
||||
149, 195, 67, 16, 187, 224, 74, 8,
|
||||
82, 169, 87, 70, 44, 4, 192, 56,
|
||||
13, 128, 44, 106, 146, 72, 194, 245);
|
||||
}
|
||||
};
|
||||
|
||||
assertMatEqual(truth, descriptors);
|
||||
}
|
||||
|
||||
public void testCreate() {
|
||||
assertNotNull(extractor);
|
||||
}
|
||||
|
||||
public void testDescriptorSize() {
|
||||
assertEquals(32, extractor.descriptorSize());
|
||||
}
|
||||
|
||||
public void testDescriptorType() {
|
||||
assertEquals(CvType.CV_8U, extractor.descriptorType());
|
||||
}
|
||||
|
||||
public void testEmpty() {
|
||||
// assertFalse(extractor.empty());
|
||||
fail("Not yet implemented"); // BRIEF does not override empty() method
|
||||
}
|
||||
|
||||
public void testRead() {
|
||||
String filename = OpenCVTestRunner.getTempFileName("yml");
|
||||
writeFile(filename, "%YAML:1.0\n---\ndescriptorSize: 64\n");
|
||||
|
||||
extractor.read(filename);
|
||||
|
||||
assertEquals(64, extractor.descriptorSize());
|
||||
}
|
||||
|
||||
public void testWrite() {
|
||||
String filename = OpenCVTestRunner.getTempFileName("xml");
|
||||
|
||||
extractor.write(filename);
|
||||
|
||||
String truth = "<?xml version=\"1.0\"?>\n<opencv_storage>\n<name>Feature2D.BRIEF</name>\n<descriptorSize>32</descriptorSize>\n<use_orientation>0</use_orientation>\n</opencv_storage>\n";
|
||||
assertEquals(truth, readFile(filename));
|
||||
}
|
||||
|
||||
public void testWriteYml() {
|
||||
String filename = OpenCVTestRunner.getTempFileName("yml");
|
||||
|
||||
extractor.write(filename);
|
||||
|
||||
String truth = "%YAML:1.0\n---\nname: \"Feature2D.BRIEF\"\ndescriptorSize: 32\nuse_orientation: 0\n";
|
||||
assertEquals(truth, readFile(filename));
|
||||
}
|
||||
|
||||
}
|
||||
@@ -0,0 +1,63 @@
|
||||
package org.opencv.test.features2d;
|
||||
|
||||
import org.opencv.test.OpenCVTestCase;
|
||||
import org.opencv.test.OpenCVTestRunner;
|
||||
import org.opencv.features2d.BRISK;
|
||||
|
||||
public class BRISKDescriptorExtractorTest extends OpenCVTestCase {
|
||||
|
||||
BRISK extractor;
|
||||
|
||||
@Override
|
||||
protected void setUp() throws Exception {
|
||||
super.setUp();
|
||||
extractor = BRISK.create(); // default (30,3,1)
|
||||
}
|
||||
|
||||
public void testCreate() {
|
||||
assertNotNull(extractor);
|
||||
}
|
||||
|
||||
public void testDetectListOfMatListOfListOfKeyPoint() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testDetectListOfMatListOfListOfKeyPointListOfMat() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testDetectMatListOfKeyPoint() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testDetectMatListOfKeyPointMat() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testEmpty() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testReadYml() {
|
||||
String filename = OpenCVTestRunner.getTempFileName("yml");
|
||||
writeFile(filename, "%YAML:1.0\n---\nname: \"Feature2D.BRISK\"\nthreshold: 31\noctaves: 4\npatternScale: 1.1\n");
|
||||
|
||||
extractor.read(filename);
|
||||
|
||||
assertEquals(31, extractor.getThreshold());
|
||||
assertEquals(4, extractor.getOctaves());
|
||||
assertEquals(1.1f, extractor.getPatternScale());
|
||||
}
|
||||
|
||||
public void testWriteYml() {
|
||||
String filename = OpenCVTestRunner.getTempFileName("yml");
|
||||
|
||||
extractor.write(filename);
|
||||
|
||||
String truth = "%YAML:1.0\n---\nname: \"Feature2D.BRISK\"\nthreshold: 30\noctaves: 3\npatternScale: 1.\n";
|
||||
String actual = readFile(filename);
|
||||
actual = actual.replaceAll("e([+-])0(\\d\\d)", "e$1$2"); // NOTE: workaround for different platforms double representation
|
||||
assertEquals(truth, actual);
|
||||
}
|
||||
|
||||
}
|
||||
@@ -0,0 +1,304 @@
|
||||
package org.opencv.test.features2d;
|
||||
|
||||
import java.util.ArrayList;
|
||||
import java.util.Arrays;
|
||||
import java.util.List;
|
||||
|
||||
import org.opencv.core.CvType;
|
||||
import org.opencv.core.Mat;
|
||||
import org.opencv.core.MatOfDMatch;
|
||||
import org.opencv.core.MatOfKeyPoint;
|
||||
import org.opencv.core.Point;
|
||||
import org.opencv.core.Scalar;
|
||||
import org.opencv.core.DMatch;
|
||||
import org.opencv.features2d.DescriptorMatcher;
|
||||
import org.opencv.features2d.BFMatcher;
|
||||
import org.opencv.core.KeyPoint;
|
||||
import org.opencv.test.OpenCVTestCase;
|
||||
import org.opencv.test.OpenCVTestRunner;
|
||||
import org.opencv.imgproc.Imgproc;
|
||||
import org.opencv.features2d.Feature2D;
|
||||
|
||||
public class BruteForceDescriptorMatcherTest extends OpenCVTestCase {
|
||||
|
||||
DescriptorMatcher matcher;
|
||||
int matSize;
|
||||
DMatch[] truth;
|
||||
|
||||
private Mat getMaskImg() {
|
||||
return new Mat(5, 2, CvType.CV_8U, new Scalar(0)) {
|
||||
{
|
||||
put(0, 0, 1, 1, 1, 1);
|
||||
}
|
||||
};
|
||||
}
|
||||
|
||||
private Mat getQueryDescriptors() {
|
||||
Mat img = getQueryImg();
|
||||
MatOfKeyPoint keypoints = new MatOfKeyPoint();
|
||||
Mat descriptors = new Mat();
|
||||
|
||||
Feature2D detector = createClassInstance(XFEATURES2D+"SURF", DEFAULT_FACTORY, null, null);
|
||||
Feature2D extractor = createClassInstance(XFEATURES2D+"SURF", DEFAULT_FACTORY, null, null);
|
||||
|
||||
setProperty(detector, "hessianThreshold", "double", 8000);
|
||||
setProperty(detector, "nOctaves", "int", 3);
|
||||
setProperty(detector, "nOctaveLayers", "int", 4);
|
||||
setProperty(detector, "upright", "boolean", false);
|
||||
|
||||
detector.detect(img, keypoints);
|
||||
extractor.compute(img, keypoints, descriptors);
|
||||
|
||||
return descriptors;
|
||||
}
|
||||
|
||||
private Mat getQueryImg() {
|
||||
Mat cross = new Mat(matSize, matSize, CvType.CV_8U, new Scalar(255));
|
||||
Imgproc.line(cross, new Point(30, matSize / 2), new Point(matSize - 31, matSize / 2), new Scalar(100), 3);
|
||||
Imgproc.line(cross, new Point(matSize / 2, 30), new Point(matSize / 2, matSize - 31), new Scalar(100), 3);
|
||||
|
||||
return cross;
|
||||
}
|
||||
|
||||
private Mat getTrainDescriptors() {
|
||||
Mat img = getTrainImg();
|
||||
MatOfKeyPoint keypoints = new MatOfKeyPoint(new KeyPoint(50, 50, 16, 0, 20000, 1, -1), new KeyPoint(42, 42, 16, 160, 10000, 1, -1));
|
||||
Mat descriptors = new Mat();
|
||||
|
||||
Feature2D extractor = createClassInstance(XFEATURES2D+"SURF", DEFAULT_FACTORY, null, null);
|
||||
|
||||
extractor.compute(img, keypoints, descriptors);
|
||||
|
||||
return descriptors;
|
||||
}
|
||||
|
||||
private Mat getTrainImg() {
|
||||
Mat cross = new Mat(matSize, matSize, CvType.CV_8U, new Scalar(255));
|
||||
Imgproc.line(cross, new Point(20, matSize / 2), new Point(matSize - 21, matSize / 2), new Scalar(100), 2);
|
||||
Imgproc.line(cross, new Point(matSize / 2, 20), new Point(matSize / 2, matSize - 21), new Scalar(100), 2);
|
||||
|
||||
return cross;
|
||||
}
|
||||
|
||||
protected void setUp() throws Exception {
|
||||
super.setUp();
|
||||
matcher = DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE);
|
||||
matSize = 100;
|
||||
|
||||
truth = new DMatch[] {
|
||||
new DMatch(0, 0, 0, 0.6159003f),
|
||||
new DMatch(1, 1, 0, 0.9177120f),
|
||||
new DMatch(2, 1, 0, 0.3112163f),
|
||||
new DMatch(3, 1, 0, 0.2925074f),
|
||||
new DMatch(4, 1, 0, 0.26520672f)
|
||||
};
|
||||
}
|
||||
|
||||
// https://github.com/opencv/opencv/issues/11268
|
||||
public void testConstructor()
|
||||
{
|
||||
BFMatcher self_created_matcher = new BFMatcher();
|
||||
Mat train = new Mat(1, 1, CvType.CV_8U, new Scalar(123));
|
||||
self_created_matcher.add(Arrays.asList(train));
|
||||
assertTrue(!self_created_matcher.empty());
|
||||
}
|
||||
|
||||
public void testAdd() {
|
||||
matcher.add(Arrays.asList(new Mat()));
|
||||
assertFalse(matcher.empty());
|
||||
}
|
||||
|
||||
public void testClear() {
|
||||
matcher.add(Arrays.asList(new Mat()));
|
||||
|
||||
matcher.clear();
|
||||
|
||||
assertTrue(matcher.empty());
|
||||
}
|
||||
|
||||
public void testClone() {
|
||||
Mat train = new Mat(1, 1, CvType.CV_8U, new Scalar(123));
|
||||
Mat truth = train.clone();
|
||||
matcher.add(Arrays.asList(train));
|
||||
|
||||
DescriptorMatcher cloned = matcher.clone();
|
||||
|
||||
assertNotNull(cloned);
|
||||
|
||||
List<Mat> descriptors = cloned.getTrainDescriptors();
|
||||
assertEquals(1, descriptors.size());
|
||||
assertMatEqual(truth, descriptors.get(0));
|
||||
}
|
||||
|
||||
public void testCloneBoolean() {
|
||||
matcher.add(Arrays.asList(new Mat()));
|
||||
|
||||
DescriptorMatcher cloned = matcher.clone(true);
|
||||
|
||||
assertNotNull(cloned);
|
||||
assertTrue(cloned.empty());
|
||||
}
|
||||
|
||||
public void testCreate() {
|
||||
assertNotNull(matcher);
|
||||
}
|
||||
|
||||
public void testEmpty() {
|
||||
assertTrue(matcher.empty());
|
||||
}
|
||||
|
||||
public void testGetTrainDescriptors() {
|
||||
Mat train = new Mat(1, 1, CvType.CV_8U, new Scalar(123));
|
||||
Mat truth = train.clone();
|
||||
matcher.add(Arrays.asList(train));
|
||||
|
||||
List<Mat> descriptors = matcher.getTrainDescriptors();
|
||||
|
||||
assertEquals(1, descriptors.size());
|
||||
assertMatEqual(truth, descriptors.get(0));
|
||||
}
|
||||
|
||||
public void testIsMaskSupported() {
|
||||
assertTrue(matcher.isMaskSupported());
|
||||
}
|
||||
|
||||
public void testKnnMatchMatListOfListOfDMatchInt() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testKnnMatchMatListOfListOfDMatchIntListOfMat() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testKnnMatchMatListOfListOfDMatchIntListOfMatBoolean() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testKnnMatchMatMatListOfListOfDMatchInt() {
|
||||
final int k = 3;
|
||||
Mat train = getTrainDescriptors();
|
||||
Mat query = getQueryDescriptors();
|
||||
List<MatOfDMatch> matches = new ArrayList<MatOfDMatch>();
|
||||
matcher.knnMatch(query, train, matches, k);
|
||||
/*
|
||||
Log.d("knnMatch", "train = " + train);
|
||||
Log.d("knnMatch", "query = " + query);
|
||||
|
||||
matcher.add(train);
|
||||
matcher.knnMatch(query, matches, k);
|
||||
*/
|
||||
assertEquals(query.rows(), matches.size());
|
||||
for(int i = 0; i<matches.size(); i++)
|
||||
{
|
||||
MatOfDMatch vdm = matches.get(i);
|
||||
//Log.d("knn", "vdm["+i+"]="+vdm.dump());
|
||||
assertTrue(Math.min(k, train.rows()) >= vdm.total());
|
||||
for(DMatch dm : vdm.toArray())
|
||||
{
|
||||
assertEquals(dm.queryIdx, i);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
public void testKnnMatchMatMatListOfListOfDMatchIntMat() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testKnnMatchMatMatListOfListOfDMatchIntMatBoolean() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testMatchMatListOfDMatch() {
|
||||
Mat train = getTrainDescriptors();
|
||||
Mat query = getQueryDescriptors();
|
||||
MatOfDMatch matches = new MatOfDMatch();
|
||||
matcher.add(Arrays.asList(train));
|
||||
|
||||
matcher.match(query, matches);
|
||||
|
||||
assertArrayDMatchEquals(truth, matches.toArray(), EPS);
|
||||
}
|
||||
|
||||
public void testMatchMatListOfDMatchListOfMat() {
|
||||
Mat train = getTrainDescriptors();
|
||||
Mat query = getQueryDescriptors();
|
||||
Mat mask = getMaskImg();
|
||||
MatOfDMatch matches = new MatOfDMatch();
|
||||
matcher.add(Arrays.asList(train));
|
||||
|
||||
matcher.match(query, matches, Arrays.asList(mask));
|
||||
|
||||
assertListDMatchEquals(Arrays.asList(truth[0], truth[1]), matches.toList(), EPS);
|
||||
}
|
||||
|
||||
public void testMatchMatMatListOfDMatch() {
|
||||
Mat train = getTrainDescriptors();
|
||||
Mat query = getQueryDescriptors();
|
||||
MatOfDMatch matches = new MatOfDMatch();
|
||||
|
||||
matcher.match(query, train, matches);
|
||||
|
||||
assertArrayDMatchEquals(truth, matches.toArray(), EPS);
|
||||
|
||||
// OpenCVTestRunner.Log("matches found: " + matches.size());
|
||||
// for (DMatch m : matches)
|
||||
// OpenCVTestRunner.Log(m.toString());
|
||||
}
|
||||
|
||||
public void testMatchMatMatListOfDMatchMat() {
|
||||
Mat train = getTrainDescriptors();
|
||||
Mat query = getQueryDescriptors();
|
||||
Mat mask = getMaskImg();
|
||||
MatOfDMatch matches = new MatOfDMatch();
|
||||
|
||||
matcher.match(query, train, matches, mask);
|
||||
|
||||
assertListDMatchEquals(Arrays.asList(truth[0], truth[1]), matches.toList(), EPS);
|
||||
}
|
||||
|
||||
public void testRadiusMatchMatListOfListOfDMatchFloat() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testRadiusMatchMatListOfListOfDMatchFloatListOfMat() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testRadiusMatchMatListOfListOfDMatchFloatListOfMatBoolean() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testRadiusMatchMatMatListOfListOfDMatchFloat() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testRadiusMatchMatMatListOfListOfDMatchFloatMat() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testRadiusMatchMatMatListOfListOfDMatchFloatMatBoolean() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testRead() {
|
||||
String filename = OpenCVTestRunner.getTempFileName("yml");
|
||||
writeFile(filename, "%YAML:1.0\n---\n");
|
||||
|
||||
matcher.read(filename);
|
||||
assertTrue(true);// BruteforceMatcher has no settings
|
||||
}
|
||||
|
||||
public void testTrain() {
|
||||
matcher.train();// BruteforceMatcher does not need to train
|
||||
}
|
||||
|
||||
public void testWrite() {
|
||||
String filename = OpenCVTestRunner.getTempFileName("yml");
|
||||
|
||||
matcher.write(filename);
|
||||
|
||||
String truth = "%YAML:1.0\n---\n";
|
||||
assertEquals(truth, readFile(filename));
|
||||
}
|
||||
|
||||
}
|
||||
@@ -0,0 +1,262 @@
|
||||
package org.opencv.test.features2d;
|
||||
|
||||
import java.util.ArrayList;
|
||||
import java.util.Arrays;
|
||||
import java.util.List;
|
||||
|
||||
import org.opencv.core.CvType;
|
||||
import org.opencv.core.Mat;
|
||||
import org.opencv.core.MatOfDMatch;
|
||||
import org.opencv.core.MatOfKeyPoint;
|
||||
import org.opencv.core.Point;
|
||||
import org.opencv.core.Scalar;
|
||||
import org.opencv.core.DMatch;
|
||||
import org.opencv.features2d.DescriptorMatcher;
|
||||
import org.opencv.features2d.FastFeatureDetector;
|
||||
import org.opencv.test.OpenCVTestCase;
|
||||
import org.opencv.test.OpenCVTestRunner;
|
||||
import org.opencv.imgproc.Imgproc;
|
||||
import org.opencv.features2d.Feature2D;
|
||||
|
||||
public class BruteForceHammingDescriptorMatcherTest extends OpenCVTestCase {
|
||||
|
||||
DescriptorMatcher matcher;
|
||||
int matSize;
|
||||
DMatch[] truth;
|
||||
|
||||
private Mat getMaskImg() {
|
||||
return new Mat(4, 4, CvType.CV_8U, new Scalar(0)) {
|
||||
{
|
||||
put(0, 0, 1, 1, 1, 1, 1, 1, 1, 1);
|
||||
}
|
||||
};
|
||||
}
|
||||
|
||||
private Mat getQueryDescriptors() {
|
||||
return getTestDescriptors(getQueryImg());
|
||||
}
|
||||
|
||||
private Mat getQueryImg() {
|
||||
Mat img = new Mat(matSize, matSize, CvType.CV_8U, new Scalar(255));
|
||||
Imgproc.line(img, new Point(40, matSize - 40), new Point(matSize - 50, 50), new Scalar(0), 8);
|
||||
return img;
|
||||
}
|
||||
|
||||
private Mat getTestDescriptors(Mat img) {
|
||||
MatOfKeyPoint keypoints = new MatOfKeyPoint();
|
||||
Mat descriptors = new Mat();
|
||||
|
||||
Feature2D detector = FastFeatureDetector.create();
|
||||
Feature2D extractor = createClassInstance(XFEATURES2D+"BriefDescriptorExtractor", DEFAULT_FACTORY, null, null);
|
||||
|
||||
detector.detect(img, keypoints);
|
||||
extractor.compute(img, keypoints, descriptors);
|
||||
|
||||
return descriptors;
|
||||
}
|
||||
|
||||
private Mat getTrainDescriptors() {
|
||||
return getTestDescriptors(getTrainImg());
|
||||
}
|
||||
|
||||
private Mat getTrainImg() {
|
||||
Mat img = new Mat(matSize, matSize, CvType.CV_8U, new Scalar(255));
|
||||
Imgproc.line(img, new Point(40, 40), new Point(matSize - 40, matSize - 40), new Scalar(0), 8);
|
||||
return img;
|
||||
}
|
||||
|
||||
protected void setUp() throws Exception {
|
||||
super.setUp();
|
||||
matcher = DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE_HAMMING);
|
||||
matSize = 100;
|
||||
|
||||
truth = new DMatch[] {
|
||||
new DMatch(0, 0, 0, 51),
|
||||
new DMatch(1, 2, 0, 42),
|
||||
new DMatch(2, 1, 0, 40),
|
||||
new DMatch(3, 3, 0, 53) };
|
||||
}
|
||||
|
||||
public void testAdd() {
|
||||
matcher.add(Arrays.asList(new Mat()));
|
||||
assertFalse(matcher.empty());
|
||||
}
|
||||
|
||||
public void testClear() {
|
||||
matcher.add(Arrays.asList(new Mat()));
|
||||
|
||||
matcher.clear();
|
||||
|
||||
assertTrue(matcher.empty());
|
||||
}
|
||||
|
||||
public void testClone() {
|
||||
Mat train = new Mat(1, 1, CvType.CV_8U, new Scalar(123));
|
||||
Mat truth = train.clone();
|
||||
matcher.add(Arrays.asList(train));
|
||||
|
||||
DescriptorMatcher cloned = matcher.clone();
|
||||
|
||||
assertNotNull(cloned);
|
||||
|
||||
List<Mat> descriptors = cloned.getTrainDescriptors();
|
||||
assertEquals(1, descriptors.size());
|
||||
assertMatEqual(truth, descriptors.get(0));
|
||||
}
|
||||
|
||||
public void testCloneBoolean() {
|
||||
matcher.add(Arrays.asList(new Mat()));
|
||||
|
||||
DescriptorMatcher cloned = matcher.clone(true);
|
||||
|
||||
assertNotNull(cloned);
|
||||
assertTrue(cloned.empty());
|
||||
}
|
||||
|
||||
public void testCreate() {
|
||||
assertNotNull(matcher);
|
||||
}
|
||||
|
||||
public void testEmpty() {
|
||||
assertTrue(matcher.empty());
|
||||
}
|
||||
|
||||
public void testGetTrainDescriptors() {
|
||||
Mat train = new Mat(1, 1, CvType.CV_8U, new Scalar(123));
|
||||
Mat truth = train.clone();
|
||||
matcher.add(Arrays.asList(train));
|
||||
|
||||
List<Mat> descriptors = matcher.getTrainDescriptors();
|
||||
|
||||
assertEquals(1, descriptors.size());
|
||||
assertMatEqual(truth, descriptors.get(0));
|
||||
}
|
||||
|
||||
public void testIsMaskSupported() {
|
||||
assertTrue(matcher.isMaskSupported());
|
||||
}
|
||||
|
||||
public void testKnnMatchMatListOfListOfDMatchInt() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testKnnMatchMatListOfListOfDMatchIntListOfMat() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testKnnMatchMatListOfListOfDMatchIntListOfMatBoolean() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testKnnMatchMatMatListOfListOfDMatchInt() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testKnnMatchMatMatListOfListOfDMatchIntMat() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testKnnMatchMatMatListOfListOfDMatchIntMatBoolean() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testMatchMatListOfDMatch() {
|
||||
Mat train = getTrainDescriptors();
|
||||
Mat query = getQueryDescriptors();
|
||||
MatOfDMatch matches = new MatOfDMatch();
|
||||
matcher.add(Arrays.asList(train));
|
||||
|
||||
matcher.match(query, matches);
|
||||
|
||||
assertListDMatchEquals(Arrays.asList(truth), matches.toList(), EPS);
|
||||
}
|
||||
|
||||
public void testMatchMatListOfDMatchListOfMat() {
|
||||
Mat train = getTrainDescriptors();
|
||||
Mat query = getQueryDescriptors();
|
||||
Mat mask = getMaskImg();
|
||||
MatOfDMatch matches = new MatOfDMatch();
|
||||
matcher.add(Arrays.asList(train));
|
||||
|
||||
matcher.match(query, matches, Arrays.asList(mask));
|
||||
|
||||
assertListDMatchEquals(Arrays.asList(truth[0], truth[1]), matches.toList(), EPS);
|
||||
}
|
||||
|
||||
public void testMatchMatMatListOfDMatch() {
|
||||
Mat train = getTrainDescriptors();
|
||||
Mat query = getQueryDescriptors();
|
||||
MatOfDMatch matches = new MatOfDMatch();
|
||||
|
||||
matcher.match(query, train, matches);
|
||||
|
||||
assertListDMatchEquals(Arrays.asList(truth), matches.toList(), EPS);
|
||||
}
|
||||
|
||||
public void testMatchMatMatListOfDMatchMat() {
|
||||
Mat train = getTrainDescriptors();
|
||||
Mat query = getQueryDescriptors();
|
||||
Mat mask = getMaskImg();
|
||||
MatOfDMatch matches = new MatOfDMatch();
|
||||
|
||||
matcher.match(query, train, matches, mask);
|
||||
|
||||
assertListDMatchEquals(Arrays.asList(truth[0], truth[1]), matches.toList(), EPS);
|
||||
}
|
||||
|
||||
public void testRadiusMatchMatListOfListOfDMatchFloat() {
|
||||
Mat train = getTrainDescriptors();
|
||||
Mat query = getQueryDescriptors();
|
||||
ArrayList<MatOfDMatch> matches = new ArrayList<MatOfDMatch>();
|
||||
|
||||
matcher.radiusMatch(query, train, matches, 50.f);
|
||||
|
||||
assertEquals(4, matches.size());
|
||||
assertTrue(matches.get(0).empty());
|
||||
assertMatEqual(matches.get(1), new MatOfDMatch(truth[1]), EPS);
|
||||
assertMatEqual(matches.get(2), new MatOfDMatch(truth[2]), EPS);
|
||||
assertTrue(matches.get(3).empty());
|
||||
}
|
||||
|
||||
public void testRadiusMatchMatListOfListOfDMatchFloatListOfMat() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testRadiusMatchMatListOfListOfDMatchFloatListOfMatBoolean() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testRadiusMatchMatMatListOfListOfDMatchFloat() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testRadiusMatchMatMatListOfListOfDMatchFloatMat() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testRadiusMatchMatMatListOfListOfDMatchFloatMatBoolean() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testRead() {
|
||||
String filename = OpenCVTestRunner.getTempFileName("yml");
|
||||
writeFile(filename, "%YAML:1.0\n---\n");
|
||||
|
||||
matcher.read(filename);
|
||||
assertTrue(true);// BruteforceMatcher has no settings
|
||||
}
|
||||
|
||||
public void testTrain() {
|
||||
matcher.train();// BruteforceMatcher does not need to train
|
||||
}
|
||||
|
||||
public void testWrite() {
|
||||
String filename = OpenCVTestRunner.getTempFileName("yml");
|
||||
|
||||
matcher.write(filename);
|
||||
|
||||
String truth = "%YAML:1.0\n---\n";
|
||||
assertEquals(truth, readFile(filename));
|
||||
}
|
||||
|
||||
}
|
||||
@@ -0,0 +1,257 @@
|
||||
package org.opencv.test.features2d;
|
||||
|
||||
import java.util.Arrays;
|
||||
import java.util.List;
|
||||
|
||||
import org.opencv.core.CvType;
|
||||
import org.opencv.core.Mat;
|
||||
import org.opencv.core.MatOfDMatch;
|
||||
import org.opencv.core.MatOfKeyPoint;
|
||||
import org.opencv.core.Point;
|
||||
import org.opencv.core.Scalar;
|
||||
import org.opencv.core.DMatch;
|
||||
import org.opencv.features2d.DescriptorMatcher;
|
||||
import org.opencv.features2d.FastFeatureDetector;
|
||||
import org.opencv.test.OpenCVTestCase;
|
||||
import org.opencv.test.OpenCVTestRunner;
|
||||
import org.opencv.imgproc.Imgproc;
|
||||
import org.opencv.features2d.Feature2D;
|
||||
|
||||
public class BruteForceHammingLUTDescriptorMatcherTest extends OpenCVTestCase {
|
||||
|
||||
DescriptorMatcher matcher;
|
||||
int matSize;
|
||||
DMatch[] truth;
|
||||
|
||||
private Mat getMaskImg() {
|
||||
return new Mat(4, 4, CvType.CV_8U, new Scalar(0)) {
|
||||
{
|
||||
put(0, 0, 1, 1, 1, 1, 1, 1, 1, 1);
|
||||
}
|
||||
};
|
||||
}
|
||||
|
||||
private Mat getQueryDescriptors() {
|
||||
return getTestDescriptors(getQueryImg());
|
||||
}
|
||||
|
||||
private Mat getQueryImg() {
|
||||
Mat img = new Mat(matSize, matSize, CvType.CV_8U, new Scalar(255));
|
||||
Imgproc.line(img, new Point(40, matSize - 40), new Point(matSize - 50, 50), new Scalar(0), 8);
|
||||
return img;
|
||||
}
|
||||
|
||||
private Mat getTestDescriptors(Mat img) {
|
||||
MatOfKeyPoint keypoints = new MatOfKeyPoint();
|
||||
Mat descriptors = new Mat();
|
||||
|
||||
Feature2D detector = FastFeatureDetector.create();
|
||||
Feature2D extractor = createClassInstance(XFEATURES2D+"BriefDescriptorExtractor", DEFAULT_FACTORY, null, null);
|
||||
|
||||
detector.detect(img, keypoints);
|
||||
extractor.compute(img, keypoints, descriptors);
|
||||
|
||||
return descriptors;
|
||||
}
|
||||
|
||||
private Mat getTrainDescriptors() {
|
||||
return getTestDescriptors(getTrainImg());
|
||||
}
|
||||
|
||||
private Mat getTrainImg() {
|
||||
Mat img = new Mat(matSize, matSize, CvType.CV_8U, new Scalar(255));
|
||||
Imgproc.line(img, new Point(40, 40), new Point(matSize - 40, matSize - 40), new Scalar(0), 8);
|
||||
return img;
|
||||
}
|
||||
|
||||
protected void setUp() throws Exception {
|
||||
super.setUp();
|
||||
matcher = DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE_HAMMINGLUT);
|
||||
matSize = 100;
|
||||
|
||||
truth = new DMatch[] {
|
||||
new DMatch(0, 0, 0, 51),
|
||||
new DMatch(1, 2, 0, 42),
|
||||
new DMatch(2, 1, 0, 40),
|
||||
new DMatch(3, 3, 0, 53) };
|
||||
}
|
||||
|
||||
public void testAdd() {
|
||||
matcher.add(Arrays.asList(new Mat()));
|
||||
assertFalse(matcher.empty());
|
||||
}
|
||||
|
||||
public void testClear() {
|
||||
matcher.add(Arrays.asList(new Mat()));
|
||||
|
||||
matcher.clear();
|
||||
|
||||
assertTrue(matcher.empty());
|
||||
}
|
||||
|
||||
public void testClone() {
|
||||
Mat train = new Mat(1, 1, CvType.CV_8U, new Scalar(123));
|
||||
Mat truth = train.clone();
|
||||
matcher.add(Arrays.asList(train));
|
||||
|
||||
DescriptorMatcher cloned = matcher.clone();
|
||||
|
||||
assertNotNull(cloned);
|
||||
|
||||
List<Mat> descriptors = cloned.getTrainDescriptors();
|
||||
assertEquals(1, descriptors.size());
|
||||
assertMatEqual(truth, descriptors.get(0));
|
||||
}
|
||||
|
||||
public void testCloneBoolean() {
|
||||
matcher.add(Arrays.asList(new Mat()));
|
||||
|
||||
DescriptorMatcher cloned = matcher.clone(true);
|
||||
|
||||
assertNotNull(cloned);
|
||||
assertTrue(cloned.empty());
|
||||
}
|
||||
|
||||
public void testCreate() {
|
||||
assertNotNull(matcher);
|
||||
}
|
||||
|
||||
public void testEmpty() {
|
||||
assertTrue(matcher.empty());
|
||||
}
|
||||
|
||||
public void testGetTrainDescriptors() {
|
||||
Mat train = new Mat(1, 1, CvType.CV_8U, new Scalar(123));
|
||||
Mat truth = train.clone();
|
||||
matcher.add(Arrays.asList(train));
|
||||
|
||||
List<Mat> descriptors = matcher.getTrainDescriptors();
|
||||
|
||||
assertEquals(1, descriptors.size());
|
||||
assertMatEqual(truth, descriptors.get(0));
|
||||
}
|
||||
|
||||
public void testIsMaskSupported() {
|
||||
assertTrue(matcher.isMaskSupported());
|
||||
}
|
||||
|
||||
public void testKnnMatchMatListOfListOfDMatchInt() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testKnnMatchMatListOfListOfDMatchIntListOfMat() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testKnnMatchMatListOfListOfDMatchIntListOfMatBoolean() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testKnnMatchMatMatListOfListOfDMatchInt() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testKnnMatchMatMatListOfListOfDMatchIntMat() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testKnnMatchMatMatListOfListOfDMatchIntMatBoolean() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testMatchMatListOfDMatch() {
|
||||
Mat train = getTrainDescriptors();
|
||||
Mat query = getQueryDescriptors();
|
||||
MatOfDMatch matches = new MatOfDMatch();
|
||||
matcher.add(Arrays.asList(train));
|
||||
|
||||
matcher.match(query, matches);
|
||||
|
||||
assertArrayDMatchEquals(truth, matches.toArray(), EPS);
|
||||
}
|
||||
|
||||
public void testMatchMatListOfDMatchListOfMat() {
|
||||
Mat train = getTrainDescriptors();
|
||||
Mat query = getQueryDescriptors();
|
||||
Mat mask = getMaskImg();
|
||||
MatOfDMatch matches = new MatOfDMatch();
|
||||
matcher.add(Arrays.asList(train));
|
||||
|
||||
matcher.match(query, matches, Arrays.asList(mask));
|
||||
|
||||
assertListDMatchEquals(Arrays.asList(truth[0], truth[1]), matches.toList(), EPS);
|
||||
}
|
||||
|
||||
public void testMatchMatMatListOfDMatch() {
|
||||
Mat train = getTrainDescriptors();
|
||||
Mat query = getQueryDescriptors();
|
||||
MatOfDMatch matches = new MatOfDMatch();
|
||||
|
||||
matcher.match(query, train, matches);
|
||||
|
||||
/*
|
||||
OpenCVTestRunner.Log("matches found: " + matches.size());
|
||||
for (DMatch m : matches.toArray())
|
||||
OpenCVTestRunner.Log(m.toString());
|
||||
*/
|
||||
|
||||
assertArrayDMatchEquals(truth, matches.toArray(), EPS);
|
||||
}
|
||||
|
||||
public void testMatchMatMatListOfDMatchMat() {
|
||||
Mat train = getTrainDescriptors();
|
||||
Mat query = getQueryDescriptors();
|
||||
Mat mask = getMaskImg();
|
||||
MatOfDMatch matches = new MatOfDMatch();
|
||||
|
||||
matcher.match(query, train, matches, mask);
|
||||
|
||||
assertListDMatchEquals(Arrays.asList(truth[0], truth[1]), matches.toList(), EPS);
|
||||
}
|
||||
|
||||
public void testRadiusMatchMatListOfListOfDMatchFloat() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testRadiusMatchMatListOfListOfDMatchFloatListOfMat() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testRadiusMatchMatListOfListOfDMatchFloatListOfMatBoolean() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testRadiusMatchMatMatListOfListOfDMatchFloat() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testRadiusMatchMatMatListOfListOfDMatchFloatMat() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testRadiusMatchMatMatListOfListOfDMatchFloatMatBoolean() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testRead() {
|
||||
String filename = OpenCVTestRunner.getTempFileName("yml");
|
||||
writeFile(filename, "%YAML:1.0\n---\n");
|
||||
|
||||
matcher.read(filename);
|
||||
assertTrue(true);// BruteforceMatcher has no settings
|
||||
}
|
||||
|
||||
public void testTrain() {
|
||||
matcher.train();// BruteforceMatcher does not need to train
|
||||
}
|
||||
|
||||
public void testWrite() {
|
||||
String filename = OpenCVTestRunner.getTempFileName("yml");
|
||||
|
||||
matcher.write(filename);
|
||||
|
||||
String truth = "%YAML:1.0\n---\n";
|
||||
assertEquals(truth, readFile(filename));
|
||||
}
|
||||
|
||||
}
|
||||
@@ -0,0 +1,268 @@
|
||||
package org.opencv.test.features2d;
|
||||
|
||||
import java.util.Arrays;
|
||||
import java.util.List;
|
||||
|
||||
import org.opencv.core.CvType;
|
||||
import org.opencv.core.Mat;
|
||||
import org.opencv.core.MatOfDMatch;
|
||||
import org.opencv.core.MatOfKeyPoint;
|
||||
import org.opencv.core.Point;
|
||||
import org.opencv.core.Scalar;
|
||||
import org.opencv.core.DMatch;
|
||||
import org.opencv.features2d.DescriptorMatcher;
|
||||
import org.opencv.core.KeyPoint;
|
||||
import org.opencv.test.OpenCVTestCase;
|
||||
import org.opencv.test.OpenCVTestRunner;
|
||||
import org.opencv.imgproc.Imgproc;
|
||||
import org.opencv.features2d.Feature2D;
|
||||
|
||||
public class BruteForceL1DescriptorMatcherTest extends OpenCVTestCase {
|
||||
|
||||
DescriptorMatcher matcher;
|
||||
int matSize;
|
||||
DMatch[] truth;
|
||||
|
||||
private Mat getMaskImg() {
|
||||
return new Mat(5, 2, CvType.CV_8U, new Scalar(0)) {
|
||||
{
|
||||
put(0, 0, 1, 1, 1, 1);
|
||||
}
|
||||
};
|
||||
}
|
||||
|
||||
private Mat getQueryDescriptors() {
|
||||
Mat img = getQueryImg();
|
||||
MatOfKeyPoint keypoints = new MatOfKeyPoint();
|
||||
Mat descriptors = new Mat();
|
||||
|
||||
Feature2D detector = createClassInstance(XFEATURES2D+"SURF", DEFAULT_FACTORY, null, null);
|
||||
Feature2D extractor = createClassInstance(XFEATURES2D+"SURF", DEFAULT_FACTORY, null, null);
|
||||
|
||||
setProperty(detector, "extended", "boolean", true);
|
||||
setProperty(detector, "hessianThreshold", "double", 8000);
|
||||
setProperty(detector, "nOctaveLayers", "int", 2);
|
||||
setProperty(detector, "nOctaves", "int", 3);
|
||||
setProperty(detector, "upright", "boolean", false);
|
||||
|
||||
detector.detect(img, keypoints);
|
||||
extractor.compute(img, keypoints, descriptors);
|
||||
|
||||
return descriptors;
|
||||
}
|
||||
|
||||
private Mat getQueryImg() {
|
||||
Mat cross = new Mat(matSize, matSize, CvType.CV_8U, new Scalar(255));
|
||||
Imgproc.line(cross, new Point(30, matSize / 2), new Point(matSize - 31, matSize / 2), new Scalar(100), 3);
|
||||
Imgproc.line(cross, new Point(matSize / 2, 30), new Point(matSize / 2, matSize - 31), new Scalar(100), 3);
|
||||
|
||||
return cross;
|
||||
}
|
||||
|
||||
private Mat getTrainDescriptors() {
|
||||
Mat img = getTrainImg();
|
||||
MatOfKeyPoint keypoints = new MatOfKeyPoint(new KeyPoint(50, 50, 16, 0, 20000, 1, -1), new KeyPoint(42, 42, 16, 160, 10000, 1, -1));
|
||||
Mat descriptors = new Mat();
|
||||
|
||||
Feature2D extractor = createClassInstance(XFEATURES2D+"SURF", DEFAULT_FACTORY, null, null);
|
||||
|
||||
extractor.compute(img, keypoints, descriptors);
|
||||
|
||||
return descriptors;
|
||||
}
|
||||
|
||||
private Mat getTrainImg() {
|
||||
Mat cross = new Mat(matSize, matSize, CvType.CV_8U, new Scalar(255));
|
||||
Imgproc.line(cross, new Point(20, matSize / 2), new Point(matSize - 21, matSize / 2), new Scalar(100), 2);
|
||||
Imgproc.line(cross, new Point(matSize / 2, 20), new Point(matSize / 2, matSize - 21), new Scalar(100), 2);
|
||||
|
||||
return cross;
|
||||
}
|
||||
|
||||
protected void setUp() throws Exception {
|
||||
super.setUp();
|
||||
matcher = DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE_L1);
|
||||
matSize = 100;
|
||||
|
||||
truth = new DMatch[] {
|
||||
new DMatch(0, 0, 0, 3.0710702f),
|
||||
new DMatch(1, 1, 0, 3.562016f),
|
||||
new DMatch(2, 1, 0, 1.3682679f),
|
||||
new DMatch(3, 1, 0, 1.3012862f),
|
||||
new DMatch(4, 1, 0, 1.1852086f)
|
||||
};
|
||||
}
|
||||
|
||||
public void testAdd() {
|
||||
matcher.add(Arrays.asList(new Mat()));
|
||||
assertFalse(matcher.empty());
|
||||
}
|
||||
|
||||
public void testClear() {
|
||||
matcher.add(Arrays.asList(new Mat()));
|
||||
|
||||
matcher.clear();
|
||||
|
||||
assertTrue(matcher.empty());
|
||||
}
|
||||
|
||||
public void testClone() {
|
||||
Mat train = new Mat(1, 1, CvType.CV_8U, new Scalar(123));
|
||||
Mat truth = train.clone();
|
||||
matcher.add(Arrays.asList(train));
|
||||
|
||||
DescriptorMatcher cloned = matcher.clone();
|
||||
|
||||
assertNotNull(cloned);
|
||||
|
||||
List<Mat> descriptors = cloned.getTrainDescriptors();
|
||||
assertEquals(1, descriptors.size());
|
||||
assertMatEqual(truth, descriptors.get(0));
|
||||
}
|
||||
|
||||
public void testCloneBoolean() {
|
||||
matcher.add(Arrays.asList(new Mat()));
|
||||
|
||||
DescriptorMatcher cloned = matcher.clone(true);
|
||||
|
||||
assertNotNull(cloned);
|
||||
assertTrue(cloned.empty());
|
||||
}
|
||||
|
||||
public void testCreate() {
|
||||
assertNotNull(matcher);
|
||||
}
|
||||
|
||||
public void testEmpty() {
|
||||
assertTrue(matcher.empty());
|
||||
}
|
||||
|
||||
public void testGetTrainDescriptors() {
|
||||
Mat train = new Mat(1, 1, CvType.CV_8U, new Scalar(123));
|
||||
Mat truth = train.clone();
|
||||
matcher.add(Arrays.asList(train));
|
||||
|
||||
List<Mat> descriptors = matcher.getTrainDescriptors();
|
||||
|
||||
assertEquals(1, descriptors.size());
|
||||
assertMatEqual(truth, descriptors.get(0));
|
||||
}
|
||||
|
||||
public void testIsMaskSupported() {
|
||||
assertTrue(matcher.isMaskSupported());
|
||||
}
|
||||
|
||||
public void testKnnMatchMatListOfListOfDMatchInt() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testKnnMatchMatListOfListOfDMatchIntListOfMat() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testKnnMatchMatListOfListOfDMatchIntListOfMatBoolean() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testKnnMatchMatMatListOfListOfDMatchInt() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testKnnMatchMatMatListOfListOfDMatchIntMat() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testKnnMatchMatMatListOfListOfDMatchIntMatBoolean() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testMatchMatListOfDMatch() {
|
||||
Mat train = getTrainDescriptors();
|
||||
Mat query = getQueryDescriptors();
|
||||
MatOfDMatch matches = new MatOfDMatch();
|
||||
matcher.add(Arrays.asList(train));
|
||||
|
||||
matcher.match(query, matches);
|
||||
|
||||
assertArrayDMatchEquals(truth, matches.toArray(), EPS);
|
||||
}
|
||||
|
||||
public void testMatchMatListOfDMatchListOfMat() {
|
||||
Mat train = getTrainDescriptors();
|
||||
Mat query = getQueryDescriptors();
|
||||
Mat mask = getMaskImg();
|
||||
MatOfDMatch matches = new MatOfDMatch();
|
||||
matcher.add(Arrays.asList(train));
|
||||
|
||||
matcher.match(query, matches, Arrays.asList(mask));
|
||||
|
||||
assertListDMatchEquals(Arrays.asList(truth[0], truth[1]), matches.toList(), EPS);
|
||||
}
|
||||
|
||||
public void testMatchMatMatListOfDMatch() {
|
||||
Mat train = getTrainDescriptors();
|
||||
Mat query = getQueryDescriptors();
|
||||
MatOfDMatch matches = new MatOfDMatch();
|
||||
|
||||
matcher.match(query, train, matches);
|
||||
|
||||
assertArrayDMatchEquals(truth, matches.toArray(), EPS);
|
||||
}
|
||||
|
||||
public void testMatchMatMatListOfDMatchMat() {
|
||||
Mat train = getTrainDescriptors();
|
||||
Mat query = getQueryDescriptors();
|
||||
Mat mask = getMaskImg();
|
||||
MatOfDMatch matches = new MatOfDMatch();
|
||||
|
||||
matcher.match(query, train, matches, mask);
|
||||
|
||||
assertListDMatchEquals(Arrays.asList(truth[0], truth[1]), matches.toList(), EPS);
|
||||
}
|
||||
|
||||
public void testRadiusMatchMatListOfListOfDMatchFloat() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testRadiusMatchMatListOfListOfDMatchFloatListOfMat() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testRadiusMatchMatListOfListOfDMatchFloatListOfMatBoolean() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testRadiusMatchMatMatListOfListOfDMatchFloat() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testRadiusMatchMatMatListOfListOfDMatchFloatMat() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testRadiusMatchMatMatListOfListOfDMatchFloatMatBoolean() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testRead() {
|
||||
String filename = OpenCVTestRunner.getTempFileName("yml");
|
||||
writeFile(filename, "%YAML:1.0\n---\n");
|
||||
|
||||
matcher.read(filename);
|
||||
assertTrue(true);// BruteforceMatcher has no settings
|
||||
}
|
||||
|
||||
public void testTrain() {
|
||||
matcher.train();// BruteforceMatcher does not need to train
|
||||
}
|
||||
|
||||
public void testWrite() {
|
||||
String filename = OpenCVTestRunner.getTempFileName("yml");
|
||||
|
||||
matcher.write(filename);
|
||||
|
||||
String truth = "%YAML:1.0\n---\n";
|
||||
assertEquals(truth, readFile(filename));
|
||||
}
|
||||
|
||||
}
|
||||
@@ -0,0 +1,280 @@
|
||||
package org.opencv.test.features2d;
|
||||
|
||||
import java.util.Arrays;
|
||||
import java.util.List;
|
||||
|
||||
import org.opencv.core.CvType;
|
||||
import org.opencv.core.Mat;
|
||||
import org.opencv.core.MatOfDMatch;
|
||||
import org.opencv.core.MatOfKeyPoint;
|
||||
import org.opencv.core.Point;
|
||||
import org.opencv.core.Scalar;
|
||||
import org.opencv.core.DMatch;
|
||||
import org.opencv.features2d.DescriptorMatcher;
|
||||
import org.opencv.core.KeyPoint;
|
||||
import org.opencv.test.OpenCVTestCase;
|
||||
import org.opencv.test.OpenCVTestRunner;
|
||||
import org.opencv.imgproc.Imgproc;
|
||||
import org.opencv.features2d.Feature2D;
|
||||
|
||||
public class BruteForceSL2DescriptorMatcherTest extends OpenCVTestCase {
|
||||
|
||||
DescriptorMatcher matcher;
|
||||
int matSize;
|
||||
DMatch[] truth;
|
||||
|
||||
private Mat getMaskImg() {
|
||||
return new Mat(5, 2, CvType.CV_8U, new Scalar(0)) {
|
||||
{
|
||||
put(0, 0, 1, 1, 1, 1);
|
||||
}
|
||||
};
|
||||
}
|
||||
|
||||
/*
|
||||
private float sqr(float val){
|
||||
return val * val;
|
||||
}
|
||||
*/
|
||||
|
||||
private Mat getQueryDescriptors() {
|
||||
Mat img = getQueryImg();
|
||||
MatOfKeyPoint keypoints = new MatOfKeyPoint();
|
||||
Mat descriptors = new Mat();
|
||||
|
||||
Feature2D detector = createClassInstance(XFEATURES2D+"SURF", DEFAULT_FACTORY, null, null);
|
||||
Feature2D extractor = createClassInstance(XFEATURES2D+"SURF", DEFAULT_FACTORY, null, null);
|
||||
|
||||
setProperty(detector, "hessianThreshold", "double", 8000);
|
||||
setProperty(detector, "nOctaves", "int", 3);
|
||||
setProperty(detector, "nOctaveLayers", "int", 4);
|
||||
setProperty(detector, "upright", "boolean", false);
|
||||
|
||||
detector.detect(img, keypoints);
|
||||
extractor.compute(img, keypoints, descriptors);
|
||||
|
||||
return descriptors;
|
||||
}
|
||||
|
||||
private Mat getQueryImg() {
|
||||
Mat cross = new Mat(matSize, matSize, CvType.CV_8U, new Scalar(255));
|
||||
Imgproc.line(cross, new Point(30, matSize / 2), new Point(matSize - 31, matSize / 2), new Scalar(100), 3);
|
||||
Imgproc.line(cross, new Point(matSize / 2, 30), new Point(matSize / 2, matSize - 31), new Scalar(100), 3);
|
||||
|
||||
return cross;
|
||||
}
|
||||
|
||||
private Mat getTrainDescriptors() {
|
||||
Mat img = getTrainImg();
|
||||
MatOfKeyPoint keypoints = new MatOfKeyPoint(new KeyPoint(50, 50, 16, 0, 20000, 1, -1), new KeyPoint(42, 42, 16, 160, 10000, 1, -1));
|
||||
Mat descriptors = new Mat();
|
||||
|
||||
Feature2D extractor = createClassInstance(XFEATURES2D+"SURF", DEFAULT_FACTORY, null, null);
|
||||
|
||||
extractor.compute(img, keypoints, descriptors);
|
||||
|
||||
return descriptors;
|
||||
}
|
||||
|
||||
private Mat getTrainImg() {
|
||||
Mat cross = new Mat(matSize, matSize, CvType.CV_8U, new Scalar(255));
|
||||
Imgproc.line(cross, new Point(20, matSize / 2), new Point(matSize - 21, matSize / 2), new Scalar(100), 2);
|
||||
Imgproc.line(cross, new Point(matSize / 2, 20), new Point(matSize / 2, matSize - 21), new Scalar(100), 2);
|
||||
|
||||
return cross;
|
||||
}
|
||||
|
||||
protected void setUp() throws Exception {
|
||||
super.setUp();
|
||||
matcher = DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE_SL2);
|
||||
matSize = 100;
|
||||
|
||||
truth = new DMatch[] {
|
||||
new DMatch(0, 0, 0, 0.37933317f),
|
||||
new DMatch(1, 1, 0, 0.8421953f),
|
||||
new DMatch(2, 1, 0, 0.0968556f),
|
||||
new DMatch(3, 1, 0, 0.0855606f),
|
||||
new DMatch(4, 1, 0, 0.07033461f)
|
||||
};
|
||||
}
|
||||
|
||||
public void testAdd() {
|
||||
matcher.add(Arrays.asList(new Mat()));
|
||||
assertFalse(matcher.empty());
|
||||
}
|
||||
|
||||
public void testClear() {
|
||||
matcher.add(Arrays.asList(new Mat()));
|
||||
|
||||
matcher.clear();
|
||||
|
||||
assertTrue(matcher.empty());
|
||||
}
|
||||
|
||||
public void testClone() {
|
||||
Mat train = new Mat(1, 1, CvType.CV_8U, new Scalar(123));
|
||||
Mat truth = train.clone();
|
||||
matcher.add(Arrays.asList(train));
|
||||
|
||||
DescriptorMatcher cloned = matcher.clone();
|
||||
|
||||
assertNotNull(cloned);
|
||||
|
||||
List<Mat> descriptors = cloned.getTrainDescriptors();
|
||||
assertEquals(1, descriptors.size());
|
||||
assertMatEqual(truth, descriptors.get(0));
|
||||
}
|
||||
|
||||
public void testCloneBoolean() {
|
||||
matcher.add(Arrays.asList(new Mat()));
|
||||
|
||||
DescriptorMatcher cloned = matcher.clone(true);
|
||||
|
||||
assertNotNull(cloned);
|
||||
assertTrue(cloned.empty());
|
||||
}
|
||||
|
||||
public void testCreate() {
|
||||
assertNotNull(matcher);
|
||||
}
|
||||
|
||||
public void testEmpty() {
|
||||
assertTrue(matcher.empty());
|
||||
}
|
||||
|
||||
public void testGetTrainDescriptors() {
|
||||
Mat train = new Mat(1, 1, CvType.CV_8U, new Scalar(123));
|
||||
Mat truth = train.clone();
|
||||
matcher.add(Arrays.asList(train));
|
||||
|
||||
List<Mat> descriptors = matcher.getTrainDescriptors();
|
||||
|
||||
assertEquals(1, descriptors.size());
|
||||
assertMatEqual(truth, descriptors.get(0));
|
||||
}
|
||||
|
||||
public void testIsMaskSupported() {
|
||||
assertTrue(matcher.isMaskSupported());
|
||||
}
|
||||
|
||||
public void testKnnMatchMatListOfListOfDMatchInt() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testKnnMatchMatListOfListOfDMatchIntListOfMat() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testKnnMatchMatListOfListOfDMatchIntListOfMatBoolean() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testKnnMatchMatMatListOfListOfDMatchInt() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testKnnMatchMatMatListOfListOfDMatchIntMat() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testKnnMatchMatMatListOfListOfDMatchIntMatBoolean() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testMatchMatListOfDMatch() {
|
||||
Mat train = getTrainDescriptors();
|
||||
Mat query = getQueryDescriptors();
|
||||
MatOfDMatch matches = new MatOfDMatch();
|
||||
matcher.add(Arrays.asList(train));
|
||||
|
||||
matcher.match(query, matches);
|
||||
OpenCVTestRunner.Log(matches);
|
||||
OpenCVTestRunner.Log(matches);
|
||||
OpenCVTestRunner.Log(matches);
|
||||
|
||||
assertArrayDMatchEquals(truth, matches.toArray(), EPS);
|
||||
}
|
||||
|
||||
public void testMatchMatListOfDMatchListOfMat() {
|
||||
Mat train = getTrainDescriptors();
|
||||
Mat query = getQueryDescriptors();
|
||||
Mat mask = getMaskImg();
|
||||
MatOfDMatch matches = new MatOfDMatch();
|
||||
matcher.add(Arrays.asList(train));
|
||||
|
||||
matcher.match(query, matches, Arrays.asList(mask));
|
||||
|
||||
assertListDMatchEquals(Arrays.asList(truth[0], truth[1]), matches.toList(), EPS);
|
||||
}
|
||||
|
||||
public void testMatchMatMatListOfDMatch() {
|
||||
Mat train = getTrainDescriptors();
|
||||
Mat query = getQueryDescriptors();
|
||||
MatOfDMatch matches = new MatOfDMatch();
|
||||
|
||||
matcher.match(query, train, matches);
|
||||
|
||||
assertArrayDMatchEquals(truth, matches.toArray(), EPS);
|
||||
|
||||
// OpenCVTestRunner.Log("matches found: " + matches.size());
|
||||
// for (DMatch m : matches)
|
||||
// OpenCVTestRunner.Log(m.toString());
|
||||
}
|
||||
|
||||
public void testMatchMatMatListOfDMatchMat() {
|
||||
Mat train = getTrainDescriptors();
|
||||
Mat query = getQueryDescriptors();
|
||||
Mat mask = getMaskImg();
|
||||
MatOfDMatch matches = new MatOfDMatch();
|
||||
|
||||
matcher.match(query, train, matches, mask);
|
||||
|
||||
assertListDMatchEquals(Arrays.asList(truth[0], truth[1]), matches.toList(), EPS);
|
||||
}
|
||||
|
||||
public void testRadiusMatchMatListOfListOfDMatchFloat() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testRadiusMatchMatListOfListOfDMatchFloatListOfMat() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testRadiusMatchMatListOfListOfDMatchFloatListOfMatBoolean() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testRadiusMatchMatMatListOfListOfDMatchFloat() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testRadiusMatchMatMatListOfListOfDMatchFloatMat() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testRadiusMatchMatMatListOfListOfDMatchFloatMatBoolean() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testRead() {
|
||||
String filename = OpenCVTestRunner.getTempFileName("yml");
|
||||
writeFile(filename, "%YAML:1.0\n---\n");
|
||||
|
||||
matcher.read(filename);
|
||||
assertTrue(true);// BruteforceMatcher has no settings
|
||||
}
|
||||
|
||||
public void testTrain() {
|
||||
matcher.train();// BruteforceMatcher does not need to train
|
||||
}
|
||||
|
||||
public void testWrite() {
|
||||
String filename = OpenCVTestRunner.getTempFileName("yml");
|
||||
|
||||
matcher.write(filename);
|
||||
|
||||
String truth = "%YAML:1.0\n---\n";
|
||||
assertEquals(truth, readFile(filename));
|
||||
}
|
||||
|
||||
}
|
||||
@@ -0,0 +1,39 @@
|
||||
package org.opencv.test.features2d;
|
||||
|
||||
import org.opencv.test.OpenCVTestCase;
|
||||
|
||||
public class DENSEFeatureDetectorTest extends OpenCVTestCase {
|
||||
|
||||
public void testCreate() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testDetectListOfMatListOfListOfKeyPoint() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testDetectListOfMatListOfListOfKeyPointListOfMat() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testDetectMatListOfKeyPoint() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testDetectMatListOfKeyPointMat() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testEmpty() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testRead() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testWrite() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
}
|
||||
@@ -0,0 +1,141 @@
|
||||
package org.opencv.test.features2d;
|
||||
|
||||
import java.util.Arrays;
|
||||
|
||||
import org.opencv.core.Core;
|
||||
import org.opencv.core.CvType;
|
||||
import org.opencv.core.Mat;
|
||||
import org.opencv.core.MatOfKeyPoint;
|
||||
import org.opencv.core.Point;
|
||||
import org.opencv.core.Scalar;
|
||||
import org.opencv.features2d.FastFeatureDetector;
|
||||
import org.opencv.core.KeyPoint;
|
||||
import org.opencv.test.OpenCVTestCase;
|
||||
import org.opencv.test.OpenCVTestRunner;
|
||||
import org.opencv.imgproc.Imgproc;
|
||||
|
||||
public class FASTFeatureDetectorTest extends OpenCVTestCase {
|
||||
|
||||
FastFeatureDetector detector;
|
||||
KeyPoint[] truth;
|
||||
|
||||
private Mat getMaskImg() {
|
||||
Mat mask = new Mat(100, 100, CvType.CV_8U, new Scalar(255));
|
||||
Mat right = mask.submat(0, 100, 50, 100);
|
||||
right.setTo(new Scalar(0));
|
||||
return mask;
|
||||
}
|
||||
|
||||
private Mat getTestImg() {
|
||||
Mat img = new Mat(100, 100, CvType.CV_8U, new Scalar(255));
|
||||
Imgproc.line(img, new Point(30, 30), new Point(70, 70), new Scalar(0), 8);
|
||||
return img;
|
||||
}
|
||||
|
||||
@Override
|
||||
protected void setUp() throws Exception {
|
||||
super.setUp();
|
||||
detector = FastFeatureDetector.create();
|
||||
truth = new KeyPoint[] { new KeyPoint(32, 27, 7, -1, 254, 0, -1), new KeyPoint(27, 32, 7, -1, 254, 0, -1), new KeyPoint(73, 68, 7, -1, 254, 0, -1),
|
||||
new KeyPoint(68, 73, 7, -1, 254, 0, -1) };
|
||||
}
|
||||
|
||||
public void testCreate() {
|
||||
assertNotNull(detector);
|
||||
}
|
||||
|
||||
public void testDetectListOfMatListOfListOfKeyPoint() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testDetectListOfMatListOfListOfKeyPointListOfMat() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testDetectMatListOfKeyPoint() {
|
||||
Mat img = getTestImg();
|
||||
MatOfKeyPoint keypoints = new MatOfKeyPoint();
|
||||
|
||||
detector.detect(img, keypoints);
|
||||
|
||||
assertListKeyPointEquals(Arrays.asList(truth), keypoints.toList(), EPS);
|
||||
|
||||
// OpenCVTestRunner.Log("points found: " + keypoints.size());
|
||||
// for (KeyPoint kp : keypoints)
|
||||
// OpenCVTestRunner.Log(kp.toString());
|
||||
}
|
||||
|
||||
public void testDetectMatListOfKeyPointMat() {
|
||||
Mat img = getTestImg();
|
||||
Mat mask = getMaskImg();
|
||||
MatOfKeyPoint keypoints = new MatOfKeyPoint();
|
||||
|
||||
detector.detect(img, keypoints, mask);
|
||||
|
||||
assertListKeyPointEquals(Arrays.asList(truth[0], truth[1]), keypoints.toList(), EPS);
|
||||
}
|
||||
|
||||
public void testEmpty() {
|
||||
// assertFalse(detector.empty());
|
||||
fail("Not yet implemented"); // FAST does not override empty() method
|
||||
}
|
||||
|
||||
public void testRead() {
|
||||
String filename = OpenCVTestRunner.getTempFileName("xml");
|
||||
|
||||
writeFile(filename, "<?xml version=\"1.0\"?>\n<opencv_storage>\n<name>Feature2D.FastFeatureDetector</name>\n<threshold>10</threshold>\n<nonmaxSuppression>1</nonmaxSuppression>\n<type>2</type>\n</opencv_storage>\n");
|
||||
detector.read(filename);
|
||||
|
||||
assertEquals(10, detector.getThreshold());
|
||||
assertEquals(true, detector.getNonmaxSuppression());
|
||||
assertEquals(2, detector.getType());
|
||||
|
||||
MatOfKeyPoint keypoints1 = new MatOfKeyPoint();
|
||||
|
||||
detector.detect(grayChess, keypoints1);
|
||||
|
||||
writeFile(filename, "<?xml version=\"1.0\"?>\n<opencv_storage>\n<name>Feature2D.FastFeatureDetector</name>\n<threshold>150</threshold>\n<nonmaxSuppression>1</nonmaxSuppression>\n<type>2</type>\n</opencv_storage>\n");
|
||||
detector.read(filename);
|
||||
|
||||
MatOfKeyPoint keypoints2 = new MatOfKeyPoint();
|
||||
|
||||
detector.detect(grayChess, keypoints2);
|
||||
|
||||
assertTrue(keypoints2.total() <= keypoints1.total());
|
||||
}
|
||||
|
||||
public void testReadYml() {
|
||||
String filename = OpenCVTestRunner.getTempFileName("yml");
|
||||
|
||||
writeFile(filename, "%YAML:1.0\n---\nthreshold: 130\nnonmaxSuppression: 1\ntype: 2\n");
|
||||
detector.read(filename);
|
||||
|
||||
assertEquals(130, detector.getThreshold());
|
||||
assertEquals(true, detector.getNonmaxSuppression());
|
||||
assertEquals(2, detector.getType());
|
||||
|
||||
MatOfKeyPoint keypoints1 = new MatOfKeyPoint();
|
||||
|
||||
detector.detect(grayChess, keypoints1);
|
||||
|
||||
writeFile(filename, "%YAML:1.0\n---\nthreshold: 150\nnonmaxSuppression: 1\ntype: 2\n");
|
||||
detector.read(filename);
|
||||
|
||||
MatOfKeyPoint keypoints2 = new MatOfKeyPoint();
|
||||
|
||||
detector.detect(grayChess, keypoints2);
|
||||
|
||||
assertTrue(keypoints2.total() <= keypoints1.total());
|
||||
}
|
||||
|
||||
public void testWriteYml() {
|
||||
String filename = OpenCVTestRunner.getTempFileName("yml");
|
||||
|
||||
detector.write(filename);
|
||||
|
||||
String truth = "%YAML:1.0\n---\nname: \"Feature2D.FastFeatureDetector\"\nthreshold: 10\nnonmaxSuppression: 1\ntype: 2\n";
|
||||
String data = readFile(filename);
|
||||
|
||||
assertEquals(truth, data);
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,172 @@
|
||||
package org.opencv.test.features2d;
|
||||
|
||||
import java.util.ArrayList;
|
||||
import java.util.Arrays;
|
||||
import java.util.List;
|
||||
|
||||
import org.opencv.calib3d.Calib3d;
|
||||
import org.opencv.core.CvType;
|
||||
import org.opencv.core.Mat;
|
||||
import org.opencv.core.MatOfInt;
|
||||
import org.opencv.core.MatOfDMatch;
|
||||
import org.opencv.core.MatOfKeyPoint;
|
||||
import org.opencv.core.MatOfPoint2f;
|
||||
import org.opencv.core.Point;
|
||||
import org.opencv.core.Range;
|
||||
import org.opencv.core.Scalar;
|
||||
import org.opencv.core.DMatch;
|
||||
import org.opencv.features2d.DescriptorMatcher;
|
||||
import org.opencv.features2d.Features2d;
|
||||
import org.opencv.core.KeyPoint;
|
||||
import org.opencv.imgcodecs.Imgcodecs;
|
||||
import org.opencv.test.OpenCVTestCase;
|
||||
import org.opencv.test.OpenCVTestRunner;
|
||||
import org.opencv.features2d.Feature2D;
|
||||
|
||||
public class Features2dTest extends OpenCVTestCase {
|
||||
|
||||
public void testDrawKeypointsMatListOfKeyPointMat() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testDrawKeypointsMatListOfKeyPointMatScalar() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testDrawKeypointsMatListOfKeyPointMatScalarInt() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testDrawMatches2MatListOfKeyPointMatListOfKeyPointListOfListOfDMatchMat() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testDrawMatches2MatListOfKeyPointMatListOfKeyPointListOfListOfDMatchMatScalar() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testDrawMatches2MatListOfKeyPointMatListOfKeyPointListOfListOfDMatchMatScalarScalar() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testDrawMatches2MatListOfKeyPointMatListOfKeyPointListOfListOfDMatchMatScalarScalarListOfListOfByte() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testDrawMatches2MatListOfKeyPointMatListOfKeyPointListOfListOfDMatchMatScalarScalarListOfListOfByteInt() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testDrawMatchesMatListOfKeyPointMatListOfKeyPointListOfDMatchMat() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testDrawMatchesMatListOfKeyPointMatListOfKeyPointListOfDMatchMatScalar() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testDrawMatchesMatListOfKeyPointMatListOfKeyPointListOfDMatchMatScalarScalar() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testDrawMatchesMatListOfKeyPointMatListOfKeyPointListOfDMatchMatScalarScalarListOfByte() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testDrawMatchesMatListOfKeyPointMatListOfKeyPointListOfDMatchMatScalarScalarListOfByteInt() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testPTOD()
|
||||
{
|
||||
String detectorCfg = "%YAML:1.0\n---\nhessianThreshold: 4000.\nextended: 0\nupright: 0\nOctaves: 4\nOctaveLayers: 3\n";
|
||||
String extractorCfg = "%YAML:1.0\n---\nhessianThreshold: 4000.\nextended: 0\nupright: 0\nOctaves: 4\nOctaveLayers: 3\n";
|
||||
|
||||
Feature2D detector = createClassInstance(XFEATURES2D+"SURF", DEFAULT_FACTORY, null, null);
|
||||
Feature2D extractor = createClassInstance(XFEATURES2D+"SURF", DEFAULT_FACTORY, null, null);
|
||||
DescriptorMatcher matcher = DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE);
|
||||
|
||||
String detectorCfgFile = OpenCVTestRunner.getTempFileName("yml");
|
||||
writeFile(detectorCfgFile, detectorCfg);
|
||||
detector.read(detectorCfgFile);
|
||||
|
||||
String extractorCfgFile = OpenCVTestRunner.getTempFileName("yml");
|
||||
writeFile(extractorCfgFile, extractorCfg);
|
||||
extractor.read(extractorCfgFile);
|
||||
|
||||
Mat imgTrain = Imgcodecs.imread(OpenCVTestRunner.LENA_PATH, Imgcodecs.IMREAD_GRAYSCALE);
|
||||
Mat imgQuery = imgTrain.submat(new Range(0, imgTrain.rows() - 100), Range.all());
|
||||
|
||||
MatOfKeyPoint trainKeypoints = new MatOfKeyPoint();
|
||||
MatOfKeyPoint queryKeypoints = new MatOfKeyPoint();
|
||||
|
||||
detector.detect(imgTrain, trainKeypoints);
|
||||
detector.detect(imgQuery, queryKeypoints);
|
||||
|
||||
// OpenCVTestRunner.Log("Keypoints found: " + trainKeypoints.size() +
|
||||
// ":" + queryKeypoints.size());
|
||||
|
||||
Mat trainDescriptors = new Mat();
|
||||
Mat queryDescriptors = new Mat();
|
||||
|
||||
extractor.compute(imgTrain, trainKeypoints, trainDescriptors);
|
||||
extractor.compute(imgQuery, queryKeypoints, queryDescriptors);
|
||||
|
||||
MatOfDMatch matches = new MatOfDMatch();
|
||||
|
||||
matcher.add(Arrays.asList(trainDescriptors));
|
||||
matcher.match(queryDescriptors, matches);
|
||||
|
||||
// OpenCVTestRunner.Log("Matches found: " + matches.size());
|
||||
|
||||
DMatch adm[] = matches.toArray();
|
||||
List<Point> lp1 = new ArrayList<Point>(adm.length);
|
||||
List<Point> lp2 = new ArrayList<Point>(adm.length);
|
||||
KeyPoint tkp[] = trainKeypoints.toArray();
|
||||
KeyPoint qkp[] = queryKeypoints.toArray();
|
||||
for (int i = 0; i < adm.length; i++) {
|
||||
DMatch dm = adm[i];
|
||||
lp1.add(tkp[dm.trainIdx].pt);
|
||||
lp2.add(qkp[dm.queryIdx].pt);
|
||||
}
|
||||
|
||||
MatOfPoint2f points1 = new MatOfPoint2f(lp1.toArray(new Point[0]));
|
||||
MatOfPoint2f points2 = new MatOfPoint2f(lp2.toArray(new Point[0]));
|
||||
|
||||
Mat hmg = Calib3d.findHomography(points1, points2, Calib3d.RANSAC, 3);
|
||||
|
||||
assertMatEqual(Mat.eye(3, 3, CvType.CV_64F), hmg, EPS);
|
||||
|
||||
Mat outimg = new Mat();
|
||||
Features2d.drawMatches(imgQuery, queryKeypoints, imgTrain, trainKeypoints, matches, outimg);
|
||||
String outputPath = OpenCVTestRunner.getOutputFileName("PTODresult.png");
|
||||
Imgcodecs.imwrite(outputPath, outimg);
|
||||
// OpenCVTestRunner.Log("Output image is saved to: " + outputPath);
|
||||
}
|
||||
|
||||
public void testDrawKeypoints()
|
||||
{
|
||||
Mat outImg = Mat.ones(11, 11, CvType.CV_8U);
|
||||
|
||||
MatOfKeyPoint kps = new MatOfKeyPoint(new KeyPoint(5, 5, 1)); // x, y, size
|
||||
Features2d.drawKeypoints(new Mat(), kps, outImg, new Scalar(255),
|
||||
Features2d.DrawMatchesFlags_DRAW_OVER_OUTIMG);
|
||||
|
||||
Mat ref = new MatOfInt(new int[] {
|
||||
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
|
||||
1, 1, 1, 1, 15, 54, 15, 1, 1, 1, 1,
|
||||
1, 1, 1, 76, 217, 217, 221, 81, 1, 1, 1,
|
||||
1, 1, 100, 224, 111, 57, 115, 225, 101, 1, 1,
|
||||
1, 44, 215, 100, 1, 1, 1, 101, 214, 44, 1,
|
||||
1, 54, 212, 57, 1, 1, 1, 55, 212, 55, 1,
|
||||
1, 40, 215, 104, 1, 1, 1, 105, 215, 40, 1,
|
||||
1, 1, 102, 221, 111, 55, 115, 222, 103, 1, 1,
|
||||
1, 1, 1, 76, 218, 217, 220, 81, 1, 1, 1,
|
||||
1, 1, 1, 1, 15, 55, 15, 1, 1, 1, 1,
|
||||
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1
|
||||
}).reshape(1, 11);
|
||||
ref.convertTo(ref, CvType.CV_8U);
|
||||
|
||||
assertMatEqual(ref, outImg);
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,389 @@
|
||||
package org.opencv.test.features2d;
|
||||
|
||||
import java.util.Arrays;
|
||||
import java.util.List;
|
||||
|
||||
import org.opencv.core.CvException;
|
||||
import org.opencv.core.CvType;
|
||||
import org.opencv.core.Mat;
|
||||
import org.opencv.core.MatOfDMatch;
|
||||
import org.opencv.core.MatOfKeyPoint;
|
||||
import org.opencv.core.Point;
|
||||
import org.opencv.core.Scalar;
|
||||
import org.opencv.core.DMatch;
|
||||
import org.opencv.features2d.DescriptorMatcher;
|
||||
import org.opencv.features2d.FlannBasedMatcher;
|
||||
import org.opencv.core.KeyPoint;
|
||||
import org.opencv.test.OpenCVTestCase;
|
||||
import org.opencv.test.OpenCVTestRunner;
|
||||
import org.opencv.imgproc.Imgproc;
|
||||
import org.opencv.features2d.Feature2D;
|
||||
|
||||
public class FlannBasedDescriptorMatcherTest extends OpenCVTestCase {
|
||||
|
||||
static final String xmlParamsDefault = "<?xml version=\"1.0\"?>\n"
|
||||
+ "<opencv_storage>\n"
|
||||
+ "<format>3</format>\n"
|
||||
+ "<indexParams>\n"
|
||||
+ " <_>\n"
|
||||
+ " <name>algorithm</name>\n"
|
||||
+ " <type>9</type>\n" // FLANN_INDEX_TYPE_ALGORITHM
|
||||
+ " <value>1</value></_>\n"
|
||||
+ " <_>\n"
|
||||
+ " <name>trees</name>\n"
|
||||
+ " <type>4</type>\n"
|
||||
+ " <value>4</value></_></indexParams>\n"
|
||||
+ "<searchParams>\n"
|
||||
+ " <_>\n"
|
||||
+ " <name>checks</name>\n"
|
||||
+ " <type>4</type>\n"
|
||||
+ " <value>32</value></_>\n"
|
||||
+ " <_>\n"
|
||||
+ " <name>eps</name>\n"
|
||||
+ " <type>5</type>\n"
|
||||
+ " <value>0.</value></_>\n"
|
||||
+ " <_>\n"
|
||||
+ " <name>explore_all_trees</name>\n"
|
||||
+ " <type>8</type>\n"
|
||||
+ " <value>0</value></_>\n"
|
||||
+ " <_>\n"
|
||||
+ " <name>sorted</name>\n"
|
||||
+ " <type>8</type>\n" // FLANN_INDEX_TYPE_BOOL
|
||||
+ " <value>1</value></_></searchParams>\n"
|
||||
+ "</opencv_storage>\n";
|
||||
static final String ymlParamsDefault = "%YAML:1.0\n---\n"
|
||||
+ "format: 3\n"
|
||||
+ "indexParams:\n"
|
||||
+ " -\n"
|
||||
+ " name: algorithm\n"
|
||||
+ " type: 9\n" // FLANN_INDEX_TYPE_ALGORITHM
|
||||
+ " value: 1\n"
|
||||
+ " -\n"
|
||||
+ " name: trees\n"
|
||||
+ " type: 4\n"
|
||||
+ " value: 4\n"
|
||||
+ "searchParams:\n"
|
||||
+ " -\n"
|
||||
+ " name: checks\n"
|
||||
+ " type: 4\n"
|
||||
+ " value: 32\n"
|
||||
+ " -\n"
|
||||
+ " name: eps\n"
|
||||
+ " type: 5\n"
|
||||
+ " value: 0.\n"
|
||||
+ " -\n"
|
||||
+ " name: explore_all_trees\n"
|
||||
+ " type: 8\n"
|
||||
+ " value: 0\n"
|
||||
+ " -\n"
|
||||
+ " name: sorted\n"
|
||||
+ " type: 8\n" // FLANN_INDEX_TYPE_BOOL
|
||||
+ " value: 1\n";
|
||||
static final String ymlParamsModified = "%YAML:1.0\n---\n"
|
||||
+ "format: 3\n"
|
||||
+ "indexParams:\n"
|
||||
+ " -\n"
|
||||
+ " name: algorithm\n"
|
||||
+ " type: 9\n" // FLANN_INDEX_TYPE_ALGORITHM
|
||||
+ " value: 6\n"// this line is changed!
|
||||
+ " -\n"
|
||||
+ " name: trees\n"
|
||||
+ " type: 4\n"
|
||||
+ " value: 4\n"
|
||||
+ "searchParams:\n"
|
||||
+ " -\n"
|
||||
+ " name: checks\n"
|
||||
+ " type: 4\n"
|
||||
+ " value: 32\n"
|
||||
+ " -\n"
|
||||
+ " name: eps\n"
|
||||
+ " type: 5\n"
|
||||
+ " value: 4.\n"// this line is changed!
|
||||
+ " -\n"
|
||||
+ " name: explore_all_trees\n"
|
||||
+ " type: 8\n"
|
||||
+ " value: 1\n"// this line is changed!
|
||||
+ " -\n"
|
||||
+ " name: sorted\n"
|
||||
+ " type: 8\n" // FLANN_INDEX_TYPE_BOOL
|
||||
+ " value: 1\n";
|
||||
|
||||
DescriptorMatcher matcher;
|
||||
|
||||
int matSize;
|
||||
|
||||
DMatch[] truth;
|
||||
|
||||
private Mat getMaskImg() {
|
||||
return new Mat(5, 2, CvType.CV_8U, new Scalar(0)) {
|
||||
{
|
||||
put(0, 0, 1, 1, 1, 1);
|
||||
}
|
||||
};
|
||||
}
|
||||
|
||||
private Mat getQueryDescriptors() {
|
||||
Mat img = getQueryImg();
|
||||
MatOfKeyPoint keypoints = new MatOfKeyPoint();
|
||||
Mat descriptors = new Mat();
|
||||
|
||||
Feature2D detector = createClassInstance(XFEATURES2D+"SURF", DEFAULT_FACTORY, null, null);
|
||||
Feature2D extractor = createClassInstance(XFEATURES2D+"SURF", DEFAULT_FACTORY, null, null);
|
||||
|
||||
setProperty(detector, "hessianThreshold", "double", 8000);
|
||||
setProperty(detector, "nOctaves", "int", 3);
|
||||
setProperty(detector, "upright", "boolean", false);
|
||||
|
||||
detector.detect(img, keypoints);
|
||||
extractor.compute(img, keypoints, descriptors);
|
||||
|
||||
return descriptors;
|
||||
}
|
||||
|
||||
private Mat getQueryImg() {
|
||||
Mat cross = new Mat(matSize, matSize, CvType.CV_8U, new Scalar(255));
|
||||
Imgproc.line(cross, new Point(30, matSize / 2), new Point(matSize - 31, matSize / 2), new Scalar(100), 3);
|
||||
Imgproc.line(cross, new Point(matSize / 2, 30), new Point(matSize / 2, matSize - 31), new Scalar(100), 3);
|
||||
|
||||
return cross;
|
||||
}
|
||||
|
||||
private Mat getTrainDescriptors() {
|
||||
Mat img = getTrainImg();
|
||||
MatOfKeyPoint keypoints = new MatOfKeyPoint(new KeyPoint(50, 50, 16, 0, 20000, 1, -1), new KeyPoint(42, 42, 16, 160, 10000, 1, -1));
|
||||
Mat descriptors = new Mat();
|
||||
|
||||
Feature2D extractor = createClassInstance(XFEATURES2D+"SURF", DEFAULT_FACTORY, null, null);
|
||||
|
||||
extractor.compute(img, keypoints, descriptors);
|
||||
|
||||
return descriptors;
|
||||
}
|
||||
|
||||
private Mat getTrainImg() {
|
||||
Mat cross = new Mat(matSize, matSize, CvType.CV_8U, new Scalar(255));
|
||||
Imgproc.line(cross, new Point(20, matSize / 2), new Point(matSize - 21, matSize / 2), new Scalar(100), 2);
|
||||
Imgproc.line(cross, new Point(matSize / 2, 20), new Point(matSize / 2, matSize - 21), new Scalar(100), 2);
|
||||
|
||||
return cross;
|
||||
}
|
||||
|
||||
protected void setUp() throws Exception {
|
||||
super.setUp();
|
||||
matcher = DescriptorMatcher.create(DescriptorMatcher.FLANNBASED);
|
||||
matSize = 100;
|
||||
truth = new DMatch[] {
|
||||
new DMatch(0, 0, 0, 0.6159003f),
|
||||
new DMatch(1, 1, 0, 0.9177120f),
|
||||
new DMatch(2, 1, 0, 0.3112163f),
|
||||
new DMatch(3, 1, 0, 0.2925075f),
|
||||
new DMatch(4, 1, 0, 0.26520672f)
|
||||
};
|
||||
}
|
||||
|
||||
// https://github.com/opencv/opencv/issues/11268
|
||||
public void testConstructor()
|
||||
{
|
||||
FlannBasedMatcher self_created_matcher = new FlannBasedMatcher();
|
||||
Mat train = new Mat(1, 1, CvType.CV_8U, new Scalar(123));
|
||||
self_created_matcher.add(Arrays.asList(train));
|
||||
assertTrue(!self_created_matcher.empty());
|
||||
}
|
||||
|
||||
public void testAdd() {
|
||||
matcher.add(Arrays.asList(new Mat()));
|
||||
assertFalse(matcher.empty());
|
||||
}
|
||||
|
||||
public void testClear() {
|
||||
matcher.add(Arrays.asList(new Mat()));
|
||||
|
||||
matcher.clear();
|
||||
|
||||
assertTrue(matcher.empty());
|
||||
}
|
||||
|
||||
public void testClone() {
|
||||
Mat train = new Mat(1, 1, CvType.CV_8U, new Scalar(123));
|
||||
matcher.add(Arrays.asList(train));
|
||||
|
||||
try {
|
||||
matcher.clone();
|
||||
fail("Expected CvException (CV_StsNotImplemented)");
|
||||
} catch (CvException cverr) {
|
||||
// expected
|
||||
}
|
||||
}
|
||||
|
||||
public void testCloneBoolean() {
|
||||
matcher.add(Arrays.asList(new Mat()));
|
||||
|
||||
DescriptorMatcher cloned = matcher.clone(true);
|
||||
|
||||
assertNotNull(cloned);
|
||||
assertTrue(cloned.empty());
|
||||
}
|
||||
|
||||
public void testCreate() {
|
||||
assertNotNull(matcher);
|
||||
}
|
||||
|
||||
public void testEmpty() {
|
||||
assertTrue(matcher.empty());
|
||||
}
|
||||
|
||||
public void testGetTrainDescriptors() {
|
||||
Mat train = new Mat(1, 1, CvType.CV_8U, new Scalar(123));
|
||||
Mat truth = train.clone();
|
||||
matcher.add(Arrays.asList(train));
|
||||
|
||||
List<Mat> descriptors = matcher.getTrainDescriptors();
|
||||
|
||||
assertEquals(1, descriptors.size());
|
||||
assertMatEqual(truth, descriptors.get(0));
|
||||
}
|
||||
|
||||
public void testIsMaskSupported() {
|
||||
assertFalse(matcher.isMaskSupported());
|
||||
}
|
||||
|
||||
public void testKnnMatchMatListOfListOfDMatchInt() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testKnnMatchMatListOfListOfDMatchIntListOfMat() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testKnnMatchMatListOfListOfDMatchIntListOfMatBoolean() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testKnnMatchMatMatListOfListOfDMatchInt() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testKnnMatchMatMatListOfListOfDMatchIntMat() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testKnnMatchMatMatListOfListOfDMatchIntMatBoolean() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testMatchMatListOfDMatch() {
|
||||
Mat train = getTrainDescriptors();
|
||||
Mat query = getQueryDescriptors();
|
||||
MatOfDMatch matches = new MatOfDMatch();
|
||||
matcher.add(Arrays.asList(train));
|
||||
matcher.train();
|
||||
|
||||
matcher.match(query, matches);
|
||||
|
||||
assertArrayDMatchEquals(truth, matches.toArray(), EPS);
|
||||
}
|
||||
|
||||
public void testMatchMatListOfDMatchListOfMat() {
|
||||
Mat train = getTrainDescriptors();
|
||||
Mat query = getQueryDescriptors();
|
||||
Mat mask = getMaskImg();
|
||||
MatOfDMatch matches = new MatOfDMatch();
|
||||
matcher.add(Arrays.asList(train));
|
||||
matcher.train();
|
||||
|
||||
matcher.match(query, matches, Arrays.asList(mask));
|
||||
|
||||
assertArrayDMatchEquals(truth, matches.toArray(), EPS);
|
||||
}
|
||||
|
||||
public void testMatchMatMatListOfDMatch() {
|
||||
Mat train = getTrainDescriptors();
|
||||
Mat query = getQueryDescriptors();
|
||||
MatOfDMatch matches = new MatOfDMatch();
|
||||
|
||||
matcher.match(query, train, matches);
|
||||
|
||||
assertArrayDMatchEquals(truth, matches.toArray(), EPS);
|
||||
|
||||
// OpenCVTestRunner.Log(matches.toString());
|
||||
// OpenCVTestRunner.Log(matches);
|
||||
}
|
||||
|
||||
public void testMatchMatMatListOfDMatchMat() {
|
||||
Mat train = getTrainDescriptors();
|
||||
Mat query = getQueryDescriptors();
|
||||
Mat mask = getMaskImg();
|
||||
MatOfDMatch matches = new MatOfDMatch();
|
||||
|
||||
matcher.match(query, train, matches, mask);
|
||||
|
||||
assertListDMatchEquals(Arrays.asList(truth), matches.toList(), EPS);
|
||||
}
|
||||
|
||||
public void testRadiusMatchMatListOfListOfDMatchFloat() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testRadiusMatchMatListOfListOfDMatchFloatListOfMat() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testRadiusMatchMatListOfListOfDMatchFloatListOfMatBoolean() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testRadiusMatchMatMatListOfListOfDMatchFloat() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testRadiusMatchMatMatListOfListOfDMatchFloatMat() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testRadiusMatchMatMatListOfListOfDMatchFloatMatBoolean() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testRead() {
|
||||
String filenameR = OpenCVTestRunner.getTempFileName("yml");
|
||||
String filenameW = OpenCVTestRunner.getTempFileName("yml");
|
||||
writeFile(filenameR, ymlParamsModified);
|
||||
|
||||
matcher.read(filenameR);
|
||||
matcher.write(filenameW);
|
||||
|
||||
assertEquals(ymlParamsModified, readFile(filenameW));
|
||||
}
|
||||
|
||||
public void testTrain() {
|
||||
Mat train = getTrainDescriptors();
|
||||
matcher.add(Arrays.asList(train));
|
||||
matcher.train();
|
||||
}
|
||||
|
||||
public void testTrainNoData() {
|
||||
try {
|
||||
matcher.train();
|
||||
fail("Expected CvException - FlannBasedMatcher::train should fail on empty train set");
|
||||
} catch (CvException cverr) {
|
||||
// expected
|
||||
}
|
||||
}
|
||||
|
||||
public void testWrite() {
|
||||
String filename = OpenCVTestRunner.getTempFileName("xml");
|
||||
|
||||
matcher.write(filename);
|
||||
|
||||
assertEquals(xmlParamsDefault, readFile(filename));
|
||||
}
|
||||
|
||||
public void testWriteYml() {
|
||||
String filename = OpenCVTestRunner.getTempFileName("yml");
|
||||
|
||||
matcher.write(filename);
|
||||
|
||||
assertEquals(ymlParamsDefault, readFile(filename));
|
||||
}
|
||||
|
||||
}
|
||||
@@ -0,0 +1,67 @@
|
||||
package org.opencv.test.features2d;
|
||||
|
||||
import org.opencv.test.OpenCVTestCase;
|
||||
import org.opencv.test.OpenCVTestRunner;
|
||||
import org.opencv.features2d.GFTTDetector;
|
||||
|
||||
public class GFTTFeatureDetectorTest extends OpenCVTestCase {
|
||||
|
||||
GFTTDetector detector;
|
||||
|
||||
@Override
|
||||
protected void setUp() throws Exception {
|
||||
super.setUp();
|
||||
detector = GFTTDetector.create(); // default constructor have (1000, 0.01, 1, 3, 3, false, 0.04)
|
||||
}
|
||||
|
||||
public void testCreate() {
|
||||
assertNotNull(detector);
|
||||
}
|
||||
|
||||
public void testDetectListOfMatListOfListOfKeyPoint() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testDetectListOfMatListOfListOfKeyPointListOfMat() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testDetectMatListOfKeyPoint() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testDetectMatListOfKeyPointMat() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testEmpty() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testReadYml() {
|
||||
String filename = OpenCVTestRunner.getTempFileName("yml");
|
||||
|
||||
writeFile(filename, "%YAML:1.0\n---\nname: \"Feature2D.GFTTDetector\"\nnfeatures: 500\nqualityLevel: 2.0000000000000000e-02\nminDistance: 2.\nblockSize: 4\ngradSize: 5\nuseHarrisDetector: 1\nk: 5.0000000000000000e-02\n");
|
||||
detector.read(filename);
|
||||
|
||||
assertEquals(500, detector.getMaxFeatures());
|
||||
assertEquals(0.02, detector.getQualityLevel());
|
||||
assertEquals(2.0, detector.getMinDistance());
|
||||
assertEquals(4, detector.getBlockSize());
|
||||
assertEquals(5, detector.getGradientSize());
|
||||
assertEquals(true, detector.getHarrisDetector());
|
||||
assertEquals(0.05, detector.getK());
|
||||
}
|
||||
|
||||
public void testWriteYml() {
|
||||
String filename = OpenCVTestRunner.getTempFileName("yml");
|
||||
|
||||
detector.write(filename);
|
||||
|
||||
String truth = "%YAML:1.0\n---\nname: \"Feature2D.GFTTDetector\"\nnfeatures: 1000\nqualityLevel: 0.01\nminDistance: 1.\nblockSize: 3\ngradSize: 3\nuseHarrisDetector: 0\nk: 0.040000000000000001\n";
|
||||
String actual = readFile(filename);
|
||||
actual = actual.replaceAll("e([+-])0(\\d\\d)", "e$1$2"); // NOTE: workaround for different platforms double representation
|
||||
assertEquals(truth, actual);
|
||||
}
|
||||
|
||||
}
|
||||
@@ -0,0 +1,39 @@
|
||||
package org.opencv.test.features2d;
|
||||
|
||||
import org.opencv.test.OpenCVTestCase;
|
||||
|
||||
public class HARRISFeatureDetectorTest extends OpenCVTestCase {
|
||||
|
||||
public void testCreate() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testDetectListOfMatListOfListOfKeyPoint() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testDetectListOfMatListOfListOfKeyPointListOfMat() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testDetectMatListOfKeyPoint() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testDetectMatListOfKeyPointMat() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testEmpty() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testRead() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testWrite() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
}
|
||||
@@ -0,0 +1,66 @@
|
||||
package org.opencv.test.features2d;
|
||||
|
||||
import org.opencv.test.OpenCVTestCase;
|
||||
import org.opencv.test.OpenCVTestRunner;
|
||||
import org.opencv.features2d.KAZE;
|
||||
|
||||
public class KAZEDescriptorExtractorTest extends OpenCVTestCase {
|
||||
|
||||
KAZE extractor;
|
||||
|
||||
@Override
|
||||
protected void setUp() throws Exception {
|
||||
super.setUp();
|
||||
extractor = KAZE.create(); // default (false,false,0.001f,4,4,1)
|
||||
}
|
||||
|
||||
public void testCreate() {
|
||||
assertNotNull(extractor);
|
||||
}
|
||||
|
||||
public void testDetectListOfMatListOfListOfKeyPoint() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testDetectListOfMatListOfListOfKeyPointListOfMat() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testDetectMatListOfKeyPoint() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testDetectMatListOfKeyPointMat() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testEmpty() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testReadYml() {
|
||||
String filename = OpenCVTestRunner.getTempFileName("yml");
|
||||
writeFile(filename, "%YAML:1.0\n---\nformat: 3\nname: \"Feature2D.KAZE\"\nextended: 1\nupright: 1\nthreshold: 0.125\noctaves: 3\nsublevels: 5\ndiffusivity: 2\n");
|
||||
|
||||
extractor.read(filename);
|
||||
|
||||
assertEquals(true, extractor.getExtended());
|
||||
assertEquals(true, extractor.getUpright());
|
||||
assertEquals(0.125, extractor.getThreshold());
|
||||
assertEquals(3, extractor.getNOctaves());
|
||||
assertEquals(5, extractor.getNOctaveLayers());
|
||||
assertEquals(2, extractor.getDiffusivity());
|
||||
}
|
||||
|
||||
public void testWriteYml() {
|
||||
String filename = OpenCVTestRunner.getTempFileName("yml");
|
||||
|
||||
extractor.write(filename);
|
||||
|
||||
String truth = "%YAML:1.0\n---\nformat: 3\nname: \"Feature2D.KAZE\"\nextended: 0\nupright: 0\nthreshold: 0.0010000000474974513\noctaves: 4\nsublevels: 4\ndiffusivity: 1\n";
|
||||
String actual = readFile(filename);
|
||||
actual = actual.replaceAll("e([+-])0(\\d\\d)", "e$1$2"); // NOTE: workaround for different platforms double representation
|
||||
assertEquals(truth, actual);
|
||||
}
|
||||
|
||||
}
|
||||
@@ -0,0 +1,70 @@
|
||||
package org.opencv.test.features2d;
|
||||
|
||||
import org.opencv.test.OpenCVTestCase;
|
||||
import org.opencv.test.OpenCVTestRunner;
|
||||
import org.opencv.features2d.MSER;
|
||||
|
||||
public class MSERFeatureDetectorTest extends OpenCVTestCase {
|
||||
|
||||
MSER detector;
|
||||
|
||||
@Override
|
||||
protected void setUp() throws Exception {
|
||||
super.setUp();
|
||||
detector = MSER.create(); // default constructor have (5, 60, 14400, .25, .2, 200, 1.01, .003, 5)
|
||||
}
|
||||
|
||||
public void testCreate() {
|
||||
assertNotNull(detector);
|
||||
}
|
||||
|
||||
public void testDetectListOfMatListOfListOfKeyPoint() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testDetectListOfMatListOfListOfKeyPointListOfMat() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testDetectMatListOfKeyPoint() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testDetectMatListOfKeyPointMat() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testEmpty() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testReadYml() {
|
||||
String filename = OpenCVTestRunner.getTempFileName("yml");
|
||||
|
||||
writeFile(filename, "%YAML:1.0\n---\nname: \"Feature2D.MSER\"\ndelta: 6\nminArea: 62\nmaxArea: 14402\nmaxVariation: .26\nminDiversity: .3\nmaxEvolution: 201\nareaThreshold: 1.02\nminMargin: 3.0e-3\nedgeBlurSize: 3\npass2Only: 1\n");
|
||||
detector.read(filename);
|
||||
|
||||
assertEquals(6, detector.getDelta());
|
||||
assertEquals(62, detector.getMinArea());
|
||||
assertEquals(14402, detector.getMaxArea());
|
||||
assertEquals(.26, detector.getMaxVariation());
|
||||
assertEquals(.3, detector.getMinDiversity());
|
||||
assertEquals(201, detector.getMaxEvolution());
|
||||
assertEquals(1.02, detector.getAreaThreshold());
|
||||
assertEquals(0.003, detector.getMinMargin());
|
||||
assertEquals(3, detector.getEdgeBlurSize());
|
||||
assertEquals(true, detector.getPass2Only());
|
||||
}
|
||||
|
||||
public void testWriteYml() {
|
||||
String filename = OpenCVTestRunner.getTempFileName("yml");
|
||||
|
||||
detector.write(filename);
|
||||
|
||||
String truth = "%YAML:1.0\n---\nname: \"Feature2D.MSER\"\ndelta: 5\nminArea: 60\nmaxArea: 14400\nmaxVariation: 0.25\nminDiversity: 0.20000000000000001\nmaxEvolution: 200\nareaThreshold: 1.01\nminMargin: 0.0030000000000000001\nedgeBlurSize: 5\npass2Only: 0\n";
|
||||
String actual = readFile(filename);
|
||||
actual = actual.replaceAll("e([+-])0(\\d\\d)", "e$1$2"); // NOTE: workaround for different platforms double representation
|
||||
assertEquals(truth, actual);
|
||||
}
|
||||
|
||||
}
|
||||
@@ -0,0 +1,121 @@
|
||||
package org.opencv.test.features2d;
|
||||
|
||||
import org.opencv.core.Core;
|
||||
import org.opencv.core.CvType;
|
||||
import org.opencv.core.Mat;
|
||||
import org.opencv.core.MatOfKeyPoint;
|
||||
import org.opencv.core.Point;
|
||||
import org.opencv.core.Scalar;
|
||||
import org.opencv.core.KeyPoint;
|
||||
import org.opencv.features2d.ORB;
|
||||
import org.opencv.test.OpenCVTestCase;
|
||||
import org.opencv.test.OpenCVTestRunner;
|
||||
import org.opencv.imgproc.Imgproc;
|
||||
|
||||
public class ORBDescriptorExtractorTest extends OpenCVTestCase {
|
||||
|
||||
ORB extractor;
|
||||
int matSize;
|
||||
|
||||
public static void assertDescriptorsClose(Mat expected, Mat actual, int allowedDistance) {
|
||||
double distance = Core.norm(expected, actual, Core.NORM_HAMMING);
|
||||
assertTrue("expected:<" + allowedDistance + "> but was:<" + distance + ">", distance <= allowedDistance);
|
||||
}
|
||||
|
||||
private Mat getTestImg() {
|
||||
Mat cross = new Mat(matSize, matSize, CvType.CV_8U, new Scalar(255));
|
||||
Imgproc.line(cross, new Point(20, matSize / 2), new Point(matSize - 21, matSize / 2), new Scalar(100), 2);
|
||||
Imgproc.line(cross, new Point(matSize / 2, 20), new Point(matSize / 2, matSize - 21), new Scalar(100), 2);
|
||||
|
||||
return cross;
|
||||
}
|
||||
|
||||
@Override
|
||||
protected void setUp() throws Exception {
|
||||
super.setUp();
|
||||
extractor = ORB.create();
|
||||
matSize = 100;
|
||||
}
|
||||
|
||||
public void testComputeListOfMatListOfListOfKeyPointListOfMat() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testComputeMatListOfKeyPointMat() {
|
||||
KeyPoint point = new KeyPoint(55.775577545166016f, 44.224422454833984f, 16, 9.754629f, 8617.863f, 1, -1);
|
||||
MatOfKeyPoint keypoints = new MatOfKeyPoint(point);
|
||||
Mat img = getTestImg();
|
||||
Mat descriptors = new Mat();
|
||||
|
||||
extractor.compute(img, keypoints, descriptors);
|
||||
|
||||
Mat truth = new Mat(1, 32, CvType.CV_8UC1) {
|
||||
{
|
||||
put(0, 0,
|
||||
6, 74, 6, 129, 2, 130, 56, 0, 44, 132, 66, 165, 172, 6, 3, 72, 102, 61, 171, 214, 0, 144, 65, 232, 4, 32, 138, 131, 4, 21, 37, 217);
|
||||
}
|
||||
};
|
||||
assertDescriptorsClose(truth, descriptors, 1);
|
||||
}
|
||||
|
||||
public void testCreate() {
|
||||
assertNotNull(extractor);
|
||||
}
|
||||
|
||||
public void testDescriptorSize() {
|
||||
assertEquals(32, extractor.descriptorSize());
|
||||
}
|
||||
|
||||
public void testDescriptorType() {
|
||||
assertEquals(CvType.CV_8U, extractor.descriptorType());
|
||||
}
|
||||
|
||||
public void testEmpty() {
|
||||
// assertFalse(extractor.empty());
|
||||
fail("Not yet implemented"); // ORB does not override empty() method
|
||||
}
|
||||
|
||||
public void testReadYml() {
|
||||
KeyPoint point = new KeyPoint(55.775577545166016f, 44.224422454833984f, 16, 9.754629f, 8617.863f, 1, -1);
|
||||
MatOfKeyPoint keypoints = new MatOfKeyPoint(point);
|
||||
Mat img = getTestImg();
|
||||
Mat descriptors = new Mat();
|
||||
|
||||
String filename = OpenCVTestRunner.getTempFileName("yml");
|
||||
writeFile(filename, "%YAML:1.0\n---\nnfeatures: 500\nscaleFactor: 1.1\nnlevels: 3\nedgeThreshold: 31\nfirstLevel: 0\nwta_k: 2\nscoreType: 0\npatchSize: 31\nfastThreshold: 20\n");
|
||||
extractor.read(filename);
|
||||
|
||||
assertEquals(500, extractor.getMaxFeatures());
|
||||
assertEquals(1.1, extractor.getScaleFactor());
|
||||
assertEquals(3, extractor.getNLevels());
|
||||
assertEquals(31, extractor.getEdgeThreshold());
|
||||
assertEquals(0, extractor.getFirstLevel());
|
||||
assertEquals(2, extractor.getWTA_K());
|
||||
assertEquals(0, extractor.getScoreType());
|
||||
assertEquals(31, extractor.getPatchSize());
|
||||
assertEquals(20, extractor.getFastThreshold());
|
||||
|
||||
extractor.compute(img, keypoints, descriptors);
|
||||
|
||||
Mat truth = new Mat(1, 32, CvType.CV_8UC1) {
|
||||
{
|
||||
put(0, 0,
|
||||
6, 10, 22, 5, 2, 130, 56, 0, 44, 164, 66, 165, 140, 6, 1, 72, 38, 61, 163, 210, 0, 208, 1, 104, 4, 32, 74, 131, 0, 37, 37, 67);
|
||||
}
|
||||
};
|
||||
assertDescriptorsClose(truth, descriptors, 1);
|
||||
}
|
||||
|
||||
public void testWriteYml() {
|
||||
String filename = OpenCVTestRunner.getTempFileName("yml");
|
||||
|
||||
extractor.write(filename);
|
||||
|
||||
String truth = "%YAML:1.0\n---\nname: \"Feature2D.ORB\"\nnfeatures: 500\nscaleFactor: 1.2000000476837158\nnlevels: 8\nedgeThreshold: 31\nfirstLevel: 0\nwta_k: 2\nscoreType: 0\npatchSize: 31\nfastThreshold: 20\n";
|
||||
// String truth = "%YAML:1.0\n---\n";
|
||||
String actual = readFile(filename);
|
||||
actual = actual.replaceAll("e\\+000", "e+00"); // NOTE: workaround for different platforms double representation
|
||||
assertEquals(truth, actual);
|
||||
}
|
||||
|
||||
}
|
||||
@@ -0,0 +1,78 @@
|
||||
package org.opencv.test.features2d;
|
||||
|
||||
import org.junit.Assert;
|
||||
import org.opencv.core.CvType;
|
||||
import org.opencv.core.KeyPoint;
|
||||
import org.opencv.core.Mat;
|
||||
import org.opencv.core.MatOfKeyPoint;
|
||||
import org.opencv.core.Scalar;
|
||||
import org.opencv.features2d.Features2d;
|
||||
import org.opencv.features2d.ORB;
|
||||
import org.opencv.test.OpenCVTestCase;
|
||||
|
||||
public class ORBFeatureDetectorTest extends OpenCVTestCase {
|
||||
|
||||
public void testCreate() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testDetectListOfMatListOfListOfKeyPoint() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testDetectListOfMatListOfListOfKeyPointListOfMat() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testDetectMatListOfKeyPoint() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testDetectMatListOfKeyPointMat() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testEmpty() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testRead() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testWrite() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testDetectTwoPoints() {
|
||||
Mat img = new Mat(256,256, CvType.CV_8UC3, new Scalar(0,0,0));
|
||||
img.put(35, 40, 255,255, 255);
|
||||
img.put(152, 98, 200,0, 0);
|
||||
|
||||
MatOfKeyPoint keypoints = new MatOfKeyPoint();
|
||||
ORB orb = ORB.create();
|
||||
Mat descriptors = new Mat();
|
||||
orb.detectAndCompute(img, new Mat(), keypoints, descriptors);
|
||||
|
||||
KeyPoint[] keypointsArray = keypoints.toArray();
|
||||
assertEquals(2, keypointsArray.length);
|
||||
|
||||
long x1 = Math.round(keypointsArray[0].pt.x);
|
||||
long y1 = Math.round(keypointsArray[0].pt.y);
|
||||
long x2 = Math.round(keypointsArray[1].pt.x);
|
||||
long y2 = Math.round(keypointsArray[1].pt.y);
|
||||
|
||||
if (x2 > x1) {
|
||||
assertEquals(40, x1);
|
||||
assertEquals(35, y1);
|
||||
assertEquals(98, x2);
|
||||
assertEquals(152, y2);
|
||||
} else {
|
||||
assertEquals(40, x2);
|
||||
assertEquals(35, y2);
|
||||
assertEquals(98, x1);
|
||||
assertEquals(152, y1);
|
||||
}
|
||||
}
|
||||
|
||||
}
|
||||
@@ -0,0 +1,109 @@
|
||||
package org.opencv.test.features2d;
|
||||
|
||||
import org.opencv.core.CvType;
|
||||
import org.opencv.core.Mat;
|
||||
import org.opencv.core.MatOfKeyPoint;
|
||||
import org.opencv.core.Point;
|
||||
import org.opencv.core.Scalar;
|
||||
import org.opencv.core.KeyPoint;
|
||||
import org.opencv.features2d.SIFT;
|
||||
import org.opencv.test.OpenCVTestCase;
|
||||
import org.opencv.test.OpenCVTestRunner;
|
||||
import org.opencv.imgproc.Imgproc;
|
||||
import org.opencv.features2d.SIFT;
|
||||
|
||||
public class SIFTDescriptorExtractorTest extends OpenCVTestCase {
|
||||
|
||||
SIFT extractor;
|
||||
KeyPoint keypoint;
|
||||
int matSize;
|
||||
Mat truth;
|
||||
|
||||
private Mat getTestImg() {
|
||||
Mat cross = new Mat(matSize, matSize, CvType.CV_8U, new Scalar(255));
|
||||
Imgproc.line(cross, new Point(20, matSize / 2), new Point(matSize - 21, matSize / 2), new Scalar(100), 2);
|
||||
Imgproc.line(cross, new Point(matSize / 2, 20), new Point(matSize / 2, matSize - 21), new Scalar(100), 2);
|
||||
|
||||
return cross;
|
||||
}
|
||||
|
||||
@Override
|
||||
protected void setUp() throws Exception {
|
||||
super.setUp();
|
||||
extractor = SIFT.create();
|
||||
keypoint = new KeyPoint(55.775577545166016f, 44.224422454833984f, 16, 9.754629f, 8617.863f, 1, -1);
|
||||
matSize = 100;
|
||||
truth = new Mat(1, 128, CvType.CV_32FC1) {
|
||||
{
|
||||
put(0, 0,
|
||||
0, 0, 0, 1, 3, 0, 0, 0, 15, 23, 22, 20, 24, 2, 0, 0, 7, 8, 2, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 27, 16, 13, 2, 0, 0, 117,
|
||||
86, 79, 68, 117, 42, 5, 5, 79, 60, 117, 25, 9, 2, 28, 19, 11, 13,
|
||||
20, 2, 0, 0, 5, 8, 0, 0, 76, 58, 34, 31, 97, 16, 95, 49, 117, 92,
|
||||
117, 112, 117, 76, 117, 54, 117, 25, 29, 22, 117, 117, 16, 11, 14,
|
||||
1, 0, 0, 22, 26, 0, 0, 0, 0, 1, 4, 15, 2, 47, 8, 0, 0, 82, 56, 31,
|
||||
17, 81, 12, 0, 0, 26, 23, 18, 23, 0, 0, 0, 0, 0, 0, 0, 0
|
||||
);
|
||||
}
|
||||
};
|
||||
}
|
||||
|
||||
public void testComputeListOfMatListOfListOfKeyPointListOfMat() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testComputeMatListOfKeyPointMat() {
|
||||
MatOfKeyPoint keypoints = new MatOfKeyPoint(keypoint);
|
||||
Mat img = getTestImg();
|
||||
Mat descriptors = new Mat();
|
||||
|
||||
extractor.compute(img, keypoints, descriptors);
|
||||
|
||||
assertMatEqual(truth, descriptors, EPS);
|
||||
}
|
||||
|
||||
public void testCreate() {
|
||||
assertNotNull(extractor);
|
||||
}
|
||||
|
||||
public void testDescriptorSize() {
|
||||
assertEquals(128, extractor.descriptorSize());
|
||||
}
|
||||
|
||||
public void testDescriptorType() {
|
||||
assertEquals(CvType.CV_32F, extractor.descriptorType());
|
||||
}
|
||||
|
||||
public void testEmpty() {
|
||||
// assertFalse(extractor.empty());
|
||||
fail("Not yet implemented"); // SIFT does not override empty() method
|
||||
}
|
||||
|
||||
public void testReadYml() {
|
||||
String filename = OpenCVTestRunner.getTempFileName("yml");
|
||||
writeFile(filename, "%YAML:1.0\n---\nname: \"Feature2D.SIFT\"\nnfeatures: 100\nnOctaveLayers: 4\ncontrastThreshold: 5.0000000000000001e-02\nedgeThreshold: 11\nsigma: 1.7\ndescriptorType: 5\n");
|
||||
|
||||
extractor.read(filename);
|
||||
|
||||
assertEquals(128, extractor.descriptorSize());
|
||||
|
||||
assertEquals(100, extractor.getNFeatures());
|
||||
assertEquals(4, extractor.getNOctaveLayers());
|
||||
assertEquals(0.05, extractor.getContrastThreshold());
|
||||
assertEquals(11., extractor.getEdgeThreshold());
|
||||
assertEquals(1.7, extractor.getSigma());
|
||||
assertEquals(5, extractor.descriptorType());
|
||||
}
|
||||
|
||||
public void testWriteYml() {
|
||||
String filename = OpenCVTestRunner.getTempFileName("yml");
|
||||
|
||||
extractor.write(filename);
|
||||
|
||||
String truth = "%YAML:1.0\n---\nname: \"Feature2D.SIFT\"\nnfeatures: 0\nnOctaveLayers: 3\ncontrastThreshold: 0.040000000000000001\nedgeThreshold: 10.\nsigma: 1.6000000000000001\ndescriptorType: 5\n";
|
||||
String actual = readFile(filename);
|
||||
actual = actual.replaceAll("e([+-])0(\\d\\d)", "e$1$2"); // NOTE: workaround for different platforms double representation
|
||||
assertEquals(truth, actual);
|
||||
}
|
||||
|
||||
}
|
||||
@@ -0,0 +1,39 @@
|
||||
package org.opencv.test.features2d;
|
||||
|
||||
import org.opencv.test.OpenCVTestCase;
|
||||
|
||||
public class SIFTFeatureDetectorTest extends OpenCVTestCase {
|
||||
|
||||
public void testCreate() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testDetectListOfMatListOfListOfKeyPoint() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testDetectListOfMatListOfListOfKeyPointListOfMat() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testDetectMatListOfKeyPoint() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testDetectMatListOfKeyPointMat() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testEmpty() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testRead() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testWrite() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
}
|
||||
@@ -0,0 +1,138 @@
|
||||
package org.opencv.test.features2d;
|
||||
|
||||
import java.util.Arrays;
|
||||
|
||||
import org.opencv.core.CvType;
|
||||
import org.opencv.core.Mat;
|
||||
import org.opencv.core.MatOfKeyPoint;
|
||||
import org.opencv.core.Point;
|
||||
import org.opencv.core.Scalar;
|
||||
import org.opencv.core.KeyPoint;
|
||||
import org.opencv.test.OpenCVTestCase;
|
||||
import org.opencv.test.OpenCVTestRunner;
|
||||
import org.opencv.imgproc.Imgproc;
|
||||
import org.opencv.features2d.SimpleBlobDetector;
|
||||
import org.opencv.features2d.SimpleBlobDetector_Params;
|
||||
|
||||
public class SIMPLEBLOBFeatureDetectorTest extends OpenCVTestCase {
|
||||
|
||||
SimpleBlobDetector detector;
|
||||
int matSize;
|
||||
KeyPoint[] truth;
|
||||
|
||||
private Mat getMaskImg() {
|
||||
Mat mask = new Mat(matSize, matSize, CvType.CV_8U, new Scalar(255));
|
||||
Mat right = mask.submat(0, matSize, matSize / 2, matSize);
|
||||
right.setTo(new Scalar(0));
|
||||
return mask;
|
||||
}
|
||||
|
||||
private Mat getTestImg() {
|
||||
|
||||
int center = matSize / 2;
|
||||
int offset = 40;
|
||||
|
||||
Mat img = new Mat(matSize, matSize, CvType.CV_8U, new Scalar(255));
|
||||
Imgproc.circle(img, new Point(center - offset, center), 24, new Scalar(0), -1);
|
||||
Imgproc.circle(img, new Point(center + offset, center), 20, new Scalar(50), -1);
|
||||
Imgproc.circle(img, new Point(center, center - offset), 18, new Scalar(100), -1);
|
||||
Imgproc.circle(img, new Point(center, center + offset), 14, new Scalar(150), -1);
|
||||
Imgproc.circle(img, new Point(center, center), 10, new Scalar(200), -1);
|
||||
return img;
|
||||
}
|
||||
|
||||
@Override
|
||||
protected void setUp() throws Exception {
|
||||
super.setUp();
|
||||
detector = SimpleBlobDetector.create();
|
||||
matSize = 200;
|
||||
truth = new KeyPoint[] {
|
||||
new KeyPoint(140, 100, 41.036568f, -1, 0, 0, -1),
|
||||
new KeyPoint(60, 100, 48.538486f, -1, 0, 0, -1),
|
||||
new KeyPoint(100, 60, 36.769554f, -1, 0, 0, -1),
|
||||
new KeyPoint(100, 140, 28.635643f, -1, 0, 0, -1),
|
||||
new KeyPoint(100, 100, 20.880613f, -1, 0, 0, -1)
|
||||
};
|
||||
}
|
||||
|
||||
public void testCreate() {
|
||||
assertNotNull(detector);
|
||||
}
|
||||
|
||||
public void testDetectListOfMatListOfListOfKeyPoint() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testDetectListOfMatListOfListOfKeyPointListOfMat() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testDetectMatListOfKeyPoint() {
|
||||
Mat img = getTestImg();
|
||||
MatOfKeyPoint keypoints = new MatOfKeyPoint();
|
||||
|
||||
detector.detect(img, keypoints);
|
||||
|
||||
assertListKeyPointEquals(Arrays.asList(truth), keypoints.toList(), EPS);
|
||||
}
|
||||
|
||||
public void testDetectMatListOfKeyPointMat() {
|
||||
Mat img = getTestImg();
|
||||
Mat mask = getMaskImg();
|
||||
MatOfKeyPoint keypoints = new MatOfKeyPoint();
|
||||
|
||||
detector.detect(img, keypoints, mask);
|
||||
|
||||
assertListKeyPointEquals(Arrays.asList(truth[1]), keypoints.toList(), EPS);
|
||||
}
|
||||
|
||||
public void testEmpty() {
|
||||
// assertFalse(detector.empty());
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testReadYml() {
|
||||
Mat img = getTestImg();
|
||||
|
||||
MatOfKeyPoint keypoints1 = new MatOfKeyPoint();
|
||||
detector.detect(img, keypoints1);
|
||||
|
||||
String filename = OpenCVTestRunner.getTempFileName("yml");
|
||||
writeFile(filename, "%YAML:1.0\nthresholdStep: 10.0\nminThreshold: 50\nmaxThreshold: 220\nminRepeatability: 2\nminDistBetweenBlobs: 10.\nfilterByColor: 1\nblobColor: 0\nfilterByArea: 1\nminArea: 800\nmaxArea: 6000\nfilterByCircularity: 0\nminCircularity: 0.7\nmaxCircularity: 10.\nfilterByInertia: 1\nminInertiaRatio: 0.2\nmaxInertiaRatio: 11.\nfilterByConvexity: true\nminConvexity: 0.9\nmaxConvexity: 12.\n");
|
||||
detector.read(filename);
|
||||
|
||||
SimpleBlobDetector_Params params = detector.getParams();
|
||||
assertEquals(10.0f, params.get_thresholdStep());
|
||||
assertEquals(50f, params.get_minThreshold());
|
||||
assertEquals(220f, params.get_maxThreshold());
|
||||
assertEquals(2, params.get_minRepeatability());
|
||||
assertEquals(10.0f, params.get_minDistBetweenBlobs());
|
||||
assertEquals(true, params.get_filterByColor());
|
||||
assertEquals(0, params.get_blobColor());
|
||||
assertEquals(true, params.get_filterByArea());
|
||||
assertEquals(800f, params.get_minArea());
|
||||
assertEquals(6000f, params.get_maxArea());
|
||||
assertEquals(false, params.get_filterByCircularity());
|
||||
assertEquals(0.7f, params.get_minCircularity());
|
||||
assertEquals(10.0f, params.get_maxCircularity());
|
||||
assertEquals(true, params.get_filterByInertia());
|
||||
assertEquals(0.2f, params.get_minInertiaRatio());
|
||||
assertEquals(11.0f, params.get_maxInertiaRatio());
|
||||
assertEquals(true, params.get_filterByConvexity());
|
||||
assertEquals(0.9f, params.get_minConvexity());
|
||||
assertEquals(12.0f, params.get_maxConvexity());
|
||||
|
||||
MatOfKeyPoint keypoints2 = new MatOfKeyPoint();
|
||||
detector.detect(img, keypoints2);
|
||||
|
||||
assertTrue(keypoints2.total() <= keypoints1.total());
|
||||
}
|
||||
|
||||
public void testWrite() {
|
||||
String filename = OpenCVTestRunner.getTempFileName("xml");
|
||||
|
||||
detector.write(filename);
|
||||
String truth = "<?xml version=\"1.0\"?>\n<opencv_storage>\n<format>3</format>\n<thresholdStep>10.</thresholdStep>\n<minThreshold>50.</minThreshold>\n<maxThreshold>220.</maxThreshold>\n<minRepeatability>2</minRepeatability>\n<minDistBetweenBlobs>10.</minDistBetweenBlobs>\n<filterByColor>1</filterByColor>\n<blobColor>0</blobColor>\n<filterByArea>1</filterByArea>\n<minArea>25.</minArea>\n<maxArea>5000.</maxArea>\n<filterByCircularity>0</filterByCircularity>\n<minCircularity>0.80000001192092896</minCircularity>\n<maxCircularity>3.4028234663852886e+38</maxCircularity>\n<filterByInertia>1</filterByInertia>\n<minInertiaRatio>0.10000000149011612</minInertiaRatio>\n<maxInertiaRatio>3.4028234663852886e+38</maxInertiaRatio>\n<filterByConvexity>1</filterByConvexity>\n<minConvexity>0.94999998807907104</minConvexity>\n<maxConvexity>3.4028234663852886e+38</maxConvexity>\n<collectContours>0</collectContours>\n</opencv_storage>\n";
|
||||
assertEquals(truth, readFile(filename));
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,19 @@
|
||||
{
|
||||
"whitelist":
|
||||
{
|
||||
"Feature2D": ["detect", "compute", "detectAndCompute", "descriptorSize", "descriptorType", "defaultNorm", "empty", "getDefaultName"],
|
||||
"BRISK": ["create", "getDefaultName"],
|
||||
"ORB": ["create", "setMaxFeatures", "setScaleFactor", "setNLevels", "setEdgeThreshold", "setFastThreshold", "setFirstLevel", "setWTA_K", "setScoreType", "setPatchSize", "getFastThreshold", "getDefaultName"],
|
||||
"MSER": ["create", "detectRegions", "setDelta", "getDelta", "setMinArea", "getMinArea", "setMaxArea", "getMaxArea", "setPass2Only", "getPass2Only", "getDefaultName"],
|
||||
"FastFeatureDetector": ["create", "setThreshold", "getThreshold", "setNonmaxSuppression", "getNonmaxSuppression", "setType", "getType", "getDefaultName"],
|
||||
"AgastFeatureDetector": ["create", "setThreshold", "getThreshold", "setNonmaxSuppression", "getNonmaxSuppression", "setType", "getType", "getDefaultName"],
|
||||
"GFTTDetector": ["create", "setMaxFeatures", "getMaxFeatures", "setQualityLevel", "getQualityLevel", "setMinDistance", "getMinDistance", "setBlockSize", "getBlockSize", "setHarrisDetector", "getHarrisDetector", "setK", "getK", "getDefaultName"],
|
||||
"SimpleBlobDetector": ["create", "setParams", "getParams", "getDefaultName"],
|
||||
"SimpleBlobDetector_Params": [],
|
||||
"KAZE": ["create", "setExtended", "getExtended", "setUpright", "getUpright", "setThreshold", "getThreshold", "setNOctaves", "getNOctaves", "setNOctaveLayers", "getNOctaveLayers", "setDiffusivity", "getDiffusivity", "getDefaultName"],
|
||||
"AKAZE": ["create", "setDescriptorType", "getDescriptorType", "setDescriptorSize", "getDescriptorSize", "setDescriptorChannels", "getDescriptorChannels", "setThreshold", "getThreshold", "setNOctaves", "getNOctaves", "setNOctaveLayers", "getNOctaveLayers", "setDiffusivity", "getDiffusivity", "getDefaultName"],
|
||||
"DescriptorMatcher": ["add", "clear", "empty", "isMaskSupported", "train", "match", "knnMatch", "radiusMatch", "clone", "create"],
|
||||
"BFMatcher": ["isMaskSupported", "create"],
|
||||
"": ["drawKeypoints", "drawMatches", "drawMatchesKnn"]
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,29 @@
|
||||
{
|
||||
"ManualFuncs" : {
|
||||
"SimpleBlobDetector": {
|
||||
"setParams": { "declaration" : [""], "implementation" : [""] },
|
||||
"getParams": { "declaration" : [""], "implementation" : [""] }
|
||||
}
|
||||
},
|
||||
"enum_fix" : {
|
||||
"FastFeatureDetector" : { "DetectorType": "FastDetectorType" },
|
||||
"AgastFeatureDetector" : { "DetectorType": "AgastDetectorType" }
|
||||
},
|
||||
"func_arg_fix" : {
|
||||
"Feature2D": {
|
||||
"(void)compute:(NSArray<Mat*>*)images keypoints:(NSMutableArray<NSMutableArray<KeyPoint*>*>*)keypoints descriptors:(NSMutableArray<Mat*>*)descriptors" : { "compute" : {"name" : "compute2"} },
|
||||
"(void)detect:(NSArray<Mat*>*)images keypoints:(NSMutableArray<NSMutableArray<KeyPoint*>*>*)keypoints masks:(NSArray<Mat*>*)masks" : { "detect" : {"name" : "detect2"} }
|
||||
},
|
||||
"DescriptorMatcher": {
|
||||
"(DescriptorMatcher*)create:(NSString*)descriptorMatcherType" : { "create" : {"name" : "create2"} }
|
||||
},
|
||||
"FlannBasedMatcher": {
|
||||
"FlannBasedMatcher": { "indexParams" : {"defval" : "cv::makePtr<cv::flann::KDTreeIndexParams>()"}, "searchParams" : {"defval" : "cv::makePtr<cv::flann::SearchParams>()"} }
|
||||
},
|
||||
"BFMatcher": {
|
||||
"BFMatcher" : { "normType" : {"ctype" : "NormTypes"} },
|
||||
"(BFMatcher*)create:(int)normType crossCheck:(BOOL)crossCheck" : { "create" : {"name" : "createBFMatcher"},
|
||||
"normType" : {"ctype" : "NormTypes"} }
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,9 @@
|
||||
#ifdef HAVE_OPENCV_FEATURES2D
|
||||
typedef SimpleBlobDetector::Params SimpleBlobDetector_Params;
|
||||
typedef AKAZE::DescriptorType AKAZE_DescriptorType;
|
||||
typedef AgastFeatureDetector::DetectorType AgastFeatureDetector_DetectorType;
|
||||
typedef FastFeatureDetector::DetectorType FastFeatureDetector_DetectorType;
|
||||
typedef DescriptorMatcher::MatcherType DescriptorMatcher_MatcherType;
|
||||
typedef KAZE::DiffusivityType KAZE_DiffusivityType;
|
||||
typedef ORB::ScoreType ORB_ScoreType;
|
||||
#endif
|
||||
@@ -0,0 +1,164 @@
|
||||
#!/usr/bin/env python
|
||||
|
||||
'''
|
||||
Feature homography
|
||||
==================
|
||||
|
||||
Example of using features2d framework for interactive video homography matching.
|
||||
ORB features and FLANN matcher are used. The actual tracking is implemented by
|
||||
PlaneTracker class in plane_tracker.py
|
||||
'''
|
||||
|
||||
# Python 2/3 compatibility
|
||||
from __future__ import print_function
|
||||
|
||||
import numpy as np
|
||||
import cv2 as cv
|
||||
import sys
|
||||
PY3 = sys.version_info[0] == 3
|
||||
|
||||
if PY3:
|
||||
xrange = range
|
||||
|
||||
# local modules
|
||||
from tst_scene_render import TestSceneRender
|
||||
|
||||
def intersectionRate(s1, s2):
|
||||
|
||||
x1, y1, x2, y2 = s1
|
||||
s1 = np.array([[x1, y1], [x2,y1], [x2, y2], [x1, y2]])
|
||||
|
||||
area, _intersection = cv.intersectConvexConvex(s1, np.array(s2))
|
||||
return 2 * area / (cv.contourArea(s1) + cv.contourArea(np.array(s2)))
|
||||
|
||||
from tests_common import NewOpenCVTests
|
||||
|
||||
class feature_homography_test(NewOpenCVTests):
|
||||
|
||||
render = None
|
||||
tracker = None
|
||||
framesCounter = 0
|
||||
frame = None
|
||||
|
||||
def test_feature_homography(self):
|
||||
|
||||
self.render = TestSceneRender(self.get_sample('samples/data/graf1.png'),
|
||||
self.get_sample('samples/data/box.png'), noise = 0.5, speed = 0.5)
|
||||
self.frame = self.render.getNextFrame()
|
||||
self.tracker = PlaneTracker()
|
||||
self.tracker.clear()
|
||||
self.tracker.add_target(self.frame, self.render.getCurrentRect())
|
||||
|
||||
while self.framesCounter < 100:
|
||||
self.framesCounter += 1
|
||||
tracked = self.tracker.track(self.frame)
|
||||
if len(tracked) > 0:
|
||||
tracked = tracked[0]
|
||||
self.assertGreater(intersectionRate(self.render.getCurrentRect(), np.int32(tracked.quad)), 0.6)
|
||||
else:
|
||||
self.assertEqual(0, 1, 'Tracking error')
|
||||
self.frame = self.render.getNextFrame()
|
||||
|
||||
|
||||
# built-in modules
|
||||
from collections import namedtuple
|
||||
|
||||
FLANN_INDEX_KDTREE = 1
|
||||
FLANN_INDEX_LSH = 6
|
||||
flann_params= dict(algorithm = FLANN_INDEX_LSH,
|
||||
table_number = 6, # 12
|
||||
key_size = 12, # 20
|
||||
multi_probe_level = 1) #2
|
||||
|
||||
MIN_MATCH_COUNT = 10
|
||||
|
||||
'''
|
||||
image - image to track
|
||||
rect - tracked rectangle (x1, y1, x2, y2)
|
||||
keypoints - keypoints detected inside rect
|
||||
descrs - their descriptors
|
||||
data - some user-provided data
|
||||
'''
|
||||
PlanarTarget = namedtuple('PlaneTarget', 'image, rect, keypoints, descrs, data')
|
||||
|
||||
'''
|
||||
target - reference to PlanarTarget
|
||||
p0 - matched points coords in target image
|
||||
p1 - matched points coords in input frame
|
||||
H - homography matrix from p0 to p1
|
||||
quad - target boundary quad in input frame
|
||||
'''
|
||||
TrackedTarget = namedtuple('TrackedTarget', 'target, p0, p1, H, quad')
|
||||
|
||||
class PlaneTracker:
|
||||
def __init__(self):
|
||||
self.detector = cv.AKAZE_create(threshold = 0.003)
|
||||
self.matcher = cv.FlannBasedMatcher(flann_params, {}) # bug : need to pass empty dict (#1329)
|
||||
self.targets = []
|
||||
self.frame_points = []
|
||||
|
||||
def add_target(self, image, rect, data=None):
|
||||
'''Add a new tracking target.'''
|
||||
x0, y0, x1, y1 = rect
|
||||
raw_points, raw_descrs = self.detect_features(image)
|
||||
points, descs = [], []
|
||||
for kp, desc in zip(raw_points, raw_descrs):
|
||||
x, y = kp.pt
|
||||
if x0 <= x <= x1 and y0 <= y <= y1:
|
||||
points.append(kp)
|
||||
descs.append(desc)
|
||||
descs = np.uint8(descs)
|
||||
self.matcher.add([descs])
|
||||
target = PlanarTarget(image = image, rect=rect, keypoints = points, descrs=descs, data=data)
|
||||
self.targets.append(target)
|
||||
|
||||
def clear(self):
|
||||
'''Remove all targets'''
|
||||
self.targets = []
|
||||
self.matcher.clear()
|
||||
|
||||
def track(self, frame):
|
||||
'''Returns a list of detected TrackedTarget objects'''
|
||||
self.frame_points, frame_descrs = self.detect_features(frame)
|
||||
if len(self.frame_points) < MIN_MATCH_COUNT:
|
||||
return []
|
||||
matches = self.matcher.knnMatch(frame_descrs, k = 2)
|
||||
matches = [m[0] for m in matches if len(m) == 2 and m[0].distance < m[1].distance * 0.75]
|
||||
if len(matches) < MIN_MATCH_COUNT:
|
||||
return []
|
||||
matches_by_id = [[] for _ in xrange(len(self.targets))]
|
||||
for m in matches:
|
||||
matches_by_id[m.imgIdx].append(m)
|
||||
tracked = []
|
||||
for imgIdx, matches in enumerate(matches_by_id):
|
||||
if len(matches) < MIN_MATCH_COUNT:
|
||||
continue
|
||||
target = self.targets[imgIdx]
|
||||
p0 = [target.keypoints[m.trainIdx].pt for m in matches]
|
||||
p1 = [self.frame_points[m.queryIdx].pt for m in matches]
|
||||
p0, p1 = np.float32((p0, p1))
|
||||
H, status = cv.findHomography(p0, p1, cv.RANSAC, 3.0)
|
||||
status = status.ravel() != 0
|
||||
if status.sum() < MIN_MATCH_COUNT:
|
||||
continue
|
||||
p0, p1 = p0[status], p1[status]
|
||||
|
||||
x0, y0, x1, y1 = target.rect
|
||||
quad = np.float32([[x0, y0], [x1, y0], [x1, y1], [x0, y1]])
|
||||
quad = cv.perspectiveTransform(quad.reshape(1, -1, 2), H).reshape(-1, 2)
|
||||
|
||||
track = TrackedTarget(target=target, p0=p0, p1=p1, H=H, quad=quad)
|
||||
tracked.append(track)
|
||||
tracked.sort(key = lambda t: len(t.p0), reverse=True)
|
||||
return tracked
|
||||
|
||||
def detect_features(self, frame):
|
||||
'''detect_features(self, frame) -> keypoints, descrs'''
|
||||
keypoints, descrs = self.detector.detectAndCompute(frame, None)
|
||||
if descrs is None: # detectAndCompute returns descs=None if no keypoints found
|
||||
descrs = []
|
||||
return keypoints, descrs
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
NewOpenCVTests.bootstrap()
|
||||
@@ -0,0 +1,127 @@
|
||||
// This file is part of OpenCV project.
|
||||
// It is subject to the license terms in the LICENSE file found in the top-level directory
|
||||
// of this distribution and at http://opencv.org/license.html
|
||||
|
||||
#include "../perf_precomp.hpp"
|
||||
#include "opencv2/ts/ocl_perf.hpp"
|
||||
|
||||
#ifdef HAVE_OPENCL
|
||||
|
||||
namespace opencv_test {
|
||||
namespace ocl {
|
||||
|
||||
typedef Size_MatType AKAZEFixture;
|
||||
|
||||
OCL_PERF_TEST_P(AKAZEFixture, detectAndCompute, ::testing::Combine(OCL_PERF_ENUM(OCL_SIZE_1, OCL_SIZE_2, OCL_SIZE_3), OCL_PERF_ENUM((MatType)CV_8UC1)))
|
||||
{
|
||||
const Size_MatType_t params = GetParam();
|
||||
const Size srcSize = get<0>(params);
|
||||
const int type = get<1>(params);
|
||||
|
||||
checkDeviceMaxMemoryAllocSize(srcSize, type);
|
||||
|
||||
UMat img(srcSize, type), mask;
|
||||
declare.in(img, WARMUP_RNG);
|
||||
|
||||
Ptr<AKAZE> akaze = AKAZE::create(AKAZE::DESCRIPTOR_MLDB_UPRIGHT, 0, 3, 0.001f, 1, 1, KAZE::DIFF_PM_G2);
|
||||
vector<KeyPoint> points;
|
||||
UMat descriptors;
|
||||
|
||||
OCL_TEST_CYCLE() akaze->detectAndCompute(img, mask, points, descriptors, false);
|
||||
|
||||
EXPECT_GT(points.size(), 20u);
|
||||
EXPECT_EQ((size_t)descriptors.rows, points.size());
|
||||
SANITY_CHECK_NOTHING();
|
||||
}
|
||||
|
||||
typedef Size_MatType AKAZEOclMldbUprightFixture;
|
||||
|
||||
OCL_PERF_TEST_P(AKAZEOclMldbUprightFixture, detectAndComputeMLDB,
|
||||
::testing::Combine(
|
||||
::testing::Values(
|
||||
cv::Size(320, 240), cv::Size(640, 480), cv::Size(960, 540),
|
||||
cv::Size(1280, 720), cv::Size(1920, 1080), cv::Size(2560, 1440), cv::Size(3840, 2160)
|
||||
),
|
||||
OCL_PERF_ENUM((MatType)CV_8UC1)
|
||||
))
|
||||
{
|
||||
const Size_MatType_t params = GetParam();
|
||||
const cv::Size srcSize = get<0>(params);
|
||||
|
||||
UMat img(srcSize, CV_8U);
|
||||
declare.in(img, WARMUP_RNG);
|
||||
|
||||
Ptr<Feature2D> det = AKAZE::create(AKAZE::DESCRIPTOR_MLDB_UPRIGHT, 0, 3, 0.001f, 4, 4, KAZE::DIFF_PM_G2);
|
||||
vector<KeyPoint> kps;
|
||||
UMat descriptors;
|
||||
|
||||
OCL_TEST_CYCLE() det->detectAndCompute(img, noArray(), kps, descriptors, false);
|
||||
|
||||
EXPECT_GT(kps.size(), 0u);
|
||||
SANITY_CHECK_NOTHING();
|
||||
}
|
||||
|
||||
typedef tuple<std::string, int> AKAZEOclRealImagesParams;
|
||||
typedef TestBaseWithParam<AKAZEOclRealImagesParams> AKAZEOclRealImagesFixture;
|
||||
|
||||
OCL_PERF_TEST_P(AKAZEOclRealImagesFixture, detectAndComputeMLDBRealImages,
|
||||
::testing::Combine(
|
||||
::testing::Values(
|
||||
std::string("stitching/boat1.jpg"),
|
||||
std::string("stitching/boat2.jpg"),
|
||||
std::string("stitching/boat3.jpg"),
|
||||
std::string("stitching/boat4.jpg"),
|
||||
std::string("stitching/boat5.jpg"),
|
||||
std::string("stitching/boat6.jpg")
|
||||
),
|
||||
::testing::Values(0)
|
||||
))
|
||||
{
|
||||
const std::string imgName = get<0>(GetParam());
|
||||
|
||||
Mat img_mat = imread(getDataPath(imgName), IMREAD_GRAYSCALE);
|
||||
if (img_mat.empty())
|
||||
throw cvtest::SkipTestException("Image not found: " + imgName);
|
||||
|
||||
UMat img;
|
||||
img_mat.copyTo(img);
|
||||
declare.in(img);
|
||||
|
||||
Ptr<Feature2D> det = AKAZE::create(AKAZE::DESCRIPTOR_MLDB_UPRIGHT, 0, 3, 0.001f, 4, 4, KAZE::DIFF_PM_G2);
|
||||
vector<KeyPoint> kps;
|
||||
UMat descriptors;
|
||||
|
||||
OCL_TEST_CYCLE() det->detectAndCompute(img, noArray(), kps, descriptors, false);
|
||||
|
||||
EXPECT_GT(kps.size(), 0u);
|
||||
SANITY_CHECK_NOTHING();
|
||||
}
|
||||
|
||||
typedef Size_MatType AKAZEOclKazeUprightFixture;
|
||||
|
||||
OCL_PERF_TEST_P(AKAZEOclKazeUprightFixture, detectAndComputeKAZEUpright,
|
||||
::testing::Combine(
|
||||
::testing::Values(cv::Size(320, 240), cv::Size(640, 480), cv::Size(1280, 720)),
|
||||
OCL_PERF_ENUM((MatType)CV_8UC1)
|
||||
))
|
||||
{
|
||||
const Size_MatType_t params = GetParam();
|
||||
const cv::Size srcSize = get<0>(params);
|
||||
|
||||
UMat img(srcSize, CV_8U);
|
||||
declare.in(img, WARMUP_RNG);
|
||||
|
||||
Ptr<Feature2D> det = AKAZE::create(AKAZE::DESCRIPTOR_KAZE_UPRIGHT, 0, 3, 0.001f, 4, 4, KAZE::DIFF_PM_G2);
|
||||
vector<KeyPoint> kps;
|
||||
UMat descriptors;
|
||||
|
||||
OCL_TEST_CYCLE() det->detectAndCompute(img, noArray(), kps, descriptors, false);
|
||||
|
||||
EXPECT_GT(kps.size(), 0u);
|
||||
SANITY_CHECK_NOTHING();
|
||||
}
|
||||
|
||||
} // ocl
|
||||
} // opencv_test
|
||||
|
||||
#endif // HAVE_OPENCL
|
||||
@@ -0,0 +1,150 @@
|
||||
/*M///////////////////////////////////////////////////////////////////////////////////////
|
||||
//
|
||||
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
|
||||
//
|
||||
// By downloading, copying, installing or using the software you agree to this license.
|
||||
// If you do not agree to this license, do not download, install,
|
||||
// copy or use the software.
|
||||
//
|
||||
//
|
||||
// License Agreement
|
||||
// For Open Source Computer Vision Library
|
||||
//
|
||||
// Copyright (C) 2010-2012, Multicoreware, Inc., all rights reserved.
|
||||
// Copyright (C) 2010-2012, Advanced Micro Devices, Inc., all rights reserved.
|
||||
// Third party copyrights are property of their respective owners.
|
||||
//
|
||||
// @Authors
|
||||
// Fangfang Bai, fangfang@multicorewareinc.com
|
||||
// Jin Ma, jin@multicorewareinc.com
|
||||
//
|
||||
// Redistribution and use in source and binary forms, with or without modification,
|
||||
// are permitted provided that the following conditions are met:
|
||||
//
|
||||
// * Redistribution's of source code must retain the above copyright notice,
|
||||
// this list of conditions and the following disclaimer.
|
||||
//
|
||||
// * Redistribution's in binary form must reproduce the above copyright notice,
|
||||
// this list of conditions and the following disclaimer in the documentation
|
||||
// and/or other materials provided with the distribution.
|
||||
//
|
||||
// * The name of the copyright holders may not be used to endorse or promote products
|
||||
// derived from this software without specific prior written permission.
|
||||
//
|
||||
// This software is provided by the copyright holders and contributors as is and
|
||||
// any express or implied warranties, including, but not limited to, the implied
|
||||
// warranties of merchantability and fitness for a particular purpose are disclaimed.
|
||||
// In no event shall the Intel Corporation or contributors be liable for any direct,
|
||||
// indirect, incidental, special, exemplary, or consequential damages
|
||||
// (including, but not limited to, procurement of substitute goods or services;
|
||||
// loss of use, data, or profits; or business interruption) however caused
|
||||
// and on any theory of liability, whether in contract, strict liability,
|
||||
// or tort (including negligence or otherwise) arising in any way out of
|
||||
// the use of this software, even if advised of the possibility of such damage.
|
||||
//
|
||||
//M*/
|
||||
#include "../perf_precomp.hpp"
|
||||
#include "opencv2/ts/ocl_perf.hpp"
|
||||
|
||||
#ifdef HAVE_OPENCL
|
||||
|
||||
namespace opencv_test {
|
||||
namespace ocl {
|
||||
|
||||
//////////////////// BruteForceMatch /////////////////
|
||||
|
||||
typedef Size_MatType BruteForceMatcherFixture;
|
||||
|
||||
OCL_PERF_TEST_P(BruteForceMatcherFixture, Match, ::testing::Combine(OCL_PERF_ENUM(OCL_SIZE_1, OCL_SIZE_2, OCL_SIZE_3), OCL_PERF_ENUM((MatType)CV_32FC1) ) )
|
||||
{
|
||||
const Size_MatType_t params = GetParam();
|
||||
const Size srcSize = get<0>(params);
|
||||
const int type = get<1>(params);
|
||||
|
||||
checkDeviceMaxMemoryAllocSize(srcSize, type);
|
||||
|
||||
vector<DMatch> matches;
|
||||
UMat uquery(srcSize, type), utrain(srcSize, type);
|
||||
|
||||
declare.in(uquery, utrain, WARMUP_RNG);
|
||||
|
||||
BFMatcher matcher(NORM_L2);
|
||||
|
||||
OCL_TEST_CYCLE()
|
||||
matcher.match(uquery, utrain, matches);
|
||||
|
||||
SANITY_CHECK_MATCHES(matches, 1e-3);
|
||||
}
|
||||
|
||||
OCL_PERF_TEST_P(BruteForceMatcherFixture, KnnMatch, ::testing::Combine(OCL_PERF_ENUM(OCL_SIZE_1, OCL_SIZE_2, OCL_SIZE_3), OCL_PERF_ENUM((MatType)CV_32FC1) ) )
|
||||
{
|
||||
const Size_MatType_t params = GetParam();
|
||||
const Size srcSize = get<0>(params);
|
||||
const int type = get<1>(params);
|
||||
|
||||
checkDeviceMaxMemoryAllocSize(srcSize, type);
|
||||
|
||||
vector< vector<DMatch> > matches;
|
||||
UMat uquery(srcSize, type), utrain(srcSize, type);
|
||||
|
||||
declare.in(uquery, utrain, WARMUP_RNG);
|
||||
|
||||
BFMatcher matcher(NORM_L2);
|
||||
|
||||
OCL_TEST_CYCLE()
|
||||
matcher.knnMatch(uquery, utrain, matches, 2);
|
||||
|
||||
vector<DMatch> & matches0 = matches[0], & matches1 = matches[1];
|
||||
SANITY_CHECK_MATCHES(matches0, 1e-3);
|
||||
SANITY_CHECK_MATCHES(matches1, 1e-3);
|
||||
|
||||
}
|
||||
|
||||
OCL_PERF_TEST_P(BruteForceMatcherFixture, RadiusMatch, ::testing::Combine(OCL_PERF_ENUM(OCL_SIZE_1, OCL_SIZE_2, OCL_SIZE_3), OCL_PERF_ENUM((MatType)CV_32FC1) ) )
|
||||
{
|
||||
const Size_MatType_t params = GetParam();
|
||||
const Size srcSize = get<0>(params);
|
||||
const int type = get<1>(params);
|
||||
|
||||
checkDeviceMaxMemoryAllocSize(srcSize, type);
|
||||
|
||||
vector< vector<DMatch> > matches;
|
||||
UMat uquery(srcSize, type), utrain(srcSize, type);
|
||||
|
||||
declare.in(uquery, utrain, WARMUP_RNG);
|
||||
|
||||
BFMatcher matcher(NORM_L2);
|
||||
|
||||
OCL_TEST_CYCLE()
|
||||
matcher.radiusMatch(uquery, utrain, matches, 2.0f);
|
||||
|
||||
vector<DMatch> & matches0 = matches[0], & matches1 = matches[1];
|
||||
SANITY_CHECK_MATCHES(matches0, 1e-3);
|
||||
SANITY_CHECK_MATCHES(matches1, 1e-3);
|
||||
}
|
||||
|
||||
OCL_PERF_TEST_P(BruteForceMatcherFixture, MatchCrossCheck, ::testing::Combine(OCL_PERF_ENUM(OCL_SIZE_1, OCL_SIZE_2, OCL_SIZE_3), OCL_PERF_ENUM((MatType)CV_32FC1) ) )
|
||||
{
|
||||
const Size_MatType_t params = GetParam();
|
||||
const Size srcSize = get<0>(params);
|
||||
const int type = get<1>(params);
|
||||
|
||||
checkDeviceMaxMemoryAllocSize(srcSize, type);
|
||||
|
||||
vector<DMatch> matches;
|
||||
UMat uquery(srcSize, type), utrain(srcSize, type);
|
||||
|
||||
declare.in(uquery, utrain, WARMUP_RNG);
|
||||
|
||||
BFMatcher matcher(NORM_L2, true /*crossCheck*/);
|
||||
|
||||
OCL_TEST_CYCLE()
|
||||
matcher.match(uquery, utrain, matches);
|
||||
|
||||
SANITY_CHECK_MATCHES(matches, 1e-3);
|
||||
}
|
||||
|
||||
} // ocl
|
||||
} // cvtest
|
||||
|
||||
#endif // HAVE_OPENCL
|
||||
@@ -0,0 +1,81 @@
|
||||
#include "../perf_precomp.hpp"
|
||||
#include "opencv2/ts/ocl_perf.hpp"
|
||||
#include "../perf_feature2d.hpp"
|
||||
|
||||
#ifdef HAVE_OPENCL
|
||||
|
||||
namespace opencv_test {
|
||||
namespace ocl {
|
||||
|
||||
OCL_PERF_TEST_P(feature2d, detect, testing::Combine(Feature2DType::all(), TEST_IMAGES))
|
||||
{
|
||||
Ptr<Feature2D> detector = getFeature2D(get<0>(GetParam()));
|
||||
std::string filename = getDataPath(get<1>(GetParam()));
|
||||
Mat mimg = imread(filename, IMREAD_GRAYSCALE);
|
||||
|
||||
ASSERT_FALSE(mimg.empty());
|
||||
ASSERT_TRUE(detector);
|
||||
|
||||
UMat img, mask;
|
||||
mimg.copyTo(img);
|
||||
declare.in(img);
|
||||
vector<KeyPoint> points;
|
||||
|
||||
OCL_TEST_CYCLE() detector->detect(img, points, mask);
|
||||
|
||||
EXPECT_GT(points.size(), 20u);
|
||||
SANITY_CHECK_NOTHING();
|
||||
}
|
||||
|
||||
OCL_PERF_TEST_P(feature2d, extract, testing::Combine(testing::Values(DETECTORS_EXTRACTORS), TEST_IMAGES))
|
||||
{
|
||||
Ptr<Feature2D> detector = AKAZE::create();
|
||||
Ptr<Feature2D> extractor = getFeature2D(get<0>(GetParam()));
|
||||
std::string filename = getDataPath(get<1>(GetParam()));
|
||||
Mat mimg = imread(filename, IMREAD_GRAYSCALE);
|
||||
|
||||
ASSERT_FALSE(mimg.empty());
|
||||
ASSERT_TRUE(extractor);
|
||||
|
||||
UMat img, mask;
|
||||
mimg.copyTo(img);
|
||||
declare.in(img);
|
||||
vector<KeyPoint> points;
|
||||
detector->detect(img, points, mask);
|
||||
|
||||
EXPECT_GT(points.size(), 20u);
|
||||
|
||||
UMat descriptors;
|
||||
|
||||
OCL_TEST_CYCLE() extractor->compute(img, points, descriptors);
|
||||
|
||||
EXPECT_EQ((size_t)descriptors.rows, points.size());
|
||||
SANITY_CHECK_NOTHING();
|
||||
}
|
||||
|
||||
OCL_PERF_TEST_P(feature2d, detectAndExtract, testing::Combine(testing::Values(DETECTORS_EXTRACTORS), TEST_IMAGES))
|
||||
{
|
||||
Ptr<Feature2D> detector = getFeature2D(get<0>(GetParam()));
|
||||
std::string filename = getDataPath(get<1>(GetParam()));
|
||||
Mat mimg = imread(filename, IMREAD_GRAYSCALE);
|
||||
|
||||
ASSERT_FALSE(mimg.empty());
|
||||
ASSERT_TRUE(detector);
|
||||
|
||||
UMat img, mask;
|
||||
mimg.copyTo(img);
|
||||
declare.in(img);
|
||||
vector<KeyPoint> points;
|
||||
UMat descriptors;
|
||||
|
||||
OCL_TEST_CYCLE() detector->detectAndCompute(img, mask, points, descriptors, false);
|
||||
|
||||
EXPECT_GT(points.size(), 20u);
|
||||
EXPECT_EQ((size_t)descriptors.rows, points.size());
|
||||
SANITY_CHECK_NOTHING();
|
||||
}
|
||||
|
||||
} // ocl
|
||||
} // cvtest
|
||||
|
||||
#endif // HAVE_OPENCL
|
||||
@@ -0,0 +1,167 @@
|
||||
#include "perf_precomp.hpp"
|
||||
|
||||
namespace opencv_test
|
||||
{
|
||||
using namespace perf;
|
||||
|
||||
CV_ENUM(NormType, NORM_L1, NORM_L2, NORM_L2SQR, NORM_HAMMING, NORM_HAMMING2)
|
||||
|
||||
typedef tuple<NormType, MatType, bool> Norm_Destination_CrossCheck_t;
|
||||
typedef perf::TestBaseWithParam<Norm_Destination_CrossCheck_t> Norm_Destination_CrossCheck;
|
||||
|
||||
typedef tuple<NormType, bool> Norm_CrossCheck_t;
|
||||
typedef perf::TestBaseWithParam<Norm_CrossCheck_t> Norm_CrossCheck;
|
||||
|
||||
typedef tuple<MatType, bool> Source_CrossCheck_t;
|
||||
typedef perf::TestBaseWithParam<Source_CrossCheck_t> Source_CrossCheck;
|
||||
|
||||
void generateData( Mat& query, Mat& train, const int sourceType );
|
||||
|
||||
PERF_TEST_P(Norm_Destination_CrossCheck, batchDistance_8U,
|
||||
testing::Combine(testing::Values((int)NORM_L1, (int)NORM_L2SQR),
|
||||
testing::Values(CV_32S, CV_32F),
|
||||
testing::Bool()
|
||||
)
|
||||
)
|
||||
{
|
||||
NormType normType = get<0>(GetParam());
|
||||
int destinationType = get<1>(GetParam());
|
||||
bool isCrossCheck = get<2>(GetParam());
|
||||
int knn = isCrossCheck ? 1 : 0;
|
||||
|
||||
Mat queryDescriptors;
|
||||
Mat trainDescriptors;
|
||||
Mat dist;
|
||||
Mat ndix;
|
||||
|
||||
generateData(queryDescriptors, trainDescriptors, CV_8U);
|
||||
|
||||
TEST_CYCLE()
|
||||
{
|
||||
batchDistance(queryDescriptors, trainDescriptors, dist, destinationType, (isCrossCheck) ? ndix : noArray(),
|
||||
normType, knn, Mat(), 0, isCrossCheck);
|
||||
}
|
||||
|
||||
SANITY_CHECK(dist);
|
||||
if (isCrossCheck) SANITY_CHECK(ndix);
|
||||
}
|
||||
|
||||
PERF_TEST_P(Norm_CrossCheck, batchDistance_Dest_32S,
|
||||
testing::Combine(testing::Values((int)NORM_HAMMING, (int)NORM_HAMMING2),
|
||||
testing::Bool()
|
||||
)
|
||||
)
|
||||
{
|
||||
NormType normType = get<0>(GetParam());
|
||||
bool isCrossCheck = get<1>(GetParam());
|
||||
int knn = isCrossCheck ? 1 : 0;
|
||||
|
||||
Mat queryDescriptors;
|
||||
Mat trainDescriptors;
|
||||
Mat dist;
|
||||
Mat ndix;
|
||||
|
||||
generateData(queryDescriptors, trainDescriptors, CV_8U);
|
||||
|
||||
TEST_CYCLE()
|
||||
{
|
||||
batchDistance(queryDescriptors, trainDescriptors, dist, CV_32S, (isCrossCheck) ? ndix : noArray(),
|
||||
normType, knn, Mat(), 0, isCrossCheck);
|
||||
}
|
||||
|
||||
SANITY_CHECK(dist);
|
||||
if (isCrossCheck) SANITY_CHECK(ndix);
|
||||
}
|
||||
|
||||
PERF_TEST_P(Source_CrossCheck, batchDistance_L2,
|
||||
testing::Combine(testing::Values(CV_8U, CV_32F),
|
||||
testing::Bool()
|
||||
)
|
||||
)
|
||||
{
|
||||
int sourceType = get<0>(GetParam());
|
||||
bool isCrossCheck = get<1>(GetParam());
|
||||
int knn = isCrossCheck ? 1 : 0;
|
||||
|
||||
Mat queryDescriptors;
|
||||
Mat trainDescriptors;
|
||||
Mat dist;
|
||||
Mat ndix;
|
||||
|
||||
generateData(queryDescriptors, trainDescriptors, sourceType);
|
||||
|
||||
declare.time(50);
|
||||
TEST_CYCLE()
|
||||
{
|
||||
batchDistance(queryDescriptors, trainDescriptors, dist, CV_32F, (isCrossCheck) ? ndix : noArray(),
|
||||
NORM_L2, knn, Mat(), 0, isCrossCheck);
|
||||
}
|
||||
|
||||
SANITY_CHECK(dist);
|
||||
if (isCrossCheck) SANITY_CHECK(ndix);
|
||||
}
|
||||
|
||||
PERF_TEST_P(Norm_CrossCheck, batchDistance_32F,
|
||||
testing::Combine(testing::Values((int)NORM_L1, (int)NORM_L2SQR),
|
||||
testing::Bool()
|
||||
)
|
||||
)
|
||||
{
|
||||
NormType normType = get<0>(GetParam());
|
||||
bool isCrossCheck = get<1>(GetParam());
|
||||
int knn = isCrossCheck ? 1 : 0;
|
||||
|
||||
Mat queryDescriptors;
|
||||
Mat trainDescriptors;
|
||||
Mat dist;
|
||||
Mat ndix;
|
||||
|
||||
generateData(queryDescriptors, trainDescriptors, CV_32F);
|
||||
declare.time(100);
|
||||
|
||||
TEST_CYCLE()
|
||||
{
|
||||
batchDistance(queryDescriptors, trainDescriptors, dist, CV_32F, (isCrossCheck) ? ndix : noArray(),
|
||||
normType, knn, Mat(), 0, isCrossCheck);
|
||||
}
|
||||
|
||||
SANITY_CHECK(dist, 1e-4);
|
||||
if (isCrossCheck) SANITY_CHECK(ndix);
|
||||
}
|
||||
|
||||
void generateData( Mat& query, Mat& train, const int sourceType )
|
||||
{
|
||||
const int dim = 500;
|
||||
const int queryDescCount = 300; // must be even number because we split train data in some cases in two
|
||||
const int countFactor = 4; // do not change it
|
||||
RNG& rng = theRNG();
|
||||
|
||||
// Generate query descriptors randomly.
|
||||
// Descriptor vector elements are integer values.
|
||||
Mat buf( queryDescCount, dim, CV_32SC1 );
|
||||
rng.fill( buf, RNG::UNIFORM, Scalar::all(0), Scalar(3) );
|
||||
buf.convertTo( query, sourceType );
|
||||
|
||||
// Generate train descriptors as follows:
|
||||
// copy each query descriptor to train set countFactor times
|
||||
// and perturb some one element of the copied descriptors in
|
||||
// in ascending order. General boundaries of the perturbation
|
||||
// are (0.f, 1.f).
|
||||
train.create( query.rows*countFactor, query.cols, sourceType );
|
||||
float step = (sourceType == CV_8U ? 256.f : 1.f) / countFactor;
|
||||
for( int qIdx = 0; qIdx < query.rows; qIdx++ )
|
||||
{
|
||||
Mat queryDescriptor = query.row(qIdx);
|
||||
for( int c = 0; c < countFactor; c++ )
|
||||
{
|
||||
int tIdx = qIdx * countFactor + c;
|
||||
Mat trainDescriptor = train.row(tIdx);
|
||||
queryDescriptor.copyTo( trainDescriptor );
|
||||
int elem = rng(dim);
|
||||
float diff = rng.uniform( step*c, step*(c+1) );
|
||||
trainDescriptor.col(elem) += diff;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
} // namespace
|
||||
@@ -0,0 +1,43 @@
|
||||
#include "perf_precomp.hpp"
|
||||
#include "perf_feature2d.hpp"
|
||||
|
||||
namespace opencv_test
|
||||
{
|
||||
using namespace perf;
|
||||
|
||||
typedef tuple<int, int, bool, string> Fast_Params_t;
|
||||
typedef perf::TestBaseWithParam<Fast_Params_t> Fast_Params;
|
||||
|
||||
PERF_TEST_P(Fast_Params, detect,
|
||||
testing::Combine(
|
||||
testing::Values(20,30,100), // threshold
|
||||
testing::Values(
|
||||
// (int)FastFeatureDetector::TYPE_5_8,
|
||||
// (int)FastFeatureDetector::TYPE_7_12,
|
||||
(int)FastFeatureDetector::TYPE_9_16 // detector_type
|
||||
),
|
||||
testing::Bool(), // nonmaxSuppression
|
||||
testing::Values("cv/inpaint/orig.png",
|
||||
"cv/cameracalibration/chess9.png")
|
||||
))
|
||||
{
|
||||
int threshold_p = get<0>(GetParam());
|
||||
int type_p = get<1>(GetParam());
|
||||
bool nonmaxSuppression_p = get<2>(GetParam());
|
||||
string filename = getDataPath(get<3>(GetParam()));
|
||||
|
||||
Mat img = imread(filename, IMREAD_GRAYSCALE);
|
||||
ASSERT_FALSE(img.empty()) << "Failed to load image: " << filename;
|
||||
|
||||
vector<KeyPoint> keypoints;
|
||||
|
||||
declare.in(img);
|
||||
TEST_CYCLE()
|
||||
{
|
||||
FAST(img, keypoints, threshold_p, nonmaxSuppression_p, (FastFeatureDetector::DetectorType)type_p);
|
||||
}
|
||||
|
||||
SANITY_CHECK_NOTHING();
|
||||
}
|
||||
|
||||
} // namespace opencv_test
|
||||
@@ -0,0 +1,72 @@
|
||||
#include "perf_feature2d.hpp"
|
||||
|
||||
namespace opencv_test
|
||||
{
|
||||
using namespace perf;
|
||||
|
||||
PERF_TEST_P(feature2d, detect, testing::Combine(Feature2DType::all(), TEST_IMAGES))
|
||||
{
|
||||
Ptr<Feature2D> detector = getFeature2D(get<0>(GetParam()));
|
||||
std::string filename = getDataPath(get<1>(GetParam()));
|
||||
Mat img = imread(filename, IMREAD_GRAYSCALE);
|
||||
|
||||
ASSERT_FALSE(img.empty());
|
||||
ASSERT_TRUE(detector);
|
||||
|
||||
declare.in(img);
|
||||
Mat mask;
|
||||
vector<KeyPoint> points;
|
||||
|
||||
TEST_CYCLE() detector->detect(img, points, mask);
|
||||
|
||||
EXPECT_GT(points.size(), 20u);
|
||||
SANITY_CHECK_NOTHING();
|
||||
}
|
||||
|
||||
PERF_TEST_P(feature2d, extract, testing::Combine(testing::Values(DETECTORS_EXTRACTORS), TEST_IMAGES))
|
||||
{
|
||||
Ptr<Feature2D> detector = AKAZE::create();
|
||||
Ptr<Feature2D> extractor = getFeature2D(get<0>(GetParam()));
|
||||
std::string filename = getDataPath(get<1>(GetParam()));
|
||||
Mat img = imread(filename, IMREAD_GRAYSCALE);
|
||||
|
||||
ASSERT_FALSE(img.empty());
|
||||
ASSERT_TRUE(extractor);
|
||||
|
||||
declare.in(img);
|
||||
Mat mask;
|
||||
vector<KeyPoint> points;
|
||||
detector->detect(img, points, mask);
|
||||
|
||||
EXPECT_GT(points.size(), 20u);
|
||||
|
||||
Mat descriptors;
|
||||
|
||||
TEST_CYCLE() extractor->compute(img, points, descriptors);
|
||||
|
||||
EXPECT_EQ((size_t)descriptors.rows, points.size());
|
||||
SANITY_CHECK_NOTHING();
|
||||
}
|
||||
|
||||
PERF_TEST_P(feature2d, detectAndExtract, testing::Combine(testing::Values(DETECTORS_EXTRACTORS), TEST_IMAGES))
|
||||
{
|
||||
Ptr<Feature2D> detector = getFeature2D(get<0>(GetParam()));
|
||||
std::string filename = getDataPath(get<1>(GetParam()));
|
||||
Mat img = imread(filename, IMREAD_GRAYSCALE);
|
||||
|
||||
ASSERT_FALSE(img.empty());
|
||||
ASSERT_TRUE(detector);
|
||||
|
||||
declare.in(img);
|
||||
Mat mask;
|
||||
vector<KeyPoint> points;
|
||||
Mat descriptors;
|
||||
|
||||
TEST_CYCLE() detector->detectAndCompute(img, mask, points, descriptors, false);
|
||||
|
||||
EXPECT_GT(points.size(), 20u);
|
||||
EXPECT_EQ((size_t)descriptors.rows, points.size());
|
||||
SANITY_CHECK_NOTHING();
|
||||
}
|
||||
|
||||
} // namespace
|
||||
@@ -0,0 +1,90 @@
|
||||
#ifndef __OPENCV_PERF_FEATURE2D_HPP__
|
||||
#define __OPENCV_PERF_FEATURE2D_HPP__
|
||||
|
||||
#include "perf_precomp.hpp"
|
||||
|
||||
namespace opencv_test
|
||||
{
|
||||
|
||||
/* configuration for tests of detectors/descriptors. shared between ocl and cpu tests. */
|
||||
|
||||
// detectors/descriptors configurations to test
|
||||
#define DETECTORS_ONLY \
|
||||
FAST_DEFAULT, FAST_20_TRUE_TYPE5_8, FAST_20_TRUE_TYPE7_12, FAST_20_TRUE_TYPE9_16, \
|
||||
FAST_20_FALSE_TYPE5_8, FAST_20_FALSE_TYPE7_12, FAST_20_FALSE_TYPE9_16, \
|
||||
\
|
||||
AGAST_DEFAULT, AGAST_5_8, AGAST_7_12d, AGAST_7_12s, AGAST_OAST_9_16, \
|
||||
\
|
||||
MSER_DEFAULT
|
||||
|
||||
#define DETECTORS_EXTRACTORS \
|
||||
ORB_DEFAULT, ORB_1500_13_1, \
|
||||
AKAZE_DEFAULT, AKAZE_DESCRIPTOR_KAZE, \
|
||||
BRISK_DEFAULT, \
|
||||
KAZE_DEFAULT, \
|
||||
SIFT_DEFAULT
|
||||
|
||||
#define CV_ENUM_EXPAND(name, ...) CV_ENUM(name, __VA_ARGS__)
|
||||
|
||||
enum Feature2DVals { DETECTORS_ONLY, DETECTORS_EXTRACTORS };
|
||||
CV_ENUM_EXPAND(Feature2DType, DETECTORS_ONLY, DETECTORS_EXTRACTORS)
|
||||
|
||||
typedef tuple<Feature2DType, string> Feature2DType_String_t;
|
||||
typedef perf::TestBaseWithParam<Feature2DType_String_t> feature2d;
|
||||
|
||||
#define TEST_IMAGES testing::Values(\
|
||||
"cv/detectors_descriptors_evaluation/images_datasets/leuven/img1.png",\
|
||||
"stitching/a3.png", \
|
||||
"stitching/s2.jpg")
|
||||
|
||||
static inline Ptr<Feature2D> getFeature2D(Feature2DType type)
|
||||
{
|
||||
switch(type) {
|
||||
case ORB_DEFAULT:
|
||||
return ORB::create();
|
||||
case ORB_1500_13_1:
|
||||
return ORB::create(1500, 1.3f, 1);
|
||||
case FAST_DEFAULT:
|
||||
return FastFeatureDetector::create();
|
||||
case FAST_20_TRUE_TYPE5_8:
|
||||
return FastFeatureDetector::create(20, true, FastFeatureDetector::TYPE_5_8);
|
||||
case FAST_20_TRUE_TYPE7_12:
|
||||
return FastFeatureDetector::create(20, true, FastFeatureDetector::TYPE_7_12);
|
||||
case FAST_20_TRUE_TYPE9_16:
|
||||
return FastFeatureDetector::create(20, true, FastFeatureDetector::TYPE_9_16);
|
||||
case FAST_20_FALSE_TYPE5_8:
|
||||
return FastFeatureDetector::create(20, false, FastFeatureDetector::TYPE_5_8);
|
||||
case FAST_20_FALSE_TYPE7_12:
|
||||
return FastFeatureDetector::create(20, false, FastFeatureDetector::TYPE_7_12);
|
||||
case FAST_20_FALSE_TYPE9_16:
|
||||
return FastFeatureDetector::create(20, false, FastFeatureDetector::TYPE_9_16);
|
||||
case AGAST_DEFAULT:
|
||||
return AgastFeatureDetector::create();
|
||||
case AGAST_5_8:
|
||||
return AgastFeatureDetector::create(70, true, AgastFeatureDetector::AGAST_5_8);
|
||||
case AGAST_7_12d:
|
||||
return AgastFeatureDetector::create(70, true, AgastFeatureDetector::AGAST_7_12d);
|
||||
case AGAST_7_12s:
|
||||
return AgastFeatureDetector::create(70, true, AgastFeatureDetector::AGAST_7_12s);
|
||||
case AGAST_OAST_9_16:
|
||||
return AgastFeatureDetector::create(70, true, AgastFeatureDetector::OAST_9_16);
|
||||
case AKAZE_DEFAULT:
|
||||
return AKAZE::create();
|
||||
case AKAZE_DESCRIPTOR_KAZE:
|
||||
return AKAZE::create(AKAZE::DESCRIPTOR_KAZE);
|
||||
case BRISK_DEFAULT:
|
||||
return BRISK::create();
|
||||
case KAZE_DEFAULT:
|
||||
return KAZE::create();
|
||||
case MSER_DEFAULT:
|
||||
return MSER::create();
|
||||
case SIFT_DEFAULT:
|
||||
return SIFT::create();
|
||||
default:
|
||||
return Ptr<Feature2D>();
|
||||
}
|
||||
}
|
||||
|
||||
} // namespace
|
||||
|
||||
#endif // __OPENCV_PERF_FEATURE2D_HPP__
|
||||
@@ -0,0 +1,7 @@
|
||||
#include "perf_precomp.hpp"
|
||||
|
||||
#if defined(HAVE_HPX)
|
||||
#include <hpx/hpx_main.hpp>
|
||||
#endif
|
||||
|
||||
CV_PERF_TEST_MAIN(features2d)
|
||||
@@ -0,0 +1,91 @@
|
||||
// This file is part of OpenCV project.
|
||||
// It is subject to the license terms in the LICENSE file found in the top-level directory
|
||||
// of this distribution and at http://opencv.org/license.html.
|
||||
//
|
||||
// Copyright (C) 2026, Advanced Micro Devices, Inc., all rights reserved.
|
||||
|
||||
// Brute-force descriptor matching perf tests. These exercise the core distance
|
||||
// kernels (hal::normL2Sqr_ / normL1_ / normHamming via cv::batchDistance) that
|
||||
// back BFMatcher, which is how float descriptors (SIFT 128-d, SURF 64-d) and
|
||||
// binary descriptors (ORB 32-byte) are matched.
|
||||
|
||||
#include "perf_precomp.hpp"
|
||||
|
||||
namespace opencv_test
|
||||
{
|
||||
using namespace perf;
|
||||
|
||||
// (descriptor dimension, query/train descriptor count)
|
||||
typedef tuple<int, int> Dim_Count_t;
|
||||
typedef TestBaseWithParam<Dim_Count_t> DescriptorMatcherFixture;
|
||||
|
||||
// Float descriptors matched with L2 (SIFT=128, SURF=64) — uses hal::normL2Sqr_.
|
||||
PERF_TEST_P(DescriptorMatcherFixture, bfmatch_knn_L2_float,
|
||||
testing::Combine(
|
||||
testing::Values(64, 128), // SURF, SIFT descriptor sizes
|
||||
testing::Values(512, 1000)
|
||||
))
|
||||
{
|
||||
const int dim = get<0>(GetParam());
|
||||
const int count = get<1>(GetParam());
|
||||
|
||||
Mat query(count, dim, CV_32F);
|
||||
Mat train(count, dim, CV_32F);
|
||||
declare.in(query, train, WARMUP_RNG);
|
||||
declare.time(60);
|
||||
|
||||
BFMatcher matcher(NORM_L2, false);
|
||||
std::vector<std::vector<DMatch> > matches;
|
||||
|
||||
TEST_CYCLE() matcher.knnMatch(query, train, matches, 2);
|
||||
|
||||
SANITY_CHECK_NOTHING();
|
||||
}
|
||||
|
||||
// Float descriptors matched with L1 — uses hal::normL1_.
|
||||
PERF_TEST_P(DescriptorMatcherFixture, bfmatch_knn_L1_float,
|
||||
testing::Combine(
|
||||
testing::Values(64, 128),
|
||||
testing::Values(512, 1000)
|
||||
))
|
||||
{
|
||||
const int dim = get<0>(GetParam());
|
||||
const int count = get<1>(GetParam());
|
||||
|
||||
Mat query(count, dim, CV_32F);
|
||||
Mat train(count, dim, CV_32F);
|
||||
declare.in(query, train, WARMUP_RNG);
|
||||
declare.time(60);
|
||||
|
||||
BFMatcher matcher(NORM_L1, false);
|
||||
std::vector<std::vector<DMatch> > matches;
|
||||
|
||||
TEST_CYCLE() matcher.knnMatch(query, train, matches, 2);
|
||||
|
||||
SANITY_CHECK_NOTHING();
|
||||
}
|
||||
|
||||
// Binary descriptors matched with Hamming (ORB/BRISK=32 bytes) — uses hal::normHamming.
|
||||
PERF_TEST_P(DescriptorMatcherFixture, bfmatch_knn_Hamming_binary,
|
||||
testing::Combine(
|
||||
testing::Values(32, 64), // ORB (32), BRISK/FREAK (64) byte sizes
|
||||
testing::Values(512, 1000)
|
||||
))
|
||||
{
|
||||
const int bytes = get<0>(GetParam());
|
||||
const int count = get<1>(GetParam());
|
||||
|
||||
Mat query(count, bytes, CV_8U);
|
||||
Mat train(count, bytes, CV_8U);
|
||||
declare.in(query, train, WARMUP_RNG);
|
||||
declare.time(60);
|
||||
|
||||
BFMatcher matcher(NORM_HAMMING, false);
|
||||
std::vector<std::vector<DMatch> > matches;
|
||||
|
||||
TEST_CYCLE() matcher.knnMatch(query, train, matches, 2);
|
||||
|
||||
SANITY_CHECK_NOTHING();
|
||||
}
|
||||
|
||||
} // namespace
|
||||
@@ -0,0 +1,7 @@
|
||||
#ifndef __OPENCV_PERF_PRECOMP_HPP__
|
||||
#define __OPENCV_PERF_PRECOMP_HPP__
|
||||
|
||||
#include "opencv2/ts.hpp"
|
||||
#include "opencv2/features2d.hpp"
|
||||
|
||||
#endif
|
||||
@@ -0,0 +1,360 @@
|
||||
// This file is part of OpenCV project.
|
||||
// It is subject to the license terms in the LICENSE file found in the top-level directory
|
||||
// of this distribution and at http://opencv.org/license.html.
|
||||
//
|
||||
// This file is based on code issued with the following license.
|
||||
/*********************************************************************
|
||||
* Software License Agreement (BSD License)
|
||||
*
|
||||
* Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
|
||||
* Copyright (C) 2008-2013, Willow Garage Inc., all rights reserved.
|
||||
* Copyright (C) 2013, Evgeny Toropov, all rights reserved.
|
||||
* Third party copyrights are property of their respective owners.
|
||||
*
|
||||
* Redistribution and use in source and binary forms, with or without
|
||||
* modification, are permitted provided that the following conditions
|
||||
* are met:
|
||||
*
|
||||
* * Redistributions of source code must retain the above copyright
|
||||
* notice, this list of conditions and the following disclaimer.
|
||||
* * Redistributions in binary form must reproduce the above
|
||||
* copyright notice, this list of conditions and the following
|
||||
* disclaimer in the documentation and/or other materials provided
|
||||
* with the distribution.
|
||||
* * The name of the copyright holders may not be used to endorse
|
||||
* or promote products derived from this software without specific
|
||||
* prior written permission.
|
||||
*
|
||||
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
|
||||
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
|
||||
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
|
||||
* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
|
||||
* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
|
||||
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
|
||||
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
|
||||
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
|
||||
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
|
||||
* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
|
||||
* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
|
||||
* POSSIBILITY OF SUCH DAMAGE.
|
||||
*********************************************************************/
|
||||
|
||||
/*
|
||||
Guoshen Yu, Jean-Michel Morel, ASIFT: An Algorithm for Fully Affine
|
||||
Invariant Comparison, Image Processing On Line, 1 (2011), pp. 11-38.
|
||||
https://doi.org/10.5201/ipol.2011.my-asift
|
||||
*/
|
||||
|
||||
#include "precomp.hpp"
|
||||
#include <iostream>
|
||||
namespace cv {
|
||||
|
||||
class AffineFeature_Impl CV_FINAL : public AffineFeature
|
||||
{
|
||||
public:
|
||||
explicit AffineFeature_Impl(const Ptr<Feature2D>& backend,
|
||||
int maxTilt, int minTilt, float tiltStep, float rotateStepBase);
|
||||
|
||||
int descriptorSize() const CV_OVERRIDE
|
||||
{
|
||||
return backend_->descriptorSize();
|
||||
}
|
||||
|
||||
int descriptorType() const CV_OVERRIDE
|
||||
{
|
||||
return backend_->descriptorType();
|
||||
}
|
||||
|
||||
int defaultNorm() const CV_OVERRIDE
|
||||
{
|
||||
return backend_->defaultNorm();
|
||||
}
|
||||
|
||||
void detectAndCompute(InputArray image, InputArray mask, std::vector<KeyPoint>& keypoints,
|
||||
OutputArray descriptors, bool useProvidedKeypoints=false) CV_OVERRIDE;
|
||||
|
||||
void setViewParams(const std::vector<float>& tilts, const std::vector<float>& rolls) CV_OVERRIDE;
|
||||
void getViewParams(std::vector<float>& tilts, std::vector<float>& rolls) const CV_OVERRIDE;
|
||||
|
||||
protected:
|
||||
void splitKeypointsByView(const std::vector<KeyPoint>& keypoints_,
|
||||
std::vector< std::vector<KeyPoint> >& keypointsByView) const;
|
||||
|
||||
const Ptr<Feature2D> backend_;
|
||||
int maxTilt_;
|
||||
int minTilt_;
|
||||
float tiltStep_;
|
||||
float rotateStepBase_;
|
||||
|
||||
// Tilt factors.
|
||||
std::vector<float> tilts_;
|
||||
// Roll factors.
|
||||
std::vector<float> rolls_;
|
||||
|
||||
private:
|
||||
AffineFeature_Impl(const AffineFeature_Impl &); // copy disabled
|
||||
AffineFeature_Impl& operator=(const AffineFeature_Impl &); // assign disabled
|
||||
};
|
||||
|
||||
AffineFeature_Impl::AffineFeature_Impl(const Ptr<FeatureDetector>& backend,
|
||||
int maxTilt, int minTilt, float tiltStep, float rotateStepBase)
|
||||
: backend_(backend), maxTilt_(maxTilt), minTilt_(minTilt), tiltStep_(tiltStep), rotateStepBase_(rotateStepBase)
|
||||
{
|
||||
int i = minTilt_;
|
||||
if( i == 0 )
|
||||
{
|
||||
tilts_.push_back(1);
|
||||
rolls_.push_back(0);
|
||||
i++;
|
||||
}
|
||||
float tilt = 1;
|
||||
for( ; i <= maxTilt_; i++ )
|
||||
{
|
||||
tilt *= tiltStep_;
|
||||
float rotateStep = rotateStepBase_ / tilt;
|
||||
int rollN = cvFloor(180.0f / rotateStep);
|
||||
if( rollN * rotateStep == 180.0f )
|
||||
rollN--;
|
||||
for( int j = 0; j <= rollN; j++ )
|
||||
{
|
||||
tilts_.push_back(tilt);
|
||||
rolls_.push_back(rotateStep * j);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
void AffineFeature_Impl::setViewParams(const std::vector<float>& tilts,
|
||||
const std::vector<float>& rolls)
|
||||
{
|
||||
CV_Assert(tilts.size() == rolls.size());
|
||||
tilts_ = tilts;
|
||||
rolls_ = rolls;
|
||||
}
|
||||
|
||||
void AffineFeature_Impl::getViewParams(std::vector<float>& tilts,
|
||||
std::vector<float>& rolls) const
|
||||
{
|
||||
tilts = tilts_;
|
||||
rolls = rolls_;
|
||||
}
|
||||
|
||||
void AffineFeature_Impl::splitKeypointsByView(const std::vector<KeyPoint>& keypoints_,
|
||||
std::vector< std::vector<KeyPoint> >& keypointsByView) const
|
||||
{
|
||||
for( size_t i = 0; i < keypoints_.size(); i++ )
|
||||
{
|
||||
const KeyPoint& kp = keypoints_[i];
|
||||
CV_Assert( kp.class_id >= 0 && kp.class_id < (int)tilts_.size() );
|
||||
keypointsByView[kp.class_id].push_back(kp);
|
||||
}
|
||||
}
|
||||
|
||||
class skewedDetectAndCompute : public ParallelLoopBody
|
||||
{
|
||||
public:
|
||||
skewedDetectAndCompute(
|
||||
const std::vector<float>& _tilts,
|
||||
const std::vector<float>& _rolls,
|
||||
std::vector< std::vector<KeyPoint> >& _keypointsCollection,
|
||||
std::vector<Mat>& _descriptorCollection,
|
||||
const Mat& _image,
|
||||
const Mat& _mask,
|
||||
const bool _do_keypoints,
|
||||
const bool _do_descriptors,
|
||||
const Ptr<Feature2D>& _backend)
|
||||
: tilts(_tilts),
|
||||
rolls(_rolls),
|
||||
keypointsCollection(_keypointsCollection),
|
||||
descriptorCollection(_descriptorCollection),
|
||||
image(_image),
|
||||
mask(_mask),
|
||||
do_keypoints(_do_keypoints),
|
||||
do_descriptors(_do_descriptors),
|
||||
backend(_backend) {}
|
||||
|
||||
void operator()( const cv::Range& range ) const CV_OVERRIDE
|
||||
{
|
||||
CV_TRACE_FUNCTION();
|
||||
|
||||
const int begin = range.start;
|
||||
const int end = range.end;
|
||||
|
||||
for( int a = begin; a < end; a++ )
|
||||
{
|
||||
Mat warpedImage, warpedMask;
|
||||
Matx23f pose, invPose;
|
||||
affineSkew(tilts[a], rolls[a], warpedImage, warpedMask, pose);
|
||||
invertAffineTransform(pose, invPose);
|
||||
|
||||
std::vector<KeyPoint> wKeypoints;
|
||||
Mat wDescriptors;
|
||||
if( !do_keypoints )
|
||||
{
|
||||
const std::vector<KeyPoint>& keypointsInView = keypointsCollection[a];
|
||||
if( keypointsInView.size() == 0 ) // when there are no keypoints in this affine view
|
||||
continue;
|
||||
|
||||
std::vector<Point2f> pts_, pts;
|
||||
KeyPoint::convert(keypointsInView, pts_);
|
||||
transform(pts_, pts, pose);
|
||||
wKeypoints.resize(keypointsInView.size());
|
||||
for( size_t wi = 0; wi < wKeypoints.size(); wi++ )
|
||||
{
|
||||
wKeypoints[wi] = keypointsInView[wi];
|
||||
wKeypoints[wi].pt = pts[wi];
|
||||
}
|
||||
}
|
||||
backend->detectAndCompute(warpedImage, warpedMask, wKeypoints, wDescriptors, !do_keypoints);
|
||||
if( do_keypoints )
|
||||
{
|
||||
// KeyPointsFilter::runByPixelsMask( wKeypoints, warpedMask );
|
||||
if( wKeypoints.size() == 0 )
|
||||
{
|
||||
keypointsCollection[a].clear();
|
||||
continue;
|
||||
}
|
||||
std::vector<Point2f> pts_, pts;
|
||||
KeyPoint::convert(wKeypoints, pts_);
|
||||
transform(pts_, pts, invPose);
|
||||
|
||||
keypointsCollection[a].resize(wKeypoints.size());
|
||||
for( size_t wi = 0; wi < wKeypoints.size(); wi++ )
|
||||
{
|
||||
keypointsCollection[a][wi] = wKeypoints[wi];
|
||||
keypointsCollection[a][wi].pt = pts[wi];
|
||||
keypointsCollection[a][wi].class_id = a;
|
||||
}
|
||||
}
|
||||
if( do_descriptors )
|
||||
wDescriptors.copyTo(descriptorCollection[a]);
|
||||
}
|
||||
}
|
||||
private:
|
||||
void affineSkew(float tilt, float phi,
|
||||
Mat& warpedImage, Mat& warpedMask, Matx23f& pose) const
|
||||
{
|
||||
int h = image.size().height;
|
||||
int w = image.size().width;
|
||||
Mat rotImage;
|
||||
|
||||
Mat mask0;
|
||||
if( mask.empty() )
|
||||
mask0 = Mat(h, w, CV_8UC1, 255);
|
||||
else
|
||||
mask0 = mask;
|
||||
pose = Matx23f(1,0,0,
|
||||
0,1,0);
|
||||
|
||||
if( phi == 0 )
|
||||
image.copyTo(rotImage);
|
||||
else
|
||||
{
|
||||
phi = phi * (float)CV_PI / 180;
|
||||
float s = std::sin(phi);
|
||||
float c = std::cos(phi);
|
||||
Matx22f A(c, -s, s, c);
|
||||
Matx<float, 4, 2> corners(0, 0, (float)w, 0, (float)w,(float)h, 0, (float)h);
|
||||
Mat tf(corners * A.t());
|
||||
Mat tcorners;
|
||||
tf.convertTo(tcorners, CV_32S);
|
||||
Rect rect = boundingRect(tcorners);
|
||||
h = rect.height; w = rect.width;
|
||||
pose = Matx23f(c, -s, -(float)rect.x,
|
||||
s, c, -(float)rect.y);
|
||||
warpAffine(image, rotImage, pose, Size(w, h), INTER_LINEAR, BORDER_REPLICATE);
|
||||
}
|
||||
if( tilt == 1 )
|
||||
warpedImage = rotImage;
|
||||
else
|
||||
{
|
||||
float s = 0.8f * sqrt(tilt * tilt - 1);
|
||||
GaussianBlur(rotImage, rotImage, Size(0, 0), s, 0.01);
|
||||
resize(rotImage, warpedImage, Size(0, 0), 1.0/tilt, 1.0, INTER_NEAREST);
|
||||
pose(0, 0) /= tilt;
|
||||
pose(0, 1) /= tilt;
|
||||
pose(0, 2) /= tilt;
|
||||
}
|
||||
if( phi != 0 || tilt != 1 )
|
||||
warpAffine(mask0, warpedMask, pose, warpedImage.size(), INTER_NEAREST);
|
||||
else
|
||||
warpedMask = mask0;
|
||||
}
|
||||
|
||||
|
||||
const std::vector<float>& tilts;
|
||||
const std::vector<float>& rolls;
|
||||
std::vector< std::vector<KeyPoint> >& keypointsCollection;
|
||||
std::vector<Mat>& descriptorCollection;
|
||||
const Mat& image;
|
||||
const Mat& mask;
|
||||
const bool do_keypoints;
|
||||
const bool do_descriptors;
|
||||
const Ptr<Feature2D>& backend;
|
||||
};
|
||||
|
||||
void AffineFeature_Impl::detectAndCompute(InputArray _image, InputArray _mask,
|
||||
std::vector<KeyPoint>& keypoints,
|
||||
OutputArray _descriptors,
|
||||
bool useProvidedKeypoints)
|
||||
{
|
||||
CV_TRACE_FUNCTION();
|
||||
|
||||
bool do_keypoints = !useProvidedKeypoints;
|
||||
bool do_descriptors = _descriptors.needed();
|
||||
Mat image = _image.getMat(), mask = _mask.getMat();
|
||||
Mat descriptors;
|
||||
|
||||
if( (!do_keypoints && !do_descriptors) || _image.empty() )
|
||||
return;
|
||||
|
||||
std::vector< std::vector<KeyPoint> > keypointsCollection(tilts_.size());
|
||||
std::vector< Mat > descriptorCollection(tilts_.size());
|
||||
|
||||
if( do_keypoints )
|
||||
keypoints.clear();
|
||||
else
|
||||
splitKeypointsByView(keypoints, keypointsCollection);
|
||||
|
||||
parallel_for_(Range(0, (int)tilts_.size()), skewedDetectAndCompute(tilts_, rolls_, keypointsCollection, descriptorCollection,
|
||||
image, mask, do_keypoints, do_descriptors, backend_));
|
||||
|
||||
if( do_keypoints )
|
||||
for( size_t i = 0; i < keypointsCollection.size(); i++ )
|
||||
{
|
||||
const std::vector<KeyPoint>& keys = keypointsCollection[i];
|
||||
keypoints.insert(keypoints.end(), keys.begin(), keys.end());
|
||||
}
|
||||
|
||||
if( do_descriptors )
|
||||
{
|
||||
_descriptors.create((int)keypoints.size(), backend_->descriptorSize(), backend_->descriptorType());
|
||||
descriptors = _descriptors.getMat();
|
||||
int iter = 0;
|
||||
for( size_t i = 0; i < descriptorCollection.size(); i++ )
|
||||
{
|
||||
const Mat& descs = descriptorCollection[i];
|
||||
if( descs.empty() )
|
||||
continue;
|
||||
Mat roi(descriptors, Rect(0, iter, descriptors.cols, descs.rows));
|
||||
descs.copyTo(roi);
|
||||
iter += descs.rows;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
Ptr<AffineFeature> AffineFeature::create(const Ptr<Feature2D>& backend,
|
||||
int maxTilt, int minTilt, float tiltStep, float rotateStepBase)
|
||||
{
|
||||
CV_Assert(minTilt < maxTilt);
|
||||
CV_Assert(tiltStep > 0);
|
||||
CV_Assert(rotateStepBase > 0);
|
||||
return makePtr<AffineFeature_Impl>(backend, maxTilt, minTilt, tiltStep, rotateStepBase);
|
||||
}
|
||||
|
||||
String AffineFeature::getDefaultName() const
|
||||
{
|
||||
return (Feature2D::getDefaultName() + ".AffineFeature");
|
||||
}
|
||||
|
||||
} // namespace
|
||||
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,69 @@
|
||||
/* This is AGAST and OAST, an optimal and accelerated corner detector
|
||||
based on the accelerated segment tests
|
||||
Below is the original copyright and the references */
|
||||
|
||||
/*
|
||||
Copyright (C) 2010 Elmar Mair
|
||||
All rights reserved.
|
||||
|
||||
Redistribution and use in source and binary forms, with or without
|
||||
modification, are permitted provided that the following conditions
|
||||
are met:
|
||||
|
||||
*Redistributions of source code must retain the above copyright
|
||||
notice, this list of conditions and the following disclaimer.
|
||||
|
||||
*Redistributions in binary form must reproduce the above copyright
|
||||
notice, this list of conditions and the following disclaimer in the
|
||||
documentation and/or other materials provided with the distribution.
|
||||
|
||||
*Neither the name of the University of Cambridge nor the names of
|
||||
its contributors may be used to endorse or promote products derived
|
||||
from this software without specific prior written permission.
|
||||
|
||||
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
|
||||
"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
|
||||
LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
|
||||
A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
|
||||
CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
|
||||
EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
|
||||
PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
|
||||
PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
|
||||
LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
|
||||
NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
|
||||
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
|
||||
*/
|
||||
|
||||
/*
|
||||
The references are:
|
||||
* Adaptive and Generic Corner Detection Based on the Accelerated Segment Test,
|
||||
Elmar Mair and Gregory D. Hager and Darius Burschka
|
||||
and Michael Suppa and Gerhard Hirzinger ECCV 2010
|
||||
URL: http://www6.in.tum.de/Main/ResearchAgast
|
||||
*/
|
||||
|
||||
|
||||
#ifndef __OPENCV_FEATURES_2D_AGAST_HPP__
|
||||
#define __OPENCV_FEATURES_2D_AGAST_HPP__
|
||||
|
||||
#ifdef __cplusplus
|
||||
|
||||
#include "precomp.hpp"
|
||||
namespace cv
|
||||
{
|
||||
|
||||
#if !(defined __i386__ || defined(_M_IX86) || defined __x86_64__ || defined(_M_X64))
|
||||
int agast_tree_search(const uint32_t table_struct32[], int pixel_[], const unsigned char* const ptr, int threshold);
|
||||
int AGAST_ALL_SCORE(const uchar* ptr, const int pixel[], int threshold, AgastFeatureDetector::DetectorType agasttype);
|
||||
#endif //!(defined __i386__ || defined(_M_IX86) || defined __x86_64__ || defined(_M_X64))
|
||||
|
||||
|
||||
void makeAgastOffsets(int pixel[16], int row_stride, AgastFeatureDetector::DetectorType type);
|
||||
|
||||
template<AgastFeatureDetector::DetectorType type>
|
||||
int agast_cornerScore(const uchar* ptr, const int pixel[], int threshold);
|
||||
|
||||
|
||||
}
|
||||
#endif
|
||||
#endif
|
||||
@@ -0,0 +1,310 @@
|
||||
/*M///////////////////////////////////////////////////////////////////////////////////////
|
||||
//
|
||||
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
|
||||
//
|
||||
// By downloading, copying, installing or using the software you agree to this license.
|
||||
// If you do not agree to this license, do not download, install,
|
||||
// copy or use the software.
|
||||
//
|
||||
//
|
||||
// License Agreement
|
||||
// For Open Source Computer Vision Library
|
||||
//
|
||||
// Copyright (C) 2008, Willow Garage Inc., all rights reserved.
|
||||
// Third party copyrights are property of their respective owners.
|
||||
//
|
||||
// Redistribution and use in source and binary forms, with or without modification,
|
||||
// are permitted provided that the following conditions are met:
|
||||
//
|
||||
// * Redistribution's of source code must retain the above copyright notice,
|
||||
// this list of conditions and the following disclaimer.
|
||||
//
|
||||
// * Redistribution's in binary form must reproduce the above copyright notice,
|
||||
// this list of conditions and the following disclaimer in the documentation
|
||||
// and/or other materials provided with the distribution.
|
||||
//
|
||||
// * The name of Intel Corporation may not be used to endorse or promote products
|
||||
// derived from this software without specific prior written permission.
|
||||
//
|
||||
// This software is provided by the copyright holders and contributors "as is" and
|
||||
// any express or implied warranties, including, but not limited to, the implied
|
||||
// warranties of merchantability and fitness for a particular purpose are disclaimed.
|
||||
// In no event shall the Intel Corporation or contributors be liable for any direct,
|
||||
// indirect, incidental, special, exemplary, or consequential damages
|
||||
// (including, but not limited to, procurement of substitute goods or services;
|
||||
// loss of use, data, or profits; or business interruption) however caused
|
||||
// and on any theory of liability, whether in contract, strict liability,
|
||||
// or tort (including negligence or otherwise) arising in any way out of
|
||||
// the use of this software, even if advised of the possibility of such damage.
|
||||
//
|
||||
//M*/
|
||||
|
||||
/*
|
||||
OpenCV wrapper of reference implementation of
|
||||
[1] Fast Explicit Diffusion for Accelerated Features in Nonlinear Scale Spaces.
|
||||
Pablo F. Alcantarilla, J. Nuevo and Adrien Bartoli.
|
||||
In British Machine Vision Conference (BMVC), Bristol, UK, September 2013
|
||||
http://www.robesafe.com/personal/pablo.alcantarilla/papers/Alcantarilla13bmvc.pdf
|
||||
@author Eugene Khvedchenya <ekhvedchenya@gmail.com>
|
||||
*/
|
||||
|
||||
#include "precomp.hpp"
|
||||
#include "kaze/AKAZEFeatures.h"
|
||||
|
||||
#include <iostream>
|
||||
|
||||
namespace cv
|
||||
{
|
||||
using namespace std;
|
||||
|
||||
class AKAZE_Impl : public AKAZE
|
||||
{
|
||||
public:
|
||||
AKAZE_Impl(DescriptorType _descriptor_type, int _descriptor_size, int _descriptor_channels,
|
||||
float _threshold, int _octaves, int _sublevels, KAZE::DiffusivityType _diffusivity, int _max_points)
|
||||
: descriptor(_descriptor_type)
|
||||
, descriptor_channels(_descriptor_channels)
|
||||
, descriptor_size(_descriptor_size)
|
||||
, threshold(_threshold)
|
||||
, octaves(_octaves)
|
||||
, sublevels(_sublevels)
|
||||
, diffusivity(_diffusivity)
|
||||
, max_points(_max_points)
|
||||
{
|
||||
}
|
||||
|
||||
virtual ~AKAZE_Impl() CV_OVERRIDE
|
||||
{
|
||||
|
||||
}
|
||||
|
||||
void setDescriptorType(DescriptorType dtype) CV_OVERRIDE{ descriptor = dtype; }
|
||||
DescriptorType getDescriptorType() const CV_OVERRIDE{ return descriptor; }
|
||||
|
||||
void setDescriptorSize(int dsize) CV_OVERRIDE { descriptor_size = dsize; }
|
||||
int getDescriptorSize() const CV_OVERRIDE { return descriptor_size; }
|
||||
|
||||
void setDescriptorChannels(int dch) CV_OVERRIDE { descriptor_channels = dch; }
|
||||
int getDescriptorChannels() const CV_OVERRIDE { return descriptor_channels; }
|
||||
|
||||
void setThreshold(double threshold_) CV_OVERRIDE { threshold = (float)threshold_; }
|
||||
double getThreshold() const CV_OVERRIDE { return threshold; }
|
||||
|
||||
void setNOctaves(int octaves_) CV_OVERRIDE { octaves = octaves_; }
|
||||
int getNOctaves() const CV_OVERRIDE { return octaves; }
|
||||
|
||||
void setNOctaveLayers(int octaveLayers_) CV_OVERRIDE { sublevels = octaveLayers_; }
|
||||
int getNOctaveLayers() const CV_OVERRIDE { return sublevels; }
|
||||
|
||||
void setDiffusivity(KAZE::DiffusivityType diff_) CV_OVERRIDE{ diffusivity = diff_; }
|
||||
KAZE::DiffusivityType getDiffusivity() const CV_OVERRIDE{ return diffusivity; }
|
||||
|
||||
void setMaxPoints(int max_points_) CV_OVERRIDE { max_points = max_points_; }
|
||||
int getMaxPoints() const CV_OVERRIDE { return max_points; }
|
||||
|
||||
// returns the descriptor size in bytes
|
||||
int descriptorSize() const CV_OVERRIDE
|
||||
{
|
||||
switch (descriptor)
|
||||
{
|
||||
case DESCRIPTOR_KAZE:
|
||||
case DESCRIPTOR_KAZE_UPRIGHT:
|
||||
return 64;
|
||||
|
||||
case DESCRIPTOR_MLDB:
|
||||
case DESCRIPTOR_MLDB_UPRIGHT:
|
||||
// We use the full length binary descriptor -> 486 bits
|
||||
if (descriptor_size == 0)
|
||||
{
|
||||
int t = (6 + 36 + 120) * descriptor_channels;
|
||||
return divUp(t, 8);
|
||||
}
|
||||
else
|
||||
{
|
||||
// We use the random bit selection length binary descriptor
|
||||
return divUp(descriptor_size, 8);
|
||||
}
|
||||
|
||||
default:
|
||||
return -1;
|
||||
}
|
||||
}
|
||||
|
||||
// returns the descriptor type
|
||||
int descriptorType() const CV_OVERRIDE
|
||||
{
|
||||
switch (descriptor)
|
||||
{
|
||||
case DESCRIPTOR_KAZE:
|
||||
case DESCRIPTOR_KAZE_UPRIGHT:
|
||||
return CV_32F;
|
||||
|
||||
case DESCRIPTOR_MLDB:
|
||||
case DESCRIPTOR_MLDB_UPRIGHT:
|
||||
return CV_8U;
|
||||
|
||||
default:
|
||||
return -1;
|
||||
}
|
||||
}
|
||||
|
||||
// returns the default norm type
|
||||
int defaultNorm() const CV_OVERRIDE
|
||||
{
|
||||
switch (descriptor)
|
||||
{
|
||||
case DESCRIPTOR_KAZE:
|
||||
case DESCRIPTOR_KAZE_UPRIGHT:
|
||||
return NORM_L2;
|
||||
|
||||
case DESCRIPTOR_MLDB:
|
||||
case DESCRIPTOR_MLDB_UPRIGHT:
|
||||
return NORM_HAMMING;
|
||||
|
||||
default:
|
||||
return -1;
|
||||
}
|
||||
}
|
||||
|
||||
void detectAndCompute(InputArray image, InputArray mask,
|
||||
std::vector<KeyPoint>& keypoints,
|
||||
OutputArray descriptors,
|
||||
bool useProvidedKeypoints) CV_OVERRIDE
|
||||
{
|
||||
CV_INSTRUMENT_REGION();
|
||||
|
||||
CV_Assert( ! image.empty() );
|
||||
|
||||
AKAZEOptions options;
|
||||
options.descriptor = descriptor;
|
||||
options.descriptor_channels = descriptor_channels;
|
||||
options.descriptor_size = descriptor_size;
|
||||
options.img_width = image.cols();
|
||||
options.img_height = image.rows();
|
||||
options.dthreshold = threshold;
|
||||
options.omax = octaves;
|
||||
options.nsublevels = sublevels;
|
||||
options.diffusivity = diffusivity;
|
||||
|
||||
AKAZEFeatures impl(options);
|
||||
|
||||
UMatPyramid uPyr;
|
||||
#ifdef HAVE_OPENCL
|
||||
// NOTE: The AKAZE kernels use excessive private memory 336 bytes per work-item:
|
||||
// float histogram[36] and float values[48] arrays. On 32-bit Windows, this
|
||||
// exceeds the driver's private memory limits, causing SEH exception (0xc0000005).
|
||||
#if defined(_M_IX86) || defined(__i386__)
|
||||
bool use_opencl = false;
|
||||
#else
|
||||
bool use_opencl = cv::ocl::useOpenCL() && image.isUMat() && descriptor == DESCRIPTOR_MLDB_UPRIGHT
|
||||
&& !ocl::Device::getDefault().hostUnifiedMemory();
|
||||
#endif
|
||||
if (use_opencl)
|
||||
{
|
||||
impl.GetEvolutionPyramid(uPyr); // Get initialized pyramid
|
||||
impl.Create_Nonlinear_Scale_Space_UMat(image, uPyr);
|
||||
if (!useProvidedKeypoints)
|
||||
{
|
||||
impl.Feature_Detection_UMat(uPyr, keypoints);
|
||||
}
|
||||
}
|
||||
else
|
||||
#endif
|
||||
{
|
||||
impl.Create_Nonlinear_Scale_Space(image);
|
||||
|
||||
if (!useProvidedKeypoints)
|
||||
{
|
||||
impl.Feature_Detection(keypoints);
|
||||
}
|
||||
}
|
||||
|
||||
if (!mask.empty())
|
||||
{
|
||||
KeyPointsFilter::runByPixelsMask(keypoints, mask.getMat());
|
||||
}
|
||||
|
||||
if (max_points > 0 && (int)keypoints.size() > max_points) {
|
||||
std::partial_sort(keypoints.begin(), keypoints.begin() + max_points, keypoints.end(),
|
||||
[](const cv::KeyPoint& k1, const cv::KeyPoint& k2) {return k1.response > k2.response;});
|
||||
keypoints.erase(keypoints.begin() + max_points, keypoints.end());
|
||||
}
|
||||
|
||||
if(descriptors.needed())
|
||||
{
|
||||
#ifdef HAVE_OPENCL
|
||||
if (use_opencl)
|
||||
{
|
||||
impl.Compute_Descriptors_UMat(keypoints, descriptors, uPyr);
|
||||
}
|
||||
else
|
||||
#endif
|
||||
{
|
||||
impl.Compute_Descriptors(keypoints, descriptors);
|
||||
}
|
||||
|
||||
CV_Assert((descriptors.empty() || descriptors.cols() == descriptorSize()));
|
||||
CV_Assert((descriptors.empty() || (descriptors.type() == descriptorType())));
|
||||
}
|
||||
}
|
||||
|
||||
void write(FileStorage& fs) const CV_OVERRIDE
|
||||
{
|
||||
writeFormat(fs);
|
||||
fs << "name" << getDefaultName();
|
||||
fs << "descriptor" << descriptor;
|
||||
fs << "descriptor_channels" << descriptor_channels;
|
||||
fs << "descriptor_size" << descriptor_size;
|
||||
fs << "threshold" << threshold;
|
||||
fs << "octaves" << octaves;
|
||||
fs << "sublevels" << sublevels;
|
||||
fs << "diffusivity" << diffusivity;
|
||||
fs << "max_points" << max_points;
|
||||
}
|
||||
|
||||
void read(const FileNode& fn) CV_OVERRIDE
|
||||
{
|
||||
// if node is empty, keep previous value
|
||||
if (!fn["descriptor"].empty())
|
||||
descriptor = static_cast<DescriptorType>((int)fn["descriptor"]);
|
||||
if (!fn["descriptor_channels"].empty())
|
||||
descriptor_channels = (int)fn["descriptor_channels"];
|
||||
if (!fn["descriptor_size"].empty())
|
||||
descriptor_size = (int)fn["descriptor_size"];
|
||||
if (!fn["threshold"].empty())
|
||||
threshold = (float)fn["threshold"];
|
||||
if (!fn["octaves"].empty())
|
||||
octaves = (int)fn["octaves"];
|
||||
if (!fn["sublevels"].empty())
|
||||
sublevels = (int)fn["sublevels"];
|
||||
if (!fn["diffusivity"].empty())
|
||||
diffusivity = static_cast<KAZE::DiffusivityType>((int)fn["diffusivity"]);
|
||||
if (!fn["max_points"].empty())
|
||||
max_points = (int)fn["max_points"];
|
||||
}
|
||||
|
||||
DescriptorType descriptor;
|
||||
int descriptor_channels;
|
||||
int descriptor_size;
|
||||
float threshold;
|
||||
int octaves;
|
||||
int sublevels;
|
||||
KAZE::DiffusivityType diffusivity;
|
||||
int max_points;
|
||||
};
|
||||
|
||||
Ptr<AKAZE> AKAZE::create(DescriptorType descriptor_type,
|
||||
int descriptor_size, int descriptor_channels,
|
||||
float threshold, int octaves,
|
||||
int sublevels, KAZE::DiffusivityType diffusivity, int max_points)
|
||||
{
|
||||
return makePtr<AKAZE_Impl>(descriptor_type, descriptor_size, descriptor_channels,
|
||||
threshold, octaves, sublevels, diffusivity, max_points);
|
||||
}
|
||||
|
||||
String AKAZE::getDefaultName() const
|
||||
{
|
||||
return (Feature2D::getDefaultName() + ".AKAZE");
|
||||
}
|
||||
|
||||
}
|
||||
@@ -0,0 +1,216 @@
|
||||
/*M///////////////////////////////////////////////////////////////////////////////////////
|
||||
//
|
||||
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
|
||||
//
|
||||
// By downloading, copying, installing or using the software you agree to this license.
|
||||
// If you do not agree to this license, do not download, install,
|
||||
// copy or use the software.
|
||||
//
|
||||
//
|
||||
// Intel License Agreement
|
||||
// For Open Source Computer Vision Library
|
||||
//
|
||||
// Copyright (C) 2000, Intel Corporation, all rights reserved.
|
||||
// Third party copyrights are property of their respective owners.
|
||||
//
|
||||
// Redistribution and use in source and binary forms, with or without modification,
|
||||
// are permitted provided that the following conditions are met:
|
||||
//
|
||||
// * Redistribution's of source code must retain the above copyright notice,
|
||||
// this list of conditions and the following disclaimer.
|
||||
//
|
||||
// * Redistribution's in binary form must reproduce the above copyright notice,
|
||||
// this list of conditions and the following disclaimer in the documentation
|
||||
// and/or other materials provided with the distribution.
|
||||
//
|
||||
// * The name of Intel Corporation may not be used to endorse or promote products
|
||||
// derived from this software without specific prior written permission.
|
||||
//
|
||||
// This software is provided by the copyright holders and contributors "as is" and
|
||||
// any express or implied warranties, including, but not limited to, the implied
|
||||
// warranties of merchantability and fitness for a particular purpose are disclaimed.
|
||||
// In no event shall the Intel Corporation or contributors be liable for any direct,
|
||||
// indirect, incidental, special, exemplary, or consequential damages
|
||||
// (including, but not limited to, procurement of substitute goods or services;
|
||||
// loss of use, data, or profits; or business interruption) however caused
|
||||
// and on any theory of liability, whether in contract, strict liability,
|
||||
// or tort (including negligence or otherwise) arising in any way out of
|
||||
// the use of this software, even if advised of the possibility of such damage.
|
||||
//
|
||||
//M*/
|
||||
|
||||
#include "precomp.hpp"
|
||||
|
||||
namespace cv
|
||||
{
|
||||
|
||||
BOWTrainer::BOWTrainer() : size(0)
|
||||
{}
|
||||
|
||||
BOWTrainer::~BOWTrainer()
|
||||
{}
|
||||
|
||||
void BOWTrainer::add( const Mat& _descriptors )
|
||||
{
|
||||
CV_Assert( !_descriptors.empty() );
|
||||
if( !descriptors.empty() )
|
||||
{
|
||||
CV_Assert( descriptors[0].cols == _descriptors.cols );
|
||||
CV_Assert( descriptors[0].type() == _descriptors.type() );
|
||||
size += _descriptors.rows;
|
||||
}
|
||||
else
|
||||
{
|
||||
size = _descriptors.rows;
|
||||
}
|
||||
|
||||
descriptors.push_back(_descriptors);
|
||||
}
|
||||
|
||||
const std::vector<Mat>& BOWTrainer::getDescriptors() const
|
||||
{
|
||||
return descriptors;
|
||||
}
|
||||
|
||||
int BOWTrainer::descriptorsCount() const
|
||||
{
|
||||
return descriptors.empty() ? 0 : size;
|
||||
}
|
||||
|
||||
void BOWTrainer::clear()
|
||||
{
|
||||
descriptors.clear();
|
||||
}
|
||||
|
||||
BOWKMeansTrainer::BOWKMeansTrainer( int _clusterCount, const TermCriteria& _termcrit,
|
||||
int _attempts, int _flags ) :
|
||||
clusterCount(_clusterCount), termcrit(_termcrit), attempts(_attempts), flags(_flags)
|
||||
{}
|
||||
|
||||
Mat BOWKMeansTrainer::cluster() const
|
||||
{
|
||||
CV_INSTRUMENT_REGION();
|
||||
|
||||
CV_Assert( !descriptors.empty() );
|
||||
|
||||
Mat mergedDescriptors( descriptorsCount(), descriptors[0].cols, descriptors[0].type() );
|
||||
for( size_t i = 0, start = 0; i < descriptors.size(); i++ )
|
||||
{
|
||||
Mat submut = mergedDescriptors.rowRange((int)start, (int)(start + descriptors[i].rows));
|
||||
descriptors[i].copyTo(submut);
|
||||
start += descriptors[i].rows;
|
||||
}
|
||||
return cluster( mergedDescriptors );
|
||||
}
|
||||
|
||||
BOWKMeansTrainer::~BOWKMeansTrainer()
|
||||
{}
|
||||
|
||||
Mat BOWKMeansTrainer::cluster( const Mat& _descriptors ) const
|
||||
{
|
||||
CV_INSTRUMENT_REGION();
|
||||
|
||||
Mat labels, vocabulary;
|
||||
kmeans( _descriptors, clusterCount, labels, termcrit, attempts, flags, vocabulary );
|
||||
return vocabulary;
|
||||
}
|
||||
|
||||
|
||||
BOWImgDescriptorExtractor::BOWImgDescriptorExtractor( const Ptr<DescriptorExtractor>& _dextractor,
|
||||
const Ptr<DescriptorMatcher>& _dmatcher ) :
|
||||
dextractor(_dextractor), dmatcher(_dmatcher)
|
||||
{}
|
||||
|
||||
BOWImgDescriptorExtractor::BOWImgDescriptorExtractor( const Ptr<DescriptorMatcher>& _dmatcher ) :
|
||||
dmatcher(_dmatcher)
|
||||
{}
|
||||
|
||||
BOWImgDescriptorExtractor::~BOWImgDescriptorExtractor()
|
||||
{}
|
||||
|
||||
void BOWImgDescriptorExtractor::setVocabulary( const Mat& _vocabulary )
|
||||
{
|
||||
dmatcher->clear();
|
||||
vocabulary = _vocabulary;
|
||||
dmatcher->add( std::vector<Mat>(1, vocabulary) );
|
||||
}
|
||||
|
||||
const Mat& BOWImgDescriptorExtractor::getVocabulary() const
|
||||
{
|
||||
return vocabulary;
|
||||
}
|
||||
|
||||
void BOWImgDescriptorExtractor::compute( InputArray image, std::vector<KeyPoint>& keypoints, OutputArray imgDescriptor,
|
||||
std::vector<std::vector<int> >* pointIdxsOfClusters, Mat* descriptors )
|
||||
{
|
||||
CV_INSTRUMENT_REGION();
|
||||
|
||||
imgDescriptor.release();
|
||||
|
||||
if( keypoints.empty() )
|
||||
return;
|
||||
|
||||
// Compute descriptors for the image.
|
||||
Mat _descriptors;
|
||||
dextractor->compute( image, keypoints, _descriptors );
|
||||
|
||||
compute( _descriptors, imgDescriptor, pointIdxsOfClusters );
|
||||
|
||||
// Add the descriptors of image keypoints
|
||||
if (descriptors) {
|
||||
*descriptors = _descriptors.clone();
|
||||
}
|
||||
}
|
||||
|
||||
int BOWImgDescriptorExtractor::descriptorSize() const
|
||||
{
|
||||
return vocabulary.empty() ? 0 : vocabulary.rows;
|
||||
}
|
||||
|
||||
int BOWImgDescriptorExtractor::descriptorType() const
|
||||
{
|
||||
return CV_32FC1;
|
||||
}
|
||||
|
||||
void BOWImgDescriptorExtractor::compute( InputArray keypointDescriptors, OutputArray _imgDescriptor, std::vector<std::vector<int> >* pointIdxsOfClusters )
|
||||
{
|
||||
CV_INSTRUMENT_REGION();
|
||||
|
||||
CV_Assert( !vocabulary.empty() );
|
||||
CV_Assert(!keypointDescriptors.empty());
|
||||
|
||||
int clusterCount = descriptorSize(); // = vocabulary.rows
|
||||
|
||||
// Match keypoint descriptors to cluster center (to vocabulary)
|
||||
std::vector<DMatch> matches;
|
||||
dmatcher->match( keypointDescriptors, matches );
|
||||
|
||||
// Compute image descriptor
|
||||
if( pointIdxsOfClusters )
|
||||
{
|
||||
pointIdxsOfClusters->clear();
|
||||
pointIdxsOfClusters->resize(clusterCount);
|
||||
}
|
||||
|
||||
_imgDescriptor.create(1, clusterCount, descriptorType());
|
||||
_imgDescriptor.setTo(Scalar::all(0));
|
||||
|
||||
Mat imgDescriptor = _imgDescriptor.getMat();
|
||||
|
||||
float *dptr = imgDescriptor.ptr<float>();
|
||||
for( size_t i = 0; i < matches.size(); i++ )
|
||||
{
|
||||
int queryIdx = matches[i].queryIdx;
|
||||
int trainIdx = matches[i].trainIdx; // cluster index
|
||||
CV_Assert( queryIdx == (int)i );
|
||||
|
||||
dptr[trainIdx] = dptr[trainIdx] + 1.f;
|
||||
if( pointIdxsOfClusters )
|
||||
(*pointIdxsOfClusters)[trainIdx].push_back( queryIdx );
|
||||
}
|
||||
|
||||
// Normalize image descriptor.
|
||||
imgDescriptor /= keypointDescriptors.size().height;
|
||||
}
|
||||
|
||||
}
|
||||
@@ -0,0 +1,503 @@
|
||||
/*M///////////////////////////////////////////////////////////////////////////////////////
|
||||
//
|
||||
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
|
||||
//
|
||||
// By downloading, copying, installing or using the software you agree to this license.
|
||||
// If you do not agree to this license, do not download, install,
|
||||
// copy or use the software.
|
||||
//
|
||||
//
|
||||
// License Agreement
|
||||
// For Open Source Computer Vision Library
|
||||
//
|
||||
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
|
||||
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
|
||||
// Third party copyrights are property of their respective owners.
|
||||
//
|
||||
// Redistribution and use in source and binary forms, with or without modification,
|
||||
// are permitted provided that the following conditions are met:
|
||||
//
|
||||
// * Redistribution's of source code must retain the above copyright notice,
|
||||
// this list of conditions and the following disclaimer.
|
||||
//
|
||||
// * Redistribution's in binary form must reproduce the above copyright notice,
|
||||
// this list of conditions and the following disclaimer in the documentation
|
||||
// and/or other materials provided with the distribution.
|
||||
//
|
||||
// * The name of the copyright holders may not be used to endorse or promote products
|
||||
// derived from this software without specific prior written permission.
|
||||
//
|
||||
// This software is provided by the copyright holders and contributors "as is" and
|
||||
// any express or implied warranties, including, but not limited to, the implied
|
||||
// warranties of merchantability and fitness for a particular purpose are disclaimed.
|
||||
// In no event shall the Intel Corporation or contributors be liable for any direct,
|
||||
// indirect, incidental, special, exemplary, or consequential damages
|
||||
// (including, but not limited to, procurement of substitute goods or services;
|
||||
// loss of use, data, or profits; or business interruption) however caused
|
||||
// and on any theory of liability, whether in contract, strict liability,
|
||||
// or tort (including negligence or otherwise) arising in any way out of
|
||||
// the use of this software, even if advised of the possibility of such damage.
|
||||
//
|
||||
//M*/
|
||||
|
||||
#include "precomp.hpp"
|
||||
#include <iterator>
|
||||
#include <limits>
|
||||
|
||||
#include <opencv2/core/utils/logger.hpp>
|
||||
|
||||
// Requires CMake flag: DEBUG_opencv_features2d=ON
|
||||
//#define DEBUG_BLOB_DETECTOR
|
||||
|
||||
#ifdef DEBUG_BLOB_DETECTOR
|
||||
#include "opencv2/highgui.hpp"
|
||||
#endif
|
||||
|
||||
namespace cv
|
||||
{
|
||||
|
||||
// TODO: To be removed in 5.x branch
|
||||
const std::vector<std::vector<cv::Point> >& SimpleBlobDetector::getBlobContours() const
|
||||
{
|
||||
CV_Error(Error::StsNotImplemented, "Method SimpleBlobDetector::getBlobContours() is not implemented");
|
||||
}
|
||||
|
||||
class CV_EXPORTS_W SimpleBlobDetectorImpl : public SimpleBlobDetector
|
||||
{
|
||||
public:
|
||||
|
||||
explicit SimpleBlobDetectorImpl(const SimpleBlobDetector::Params ¶meters = SimpleBlobDetector::Params());
|
||||
|
||||
virtual void read( const FileNode& fn ) CV_OVERRIDE;
|
||||
virtual void write( FileStorage& fs ) const CV_OVERRIDE;
|
||||
|
||||
void setParams(const SimpleBlobDetector::Params& _params ) CV_OVERRIDE {
|
||||
SimpleBlobDetectorImpl::validateParameters(_params);
|
||||
params = _params;
|
||||
}
|
||||
|
||||
SimpleBlobDetector::Params getParams() const CV_OVERRIDE { return params; }
|
||||
|
||||
static void validateParameters(const SimpleBlobDetector::Params& p)
|
||||
{
|
||||
if (p.thresholdStep <= 0)
|
||||
CV_Error(Error::StsBadArg, "thresholdStep>0");
|
||||
|
||||
if (p.minThreshold > p.maxThreshold || p.minThreshold < 0)
|
||||
CV_Error(Error::StsBadArg, "0<=minThreshold<=maxThreshold");
|
||||
|
||||
if (p.minDistBetweenBlobs <=0 )
|
||||
CV_Error(Error::StsBadArg, "minDistBetweenBlobs>0");
|
||||
|
||||
if (p.minArea > p.maxArea || p.minArea <=0)
|
||||
CV_Error(Error::StsBadArg, "0<minArea<=maxArea");
|
||||
|
||||
if (p.minCircularity > p.maxCircularity || p.minCircularity <= 0)
|
||||
CV_Error(Error::StsBadArg, "0<minCircularity<=maxCircularity");
|
||||
|
||||
if (p.minInertiaRatio > p.maxInertiaRatio || p.minInertiaRatio <= 0)
|
||||
CV_Error(Error::StsBadArg, "0<minInertiaRatio<=maxInertiaRatio");
|
||||
|
||||
if (p.minConvexity > p.maxConvexity || p.minConvexity <= 0)
|
||||
CV_Error(Error::StsBadArg, "0<minConvexity<=maxConvexity");
|
||||
}
|
||||
|
||||
protected:
|
||||
struct CV_EXPORTS Center
|
||||
{
|
||||
Point2d location;
|
||||
double radius;
|
||||
double confidence;
|
||||
};
|
||||
|
||||
virtual void detect( InputArray image, std::vector<KeyPoint>& keypoints, InputArray mask=noArray() ) CV_OVERRIDE;
|
||||
virtual void findBlobs(InputArray image, InputArray binaryImage, std::vector<Center> ¢ers,
|
||||
std::vector<std::vector<Point> > &contours, std::vector<Moments> &moments) const;
|
||||
virtual const std::vector<std::vector<Point> >& getBlobContours() const CV_OVERRIDE;
|
||||
|
||||
Params params;
|
||||
std::vector<std::vector<Point> > blobContours;
|
||||
};
|
||||
|
||||
/*
|
||||
* SimpleBlobDetector
|
||||
*/
|
||||
SimpleBlobDetector::Params::Params()
|
||||
{
|
||||
thresholdStep = 10;
|
||||
minThreshold = 50;
|
||||
maxThreshold = 220;
|
||||
minRepeatability = 2;
|
||||
minDistBetweenBlobs = 10;
|
||||
|
||||
filterByColor = true;
|
||||
blobColor = 0;
|
||||
|
||||
filterByArea = true;
|
||||
minArea = 25;
|
||||
maxArea = 5000;
|
||||
|
||||
filterByCircularity = false;
|
||||
minCircularity = 0.8f;
|
||||
maxCircularity = std::numeric_limits<float>::max();
|
||||
|
||||
filterByInertia = true;
|
||||
//minInertiaRatio = 0.6;
|
||||
minInertiaRatio = 0.1f;
|
||||
maxInertiaRatio = std::numeric_limits<float>::max();
|
||||
|
||||
filterByConvexity = true;
|
||||
//minConvexity = 0.8;
|
||||
minConvexity = 0.95f;
|
||||
maxConvexity = std::numeric_limits<float>::max();
|
||||
|
||||
collectContours = false;
|
||||
}
|
||||
|
||||
void SimpleBlobDetector::Params::read(const cv::FileNode& fn )
|
||||
{
|
||||
thresholdStep = fn["thresholdStep"];
|
||||
minThreshold = fn["minThreshold"];
|
||||
maxThreshold = fn["maxThreshold"];
|
||||
|
||||
minRepeatability = (size_t)(int)fn["minRepeatability"];
|
||||
minDistBetweenBlobs = fn["minDistBetweenBlobs"];
|
||||
|
||||
filterByColor = (int)fn["filterByColor"] != 0 ? true : false;
|
||||
blobColor = (uchar)(int)fn["blobColor"];
|
||||
|
||||
filterByArea = (int)fn["filterByArea"] != 0 ? true : false;
|
||||
minArea = fn["minArea"];
|
||||
maxArea = fn["maxArea"];
|
||||
|
||||
filterByCircularity = (int)fn["filterByCircularity"] != 0 ? true : false;
|
||||
minCircularity = fn["minCircularity"];
|
||||
maxCircularity = fn["maxCircularity"];
|
||||
|
||||
filterByInertia = (int)fn["filterByInertia"] != 0 ? true : false;
|
||||
minInertiaRatio = fn["minInertiaRatio"];
|
||||
maxInertiaRatio = fn["maxInertiaRatio"];
|
||||
|
||||
filterByConvexity = (int)fn["filterByConvexity"] != 0 ? true : false;
|
||||
minConvexity = fn["minConvexity"];
|
||||
maxConvexity = fn["maxConvexity"];
|
||||
|
||||
collectContours = (int)fn["collectContours"] != 0 ? true : false;
|
||||
}
|
||||
|
||||
void SimpleBlobDetector::Params::write(cv::FileStorage& fs) const
|
||||
{
|
||||
fs << "thresholdStep" << thresholdStep;
|
||||
fs << "minThreshold" << minThreshold;
|
||||
fs << "maxThreshold" << maxThreshold;
|
||||
|
||||
fs << "minRepeatability" << (int)minRepeatability;
|
||||
fs << "minDistBetweenBlobs" << minDistBetweenBlobs;
|
||||
|
||||
fs << "filterByColor" << (int)filterByColor;
|
||||
fs << "blobColor" << (int)blobColor;
|
||||
|
||||
fs << "filterByArea" << (int)filterByArea;
|
||||
fs << "minArea" << minArea;
|
||||
fs << "maxArea" << maxArea;
|
||||
|
||||
fs << "filterByCircularity" << (int)filterByCircularity;
|
||||
fs << "minCircularity" << minCircularity;
|
||||
fs << "maxCircularity" << maxCircularity;
|
||||
|
||||
fs << "filterByInertia" << (int)filterByInertia;
|
||||
fs << "minInertiaRatio" << minInertiaRatio;
|
||||
fs << "maxInertiaRatio" << maxInertiaRatio;
|
||||
|
||||
fs << "filterByConvexity" << (int)filterByConvexity;
|
||||
fs << "minConvexity" << minConvexity;
|
||||
fs << "maxConvexity" << maxConvexity;
|
||||
|
||||
fs << "collectContours" << (int)collectContours;
|
||||
}
|
||||
|
||||
SimpleBlobDetectorImpl::SimpleBlobDetectorImpl(const SimpleBlobDetector::Params ¶meters) :
|
||||
params(parameters)
|
||||
{
|
||||
}
|
||||
|
||||
void SimpleBlobDetectorImpl::read( const cv::FileNode& fn )
|
||||
{
|
||||
SimpleBlobDetector::Params rp;
|
||||
rp.read(fn);
|
||||
SimpleBlobDetectorImpl::validateParameters(rp);
|
||||
params = rp;
|
||||
}
|
||||
|
||||
void SimpleBlobDetectorImpl::write( cv::FileStorage& fs ) const
|
||||
{
|
||||
writeFormat(fs);
|
||||
params.write(fs);
|
||||
}
|
||||
|
||||
void SimpleBlobDetectorImpl::findBlobs(InputArray _image, InputArray _binaryImage, std::vector<Center> ¢ers,
|
||||
std::vector<std::vector<Point> > &contoursOut, std::vector<Moments> &momentss) const
|
||||
{
|
||||
CV_INSTRUMENT_REGION();
|
||||
|
||||
Mat image = _image.getMat(), binaryImage = _binaryImage.getMat();
|
||||
CV_UNUSED(image);
|
||||
centers.clear();
|
||||
contoursOut.clear();
|
||||
momentss.clear();
|
||||
|
||||
std::vector < std::vector<Point> > contours;
|
||||
findContours(binaryImage, contours, RETR_LIST, CHAIN_APPROX_NONE);
|
||||
|
||||
#ifdef DEBUG_BLOB_DETECTOR
|
||||
Mat keypointsImage;
|
||||
cvtColor(binaryImage, keypointsImage, COLOR_GRAY2RGB);
|
||||
|
||||
Mat contoursImage;
|
||||
cvtColor(binaryImage, contoursImage, COLOR_GRAY2RGB);
|
||||
drawContours( contoursImage, contours, -1, Scalar(0,255,0) );
|
||||
imshow("contours", contoursImage );
|
||||
#endif
|
||||
|
||||
for (size_t contourIdx = 0; contourIdx < contours.size(); contourIdx++)
|
||||
{
|
||||
Center center;
|
||||
center.confidence = 1;
|
||||
Moments moms = moments(contours[contourIdx]);
|
||||
if (params.filterByArea)
|
||||
{
|
||||
double area = moms.m00;
|
||||
if (area < params.minArea || area >= params.maxArea)
|
||||
continue;
|
||||
}
|
||||
|
||||
if (params.filterByCircularity)
|
||||
{
|
||||
double area = moms.m00;
|
||||
double perimeter = arcLength(contours[contourIdx], true);
|
||||
double ratio = 4 * CV_PI * area / (perimeter * perimeter);
|
||||
if (ratio < params.minCircularity || ratio >= params.maxCircularity)
|
||||
continue;
|
||||
}
|
||||
|
||||
if (params.filterByInertia)
|
||||
{
|
||||
double denominator = std::sqrt(std::pow(2 * moms.mu11, 2) + std::pow(moms.mu20 - moms.mu02, 2));
|
||||
const double eps = 1e-2;
|
||||
double ratio;
|
||||
if (denominator > eps)
|
||||
{
|
||||
double cosmin = (moms.mu20 - moms.mu02) / denominator;
|
||||
double sinmin = 2 * moms.mu11 / denominator;
|
||||
double cosmax = -cosmin;
|
||||
double sinmax = -sinmin;
|
||||
|
||||
double imin = 0.5 * (moms.mu20 + moms.mu02) - 0.5 * (moms.mu20 - moms.mu02) * cosmin - moms.mu11 * sinmin;
|
||||
double imax = 0.5 * (moms.mu20 + moms.mu02) - 0.5 * (moms.mu20 - moms.mu02) * cosmax - moms.mu11 * sinmax;
|
||||
ratio = imin / imax;
|
||||
}
|
||||
else
|
||||
{
|
||||
ratio = 1;
|
||||
}
|
||||
|
||||
if (ratio < params.minInertiaRatio || ratio >= params.maxInertiaRatio)
|
||||
continue;
|
||||
|
||||
center.confidence = ratio * ratio;
|
||||
}
|
||||
|
||||
if (params.filterByConvexity)
|
||||
{
|
||||
std::vector < Point > hull;
|
||||
convexHull(contours[contourIdx], hull);
|
||||
double area = moms.m00;
|
||||
double hullArea = contourArea(hull);
|
||||
if (fabs(hullArea) < DBL_EPSILON)
|
||||
continue;
|
||||
double ratio = area / hullArea;
|
||||
if (ratio < params.minConvexity || ratio >= params.maxConvexity)
|
||||
continue;
|
||||
}
|
||||
|
||||
if(moms.m00 == 0.0)
|
||||
continue;
|
||||
center.location = Point2d(moms.m10 / moms.m00, moms.m01 / moms.m00);
|
||||
|
||||
if (params.filterByColor)
|
||||
{
|
||||
if (binaryImage.at<uchar> (cvRound(center.location.y), cvRound(center.location.x)) != params.blobColor)
|
||||
continue;
|
||||
}
|
||||
|
||||
//compute blob radius
|
||||
{
|
||||
const std::vector<cv::Point>& contour = contours[contourIdx];
|
||||
const size_t contourSize = contour.size();
|
||||
AutoBuffer<double> dists(contourSize);
|
||||
for (size_t pointIdx = 0; pointIdx < contourSize; pointIdx++)
|
||||
{
|
||||
const Point2d& pt = contour[pointIdx];
|
||||
dists[pointIdx] = norm(center.location - pt);
|
||||
}
|
||||
std::sort(dists.begin(), dists.end());
|
||||
center.radius = (dists[(dists.size() - 1) / 2] + dists[dists.size() / 2]) / 2.;
|
||||
}
|
||||
|
||||
centers.push_back(center);
|
||||
if (params.collectContours)
|
||||
{
|
||||
contoursOut.push_back(contours[contourIdx]);
|
||||
momentss.push_back(moms);
|
||||
}
|
||||
|
||||
#ifdef DEBUG_BLOB_DETECTOR
|
||||
circle( keypointsImage, center.location, 1, Scalar(0,0,255), 1 );
|
||||
#endif
|
||||
}
|
||||
#ifdef DEBUG_BLOB_DETECTOR
|
||||
imshow("bk", keypointsImage );
|
||||
waitKey();
|
||||
#endif
|
||||
}
|
||||
|
||||
void SimpleBlobDetectorImpl::detect(InputArray image, std::vector<cv::KeyPoint>& keypoints, InputArray mask)
|
||||
{
|
||||
CV_INSTRUMENT_REGION();
|
||||
|
||||
keypoints.clear();
|
||||
blobContours.clear();
|
||||
|
||||
CV_Assert(params.minRepeatability != 0);
|
||||
Mat grayscaleImage;
|
||||
if (image.channels() == 3 || image.channels() == 4)
|
||||
cvtColor(image, grayscaleImage, COLOR_BGR2GRAY);
|
||||
else
|
||||
grayscaleImage = image.getMat();
|
||||
|
||||
if (grayscaleImage.type() != CV_8UC1) {
|
||||
CV_Error(Error::StsUnsupportedFormat, "Blob detector only supports 8-bit images!");
|
||||
}
|
||||
|
||||
CV_CheckGT(params.thresholdStep, 0.0f, "");
|
||||
if (params.minThreshold + params.thresholdStep >= params.maxThreshold)
|
||||
{
|
||||
// https://github.com/opencv/opencv/issues/6667
|
||||
CV_LOG_ONCE_INFO(NULL, "SimpleBlobDetector: params.minDistBetweenBlobs is ignored for case with single threshold");
|
||||
#if 0 // OpenCV 5.0
|
||||
CV_CheckEQ(params.minRepeatability, 1u, "Incompatible parameters for case with single threshold");
|
||||
#else
|
||||
if (params.minRepeatability != 1)
|
||||
CV_LOG_WARNING(NULL, "SimpleBlobDetector: params.minRepeatability=" << params.minRepeatability << " is incompatible for case with single threshold. Empty result is expected.");
|
||||
#endif
|
||||
}
|
||||
|
||||
std::vector < std::vector<Center> > centers;
|
||||
std::vector<Moments> momentss;
|
||||
for (double thresh = params.minThreshold; thresh < params.maxThreshold; thresh += params.thresholdStep)
|
||||
{
|
||||
Mat binarizedImage;
|
||||
threshold(grayscaleImage, binarizedImage, thresh, 255, THRESH_BINARY);
|
||||
|
||||
std::vector < Center > curCenters;
|
||||
std::vector<std::vector<Point> > curContours;
|
||||
std::vector<Moments> curMomentss;
|
||||
findBlobs(grayscaleImage, binarizedImage, curCenters, curContours, curMomentss);
|
||||
std::vector < std::vector<Center> > newCenters;
|
||||
std::vector<std::vector<Point> > newContours;
|
||||
std::vector<Moments> newMomentss;
|
||||
for (size_t i = 0; i < curCenters.size(); i++)
|
||||
{
|
||||
bool isNew = true;
|
||||
for (size_t j = 0; j < centers.size(); j++)
|
||||
{
|
||||
double dist = norm(centers[j][ centers[j].size() / 2 ].location - curCenters[i].location);
|
||||
isNew = dist >= params.minDistBetweenBlobs && dist >= centers[j][ centers[j].size() / 2 ].radius && dist >= curCenters[i].radius;
|
||||
if (!isNew)
|
||||
{
|
||||
centers[j].push_back(curCenters[i]);
|
||||
|
||||
size_t k = centers[j].size() - 1;
|
||||
while( k > 0 && curCenters[i].radius < centers[j][k-1].radius )
|
||||
{
|
||||
centers[j][k] = centers[j][k-1];
|
||||
k--;
|
||||
}
|
||||
|
||||
if (params.collectContours)
|
||||
{
|
||||
if (curCenters[i].confidence > centers[j][k].confidence
|
||||
|| (curCenters[i].confidence == centers[j][k].confidence && curMomentss[i].m00 > momentss[j].m00))
|
||||
{
|
||||
blobContours[j] = curContours[i];
|
||||
momentss[j] = curMomentss[i];
|
||||
}
|
||||
}
|
||||
centers[j][k] = curCenters[i];
|
||||
|
||||
break;
|
||||
}
|
||||
}
|
||||
if (isNew)
|
||||
{
|
||||
newCenters.push_back(std::vector<Center> (1, curCenters[i]));
|
||||
if (params.collectContours)
|
||||
{
|
||||
newContours.push_back(curContours[i]);
|
||||
newMomentss.push_back(curMomentss[i]);
|
||||
}
|
||||
}
|
||||
}
|
||||
std::copy(newCenters.begin(), newCenters.end(), std::back_inserter(centers));
|
||||
if (params.collectContours)
|
||||
{
|
||||
std::copy(newContours.begin(), newContours.end(), std::back_inserter(blobContours));
|
||||
std::copy(newMomentss.begin(), newMomentss.end(), std::back_inserter(momentss));
|
||||
}
|
||||
}
|
||||
|
||||
for (size_t i = 0; i < centers.size(); i++)
|
||||
{
|
||||
if (centers[i].size() < params.minRepeatability)
|
||||
continue;
|
||||
Point2d sumPoint(0, 0);
|
||||
double normalizer = 0;
|
||||
for (size_t j = 0; j < centers[i].size(); j++)
|
||||
{
|
||||
sumPoint += centers[i][j].confidence * centers[i][j].location;
|
||||
normalizer += centers[i][j].confidence;
|
||||
}
|
||||
sumPoint *= (1. / normalizer);
|
||||
KeyPoint kpt(sumPoint, (float)(centers[i][centers[i].size() / 2].radius) * 2.0f);
|
||||
keypoints.push_back(kpt);
|
||||
}
|
||||
|
||||
if (!mask.empty())
|
||||
{
|
||||
if (params.collectContours)
|
||||
{
|
||||
KeyPointsFilter::runByPixelsMask2VectorPoint(keypoints, blobContours, mask.getMat());
|
||||
}
|
||||
else
|
||||
{
|
||||
KeyPointsFilter::runByPixelsMask(keypoints, mask.getMat());
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
const std::vector<std::vector<Point> >& SimpleBlobDetectorImpl::getBlobContours() const {
|
||||
return blobContours;
|
||||
}
|
||||
|
||||
Ptr<SimpleBlobDetector> SimpleBlobDetector::create(const SimpleBlobDetector::Params& params)
|
||||
{
|
||||
SimpleBlobDetectorImpl::validateParameters(params);
|
||||
return makePtr<SimpleBlobDetectorImpl>(params);
|
||||
}
|
||||
|
||||
String SimpleBlobDetector::getDefaultName() const
|
||||
{
|
||||
return (Feature2D::getDefaultName() + ".SimpleBlobDetector");
|
||||
}
|
||||
|
||||
}
|
||||
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,279 @@
|
||||
/*M///////////////////////////////////////////////////////////////////////////////////////
|
||||
//
|
||||
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
|
||||
//
|
||||
// By downloading, copying, installing or using the software you agree to this license.
|
||||
// If you do not agree to this license, do not download, install,
|
||||
// copy or use the software.
|
||||
//
|
||||
//
|
||||
// Intel License Agreement
|
||||
// For Open Source Computer Vision Library
|
||||
//
|
||||
// Copyright (C) 2000, Intel Corporation, all rights reserved.
|
||||
// Third party copyrights are property of their respective owners.
|
||||
//
|
||||
// Redistribution and use in source and binary forms, with or without modification,
|
||||
// are permitted provided that the following conditions are met:
|
||||
//
|
||||
// * Redistribution's of source code must retain the above copyright notice,
|
||||
// this list of conditions and the following disclaimer.
|
||||
//
|
||||
// * Redistribution's in binary form must reproduce the above copyright notice,
|
||||
// this list of conditions and the following disclaimer in the documentation
|
||||
// and/or other materials provided with the distribution.
|
||||
//
|
||||
// * The name of Intel Corporation may not be used to endorse or promote products
|
||||
// derived from this software without specific prior written permission.
|
||||
//
|
||||
// This software is provided by the copyright holders and contributors "as is" and
|
||||
// any express or implied warranties, including, but not limited to, the implied
|
||||
// warranties of merchantability and fitness for a particular purpose are disclaimed.
|
||||
// In no event shall the Intel Corporation or contributors be liable for any direct,
|
||||
// indirect, incidental, special, exemplary, or consequential damages
|
||||
// (including, but not limited to, procurement of substitute goods or services;
|
||||
// loss of use, data, or profits; or business interruption) however caused
|
||||
// and on any theory of liability, whether in contract, strict liability,
|
||||
// or tort (including negligence or otherwise) arising in any way out of
|
||||
// the use of this software, even if advised of the possibility of such damage.
|
||||
//
|
||||
//M*/
|
||||
|
||||
#include "precomp.hpp"
|
||||
|
||||
const int draw_shift_bits = 4;
|
||||
const int draw_multiplier = 1 << draw_shift_bits;
|
||||
|
||||
namespace cv
|
||||
{
|
||||
|
||||
/*
|
||||
* Functions to draw keypoints and matches.
|
||||
*/
|
||||
static inline void _drawKeypoint( InputOutputArray img, const KeyPoint& p, const Scalar& color, DrawMatchesFlags flags )
|
||||
{
|
||||
CV_Assert( !img.empty() );
|
||||
Point center( cvRound(p.pt.x * draw_multiplier), cvRound(p.pt.y * draw_multiplier) );
|
||||
|
||||
if( !!(flags & DrawMatchesFlags::DRAW_RICH_KEYPOINTS) )
|
||||
{
|
||||
int radius = cvRound(p.size/2 * draw_multiplier); // KeyPoint::size is a diameter
|
||||
|
||||
// draw the circles around keypoints with the keypoints size
|
||||
circle( img, center, radius, color, 1, LINE_AA, draw_shift_bits );
|
||||
|
||||
// draw orientation of the keypoint, if it is applicable
|
||||
if( p.angle != -1 )
|
||||
{
|
||||
float srcAngleRad = p.angle*(float)CV_PI/180.f;
|
||||
Point orient( cvRound(cos(srcAngleRad)*radius ),
|
||||
cvRound(sin(srcAngleRad)*radius )
|
||||
);
|
||||
line( img, center, center+orient, color, 1, LINE_AA, draw_shift_bits );
|
||||
}
|
||||
#if 0
|
||||
else
|
||||
{
|
||||
// draw center with R=1
|
||||
int radius = 1 * draw_multiplier;
|
||||
circle( img, center, radius, color, 1, LINE_AA, draw_shift_bits );
|
||||
}
|
||||
#endif
|
||||
}
|
||||
else
|
||||
{
|
||||
// draw center with R=3
|
||||
int radius = 3 * draw_multiplier;
|
||||
circle( img, center, radius, color, 1, LINE_AA, draw_shift_bits );
|
||||
}
|
||||
}
|
||||
|
||||
void drawKeypoints( InputArray image, const std::vector<KeyPoint>& keypoints, InputOutputArray outImage,
|
||||
const Scalar& _color, DrawMatchesFlags flags )
|
||||
{
|
||||
CV_INSTRUMENT_REGION();
|
||||
|
||||
if( !(flags & DrawMatchesFlags::DRAW_OVER_OUTIMG) )
|
||||
{
|
||||
if (image.type() == CV_8UC3 || image.type() == CV_8UC4)
|
||||
{
|
||||
image.copyTo(outImage);
|
||||
}
|
||||
else if( image.type() == CV_8UC1 )
|
||||
{
|
||||
cvtColor( image, outImage, COLOR_GRAY2BGR );
|
||||
}
|
||||
else
|
||||
{
|
||||
CV_Error( Error::StsBadArg, "Incorrect type of input image: " + typeToString(image.type()) );
|
||||
}
|
||||
}
|
||||
|
||||
RNG& rng=theRNG();
|
||||
bool isRandColor = _color == Scalar::all(-1);
|
||||
|
||||
CV_Assert( !outImage.empty() );
|
||||
std::vector<KeyPoint>::const_iterator it = keypoints.begin(),
|
||||
end = keypoints.end();
|
||||
for( ; it != end; ++it )
|
||||
{
|
||||
Scalar color = isRandColor ? Scalar( rng(256), rng(256), rng(256), 255 ) : _color;
|
||||
_drawKeypoint( outImage, *it, color, flags );
|
||||
}
|
||||
}
|
||||
|
||||
static void _prepareImage(InputArray src, const Mat& dst)
|
||||
{
|
||||
CV_CheckType(src.type(), src.type() == CV_8UC1 || src.type() == CV_8UC3 || src.type() == CV_8UC4, "Unsupported source image");
|
||||
CV_CheckType(dst.type(), dst.type() == CV_8UC3 || dst.type() == CV_8UC4, "Unsupported destination image");
|
||||
const int src_cn = src.channels();
|
||||
const int dst_cn = dst.channels();
|
||||
|
||||
if (src_cn == dst_cn)
|
||||
src.copyTo(dst);
|
||||
else if (src_cn == 1)
|
||||
cvtColor(src, dst, dst_cn == 3 ? COLOR_GRAY2BGR : COLOR_GRAY2BGRA);
|
||||
else if (src_cn == 3 && dst_cn == 4)
|
||||
cvtColor(src, dst, COLOR_BGR2BGRA);
|
||||
else if (src_cn == 4 && dst_cn == 3)
|
||||
cvtColor(src, dst, COLOR_BGRA2BGR);
|
||||
else
|
||||
CV_Error(Error::StsInternal, "");
|
||||
}
|
||||
|
||||
static void _prepareImgAndDrawKeypoints( InputArray img1, const std::vector<KeyPoint>& keypoints1,
|
||||
InputArray img2, const std::vector<KeyPoint>& keypoints2,
|
||||
InputOutputArray _outImg, Mat& outImg1, Mat& outImg2,
|
||||
const Scalar& singlePointColor, DrawMatchesFlags flags )
|
||||
{
|
||||
Mat outImg;
|
||||
Size img1size = img1.size(), img2size = img2.size();
|
||||
Size size( img1size.width + img2size.width, MAX(img1size.height, img2size.height) );
|
||||
if( !!(flags & DrawMatchesFlags::DRAW_OVER_OUTIMG) )
|
||||
{
|
||||
outImg = _outImg.getMat();
|
||||
if( size.width > outImg.cols || size.height > outImg.rows )
|
||||
CV_Error( Error::StsBadSize, "outImg has size less than need to draw img1 and img2 together" );
|
||||
outImg1 = outImg( Rect(0, 0, img1size.width, img1size.height) );
|
||||
outImg2 = outImg( Rect(img1size.width, 0, img2size.width, img2size.height) );
|
||||
}
|
||||
else
|
||||
{
|
||||
const int cn1 = img1.channels(), cn2 = img2.channels();
|
||||
const int out_cn = std::max(3, std::max(cn1, cn2));
|
||||
_outImg.create(size, CV_MAKETYPE(img1.depth(), out_cn));
|
||||
outImg = _outImg.getMat();
|
||||
outImg = Scalar::all(0);
|
||||
outImg1 = outImg( Rect(0, 0, img1size.width, img1size.height) );
|
||||
outImg2 = outImg( Rect(img1size.width, 0, img2size.width, img2size.height) );
|
||||
|
||||
_prepareImage(img1, outImg1);
|
||||
_prepareImage(img2, outImg2);
|
||||
}
|
||||
|
||||
// draw keypoints
|
||||
if( !(flags & DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS) )
|
||||
{
|
||||
Mat _outImg1 = outImg( Rect(0, 0, img1size.width, img1size.height) );
|
||||
drawKeypoints( _outImg1, keypoints1, _outImg1, singlePointColor, flags | DrawMatchesFlags::DRAW_OVER_OUTIMG );
|
||||
|
||||
Mat _outImg2 = outImg( Rect(img1size.width, 0, img2size.width, img2size.height) );
|
||||
drawKeypoints( _outImg2, keypoints2, _outImg2, singlePointColor, flags | DrawMatchesFlags::DRAW_OVER_OUTIMG );
|
||||
}
|
||||
}
|
||||
|
||||
static inline void _drawMatch( InputOutputArray outImg, InputOutputArray outImg1, InputOutputArray outImg2 ,
|
||||
const KeyPoint& kp1, const KeyPoint& kp2, const Scalar& matchColor, DrawMatchesFlags flags,
|
||||
const int matchesThickness )
|
||||
{
|
||||
RNG& rng = theRNG();
|
||||
bool isRandMatchColor = matchColor == Scalar::all(-1);
|
||||
Scalar color = isRandMatchColor ? Scalar( rng(256), rng(256), rng(256), 255 ) : matchColor;
|
||||
|
||||
_drawKeypoint( outImg1, kp1, color, flags );
|
||||
_drawKeypoint( outImg2, kp2, color, flags );
|
||||
|
||||
Point2f pt1 = kp1.pt,
|
||||
pt2 = kp2.pt,
|
||||
dpt2 = Point2f( std::min(pt2.x+outImg1.size().width, float(outImg.size().width-1)), pt2.y );
|
||||
|
||||
line( outImg,
|
||||
Point(cvRound(pt1.x*draw_multiplier), cvRound(pt1.y*draw_multiplier)),
|
||||
Point(cvRound(dpt2.x*draw_multiplier), cvRound(dpt2.y*draw_multiplier)),
|
||||
color, matchesThickness, LINE_AA, draw_shift_bits );
|
||||
}
|
||||
|
||||
void drawMatches( InputArray img1, const std::vector<KeyPoint>& keypoints1,
|
||||
InputArray img2, const std::vector<KeyPoint>& keypoints2,
|
||||
const std::vector<DMatch>& matches1to2, InputOutputArray outImg,
|
||||
const Scalar& matchColor, const Scalar& singlePointColor,
|
||||
const std::vector<char>& matchesMask, DrawMatchesFlags flags )
|
||||
{
|
||||
drawMatches( img1, keypoints1,
|
||||
img2, keypoints2,
|
||||
matches1to2, outImg,
|
||||
1, matchColor,
|
||||
singlePointColor, matchesMask,
|
||||
flags);
|
||||
}
|
||||
|
||||
void drawMatches( InputArray img1, const std::vector<KeyPoint>& keypoints1,
|
||||
InputArray img2, const std::vector<KeyPoint>& keypoints2,
|
||||
const std::vector<DMatch>& matches1to2, InputOutputArray outImg,
|
||||
const int matchesThickness, const Scalar& matchColor,
|
||||
const Scalar& singlePointColor, const std::vector<char>& matchesMask,
|
||||
DrawMatchesFlags flags )
|
||||
{
|
||||
if( !matchesMask.empty() && matchesMask.size() != matches1to2.size() )
|
||||
CV_Error( Error::StsBadSize, "matchesMask must have the same size as matches1to2" );
|
||||
|
||||
Mat outImg1, outImg2;
|
||||
_prepareImgAndDrawKeypoints( img1, keypoints1, img2, keypoints2,
|
||||
outImg, outImg1, outImg2, singlePointColor, flags );
|
||||
|
||||
// draw matches
|
||||
for( size_t m = 0; m < matches1to2.size(); m++ )
|
||||
{
|
||||
if( matchesMask.empty() || matchesMask[m] )
|
||||
{
|
||||
int i1 = matches1to2[m].queryIdx;
|
||||
int i2 = matches1to2[m].trainIdx;
|
||||
CV_Assert(i1 >= 0 && i1 < static_cast<int>(keypoints1.size()));
|
||||
CV_Assert(i2 >= 0 && i2 < static_cast<int>(keypoints2.size()));
|
||||
|
||||
const KeyPoint &kp1 = keypoints1[i1], &kp2 = keypoints2[i2];
|
||||
_drawMatch( outImg, outImg1, outImg2, kp1, kp2, matchColor, flags, matchesThickness );
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
void drawMatches( InputArray img1, const std::vector<KeyPoint>& keypoints1,
|
||||
InputArray img2, const std::vector<KeyPoint>& keypoints2,
|
||||
const std::vector<std::vector<DMatch> >& matches1to2, InputOutputArray outImg,
|
||||
const Scalar& matchColor, const Scalar& singlePointColor,
|
||||
const std::vector<std::vector<char> >& matchesMask, DrawMatchesFlags flags )
|
||||
{
|
||||
if( !matchesMask.empty() && matchesMask.size() != matches1to2.size() )
|
||||
CV_Error( Error::StsBadSize, "matchesMask must have the same size as matches1to2" );
|
||||
|
||||
Mat outImg1, outImg2;
|
||||
_prepareImgAndDrawKeypoints( img1, keypoints1, img2, keypoints2,
|
||||
outImg, outImg1, outImg2, singlePointColor, flags );
|
||||
|
||||
// draw matches
|
||||
for( size_t i = 0; i < matches1to2.size(); i++ )
|
||||
{
|
||||
for( size_t j = 0; j < matches1to2[i].size(); j++ )
|
||||
{
|
||||
int i1 = matches1to2[i][j].queryIdx;
|
||||
int i2 = matches1to2[i][j].trainIdx;
|
||||
if( matchesMask.empty() || matchesMask[i][j] )
|
||||
{
|
||||
const KeyPoint &kp1 = keypoints1[i1], &kp2 = keypoints2[i2];
|
||||
_drawMatch( outImg, outImg1, outImg2, kp1, kp2, matchColor, flags, 1 );
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,47 @@
|
||||
/*M///////////////////////////////////////////////////////////////////////////////////////
|
||||
//
|
||||
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
|
||||
//
|
||||
// By downloading, copying, installing or using the software you agree to this license.
|
||||
// If you do not agree to this license, do not download, install,
|
||||
// copy or use the software.
|
||||
//
|
||||
//
|
||||
// License Agreement
|
||||
// For Open Source Computer Vision Library
|
||||
//
|
||||
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
|
||||
// Copyright (C) 2009-2010, Willow Garage Inc., all rights reserved.
|
||||
// Third party copyrights are property of their respective owners.
|
||||
//
|
||||
// Redistribution and use in source and binary forms, with or without modification,
|
||||
// are permitted provided that the following conditions are met:
|
||||
//
|
||||
// * Redistribution's of source code must retain the above copyright notice,
|
||||
// this list of conditions and the following disclaimer.
|
||||
//
|
||||
// * Redistribution's in binary form must reproduce the above copyright notice,
|
||||
// this list of conditions and the following disclaimer in the documentation
|
||||
// and/or other materials provided with the distribution.
|
||||
//
|
||||
// * The name of the copyright holders may not be used to endorse or promote products
|
||||
// derived from this software without specific prior written permission.
|
||||
//
|
||||
// This software is provided by the copyright holders and contributors "as is" and
|
||||
// any express or implied warranties, including, but not limited to, the implied
|
||||
// warranties of merchantability and fitness for a particular purpose are disclaimed.
|
||||
// In no event shall the Intel Corporation or contributors be liable for any direct,
|
||||
// indirect, incidental, special, exemplary, or consequential damages
|
||||
// (including, but not limited to, procurement of substitute goods or services;
|
||||
// loss of use, data, or profits; or business interruption) however caused
|
||||
// and on any theory of liability, whether in contract, strict liability,
|
||||
// or tort (including negligence or otherwise) arising in any way out of
|
||||
// the use of this software, even if advised of the possibility of such damage.
|
||||
//
|
||||
//M*/
|
||||
|
||||
#include "precomp.hpp"
|
||||
namespace cv
|
||||
{
|
||||
|
||||
}
|
||||
@@ -0,0 +1,572 @@
|
||||
//*M///////////////////////////////////////////////////////////////////////////////////////
|
||||
//
|
||||
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
|
||||
//
|
||||
// By downloading, copying, installing or using the software you agree to this license.
|
||||
// If you do not agree to this license, do not download, install,
|
||||
// copy or use the software.
|
||||
//
|
||||
//
|
||||
// License Agreement
|
||||
// For Open Source Computer Vision Library
|
||||
//
|
||||
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
|
||||
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
|
||||
// Third party copyrights are property of their respective owners.
|
||||
//
|
||||
// Redistribution and use in source and binary forms, with or without modification,
|
||||
// are permitted provided that the following conditions are met:
|
||||
//
|
||||
// * Redistribution's of source code must retain the above copyright notice,
|
||||
// this list of conditions and the following disclaimer.
|
||||
//
|
||||
// * Redistribution's in binary form must reproduce the above copyright notice,
|
||||
// this list of conditions and the following disclaimer in the documentation
|
||||
// and/or other materials provided with the distribution.
|
||||
//
|
||||
// * The name of the copyright holders may not be used to endorse or promote products
|
||||
// derived from this software without specific prior written permission.
|
||||
//
|
||||
// This software is provided by the copyright holders and contributors "as is" and
|
||||
// any express or implied warranties, including, but not limited to, the implied
|
||||
// warranties of merchantability and fitness for a particular purpose are disclaimed.
|
||||
// In no event shall the Intel Corporation or contributors be liable for any direct,
|
||||
// indirect, incidental, special, exemplary, or consequential damages
|
||||
// (including, but not limited to, procurement of substitute goods or services;
|
||||
// loss of use, data, or profits; or business interruption) however caused
|
||||
// and on any theory of liability, whether in contract, strict liability,
|
||||
// or tort (including negligence or otherwise) arising in any way out of
|
||||
// the use of this software, even if advised of the possibility of such damage.
|
||||
//
|
||||
//M*/
|
||||
|
||||
#include "precomp.hpp"
|
||||
#include <limits>
|
||||
|
||||
using namespace cv;
|
||||
|
||||
template<typename _Tp> static int solveQuadratic(_Tp a, _Tp b, _Tp c, _Tp& x1, _Tp& x2)
|
||||
{
|
||||
if( a == 0 )
|
||||
{
|
||||
if( b == 0 )
|
||||
{
|
||||
x1 = x2 = 0;
|
||||
return c == 0;
|
||||
}
|
||||
x1 = x2 = -c/b;
|
||||
return 1;
|
||||
}
|
||||
|
||||
_Tp d = b*b - 4*a*c;
|
||||
if( d < 0 )
|
||||
{
|
||||
x1 = x2 = 0;
|
||||
return 0;
|
||||
}
|
||||
if( d > 0 )
|
||||
{
|
||||
d = std::sqrt(d);
|
||||
double s = 1/(2*a);
|
||||
x1 = (-b - d)*s;
|
||||
x2 = (-b + d)*s;
|
||||
if( x1 > x2 )
|
||||
std::swap(x1, x2);
|
||||
return 2;
|
||||
}
|
||||
x1 = x2 = -b/(2*a);
|
||||
return 1;
|
||||
}
|
||||
|
||||
//for android ndk
|
||||
#undef _S
|
||||
static inline Point2f applyHomography( const Mat_<double>& H, const Point2f& pt )
|
||||
{
|
||||
double z = H(2,0)*pt.x + H(2,1)*pt.y + H(2,2);
|
||||
if( z )
|
||||
{
|
||||
double w = 1./z;
|
||||
return Point2f( (float)((H(0,0)*pt.x + H(0,1)*pt.y + H(0,2))*w), (float)((H(1,0)*pt.x + H(1,1)*pt.y + H(1,2))*w) );
|
||||
}
|
||||
return Point2f( std::numeric_limits<float>::max(), std::numeric_limits<float>::max() );
|
||||
}
|
||||
|
||||
static inline void linearizeHomographyAt( const Mat_<double>& H, const Point2f& pt, Mat_<double>& A )
|
||||
{
|
||||
A.create(2,2);
|
||||
double p1 = H(0,0)*pt.x + H(0,1)*pt.y + H(0,2),
|
||||
p2 = H(1,0)*pt.x + H(1,1)*pt.y + H(1,2),
|
||||
p3 = H(2,0)*pt.x + H(2,1)*pt.y + H(2,2),
|
||||
p3_2 = p3*p3;
|
||||
if( p3 )
|
||||
{
|
||||
A(0,0) = H(0,0)/p3 - p1*H(2,0)/p3_2; // fxdx
|
||||
A(0,1) = H(0,1)/p3 - p1*H(2,1)/p3_2; // fxdy
|
||||
|
||||
A(1,0) = H(1,0)/p3 - p2*H(2,0)/p3_2; // fydx
|
||||
A(1,1) = H(1,1)/p3 - p2*H(2,1)/p3_2; // fydx
|
||||
}
|
||||
else
|
||||
A.setTo(Scalar::all(std::numeric_limits<double>::max()));
|
||||
}
|
||||
|
||||
class EllipticKeyPoint
|
||||
{
|
||||
public:
|
||||
EllipticKeyPoint();
|
||||
EllipticKeyPoint( const Point2f& _center, const Scalar& _ellipse );
|
||||
|
||||
static void convert( const std::vector<KeyPoint>& src, std::vector<EllipticKeyPoint>& dst );
|
||||
static void convert( const std::vector<EllipticKeyPoint>& src, std::vector<KeyPoint>& dst );
|
||||
|
||||
static Mat_<double> getSecondMomentsMatrix( const Scalar& _ellipse );
|
||||
Mat_<double> getSecondMomentsMatrix() const;
|
||||
|
||||
void calcProjection( const Mat_<double>& H, EllipticKeyPoint& projection ) const;
|
||||
static void calcProjection( const std::vector<EllipticKeyPoint>& src, const Mat_<double>& H, std::vector<EllipticKeyPoint>& dst );
|
||||
|
||||
Point2f center;
|
||||
Scalar ellipse; // 3 elements a, b, c: ax^2+2bxy+cy^2=1
|
||||
Size_<float> axes; // half length of ellipse axes
|
||||
Size_<float> boundingBox; // half sizes of bounding box which sides are parallel to the coordinate axes
|
||||
};
|
||||
|
||||
EllipticKeyPoint::EllipticKeyPoint()
|
||||
{
|
||||
*this = EllipticKeyPoint(Point2f(0,0), Scalar(1, 0, 1) );
|
||||
}
|
||||
|
||||
EllipticKeyPoint::EllipticKeyPoint( const Point2f& _center, const Scalar& _ellipse )
|
||||
{
|
||||
center = _center;
|
||||
ellipse = _ellipse;
|
||||
|
||||
double a = ellipse[0], b = ellipse[1], c = ellipse[2];
|
||||
double ac_b2 = a*c - b*b;
|
||||
double x1, x2;
|
||||
solveQuadratic(1., -(a+c), ac_b2, x1, x2);
|
||||
axes.width = (float)(1/sqrt(x1));
|
||||
axes.height = (float)(1/sqrt(x2));
|
||||
|
||||
boundingBox.width = (float)sqrt(ellipse[2]/ac_b2);
|
||||
boundingBox.height = (float)sqrt(ellipse[0]/ac_b2);
|
||||
}
|
||||
|
||||
Mat_<double> EllipticKeyPoint::getSecondMomentsMatrix( const Scalar& _ellipse )
|
||||
{
|
||||
Mat_<double> M(2, 2);
|
||||
M(0,0) = _ellipse[0];
|
||||
M(1,0) = M(0,1) = _ellipse[1];
|
||||
M(1,1) = _ellipse[2];
|
||||
return M;
|
||||
}
|
||||
|
||||
Mat_<double> EllipticKeyPoint::getSecondMomentsMatrix() const
|
||||
{
|
||||
return getSecondMomentsMatrix(ellipse);
|
||||
}
|
||||
|
||||
void EllipticKeyPoint::calcProjection( const Mat_<double>& H, EllipticKeyPoint& projection ) const
|
||||
{
|
||||
Point2f dstCenter = applyHomography(H, center);
|
||||
|
||||
Mat_<double> invM; invert(getSecondMomentsMatrix(), invM);
|
||||
Mat_<double> Aff; linearizeHomographyAt(H, center, Aff);
|
||||
Mat_<double> dstM; invert(Aff*invM*Aff.t(), dstM);
|
||||
|
||||
projection = EllipticKeyPoint( dstCenter, Scalar(dstM(0,0), dstM(0,1), dstM(1,1)) );
|
||||
}
|
||||
|
||||
void EllipticKeyPoint::convert( const std::vector<KeyPoint>& src, std::vector<EllipticKeyPoint>& dst )
|
||||
{
|
||||
CV_INSTRUMENT_REGION();
|
||||
|
||||
if( !src.empty() )
|
||||
{
|
||||
dst.resize(src.size());
|
||||
for( size_t i = 0; i < src.size(); i++ )
|
||||
{
|
||||
float rad = src[i].size/2;
|
||||
CV_Assert( rad );
|
||||
float fac = 1.f/(rad*rad);
|
||||
dst[i] = EllipticKeyPoint( src[i].pt, Scalar(fac, 0, fac) );
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
void EllipticKeyPoint::convert( const std::vector<EllipticKeyPoint>& src, std::vector<KeyPoint>& dst )
|
||||
{
|
||||
CV_INSTRUMENT_REGION();
|
||||
|
||||
if( !src.empty() )
|
||||
{
|
||||
dst.resize(src.size());
|
||||
for( size_t i = 0; i < src.size(); i++ )
|
||||
{
|
||||
Size_<float> axes = src[i].axes;
|
||||
float rad = sqrt(axes.height*axes.width);
|
||||
dst[i] = KeyPoint(src[i].center, 2*rad );
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
void EllipticKeyPoint::calcProjection( const std::vector<EllipticKeyPoint>& src, const Mat_<double>& H, std::vector<EllipticKeyPoint>& dst )
|
||||
{
|
||||
if( !src.empty() )
|
||||
{
|
||||
CV_Assert( !H.empty() && H.cols == 3 && H.rows == 3);
|
||||
dst.resize(src.size());
|
||||
std::vector<EllipticKeyPoint>::const_iterator srcIt = src.begin();
|
||||
std::vector<EllipticKeyPoint>::iterator dstIt = dst.begin();
|
||||
for( ; srcIt != src.end() && dstIt != dst.end(); ++srcIt, ++dstIt )
|
||||
srcIt->calcProjection(H, *dstIt);
|
||||
}
|
||||
}
|
||||
|
||||
static void filterEllipticKeyPointsByImageSize( std::vector<EllipticKeyPoint>& keypoints, const Size& imgSize )
|
||||
{
|
||||
if( !keypoints.empty() )
|
||||
{
|
||||
std::vector<EllipticKeyPoint> filtered;
|
||||
filtered.reserve(keypoints.size());
|
||||
std::vector<EllipticKeyPoint>::const_iterator it = keypoints.begin();
|
||||
for( ; it != keypoints.end(); ++it )
|
||||
{
|
||||
if( it->center.x + it->boundingBox.width < imgSize.width &&
|
||||
it->center.x - it->boundingBox.width > 0 &&
|
||||
it->center.y + it->boundingBox.height < imgSize.height &&
|
||||
it->center.y - it->boundingBox.height > 0 )
|
||||
filtered.push_back(*it);
|
||||
}
|
||||
keypoints.assign(filtered.begin(), filtered.end());
|
||||
}
|
||||
}
|
||||
|
||||
struct IntersectAreaCounter
|
||||
{
|
||||
IntersectAreaCounter( float _dr, int _minx,
|
||||
int _miny, int _maxy,
|
||||
const Point2f& _diff,
|
||||
const Scalar& _ellipse1, const Scalar& _ellipse2 ) :
|
||||
dr(_dr), bua(0), bna(0), minx(_minx), miny(_miny), maxy(_maxy),
|
||||
diff(_diff), ellipse1(_ellipse1), ellipse2(_ellipse2) {}
|
||||
IntersectAreaCounter( const IntersectAreaCounter& counter, Split )
|
||||
{
|
||||
*this = counter;
|
||||
bua = 0;
|
||||
bna = 0;
|
||||
}
|
||||
|
||||
void operator()( const BlockedRange& range )
|
||||
{
|
||||
CV_Assert( miny < maxy );
|
||||
CV_Assert( dr > FLT_EPSILON );
|
||||
|
||||
int temp_bua = bua, temp_bna = bna;
|
||||
for( int i = range.begin(); i != range.end(); i++ )
|
||||
{
|
||||
float rx1 = minx + i*dr;
|
||||
float rx2 = rx1 - diff.x;
|
||||
for( float ry1 = (float)miny; ry1 <= (float)maxy; ry1 += dr )
|
||||
{
|
||||
float ry2 = ry1 - diff.y;
|
||||
//compute the distance from the ellipse center
|
||||
float e1 = (float)(ellipse1[0]*rx1*rx1 + 2*ellipse1[1]*rx1*ry1 + ellipse1[2]*ry1*ry1);
|
||||
float e2 = (float)(ellipse2[0]*rx2*rx2 + 2*ellipse2[1]*rx2*ry2 + ellipse2[2]*ry2*ry2);
|
||||
//compute the area
|
||||
if( e1<1 && e2<1 ) temp_bna++;
|
||||
if( e1<1 || e2<1 ) temp_bua++;
|
||||
}
|
||||
}
|
||||
bua = temp_bua;
|
||||
bna = temp_bna;
|
||||
}
|
||||
|
||||
void join( IntersectAreaCounter& ac )
|
||||
{
|
||||
bua += ac.bua;
|
||||
bna += ac.bna;
|
||||
}
|
||||
|
||||
float dr;
|
||||
int bua, bna;
|
||||
|
||||
int minx;
|
||||
int miny, maxy;
|
||||
|
||||
Point2f diff;
|
||||
Scalar ellipse1, ellipse2;
|
||||
|
||||
};
|
||||
|
||||
struct SIdx
|
||||
{
|
||||
SIdx() : S(-1), i1(-1), i2(-1) {}
|
||||
SIdx(float _S, int _i1, int _i2) : S(_S), i1(_i1), i2(_i2) {}
|
||||
float S;
|
||||
int i1;
|
||||
int i2;
|
||||
|
||||
bool operator<(const SIdx& v) const { return S > v.S; }
|
||||
|
||||
struct UsedFinder
|
||||
{
|
||||
UsedFinder(const SIdx& _used) : used(_used) {}
|
||||
const SIdx& used;
|
||||
bool operator()(const SIdx& v) const { return (v.i1 == used.i1 || v.i2 == used.i2); }
|
||||
UsedFinder& operator=(const UsedFinder&) = delete;
|
||||
// To avoid -Wdeprecated-copy warning, copy constructor is needed.
|
||||
UsedFinder(const UsedFinder&) = default;
|
||||
};
|
||||
};
|
||||
|
||||
static void computeOneToOneMatchedOverlaps( const std::vector<EllipticKeyPoint>& keypoints1, const std::vector<EllipticKeyPoint>& keypoints2t,
|
||||
bool commonPart, std::vector<SIdx>& overlaps, float minOverlap )
|
||||
{
|
||||
CV_Assert( minOverlap >= 0.f );
|
||||
overlaps.clear();
|
||||
if( keypoints1.empty() || keypoints2t.empty() )
|
||||
return;
|
||||
|
||||
overlaps.clear();
|
||||
overlaps.reserve(cvRound(keypoints1.size() * keypoints2t.size() * 0.01));
|
||||
|
||||
for( size_t i1 = 0; i1 < keypoints1.size(); i1++ )
|
||||
{
|
||||
EllipticKeyPoint kp1 = keypoints1[i1];
|
||||
float maxDist = sqrt(kp1.axes.width*kp1.axes.height),
|
||||
fac = 30.f/maxDist;
|
||||
if( !commonPart )
|
||||
fac=3;
|
||||
|
||||
maxDist = maxDist*4;
|
||||
fac = 1.f/(fac*fac);
|
||||
|
||||
EllipticKeyPoint keypoint1a = EllipticKeyPoint( kp1.center, Scalar(fac*kp1.ellipse[0], fac*kp1.ellipse[1], fac*kp1.ellipse[2]) );
|
||||
|
||||
for( size_t i2 = 0; i2 < keypoints2t.size(); i2++ )
|
||||
{
|
||||
EllipticKeyPoint kp2 = keypoints2t[i2];
|
||||
Point2f diff = kp2.center - kp1.center;
|
||||
|
||||
if( norm(diff) < maxDist )
|
||||
{
|
||||
EllipticKeyPoint keypoint2a = EllipticKeyPoint( kp2.center, Scalar(fac*kp2.ellipse[0], fac*kp2.ellipse[1], fac*kp2.ellipse[2]) );
|
||||
//find the largest eigenvalue
|
||||
int maxx = (int)ceil(( keypoint1a.boundingBox.width > (diff.x+keypoint2a.boundingBox.width)) ?
|
||||
keypoint1a.boundingBox.width : (diff.x+keypoint2a.boundingBox.width));
|
||||
int minx = (int)floor((-keypoint1a.boundingBox.width < (diff.x-keypoint2a.boundingBox.width)) ?
|
||||
-keypoint1a.boundingBox.width : (diff.x-keypoint2a.boundingBox.width));
|
||||
|
||||
int maxy = (int)ceil(( keypoint1a.boundingBox.height > (diff.y+keypoint2a.boundingBox.height)) ?
|
||||
keypoint1a.boundingBox.height : (diff.y+keypoint2a.boundingBox.height));
|
||||
int miny = (int)floor((-keypoint1a.boundingBox.height < (diff.y-keypoint2a.boundingBox.height)) ?
|
||||
-keypoint1a.boundingBox.height : (diff.y-keypoint2a.boundingBox.height));
|
||||
int mina = (maxx-minx) < (maxy-miny) ? (maxx-minx) : (maxy-miny) ;
|
||||
|
||||
//compute the area
|
||||
float dr = (float)mina/50.f;
|
||||
int N = (int)floor((float)(maxx - minx) / dr);
|
||||
IntersectAreaCounter ac( dr, minx, miny, maxy, diff, keypoint1a.ellipse, keypoint2a.ellipse );
|
||||
parallel_reduce( BlockedRange(0, N+1), ac );
|
||||
if( ac.bna > 0 )
|
||||
{
|
||||
float ov = (float)ac.bna / (float)ac.bua;
|
||||
if( ov >= minOverlap )
|
||||
overlaps.push_back(SIdx(ov, (int)i1, (int)i2));
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
std::sort( overlaps.begin(), overlaps.end() );
|
||||
|
||||
typedef std::vector<SIdx>::iterator It;
|
||||
|
||||
It pos = overlaps.begin();
|
||||
It end = overlaps.end();
|
||||
|
||||
while(pos != end)
|
||||
{
|
||||
It prev = pos++;
|
||||
end = std::remove_if(pos, end, SIdx::UsedFinder(*prev));
|
||||
}
|
||||
overlaps.erase(pos, overlaps.end());
|
||||
}
|
||||
|
||||
static void calculateRepeatability( const Mat& img1, const Mat& img2, const Mat& H1to2,
|
||||
const std::vector<KeyPoint>& _keypoints1, const std::vector<KeyPoint>& _keypoints2,
|
||||
float& repeatability, int& correspondencesCount,
|
||||
Mat* thresholdedOverlapMask=0 )
|
||||
{
|
||||
std::vector<EllipticKeyPoint> keypoints1, keypoints2, keypoints1t, keypoints2t;
|
||||
EllipticKeyPoint::convert( _keypoints1, keypoints1 );
|
||||
EllipticKeyPoint::convert( _keypoints2, keypoints2 );
|
||||
|
||||
// calculate projections of key points
|
||||
EllipticKeyPoint::calcProjection( keypoints1, H1to2, keypoints1t );
|
||||
Mat H2to1; invert(H1to2, H2to1);
|
||||
EllipticKeyPoint::calcProjection( keypoints2, H2to1, keypoints2t );
|
||||
|
||||
float overlapThreshold;
|
||||
bool ifEvaluateDetectors = thresholdedOverlapMask == 0;
|
||||
if( ifEvaluateDetectors )
|
||||
{
|
||||
overlapThreshold = 1.f - 0.4f;
|
||||
|
||||
// remove key points from outside of the common image part
|
||||
Size sz1 = img1.size(), sz2 = img2.size();
|
||||
filterEllipticKeyPointsByImageSize( keypoints1, sz1 );
|
||||
filterEllipticKeyPointsByImageSize( keypoints1t, sz2 );
|
||||
filterEllipticKeyPointsByImageSize( keypoints2, sz2 );
|
||||
filterEllipticKeyPointsByImageSize( keypoints2t, sz1 );
|
||||
}
|
||||
else
|
||||
{
|
||||
overlapThreshold = 1.f - 0.5f;
|
||||
|
||||
thresholdedOverlapMask->create( (int)keypoints1.size(), (int)keypoints2t.size(), CV_8UC1 );
|
||||
thresholdedOverlapMask->setTo( Scalar::all(0) );
|
||||
}
|
||||
size_t size1 = keypoints1.size(), size2 = keypoints2t.size();
|
||||
size_t minCount = MIN( size1, size2 );
|
||||
|
||||
// calculate overlap errors
|
||||
std::vector<SIdx> overlaps;
|
||||
computeOneToOneMatchedOverlaps( keypoints1, keypoints2t, ifEvaluateDetectors, overlaps, overlapThreshold/*min overlap*/ );
|
||||
|
||||
correspondencesCount = -1;
|
||||
repeatability = -1.f;
|
||||
if( overlaps.empty() )
|
||||
return;
|
||||
|
||||
if( ifEvaluateDetectors )
|
||||
{
|
||||
// regions one-to-one matching
|
||||
correspondencesCount = (int)overlaps.size();
|
||||
repeatability = minCount ? (float)correspondencesCount / minCount : -1;
|
||||
}
|
||||
else
|
||||
{
|
||||
for( size_t i = 0; i < overlaps.size(); i++ )
|
||||
{
|
||||
int y = overlaps[i].i1;
|
||||
int x = overlaps[i].i2;
|
||||
thresholdedOverlapMask->at<uchar>(y,x) = 1;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
void cv::evaluateFeatureDetector( const Mat& img1, const Mat& img2, const Mat& H1to2,
|
||||
std::vector<KeyPoint>* _keypoints1, std::vector<KeyPoint>* _keypoints2,
|
||||
float& repeatability, int& correspCount,
|
||||
const Ptr<FeatureDetector>& _fdetector )
|
||||
{
|
||||
CV_INSTRUMENT_REGION();
|
||||
|
||||
Ptr<FeatureDetector> fdetector(_fdetector);
|
||||
std::vector<KeyPoint> *keypoints1, *keypoints2, buf1, buf2;
|
||||
keypoints1 = _keypoints1 != 0 ? _keypoints1 : &buf1;
|
||||
keypoints2 = _keypoints2 != 0 ? _keypoints2 : &buf2;
|
||||
|
||||
if( (keypoints1->empty() || keypoints2->empty()) && !fdetector )
|
||||
CV_Error( Error::StsBadArg, "fdetector must not be empty when keypoints1 or keypoints2 is empty" );
|
||||
|
||||
if( keypoints1->empty() )
|
||||
fdetector->detect( img1, *keypoints1 );
|
||||
if( keypoints2->empty() )
|
||||
fdetector->detect( img2, *keypoints2 );
|
||||
|
||||
calculateRepeatability( img1, img2, H1to2, *keypoints1, *keypoints2, repeatability, correspCount );
|
||||
}
|
||||
|
||||
struct DMatchForEvaluation : public DMatch
|
||||
{
|
||||
uchar isCorrect;
|
||||
DMatchForEvaluation( const DMatch &dm ) : DMatch( dm ), isCorrect(0) {}
|
||||
};
|
||||
|
||||
static inline float recall( int correctMatchCount, int correspondenceCount )
|
||||
{
|
||||
return correspondenceCount ? (float)correctMatchCount / (float)correspondenceCount : -1;
|
||||
}
|
||||
|
||||
static inline float precision( int correctMatchCount, int falseMatchCount )
|
||||
{
|
||||
return correctMatchCount + falseMatchCount ? (float)correctMatchCount / (float)(correctMatchCount + falseMatchCount) : -1;
|
||||
}
|
||||
|
||||
void cv::computeRecallPrecisionCurve( const std::vector<std::vector<DMatch> >& matches1to2,
|
||||
const std::vector<std::vector<uchar> >& correctMatches1to2Mask,
|
||||
std::vector<Point2f>& recallPrecisionCurve )
|
||||
{
|
||||
CV_INSTRUMENT_REGION();
|
||||
|
||||
CV_Assert( matches1to2.size() == correctMatches1to2Mask.size() );
|
||||
|
||||
std::vector<DMatchForEvaluation> allMatches;
|
||||
int correspondenceCount = 0;
|
||||
for( size_t i = 0; i < matches1to2.size(); i++ )
|
||||
{
|
||||
for( size_t j = 0; j < matches1to2[i].size(); j++ )
|
||||
{
|
||||
DMatchForEvaluation match = matches1to2[i][j];
|
||||
match.isCorrect = correctMatches1to2Mask[i][j] ;
|
||||
allMatches.push_back( match );
|
||||
correspondenceCount += match.isCorrect != 0 ? 1 : 0;
|
||||
}
|
||||
}
|
||||
|
||||
std::sort( allMatches.begin(), allMatches.end() );
|
||||
|
||||
int correctMatchCount = 0, falseMatchCount = 0;
|
||||
recallPrecisionCurve.resize( allMatches.size() );
|
||||
for( size_t i = 0; i < allMatches.size(); i++ )
|
||||
{
|
||||
if( allMatches[i].isCorrect )
|
||||
correctMatchCount++;
|
||||
else
|
||||
falseMatchCount++;
|
||||
|
||||
float r = recall( correctMatchCount, correspondenceCount );
|
||||
float p = precision( correctMatchCount, falseMatchCount );
|
||||
recallPrecisionCurve[i] = Point2f(1-p, r);
|
||||
}
|
||||
}
|
||||
|
||||
float cv::getRecall( const std::vector<Point2f>& recallPrecisionCurve, float l_precision )
|
||||
{
|
||||
CV_INSTRUMENT_REGION();
|
||||
|
||||
int nearestPointIndex = getNearestPoint( recallPrecisionCurve, l_precision );
|
||||
|
||||
float recall = -1.f;
|
||||
|
||||
if( nearestPointIndex >= 0 )
|
||||
recall = recallPrecisionCurve[nearestPointIndex].y;
|
||||
|
||||
return recall;
|
||||
}
|
||||
|
||||
int cv::getNearestPoint( const std::vector<Point2f>& recallPrecisionCurve, float l_precision )
|
||||
{
|
||||
CV_INSTRUMENT_REGION();
|
||||
|
||||
int nearestPointIndex = -1;
|
||||
|
||||
if( l_precision >= 0 && l_precision <= 1 )
|
||||
{
|
||||
float minDiff = FLT_MAX;
|
||||
for( size_t i = 0; i < recallPrecisionCurve.size(); i++ )
|
||||
{
|
||||
float curDiff = std::fabs(l_precision - recallPrecisionCurve[i].x);
|
||||
if( curDiff <= minDiff )
|
||||
{
|
||||
nearestPointIndex = (int)i;
|
||||
minDiff = curDiff;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return nearestPointIndex;
|
||||
}
|
||||
@@ -0,0 +1,184 @@
|
||||
/* This is FAST corner detector, contributed to OpenCV by the author, Edward Rosten.
|
||||
Below is the original copyright and the references */
|
||||
|
||||
/*
|
||||
Copyright (c) 2006, 2008 Edward Rosten
|
||||
All rights reserved.
|
||||
|
||||
Redistribution and use in source and binary forms, with or without
|
||||
modification, are permitted provided that the following conditions
|
||||
are met:
|
||||
|
||||
*Redistributions of source code must retain the above copyright
|
||||
notice, this list of conditions and the following disclaimer.
|
||||
|
||||
*Redistributions in binary form must reproduce the above copyright
|
||||
notice, this list of conditions and the following disclaimer in the
|
||||
documentation and/or other materials provided with the distribution.
|
||||
|
||||
*Neither the name of the University of Cambridge nor the names of
|
||||
its contributors may be used to endorse or promote products derived
|
||||
from this software without specific prior written permission.
|
||||
|
||||
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
|
||||
"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
|
||||
LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
|
||||
A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
|
||||
CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
|
||||
EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
|
||||
PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
|
||||
PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
|
||||
LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
|
||||
NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
|
||||
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
|
||||
*/
|
||||
|
||||
/*
|
||||
The references are:
|
||||
* Machine learning for high-speed corner detection,
|
||||
E. Rosten and T. Drummond, ECCV 2006
|
||||
* Faster and better: A machine learning approach to corner detection
|
||||
E. Rosten, R. Porter and T. Drummond, PAMI, 2009
|
||||
*/
|
||||
|
||||
#include "precomp.hpp"
|
||||
#include "fast.hpp"
|
||||
#include "opencv2/core/hal/intrin.hpp"
|
||||
|
||||
namespace cv
|
||||
{
|
||||
namespace opt_AVX2
|
||||
{
|
||||
|
||||
class FAST_t_patternSize16_AVX2_Impl CV_FINAL: public FAST_t_patternSize16_AVX2
|
||||
{
|
||||
public:
|
||||
FAST_t_patternSize16_AVX2_Impl(int _cols, int _threshold, bool _nonmax_suppression, const int* _pixel):
|
||||
cols(_cols), nonmax_suppression(_nonmax_suppression), pixel(_pixel)
|
||||
{
|
||||
//patternSize = 16
|
||||
t256c = (char)_threshold;
|
||||
threshold = std::min(std::max(_threshold, 0), 255);
|
||||
}
|
||||
|
||||
virtual void process(int &j, const uchar* &ptr, uchar* curr, int* cornerpos, int &ncorners) CV_OVERRIDE
|
||||
{
|
||||
static const __m256i delta256 = _mm256_broadcastsi128_si256(_mm_set1_epi8((char)(-128))), K16_256 = _mm256_broadcastsi128_si256(_mm_set1_epi8((char)8));
|
||||
const __m256i t256 = _mm256_broadcastsi128_si256(_mm_set1_epi8(t256c));
|
||||
for (; j < cols - 32 - 3; j += 32, ptr += 32)
|
||||
{
|
||||
__m256i m0, m1;
|
||||
__m256i v0 = _mm256_loadu_si256((const __m256i*)ptr);
|
||||
|
||||
__m256i v1 = _mm256_xor_si256(_mm256_subs_epu8(v0, t256), delta256);
|
||||
v0 = _mm256_xor_si256(_mm256_adds_epu8(v0, t256), delta256);
|
||||
|
||||
__m256i x0 = _mm256_sub_epi8(_mm256_loadu_si256((const __m256i*)(ptr + pixel[0])), delta256);
|
||||
__m256i x1 = _mm256_sub_epi8(_mm256_loadu_si256((const __m256i*)(ptr + pixel[4])), delta256);
|
||||
__m256i x2 = _mm256_sub_epi8(_mm256_loadu_si256((const __m256i*)(ptr + pixel[8])), delta256);
|
||||
__m256i x3 = _mm256_sub_epi8(_mm256_loadu_si256((const __m256i*)(ptr + pixel[12])), delta256);
|
||||
|
||||
m0 = _mm256_and_si256(_mm256_cmpgt_epi8(x0, v0), _mm256_cmpgt_epi8(x1, v0));
|
||||
m1 = _mm256_and_si256(_mm256_cmpgt_epi8(v1, x0), _mm256_cmpgt_epi8(v1, x1));
|
||||
m0 = _mm256_or_si256(m0, _mm256_and_si256(_mm256_cmpgt_epi8(x1, v0), _mm256_cmpgt_epi8(x2, v0)));
|
||||
m1 = _mm256_or_si256(m1, _mm256_and_si256(_mm256_cmpgt_epi8(v1, x1), _mm256_cmpgt_epi8(v1, x2)));
|
||||
m0 = _mm256_or_si256(m0, _mm256_and_si256(_mm256_cmpgt_epi8(x2, v0), _mm256_cmpgt_epi8(x3, v0)));
|
||||
m1 = _mm256_or_si256(m1, _mm256_and_si256(_mm256_cmpgt_epi8(v1, x2), _mm256_cmpgt_epi8(v1, x3)));
|
||||
m0 = _mm256_or_si256(m0, _mm256_and_si256(_mm256_cmpgt_epi8(x3, v0), _mm256_cmpgt_epi8(x0, v0)));
|
||||
m1 = _mm256_or_si256(m1, _mm256_and_si256(_mm256_cmpgt_epi8(v1, x3), _mm256_cmpgt_epi8(v1, x0)));
|
||||
m0 = _mm256_or_si256(m0, m1);
|
||||
|
||||
unsigned int mask = _mm256_movemask_epi8(m0); //unsigned is important!
|
||||
if (mask == 0){
|
||||
continue;
|
||||
}
|
||||
if ((mask & 0xffff) == 0)
|
||||
{
|
||||
j -= 16;
|
||||
ptr -= 16;
|
||||
continue;
|
||||
}
|
||||
|
||||
__m256i c0 = _mm256_setzero_si256(), c1 = c0, max0 = c0, max1 = c0;
|
||||
for (int k = 0; k < 25; k++)
|
||||
{
|
||||
__m256i x = _mm256_xor_si256(_mm256_loadu_si256((const __m256i*)(ptr + pixel[k])), delta256);
|
||||
m0 = _mm256_cmpgt_epi8(x, v0);
|
||||
m1 = _mm256_cmpgt_epi8(v1, x);
|
||||
|
||||
c0 = _mm256_and_si256(_mm256_sub_epi8(c0, m0), m0);
|
||||
c1 = _mm256_and_si256(_mm256_sub_epi8(c1, m1), m1);
|
||||
|
||||
max0 = _mm256_max_epu8(max0, c0);
|
||||
max1 = _mm256_max_epu8(max1, c1);
|
||||
}
|
||||
|
||||
max0 = _mm256_max_epu8(max0, max1);
|
||||
unsigned int m = _mm256_movemask_epi8(_mm256_cmpgt_epi8(max0, K16_256));
|
||||
|
||||
for (int k = 0; m > 0 && k < 32; k++, m >>= 1)
|
||||
if (m & 1)
|
||||
{
|
||||
cornerpos[ncorners++] = j + k;
|
||||
if (nonmax_suppression)
|
||||
{
|
||||
short d[25];
|
||||
for (int q = 0; q < 25; q++)
|
||||
d[q] = (short)(ptr[k] - ptr[k + pixel[q]]);
|
||||
v_int16x8 q0 = v_setall_s16(-1000), q1 = v_setall_s16(1000);
|
||||
for (int q = 0; q < 16; q += 8)
|
||||
{
|
||||
v_int16x8 v0_ = v_load(d + q + 1);
|
||||
v_int16x8 v1_ = v_load(d + q + 2);
|
||||
v_int16x8 a = v_min(v0_, v1_);
|
||||
v_int16x8 b = v_max(v0_, v1_);
|
||||
v0_ = v_load(d + q + 3);
|
||||
a = v_min(a, v0_);
|
||||
b = v_max(b, v0_);
|
||||
v0_ = v_load(d + q + 4);
|
||||
a = v_min(a, v0_);
|
||||
b = v_max(b, v0_);
|
||||
v0_ = v_load(d + q + 5);
|
||||
a = v_min(a, v0_);
|
||||
b = v_max(b, v0_);
|
||||
v0_ = v_load(d + q + 6);
|
||||
a = v_min(a, v0_);
|
||||
b = v_max(b, v0_);
|
||||
v0_ = v_load(d + q + 7);
|
||||
a = v_min(a, v0_);
|
||||
b = v_max(b, v0_);
|
||||
v0_ = v_load(d + q + 8);
|
||||
a = v_min(a, v0_);
|
||||
b = v_max(b, v0_);
|
||||
v0_ = v_load(d + q);
|
||||
q0 = v_max(q0, v_min(a, v0_));
|
||||
q1 = v_min(q1, v_max(b, v0_));
|
||||
v0_ = v_load(d + q + 9);
|
||||
q0 = v_max(q0, v_min(a, v0_));
|
||||
q1 = v_min(q1, v_max(b, v0_));
|
||||
}
|
||||
q0 = v_max(q0, v_sub(v_setzero_s16(), q1));
|
||||
curr[j + k] = (uchar)(v_reduce_max(q0) - 1);
|
||||
}
|
||||
}
|
||||
}
|
||||
_mm256_zeroupper();
|
||||
}
|
||||
|
||||
virtual ~FAST_t_patternSize16_AVX2_Impl() CV_OVERRIDE {}
|
||||
|
||||
private:
|
||||
int cols;
|
||||
char t256c;
|
||||
int threshold;
|
||||
bool nonmax_suppression;
|
||||
const int* pixel;
|
||||
};
|
||||
|
||||
Ptr<FAST_t_patternSize16_AVX2> FAST_t_patternSize16_AVX2::getImpl(int _cols, int _threshold, bool _nonmax_suppression, const int* _pixel)
|
||||
{
|
||||
return Ptr<FAST_t_patternSize16_AVX2>(new FAST_t_patternSize16_AVX2_Impl(_cols, _threshold, _nonmax_suppression, _pixel));
|
||||
}
|
||||
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,599 @@
|
||||
/* This is FAST corner detector, contributed to OpenCV by the author, Edward Rosten.
|
||||
Below is the original copyright and the references */
|
||||
|
||||
/*
|
||||
Copyright (c) 2006, 2008 Edward Rosten
|
||||
All rights reserved.
|
||||
|
||||
Redistribution and use in source and binary forms, with or without
|
||||
modification, are permitted provided that the following conditions
|
||||
are met:
|
||||
|
||||
*Redistributions of source code must retain the above copyright
|
||||
notice, this list of conditions and the following disclaimer.
|
||||
|
||||
*Redistributions in binary form must reproduce the above copyright
|
||||
notice, this list of conditions and the following disclaimer in the
|
||||
documentation and/or other materials provided with the distribution.
|
||||
|
||||
*Neither the name of the University of Cambridge nor the names of
|
||||
its contributors may be used to endorse or promote products derived
|
||||
from this software without specific prior written permission.
|
||||
|
||||
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
|
||||
"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
|
||||
LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
|
||||
A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
|
||||
CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
|
||||
EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
|
||||
PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
|
||||
PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
|
||||
LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
|
||||
NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
|
||||
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
|
||||
*/
|
||||
|
||||
/*
|
||||
The references are:
|
||||
* Machine learning for high-speed corner detection,
|
||||
E. Rosten and T. Drummond, ECCV 2006
|
||||
* Faster and better: A machine learning approach to corner detection
|
||||
E. Rosten, R. Porter and T. Drummond, PAMI, 2009
|
||||
*/
|
||||
|
||||
#include "precomp.hpp"
|
||||
#include "fast.hpp"
|
||||
#include "fast_score.hpp"
|
||||
#include "opencl_kernels_features2d.hpp"
|
||||
#include "hal_replacement.hpp"
|
||||
#include "opencv2/core/hal/intrin.hpp"
|
||||
#include "opencv2/core/utils/buffer_area.private.hpp"
|
||||
|
||||
namespace cv
|
||||
{
|
||||
|
||||
template<int patternSize>
|
||||
void FAST_t(InputArray _img, std::vector<KeyPoint>& keypoints, int threshold, bool nonmax_suppression)
|
||||
{
|
||||
Mat img = _img.getMat();
|
||||
const int K = patternSize/2, N = patternSize + K + 1;
|
||||
int i, j, k, pixel[25];
|
||||
makeOffsets(pixel, (int)img.step, patternSize);
|
||||
|
||||
#if CV_SIMD128
|
||||
const int quarterPatternSize = patternSize/4;
|
||||
v_uint8x16 delta = v_setall_u8(0x80), t = v_setall_u8((char)threshold), K16 = v_setall_u8((char)K);
|
||||
#if CV_TRY_AVX2
|
||||
Ptr<opt_AVX2::FAST_t_patternSize16_AVX2> fast_t_impl_avx2;
|
||||
if(CV_CPU_HAS_SUPPORT_AVX2)
|
||||
fast_t_impl_avx2 = opt_AVX2::FAST_t_patternSize16_AVX2::getImpl(img.cols, threshold, nonmax_suppression, pixel);
|
||||
#endif
|
||||
|
||||
#endif
|
||||
|
||||
keypoints.clear();
|
||||
|
||||
threshold = std::min(std::max(threshold, 0), 255);
|
||||
|
||||
uchar threshold_tab[512];
|
||||
for( i = -255; i <= 255; i++ )
|
||||
threshold_tab[i+255] = (uchar)(i < -threshold ? 1 : i > threshold ? 2 : 0);
|
||||
|
||||
uchar* buf[3] = { 0 };
|
||||
int* cpbuf[3] = { 0 };
|
||||
utils::BufferArea area;
|
||||
for (unsigned idx = 0; idx < 3; ++idx)
|
||||
{
|
||||
area.allocate(buf[idx], img.cols);
|
||||
area.allocate(cpbuf[idx], img.cols + 1);
|
||||
}
|
||||
area.commit();
|
||||
|
||||
for (unsigned idx = 0; idx < 3; ++idx)
|
||||
{
|
||||
memset(buf[idx], 0, img.cols);
|
||||
}
|
||||
|
||||
for(i = 3; i < img.rows-2; i++)
|
||||
{
|
||||
const uchar* ptr = img.ptr<uchar>(i) + 3;
|
||||
uchar* curr = buf[(i - 3)%3];
|
||||
int* cornerpos = cpbuf[(i - 3)%3] + 1; // cornerpos[-1] is used to store a value
|
||||
memset(curr, 0, img.cols);
|
||||
int ncorners = 0;
|
||||
|
||||
if( i < img.rows - 3 )
|
||||
{
|
||||
j = 3;
|
||||
#if CV_SIMD128
|
||||
{
|
||||
if( patternSize == 16 )
|
||||
{
|
||||
#if CV_TRY_AVX2
|
||||
if (fast_t_impl_avx2)
|
||||
fast_t_impl_avx2->process(j, ptr, curr, cornerpos, ncorners);
|
||||
#endif
|
||||
//vz if (j <= (img.cols - 27)) //it doesn't make sense using vectors for less than 8 elements
|
||||
{
|
||||
for (; j < img.cols - 16 - 3; j += 16, ptr += 16)
|
||||
{
|
||||
v_uint8x16 v = v_load(ptr);
|
||||
v_int8x16 v0 = v_reinterpret_as_s8(v_xor(v_add(v, t), delta));
|
||||
v_int8x16 v1 = v_reinterpret_as_s8(v_xor(v_sub(v, t), delta));
|
||||
|
||||
v_int8x16 x0 = v_reinterpret_as_s8(v_sub_wrap(v_load(ptr + pixel[0]), delta));
|
||||
v_int8x16 x1 = v_reinterpret_as_s8(v_sub_wrap(v_load(ptr + pixel[quarterPatternSize]), delta));
|
||||
v_int8x16 x2 = v_reinterpret_as_s8(v_sub_wrap(v_load(ptr + pixel[2*quarterPatternSize]), delta));
|
||||
v_int8x16 x3 = v_reinterpret_as_s8(v_sub_wrap(v_load(ptr + pixel[3*quarterPatternSize]), delta));
|
||||
|
||||
v_int8x16 m0, m1;
|
||||
m0 = v_and(v_lt(v0, x0), v_lt(v0, x1));
|
||||
m1 = v_and(v_lt(x0, v1), v_lt(x1, v1));
|
||||
m0 = v_or(m0, v_and(v_lt(v0, x1), v_lt(v0, x2)));
|
||||
m1 = v_or(m1, v_and(v_lt(x1, v1), v_lt(x2, v1)));
|
||||
m0 = v_or(m0, v_and(v_lt(v0, x2), v_lt(v0, x3)));
|
||||
m1 = v_or(m1, v_and(v_lt(x2, v1), v_lt(x3, v1)));
|
||||
m0 = v_or(m0, v_and(v_lt(v0, x3), v_lt(v0, x0)));
|
||||
m1 = v_or(m1, v_and(v_lt(x3, v1), v_lt(x0, v1)));
|
||||
m0 = v_or(m0, m1);
|
||||
|
||||
if( !v_check_any(m0) )
|
||||
continue;
|
||||
if( !v_check_any(v_combine_low(m0, m0)) )
|
||||
{
|
||||
j -= 8;
|
||||
ptr -= 8;
|
||||
continue;
|
||||
}
|
||||
|
||||
v_int8x16 c0 = v_setzero_s8();
|
||||
v_int8x16 c1 = v_setzero_s8();
|
||||
v_uint8x16 max0 = v_setzero_u8();
|
||||
v_uint8x16 max1 = v_setzero_u8();
|
||||
for( k = 0; k < N; k++ )
|
||||
{
|
||||
v_int8x16 x = v_reinterpret_as_s8(v_xor(v_load((ptr + pixel[k])), delta));
|
||||
m0 = v_lt(v0, x);
|
||||
m1 = v_lt(x, v1);
|
||||
|
||||
c0 = v_and(v_sub_wrap(c0, m0), m0);
|
||||
c1 = v_and(v_sub_wrap(c1, m1), m1);
|
||||
|
||||
max0 = v_max(max0, v_reinterpret_as_u8(c0));
|
||||
max1 = v_max(max1, v_reinterpret_as_u8(c1));
|
||||
}
|
||||
|
||||
max0 = v_lt(K16, v_max(max0, max1));
|
||||
unsigned int m = v_signmask(v_reinterpret_as_s8(max0));
|
||||
|
||||
for( k = 0; m > 0 && k < 16; k++, m >>= 1 )
|
||||
{
|
||||
if( m & 1 )
|
||||
{
|
||||
cornerpos[ncorners++] = j+k;
|
||||
if(nonmax_suppression)
|
||||
{
|
||||
short d[25];
|
||||
int _k = 0;
|
||||
#if CV_ENABLE_UNROLLED
|
||||
for (; _k + 4 < 25; _k += 5)
|
||||
{
|
||||
d[_k] = (short)(ptr[k] - ptr[k + pixel[_k]]);
|
||||
d[_k + 1] = (short)(ptr[k] - ptr[k + pixel[_k + 1]]);
|
||||
d[_k + 2] = (short)(ptr[k] - ptr[k + pixel[_k + 2]]);
|
||||
d[_k + 3] = (short)(ptr[k] - ptr[k + pixel[_k + 3]]);
|
||||
d[_k + 4] = (short)(ptr[k] - ptr[k + pixel[_k + 4]]);
|
||||
}
|
||||
#else
|
||||
for ( ; _k < 25; _k++)
|
||||
d[_k] = (short)(ptr[k] - ptr[k + pixel[_k]]);
|
||||
#endif
|
||||
|
||||
v_int16x8 a0, b0, a1, b1;
|
||||
a0 = b0 = a1 = b1 = v_load(d + 8);
|
||||
for(int shift = 0; shift < 8; ++shift)
|
||||
{
|
||||
v_int16x8 v_nms = v_load(d + shift);
|
||||
a0 = v_min(a0, v_nms);
|
||||
b0 = v_max(b0, v_nms);
|
||||
v_nms = v_load(d + 9 + shift);
|
||||
a1 = v_min(a1, v_nms);
|
||||
b1 = v_max(b1, v_nms);
|
||||
}
|
||||
curr[j + k] = (uchar)(v_reduce_max(v_max(v_max(a0, a1), v_sub(v_setzero_s16(), v_min(b0, b1)))) - 1);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
#endif
|
||||
for( ; j < img.cols - 3; j++, ptr++ )
|
||||
{
|
||||
int v = ptr[0];
|
||||
const uchar* tab = &threshold_tab[0] - v + 255;
|
||||
int d = tab[ptr[pixel[0]]] | tab[ptr[pixel[8]]];
|
||||
|
||||
if( d == 0 )
|
||||
continue;
|
||||
|
||||
d &= tab[ptr[pixel[2]]] | tab[ptr[pixel[10]]];
|
||||
d &= tab[ptr[pixel[4]]] | tab[ptr[pixel[12]]];
|
||||
d &= tab[ptr[pixel[6]]] | tab[ptr[pixel[14]]];
|
||||
|
||||
if( d == 0 )
|
||||
continue;
|
||||
|
||||
d &= tab[ptr[pixel[1]]] | tab[ptr[pixel[9]]];
|
||||
d &= tab[ptr[pixel[3]]] | tab[ptr[pixel[11]]];
|
||||
d &= tab[ptr[pixel[5]]] | tab[ptr[pixel[13]]];
|
||||
d &= tab[ptr[pixel[7]]] | tab[ptr[pixel[15]]];
|
||||
|
||||
if( d & 1 )
|
||||
{
|
||||
int vt = v - threshold, count = 0;
|
||||
|
||||
for( k = 0; k < N; k++ )
|
||||
{
|
||||
int x = ptr[pixel[k]];
|
||||
if(x < vt)
|
||||
{
|
||||
if( ++count > K )
|
||||
{
|
||||
cornerpos[ncorners++] = j;
|
||||
if(nonmax_suppression)
|
||||
curr[j] = (uchar)cornerScore<patternSize>(ptr, pixel, threshold);
|
||||
break;
|
||||
}
|
||||
}
|
||||
else
|
||||
count = 0;
|
||||
}
|
||||
}
|
||||
|
||||
if( d & 2 )
|
||||
{
|
||||
int vt = v + threshold, count = 0;
|
||||
|
||||
for( k = 0; k < N; k++ )
|
||||
{
|
||||
int x = ptr[pixel[k]];
|
||||
if(x > vt)
|
||||
{
|
||||
if( ++count > K )
|
||||
{
|
||||
cornerpos[ncorners++] = j;
|
||||
if(nonmax_suppression)
|
||||
curr[j] = (uchar)cornerScore<patternSize>(ptr, pixel, threshold);
|
||||
break;
|
||||
}
|
||||
}
|
||||
else
|
||||
count = 0;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
cornerpos[-1] = ncorners;
|
||||
|
||||
if( i == 3 )
|
||||
continue;
|
||||
|
||||
const uchar* prev = buf[(i - 4 + 3)%3];
|
||||
const uchar* pprev = buf[(i - 5 + 3)%3];
|
||||
cornerpos = cpbuf[(i - 4 + 3)%3] + 1; // cornerpos[-1] is used to store a value
|
||||
ncorners = cornerpos[-1];
|
||||
|
||||
for( k = 0; k < ncorners; k++ )
|
||||
{
|
||||
j = cornerpos[k];
|
||||
int score = prev[j];
|
||||
if( !nonmax_suppression ||
|
||||
(score > prev[j+1] && score > prev[j-1] &&
|
||||
score > pprev[j-1] && score > pprev[j] && score > pprev[j+1] &&
|
||||
score > curr[j-1] && score > curr[j] && score > curr[j+1]) )
|
||||
{
|
||||
keypoints.push_back(KeyPoint((float)j, (float)(i-1), 7.f, -1, (float)score));
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
#ifdef HAVE_OPENCL
|
||||
template<typename pt>
|
||||
struct cmp_pt
|
||||
{
|
||||
bool operator ()(const pt& a, const pt& b) const { return a.y < b.y || (a.y == b.y && a.x < b.x); }
|
||||
};
|
||||
|
||||
static bool ocl_FAST( InputArray _img, std::vector<KeyPoint>& keypoints,
|
||||
int threshold, bool nonmax_suppression, int maxKeypoints )
|
||||
{
|
||||
UMat img = _img.getUMat();
|
||||
if( img.cols < 7 || img.rows < 7 )
|
||||
return false;
|
||||
size_t globalsize[] = { (size_t)img.cols-6, (size_t)img.rows-6 };
|
||||
|
||||
ocl::Kernel fastKptKernel("FAST_findKeypoints", ocl::features2d::fast_oclsrc);
|
||||
if (fastKptKernel.empty())
|
||||
return false;
|
||||
|
||||
UMat kp1(1, maxKeypoints*2+1, CV_32S);
|
||||
|
||||
UMat ucounter1(kp1, Rect(0,0,1,1));
|
||||
ucounter1.setTo(Scalar::all(0));
|
||||
|
||||
if( !fastKptKernel.args(ocl::KernelArg::ReadOnly(img),
|
||||
ocl::KernelArg::PtrReadWrite(kp1),
|
||||
maxKeypoints, threshold).run(2, globalsize, 0, true))
|
||||
return false;
|
||||
|
||||
Mat mcounter;
|
||||
ucounter1.copyTo(mcounter);
|
||||
int i, counter = mcounter.at<int>(0);
|
||||
counter = std::min(counter, maxKeypoints);
|
||||
|
||||
keypoints.clear();
|
||||
|
||||
if( counter == 0 )
|
||||
return true;
|
||||
|
||||
if( !nonmax_suppression )
|
||||
{
|
||||
Mat m;
|
||||
kp1(Rect(0, 0, counter*2+1, 1)).copyTo(m);
|
||||
const Point* pt = (const Point*)(m.ptr<int>() + 1);
|
||||
for( i = 0; i < counter; i++ )
|
||||
keypoints.push_back(KeyPoint((float)pt[i].x, (float)pt[i].y, 7.f, -1, 1.f));
|
||||
}
|
||||
else
|
||||
{
|
||||
UMat kp2(1, maxKeypoints*3+1, CV_32S);
|
||||
UMat ucounter2 = kp2(Rect(0,0,1,1));
|
||||
ucounter2.setTo(Scalar::all(0));
|
||||
|
||||
ocl::Kernel fastNMSKernel("FAST_nonmaxSupression", ocl::features2d::fast_oclsrc);
|
||||
if (fastNMSKernel.empty())
|
||||
return false;
|
||||
|
||||
size_t globalsize_nms[] = { (size_t)counter };
|
||||
if( !fastNMSKernel.args(ocl::KernelArg::PtrReadOnly(kp1),
|
||||
ocl::KernelArg::PtrReadWrite(kp2),
|
||||
ocl::KernelArg::ReadOnly(img),
|
||||
counter, counter).run(1, globalsize_nms, 0, true))
|
||||
return false;
|
||||
|
||||
Mat m2;
|
||||
kp2(Rect(0, 0, counter*3+1, 1)).copyTo(m2);
|
||||
Point3i* pt2 = (Point3i*)(m2.ptr<int>() + 1);
|
||||
int newcounter = std::min(m2.at<int>(0), counter);
|
||||
|
||||
std::sort(pt2, pt2 + newcounter, cmp_pt<Point3i>());
|
||||
|
||||
for( i = 0; i < newcounter; i++ )
|
||||
keypoints.push_back(KeyPoint((float)pt2[i].x, (float)pt2[i].y, 7.f, -1, (float)pt2[i].z));
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
#endif
|
||||
|
||||
static inline int hal_FAST(cv::Mat& src, std::vector<KeyPoint>& keypoints, int threshold, bool nonmax_suppression, FastFeatureDetector::DetectorType type)
|
||||
{
|
||||
if (threshold > 20)
|
||||
return CV_HAL_ERROR_NOT_IMPLEMENTED;
|
||||
|
||||
cv::Mat scores(src.size(), src.type());
|
||||
|
||||
int error = cv_hal_FAST_dense(src.data, src.step, scores.data, scores.step, src.cols, src.rows, type);
|
||||
|
||||
if (error != CV_HAL_ERROR_OK)
|
||||
return error;
|
||||
|
||||
cv::Mat suppressedScores(src.size(), src.type());
|
||||
|
||||
if (nonmax_suppression)
|
||||
{
|
||||
error = cv_hal_FAST_NMS(scores.data, scores.step, suppressedScores.data, suppressedScores.step, scores.cols, scores.rows);
|
||||
|
||||
if (error != CV_HAL_ERROR_OK)
|
||||
return error;
|
||||
}
|
||||
else
|
||||
{
|
||||
suppressedScores = scores;
|
||||
}
|
||||
|
||||
if (!threshold && nonmax_suppression) threshold = 1;
|
||||
|
||||
cv::KeyPoint kpt(0, 0, 7.f, -1, 0);
|
||||
|
||||
unsigned uthreshold = (unsigned) threshold;
|
||||
|
||||
int ofs = 3;
|
||||
|
||||
int stride = (int)suppressedScores.step;
|
||||
const unsigned char* pscore = suppressedScores.data;
|
||||
|
||||
keypoints.clear();
|
||||
|
||||
for (int y = ofs; y + ofs < suppressedScores.rows; ++y)
|
||||
{
|
||||
kpt.pt.y = (float)(y);
|
||||
for (int x = ofs; x + ofs < suppressedScores.cols; ++x)
|
||||
{
|
||||
unsigned score = pscore[y * stride + x];
|
||||
if (score > uthreshold)
|
||||
{
|
||||
kpt.pt.x = (float)(x);
|
||||
kpt.response = (nonmax_suppression != 0) ? (float)((int)score - 1) : 0.f;
|
||||
keypoints.push_back(kpt);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return CV_HAL_ERROR_OK;
|
||||
}
|
||||
|
||||
void FAST(InputArray _img, std::vector<KeyPoint>& keypoints, int threshold, bool nonmax_suppression, FastFeatureDetector::DetectorType type)
|
||||
{
|
||||
CV_INSTRUMENT_REGION();
|
||||
|
||||
if (type != FastFeatureDetector::TYPE_5_8 &&
|
||||
type != FastFeatureDetector::TYPE_7_12 &&
|
||||
type != FastFeatureDetector::TYPE_9_16)
|
||||
{
|
||||
CV_Error_(Error::StsBadArg,
|
||||
("Unknown FastFeatureDetector detector type: %d", static_cast<int>(type)));
|
||||
}
|
||||
|
||||
const size_t max_fast_features = std::max(_img.total()/100, size_t(1000)); // Simple heuristic that depends on resolution.
|
||||
|
||||
CV_OCL_RUN(_img.isUMat() && type == FastFeatureDetector::TYPE_9_16,
|
||||
ocl_FAST(_img, keypoints, threshold, nonmax_suppression, (int)max_fast_features));
|
||||
|
||||
cv::Mat img = _img.getMat();
|
||||
CALL_HAL(fast_dense, hal_FAST, img, keypoints, threshold, nonmax_suppression, type);
|
||||
|
||||
size_t keypoints_count = 1;
|
||||
keypoints.clear();
|
||||
KeyPoint* kps = (KeyPoint*)malloc(sizeof(KeyPoint) * keypoints_count);
|
||||
int hal_ret = cv_hal_FASTv2(img.data, img.step, img.cols, img.rows, (void**)&kps,
|
||||
&keypoints_count, threshold, nonmax_suppression, type, realloc);
|
||||
if (hal_ret == CV_HAL_ERROR_OK) {
|
||||
keypoints.assign(kps, kps + keypoints_count);
|
||||
free(kps);
|
||||
return;
|
||||
} else {
|
||||
free(kps);
|
||||
keypoints_count = max_fast_features;
|
||||
keypoints.clear();
|
||||
keypoints.resize(keypoints_count);
|
||||
CALL_HAL(fast, cv_hal_FAST, img.data, img.step, img.cols, img.rows,
|
||||
(uchar*)(keypoints.data()), &keypoints_count, threshold, nonmax_suppression, type);
|
||||
}
|
||||
|
||||
switch(type) {
|
||||
case FastFeatureDetector::TYPE_5_8:
|
||||
FAST_t<8>(_img, keypoints, threshold, nonmax_suppression);
|
||||
break;
|
||||
case FastFeatureDetector::TYPE_7_12:
|
||||
FAST_t<12>(_img, keypoints, threshold, nonmax_suppression);
|
||||
break;
|
||||
case FastFeatureDetector::TYPE_9_16:
|
||||
FAST_t<16>(_img, keypoints, threshold, nonmax_suppression);
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
void FAST(InputArray _img, std::vector<KeyPoint>& keypoints, int threshold, bool nonmax_suppression)
|
||||
{
|
||||
CV_INSTRUMENT_REGION();
|
||||
|
||||
FAST(_img, keypoints, threshold, nonmax_suppression, FastFeatureDetector::TYPE_9_16);
|
||||
}
|
||||
|
||||
|
||||
class FastFeatureDetector_Impl CV_FINAL : public FastFeatureDetector
|
||||
{
|
||||
public:
|
||||
FastFeatureDetector_Impl( int _threshold, bool _nonmaxSuppression, FastFeatureDetector::DetectorType _type )
|
||||
: threshold(_threshold), nonmaxSuppression(_nonmaxSuppression), type(_type)
|
||||
{}
|
||||
|
||||
void read( const FileNode& fn) CV_OVERRIDE
|
||||
{
|
||||
// if node is empty, keep previous value
|
||||
if (!fn["threshold"].empty())
|
||||
fn["threshold"] >> threshold;
|
||||
if (!fn["nonmaxSuppression"].empty())
|
||||
fn["nonmaxSuppression"] >> nonmaxSuppression;
|
||||
if (!fn["type"].empty())
|
||||
fn["type"] >> type;
|
||||
}
|
||||
void write( FileStorage& fs) const CV_OVERRIDE
|
||||
{
|
||||
if(fs.isOpened())
|
||||
{
|
||||
fs << "name" << getDefaultName();
|
||||
fs << "threshold" << threshold;
|
||||
fs << "nonmaxSuppression" << nonmaxSuppression;
|
||||
fs << "type" << type;
|
||||
}
|
||||
}
|
||||
|
||||
void detect( InputArray _image, std::vector<KeyPoint>& keypoints, InputArray _mask ) CV_OVERRIDE
|
||||
{
|
||||
CV_INSTRUMENT_REGION();
|
||||
|
||||
if(_image.empty())
|
||||
{
|
||||
keypoints.clear();
|
||||
return;
|
||||
}
|
||||
|
||||
Mat mask = _mask.getMat(), grayImage;
|
||||
UMat ugrayImage;
|
||||
_InputArray gray = _image;
|
||||
if( _image.type() != CV_8U )
|
||||
{
|
||||
_OutputArray ogray = _image.isUMat() ? _OutputArray(ugrayImage) : _OutputArray(grayImage);
|
||||
cvtColor( _image, ogray, COLOR_BGR2GRAY );
|
||||
gray = ogray;
|
||||
}
|
||||
FAST( gray, keypoints, threshold, nonmaxSuppression, type );
|
||||
KeyPointsFilter::runByPixelsMask( keypoints, mask );
|
||||
}
|
||||
|
||||
void set(int prop, double value)
|
||||
{
|
||||
if(prop == THRESHOLD)
|
||||
threshold = cvRound(value);
|
||||
else if(prop == NONMAX_SUPPRESSION)
|
||||
nonmaxSuppression = value != 0;
|
||||
else if(prop == FAST_N)
|
||||
type = static_cast<FastFeatureDetector::DetectorType>(cvRound(value));
|
||||
else
|
||||
CV_Error_(Error::StsBadArg, ("Unknown FastFeatureDetector property: %d", prop));
|
||||
}
|
||||
|
||||
double get(int prop) const
|
||||
{
|
||||
if(prop == THRESHOLD)
|
||||
return threshold;
|
||||
if(prop == NONMAX_SUPPRESSION)
|
||||
return nonmaxSuppression;
|
||||
if(prop == FAST_N)
|
||||
return static_cast<int>(type);
|
||||
CV_Error_(Error::StsBadArg, ("Unknown FastFeatureDetector property: %d", prop));
|
||||
return 0;
|
||||
}
|
||||
|
||||
void setThreshold(int threshold_) CV_OVERRIDE { threshold = threshold_; }
|
||||
int getThreshold() const CV_OVERRIDE { return threshold; }
|
||||
|
||||
void setNonmaxSuppression(bool f) CV_OVERRIDE { nonmaxSuppression = f; }
|
||||
bool getNonmaxSuppression() const CV_OVERRIDE { return nonmaxSuppression; }
|
||||
|
||||
void setType(FastFeatureDetector::DetectorType type_) CV_OVERRIDE{ type = type_; }
|
||||
FastFeatureDetector::DetectorType getType() const CV_OVERRIDE{ return type; }
|
||||
|
||||
int threshold;
|
||||
bool nonmaxSuppression;
|
||||
FastFeatureDetector::DetectorType type;
|
||||
};
|
||||
|
||||
Ptr<FastFeatureDetector> FastFeatureDetector::create( int threshold, bool nonmaxSuppression, FastFeatureDetector::DetectorType type )
|
||||
{
|
||||
return makePtr<FastFeatureDetector_Impl>(threshold, nonmaxSuppression, type);
|
||||
}
|
||||
|
||||
String FastFeatureDetector::getDefaultName() const
|
||||
{
|
||||
return (Feature2D::getDefaultName() + ".FastFeatureDetector");
|
||||
}
|
||||
|
||||
}
|
||||
@@ -0,0 +1,62 @@
|
||||
/* This is FAST corner detector, contributed to OpenCV by the author, Edward Rosten.
|
||||
Below is the original copyright and the references */
|
||||
|
||||
/*
|
||||
Copyright (c) 2006, 2008 Edward Rosten
|
||||
All rights reserved.
|
||||
|
||||
Redistribution and use in source and binary forms, with or without
|
||||
modification, are permitted provided that the following conditions
|
||||
are met:
|
||||
|
||||
*Redistributions of source code must retain the above copyright
|
||||
notice, this list of conditions and the following disclaimer.
|
||||
|
||||
*Redistributions in binary form must reproduce the above copyright
|
||||
notice, this list of conditions and the following disclaimer in the
|
||||
documentation and/or other materials provided with the distribution.
|
||||
|
||||
*Neither the name of the University of Cambridge nor the names of
|
||||
its contributors may be used to endorse or promote products derived
|
||||
from this software without specific prior written permission.
|
||||
|
||||
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
|
||||
"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
|
||||
LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
|
||||
A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
|
||||
CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
|
||||
EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
|
||||
PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
|
||||
PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
|
||||
LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
|
||||
NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
|
||||
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
|
||||
*/
|
||||
|
||||
/*
|
||||
The references are:
|
||||
* Machine learning for high-speed corner detection,
|
||||
E. Rosten and T. Drummond, ECCV 2006
|
||||
* Faster and better: A machine learning approach to corner detection
|
||||
E. Rosten, R. Porter and T. Drummond, PAMI, 2009
|
||||
*/
|
||||
|
||||
#ifndef OPENCV_FEATURES2D_FAST_HPP
|
||||
#define OPENCV_FEATURES2D_FAST_HPP
|
||||
|
||||
namespace cv
|
||||
{
|
||||
namespace opt_AVX2
|
||||
{
|
||||
#if CV_TRY_AVX2
|
||||
class FAST_t_patternSize16_AVX2
|
||||
{
|
||||
public:
|
||||
static Ptr<FAST_t_patternSize16_AVX2> getImpl(int _cols, int _threshold, bool _nonmax_suppression, const int* _pixel);
|
||||
virtual void process(int &j, const uchar* &ptr, uchar* curr, int* cornerpos, int &ncorners) = 0;
|
||||
virtual ~FAST_t_patternSize16_AVX2() {}
|
||||
};
|
||||
#endif
|
||||
}
|
||||
}
|
||||
#endif
|
||||
@@ -0,0 +1,366 @@
|
||||
/* This is FAST corner detector, contributed to OpenCV by the author, Edward Rosten.
|
||||
Below is the original copyright and the references */
|
||||
|
||||
/*
|
||||
Copyright (c) 2006, 2008 Edward Rosten
|
||||
All rights reserved.
|
||||
|
||||
Redistribution and use in source and binary forms, with or without
|
||||
modification, are permitted provided that the following conditions
|
||||
are met:
|
||||
|
||||
*Redistributions of source code must retain the above copyright
|
||||
notice, this list of conditions and the following disclaimer.
|
||||
|
||||
*Redistributions in binary form must reproduce the above copyright
|
||||
notice, this list of conditions and the following disclaimer in the
|
||||
documentation and/or other materials provided with the distribution.
|
||||
|
||||
*Neither the name of the University of Cambridge nor the names of
|
||||
its contributors may be used to endorse or promote products derived
|
||||
from this software without specific prior written permission.
|
||||
|
||||
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
|
||||
"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
|
||||
LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
|
||||
A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
|
||||
CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
|
||||
EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
|
||||
PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
|
||||
PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
|
||||
LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
|
||||
NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
|
||||
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
|
||||
*/
|
||||
|
||||
/*
|
||||
The references are:
|
||||
* Machine learning for high-speed corner detection,
|
||||
E. Rosten and T. Drummond, ECCV 2006
|
||||
* Faster and better: A machine learning approach to corner detection
|
||||
E. Rosten, R. Porter and T. Drummond, PAMI, 2009
|
||||
*/
|
||||
|
||||
#include "fast_score.hpp"
|
||||
#include "opencv2/core/hal/intrin.hpp"
|
||||
#define VERIFY_CORNERS 0
|
||||
|
||||
namespace cv {
|
||||
|
||||
void makeOffsets(int pixel[25], int rowStride, int patternSize)
|
||||
{
|
||||
static const int offsets16[][2] =
|
||||
{
|
||||
{0, 3}, { 1, 3}, { 2, 2}, { 3, 1}, { 3, 0}, { 3, -1}, { 2, -2}, { 1, -3},
|
||||
{0, -3}, {-1, -3}, {-2, -2}, {-3, -1}, {-3, 0}, {-3, 1}, {-2, 2}, {-1, 3}
|
||||
};
|
||||
|
||||
static const int offsets12[][2] =
|
||||
{
|
||||
{0, 2}, { 1, 2}, { 2, 1}, { 2, 0}, { 2, -1}, { 1, -2},
|
||||
{0, -2}, {-1, -2}, {-2, -1}, {-2, 0}, {-2, 1}, {-1, 2}
|
||||
};
|
||||
|
||||
static const int offsets8[][2] =
|
||||
{
|
||||
{0, 1}, { 1, 1}, { 1, 0}, { 1, -1},
|
||||
{0, -1}, {-1, -1}, {-1, 0}, {-1, 1}
|
||||
};
|
||||
|
||||
const int (*offsets)[2] = patternSize == 16 ? offsets16 :
|
||||
patternSize == 12 ? offsets12 :
|
||||
patternSize == 8 ? offsets8 : 0;
|
||||
|
||||
CV_Assert(pixel && offsets);
|
||||
|
||||
int k = 0;
|
||||
for( ; k < patternSize; k++ )
|
||||
pixel[k] = offsets[k][0] + offsets[k][1] * rowStride;
|
||||
for( ; k < 25; k++ )
|
||||
pixel[k] = pixel[k - patternSize];
|
||||
}
|
||||
|
||||
#if VERIFY_CORNERS
|
||||
static void testCorner(const uchar* ptr, const int pixel[], int K, int N, int threshold) {
|
||||
// check that with the computed "threshold" the pixel is still a corner
|
||||
// and that with the increased-by-1 "threshold" the pixel is not a corner anymore
|
||||
for( int delta = 0; delta <= 1; delta++ )
|
||||
{
|
||||
int v0 = std::min(ptr[0] + threshold + delta, 255);
|
||||
int v1 = std::max(ptr[0] - threshold - delta, 0);
|
||||
int c0 = 0, c1 = 0;
|
||||
|
||||
for( int k = 0; k < N; k++ )
|
||||
{
|
||||
int x = ptr[pixel[k]];
|
||||
if(x > v0)
|
||||
{
|
||||
if( ++c0 > K )
|
||||
break;
|
||||
c1 = 0;
|
||||
}
|
||||
else if( x < v1 )
|
||||
{
|
||||
if( ++c1 > K )
|
||||
break;
|
||||
c0 = 0;
|
||||
}
|
||||
else
|
||||
{
|
||||
c0 = c1 = 0;
|
||||
}
|
||||
}
|
||||
CV_Assert( (delta == 0 && std::max(c0, c1) > K) ||
|
||||
(delta == 1 && std::max(c0, c1) <= K) );
|
||||
}
|
||||
}
|
||||
#endif
|
||||
|
||||
template<>
|
||||
int cornerScore<16>(const uchar* ptr, const int pixel[], int threshold)
|
||||
{
|
||||
const int K = 8, N = K*3 + 1;
|
||||
int k, v = ptr[0];
|
||||
short d[N];
|
||||
for( k = 0; k < N; k++ )
|
||||
d[k] = (short)(v - ptr[pixel[k]]);
|
||||
|
||||
#if CV_SIMD128
|
||||
if (true)
|
||||
{
|
||||
v_int16x8 q0 = v_setall_s16(-1000), q1 = v_setall_s16(1000);
|
||||
for (k = 0; k < 16; k += 8)
|
||||
{
|
||||
v_int16x8 v0 = v_load(d + k + 1);
|
||||
v_int16x8 v1 = v_load(d + k + 2);
|
||||
v_int16x8 a = v_min(v0, v1);
|
||||
v_int16x8 b = v_max(v0, v1);
|
||||
v0 = v_load(d + k + 3);
|
||||
a = v_min(a, v0);
|
||||
b = v_max(b, v0);
|
||||
v0 = v_load(d + k + 4);
|
||||
a = v_min(a, v0);
|
||||
b = v_max(b, v0);
|
||||
v0 = v_load(d + k + 5);
|
||||
a = v_min(a, v0);
|
||||
b = v_max(b, v0);
|
||||
v0 = v_load(d + k + 6);
|
||||
a = v_min(a, v0);
|
||||
b = v_max(b, v0);
|
||||
v0 = v_load(d + k + 7);
|
||||
a = v_min(a, v0);
|
||||
b = v_max(b, v0);
|
||||
v0 = v_load(d + k + 8);
|
||||
a = v_min(a, v0);
|
||||
b = v_max(b, v0);
|
||||
v0 = v_load(d + k);
|
||||
q0 = v_max(q0, v_min(a, v0));
|
||||
q1 = v_min(q1, v_max(b, v0));
|
||||
v0 = v_load(d + k + 9);
|
||||
q0 = v_max(q0, v_min(a, v0));
|
||||
q1 = v_min(q1, v_max(b, v0));
|
||||
}
|
||||
q0 = v_max(q0, v_sub(v_setzero_s16(), q1));
|
||||
threshold = v_reduce_max(q0) - 1;
|
||||
}
|
||||
else
|
||||
#endif
|
||||
{
|
||||
|
||||
int a0 = threshold;
|
||||
for( k = 0; k < 16; k += 2 )
|
||||
{
|
||||
int a = std::min((int)d[k+1], (int)d[k+2]);
|
||||
a = std::min(a, (int)d[k+3]);
|
||||
if( a <= a0 )
|
||||
continue;
|
||||
a = std::min(a, (int)d[k+4]);
|
||||
a = std::min(a, (int)d[k+5]);
|
||||
a = std::min(a, (int)d[k+6]);
|
||||
a = std::min(a, (int)d[k+7]);
|
||||
a = std::min(a, (int)d[k+8]);
|
||||
a0 = std::max(a0, std::min(a, (int)d[k]));
|
||||
a0 = std::max(a0, std::min(a, (int)d[k+9]));
|
||||
}
|
||||
|
||||
int b0 = -a0;
|
||||
for( k = 0; k < 16; k += 2 )
|
||||
{
|
||||
int b = std::max((int)d[k+1], (int)d[k+2]);
|
||||
b = std::max(b, (int)d[k+3]);
|
||||
b = std::max(b, (int)d[k+4]);
|
||||
b = std::max(b, (int)d[k+5]);
|
||||
if( b >= b0 )
|
||||
continue;
|
||||
b = std::max(b, (int)d[k+6]);
|
||||
b = std::max(b, (int)d[k+7]);
|
||||
b = std::max(b, (int)d[k+8]);
|
||||
|
||||
b0 = std::min(b0, std::max(b, (int)d[k]));
|
||||
b0 = std::min(b0, std::max(b, (int)d[k+9]));
|
||||
}
|
||||
|
||||
threshold = -b0 - 1;
|
||||
}
|
||||
|
||||
#if VERIFY_CORNERS
|
||||
testCorner(ptr, pixel, K, N, threshold);
|
||||
#endif
|
||||
return threshold;
|
||||
}
|
||||
|
||||
template<>
|
||||
int cornerScore<12>(const uchar* ptr, const int pixel[], int threshold)
|
||||
{
|
||||
const int K = 6, N = K*3 + 1;
|
||||
int k, v = ptr[0];
|
||||
short d[N + 4];
|
||||
for( k = 0; k < N; k++ )
|
||||
d[k] = (short)(v - ptr[pixel[k]]);
|
||||
#if CV_SIMD128
|
||||
for( k = 0; k < 4; k++ )
|
||||
d[N+k] = d[k];
|
||||
#endif
|
||||
|
||||
#if CV_SIMD128
|
||||
if (true)
|
||||
{
|
||||
v_int16x8 q0 = v_setall_s16(-1000), q1 = v_setall_s16(1000);
|
||||
for (k = 0; k < 16; k += 8)
|
||||
{
|
||||
v_int16x8 v0 = v_load(d + k + 1);
|
||||
v_int16x8 v1 = v_load(d + k + 2);
|
||||
v_int16x8 a = v_min(v0, v1);
|
||||
v_int16x8 b = v_max(v0, v1);
|
||||
v0 = v_load(d + k + 3);
|
||||
a = v_min(a, v0);
|
||||
b = v_max(b, v0);
|
||||
v0 = v_load(d + k + 4);
|
||||
a = v_min(a, v0);
|
||||
b = v_max(b, v0);
|
||||
v0 = v_load(d + k + 5);
|
||||
a = v_min(a, v0);
|
||||
b = v_max(b, v0);
|
||||
v0 = v_load(d + k + 6);
|
||||
a = v_min(a, v0);
|
||||
b = v_max(b, v0);
|
||||
v0 = v_load(d + k);
|
||||
q0 = v_max(q0, v_min(a, v0));
|
||||
q1 = v_min(q1, v_max(b, v0));
|
||||
v0 = v_load(d + k + 7);
|
||||
q0 = v_max(q0, v_min(a, v0));
|
||||
q1 = v_min(q1, v_max(b, v0));
|
||||
}
|
||||
q0 = v_max(q0, v_sub(v_setzero_s16(), q1));
|
||||
threshold = v_reduce_max(q0) - 1;
|
||||
}
|
||||
else
|
||||
#endif
|
||||
{
|
||||
int a0 = threshold;
|
||||
for( k = 0; k < 12; k += 2 )
|
||||
{
|
||||
int a = std::min((int)d[k+1], (int)d[k+2]);
|
||||
if( a <= a0 )
|
||||
continue;
|
||||
a = std::min(a, (int)d[k+3]);
|
||||
a = std::min(a, (int)d[k+4]);
|
||||
a = std::min(a, (int)d[k+5]);
|
||||
a = std::min(a, (int)d[k+6]);
|
||||
a0 = std::max(a0, std::min(a, (int)d[k]));
|
||||
a0 = std::max(a0, std::min(a, (int)d[k+7]));
|
||||
}
|
||||
|
||||
int b0 = -a0;
|
||||
for( k = 0; k < 12; k += 2 )
|
||||
{
|
||||
int b = std::max((int)d[k+1], (int)d[k+2]);
|
||||
b = std::max(b, (int)d[k+3]);
|
||||
b = std::max(b, (int)d[k+4]);
|
||||
if( b >= b0 )
|
||||
continue;
|
||||
b = std::max(b, (int)d[k+5]);
|
||||
b = std::max(b, (int)d[k+6]);
|
||||
|
||||
b0 = std::min(b0, std::max(b, (int)d[k]));
|
||||
b0 = std::min(b0, std::max(b, (int)d[k+7]));
|
||||
}
|
||||
|
||||
threshold = -b0-1;
|
||||
}
|
||||
#if VERIFY_CORNERS
|
||||
testCorner(ptr, pixel, K, N, threshold);
|
||||
#endif
|
||||
return threshold;
|
||||
}
|
||||
|
||||
template<>
|
||||
int cornerScore<8>(const uchar* ptr, const int pixel[], int threshold)
|
||||
{
|
||||
const int K = 4, N = K * 3 + 1;
|
||||
int k, v = ptr[0];
|
||||
short d[N];
|
||||
for (k = 0; k < N; k++)
|
||||
d[k] = (short)(v - ptr[pixel[k]]);
|
||||
|
||||
#if CV_SIMD128 \
|
||||
&& (!defined(CV_SIMD128_CPP) || (!defined(__GNUC__) || __GNUC__ != 5)) // "movdqa" bug on "v_load(d + 1)" line (Ubuntu 16.04 + GCC 5.4)
|
||||
if (true)
|
||||
{
|
||||
v_int16x8 v0 = v_load(d + 1);
|
||||
v_int16x8 v1 = v_load(d + 2);
|
||||
v_int16x8 a = v_min(v0, v1);
|
||||
v_int16x8 b = v_max(v0, v1);
|
||||
v0 = v_load(d + 3);
|
||||
a = v_min(a, v0);
|
||||
b = v_max(b, v0);
|
||||
v0 = v_load(d + 4);
|
||||
a = v_min(a, v0);
|
||||
b = v_max(b, v0);
|
||||
v0 = v_load(d);
|
||||
v_int16x8 q0 = v_min(a, v0);
|
||||
v_int16x8 q1 = v_max(b, v0);
|
||||
v0 = v_load(d + 5);
|
||||
q0 = v_max(q0, v_min(a, v0));
|
||||
q1 = v_min(q1, v_max(b, v0));
|
||||
q0 = v_max(q0, v_sub(v_setzero_s16(), q1));
|
||||
threshold = v_reduce_max(q0) - 1;
|
||||
}
|
||||
else
|
||||
#endif
|
||||
{
|
||||
int a0 = threshold;
|
||||
for( k = 0; k < 8; k += 2 )
|
||||
{
|
||||
int a = std::min((int)d[k+1], (int)d[k+2]);
|
||||
if( a <= a0 )
|
||||
continue;
|
||||
a = std::min(a, (int)d[k+3]);
|
||||
a = std::min(a, (int)d[k+4]);
|
||||
a0 = std::max(a0, std::min(a, (int)d[k]));
|
||||
a0 = std::max(a0, std::min(a, (int)d[k+5]));
|
||||
}
|
||||
|
||||
int b0 = -a0;
|
||||
for( k = 0; k < 8; k += 2 )
|
||||
{
|
||||
int b = std::max((int)d[k+1], (int)d[k+2]);
|
||||
b = std::max(b, (int)d[k+3]);
|
||||
if( b >= b0 )
|
||||
continue;
|
||||
b = std::max(b, (int)d[k+4]);
|
||||
|
||||
b0 = std::min(b0, std::max(b, (int)d[k]));
|
||||
b0 = std::min(b0, std::max(b, (int)d[k+5]));
|
||||
}
|
||||
|
||||
threshold = -b0-1;
|
||||
}
|
||||
|
||||
#if VERIFY_CORNERS
|
||||
testCorner(ptr, pixel, K, N, threshold);
|
||||
#endif
|
||||
return threshold;
|
||||
}
|
||||
|
||||
} // namespace cv
|
||||
@@ -0,0 +1,62 @@
|
||||
/* This is FAST corner detector, contributed to OpenCV by the author, Edward Rosten.
|
||||
Below is the original copyright and the references */
|
||||
|
||||
/*
|
||||
Copyright (c) 2006, 2008 Edward Rosten
|
||||
All rights reserved.
|
||||
|
||||
Redistribution and use in source and binary forms, with or without
|
||||
modification, are permitted provided that the following conditions
|
||||
are met:
|
||||
|
||||
*Redistributions of source code must retain the above copyright
|
||||
notice, this list of conditions and the following disclaimer.
|
||||
|
||||
*Redistributions in binary form must reproduce the above copyright
|
||||
notice, this list of conditions and the following disclaimer in the
|
||||
documentation and/or other materials provided with the distribution.
|
||||
|
||||
*Neither the name of the University of Cambridge nor the names of
|
||||
its contributors may be used to endorse or promote products derived
|
||||
from this software without specific prior written permission.
|
||||
|
||||
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
|
||||
"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
|
||||
LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
|
||||
A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
|
||||
CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
|
||||
EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
|
||||
PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
|
||||
PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
|
||||
LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
|
||||
NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
|
||||
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
|
||||
*/
|
||||
|
||||
/*
|
||||
The references are:
|
||||
* Machine learning for high-speed corner detection,
|
||||
E. Rosten and T. Drummond, ECCV 2006
|
||||
* Faster and better: A machine learning approach to corner detection
|
||||
E. Rosten, R. Porter and T. Drummond, PAMI, 2009
|
||||
*/
|
||||
|
||||
#ifndef __OPENCV_FEATURES_2D_FAST_HPP__
|
||||
#define __OPENCV_FEATURES_2D_FAST_HPP__
|
||||
|
||||
#ifdef __cplusplus
|
||||
|
||||
#include "precomp.hpp"
|
||||
|
||||
namespace cv
|
||||
{
|
||||
|
||||
void makeOffsets(int pixel[25], int row_stride, int patternSize);
|
||||
|
||||
template<int patternSize>
|
||||
int cornerScore(const uchar* ptr, const int pixel[], int threshold);
|
||||
|
||||
}
|
||||
|
||||
#endif
|
||||
#endif
|
||||
@@ -0,0 +1,224 @@
|
||||
/*M///////////////////////////////////////////////////////////////////////////////////////
|
||||
//
|
||||
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
|
||||
//
|
||||
// By downloading, copying, installing or using the software you agree to this license.
|
||||
// If you do not agree to this license, do not download, install,
|
||||
// copy or use the software.
|
||||
//
|
||||
//
|
||||
// License Agreement
|
||||
// For Open Source Computer Vision Library
|
||||
//
|
||||
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
|
||||
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
|
||||
// Third party copyrights are property of their respective owners.
|
||||
//
|
||||
// Redistribution and use in source and binary forms, with or without modification,
|
||||
// are permitted provided that the following conditions are met:
|
||||
//
|
||||
// * Redistribution's of source code must retain the above copyright notice,
|
||||
// this list of conditions and the following disclaimer.
|
||||
//
|
||||
// * Redistribution's in binary form must reproduce the above copyright notice,
|
||||
// this list of conditions and the following disclaimer in the documentation
|
||||
// and/or other materials provided with the distribution.
|
||||
//
|
||||
// * The name of the copyright holders may not be used to endorse or promote products
|
||||
// derived from this software without specific prior written permission.
|
||||
//
|
||||
// This software is provided by the copyright holders and contributors "as is" and
|
||||
// any express or implied warranties, including, but not limited to, the implied
|
||||
// warranties of merchantability and fitness for a particular purpose are disclaimed.
|
||||
// In no event shall the Intel Corporation or contributors be liable for any direct,
|
||||
// indirect, incidental, special, exemplary, or consequential damages
|
||||
// (including, but not limited to, procurement of substitute goods or services;
|
||||
// loss of use, data, or profits; or business interruption) however caused
|
||||
// and on any theory of liability, whether in contract, strict liability,
|
||||
// or tort (including negligence or otherwise) arising in any way out of
|
||||
// the use of this software, even if advised of the possibility of such damage.
|
||||
//
|
||||
//M*/
|
||||
|
||||
#include "precomp.hpp"
|
||||
|
||||
namespace cv
|
||||
{
|
||||
|
||||
using std::vector;
|
||||
|
||||
Feature2D::~Feature2D() {}
|
||||
|
||||
/*
|
||||
* Detect keypoints in an image.
|
||||
* image The image.
|
||||
* keypoints The detected keypoints.
|
||||
* mask Mask specifying where to look for keypoints (optional). Must be a char
|
||||
* matrix with non-zero values in the region of interest.
|
||||
*/
|
||||
void Feature2D::detect( InputArray image,
|
||||
std::vector<KeyPoint>& keypoints,
|
||||
InputArray mask )
|
||||
{
|
||||
CV_INSTRUMENT_REGION();
|
||||
|
||||
if( image.empty() )
|
||||
{
|
||||
keypoints.clear();
|
||||
return;
|
||||
}
|
||||
detectAndCompute(image, mask, keypoints, noArray(), false);
|
||||
}
|
||||
|
||||
|
||||
void Feature2D::detect( InputArrayOfArrays images,
|
||||
std::vector<std::vector<KeyPoint> >& keypoints,
|
||||
InputArrayOfArrays masks )
|
||||
{
|
||||
CV_INSTRUMENT_REGION();
|
||||
|
||||
int nimages = (int)images.total();
|
||||
|
||||
if (!masks.empty())
|
||||
{
|
||||
CV_Assert(masks.total() == (size_t)nimages);
|
||||
}
|
||||
|
||||
keypoints.resize(nimages);
|
||||
|
||||
if (images.isMatVector())
|
||||
{
|
||||
for (int i = 0; i < nimages; i++)
|
||||
{
|
||||
detect(images.getMat(i), keypoints[i], masks.empty() ? noArray() : masks.getMat(i));
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
// assume UMats
|
||||
for (int i = 0; i < nimages; i++)
|
||||
{
|
||||
detect(images.getUMat(i), keypoints[i], masks.empty() ? noArray() : masks.getUMat(i));
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
}
|
||||
|
||||
/*
|
||||
* Compute the descriptors for a set of keypoints in an image.
|
||||
* image The image.
|
||||
* keypoints The input keypoints. Keypoints for which a descriptor cannot be computed are removed.
|
||||
* descriptors Copmputed descriptors. Row i is the descriptor for keypoint i.
|
||||
*/
|
||||
void Feature2D::compute( InputArray image,
|
||||
std::vector<KeyPoint>& keypoints,
|
||||
OutputArray descriptors )
|
||||
{
|
||||
CV_INSTRUMENT_REGION();
|
||||
|
||||
if( image.empty() )
|
||||
{
|
||||
descriptors.release();
|
||||
return;
|
||||
}
|
||||
detectAndCompute(image, noArray(), keypoints, descriptors, true);
|
||||
}
|
||||
|
||||
void Feature2D::compute( InputArrayOfArrays images,
|
||||
std::vector<std::vector<KeyPoint> >& keypoints,
|
||||
OutputArrayOfArrays descriptors )
|
||||
{
|
||||
CV_INSTRUMENT_REGION();
|
||||
|
||||
if( !descriptors.needed() )
|
||||
return;
|
||||
|
||||
int nimages = (int)images.total();
|
||||
|
||||
CV_Assert( keypoints.size() == (size_t)nimages );
|
||||
// resize descriptors to appropriate size and compute
|
||||
if (descriptors.isMatVector())
|
||||
{
|
||||
vector<Mat>& vec = *(vector<Mat>*)descriptors.getObj();
|
||||
vec.resize(nimages);
|
||||
for (int i = 0; i < nimages; i++)
|
||||
{
|
||||
compute(images.getMat(i), keypoints[i], vec[i]);
|
||||
}
|
||||
}
|
||||
else if (descriptors.isUMatVector())
|
||||
{
|
||||
vector<UMat>& vec = *(vector<UMat>*)descriptors.getObj();
|
||||
vec.resize(nimages);
|
||||
for (int i = 0; i < nimages; i++)
|
||||
{
|
||||
compute(images.getUMat(i), keypoints[i], vec[i]);
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
CV_Error(Error::StsBadArg, "descriptors must be vector<Mat> or vector<UMat>");
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
/* Detects keypoints and computes the descriptors */
|
||||
void Feature2D::detectAndCompute( InputArray, InputArray,
|
||||
std::vector<KeyPoint>&,
|
||||
OutputArray,
|
||||
bool )
|
||||
{
|
||||
CV_INSTRUMENT_REGION();
|
||||
|
||||
CV_Error(Error::StsNotImplemented, "");
|
||||
}
|
||||
|
||||
void Feature2D::write( const String& fileName ) const
|
||||
{
|
||||
FileStorage fs(fileName, FileStorage::WRITE);
|
||||
write(fs);
|
||||
}
|
||||
|
||||
void Feature2D::read( const String& fileName )
|
||||
{
|
||||
FileStorage fs(fileName, FileStorage::READ);
|
||||
read(fs.root());
|
||||
}
|
||||
|
||||
void Feature2D::write( FileStorage&) const
|
||||
{
|
||||
}
|
||||
|
||||
void Feature2D::read( const FileNode&)
|
||||
{
|
||||
}
|
||||
|
||||
int Feature2D::descriptorSize() const
|
||||
{
|
||||
return 0;
|
||||
}
|
||||
|
||||
int Feature2D::descriptorType() const
|
||||
{
|
||||
return CV_32F;
|
||||
}
|
||||
|
||||
int Feature2D::defaultNorm() const
|
||||
{
|
||||
int tp = descriptorType();
|
||||
return tp == CV_8U ? NORM_HAMMING : NORM_L2;
|
||||
}
|
||||
|
||||
// Return true if detector object is empty
|
||||
bool Feature2D::empty() const
|
||||
{
|
||||
return true;
|
||||
}
|
||||
|
||||
String Feature2D::getDefaultName() const
|
||||
{
|
||||
return "Feature2D";
|
||||
}
|
||||
|
||||
}
|
||||
@@ -0,0 +1,185 @@
|
||||
/*M///////////////////////////////////////////////////////////////////////////////////////
|
||||
//
|
||||
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
|
||||
//
|
||||
// By downloading, copying, installing or using the software you agree to this license.
|
||||
// If you do not agree to this license, do not download, install,
|
||||
// copy or use the software.
|
||||
//
|
||||
//
|
||||
// Intel License Agreement
|
||||
// For Open Source Computer Vision Library
|
||||
//
|
||||
// Copyright (C) 2000, Intel Corporation, all rights reserved.
|
||||
// Third party copyrights are property of their respective owners.
|
||||
//
|
||||
// Redistribution and use in source and binary forms, with or without modification,
|
||||
// are permitted provided that the following conditions are met:
|
||||
//
|
||||
// * Redistribution's of source code must retain the above copyright notice,
|
||||
// this list of conditions and the following disclaimer.
|
||||
//
|
||||
// * Redistribution's in binary form must reproduce the above copyright notice,
|
||||
// this list of conditions and the following disclaimer in the documentation
|
||||
// and/or other materials provided with the distribution.
|
||||
//
|
||||
// * The name of Intel Corporation may not be used to endorse or promote products
|
||||
// derived from this software without specific prior written permission.
|
||||
//
|
||||
// This software is provided by the copyright holders and contributors "as is" and
|
||||
// any express or implied warranties, including, but not limited to, the implied
|
||||
// warranties of merchantability and fitness for a particular purpose are disclaimed.
|
||||
// In no event shall the Intel Corporation or contributors be liable for any direct,
|
||||
// indirect, incidental, special, exemplary, or consequential damages
|
||||
// (including, but not limited to, procurement of substitute goods or services;
|
||||
// loss of use, data, or profits; or business interruption) however caused
|
||||
// and on any theory of liability, whether in contract, strict liability,
|
||||
// or tort (including negligence or otherwise) arising in any way out of
|
||||
// the use of this software, even if advised of the possibility of such damage.
|
||||
//
|
||||
//M*/
|
||||
|
||||
#include "precomp.hpp"
|
||||
|
||||
namespace cv
|
||||
{
|
||||
|
||||
class GFTTDetector_Impl CV_FINAL : public GFTTDetector
|
||||
{
|
||||
public:
|
||||
GFTTDetector_Impl( int _nfeatures, double _qualityLevel,
|
||||
double _minDistance, int _blockSize, int _gradientSize,
|
||||
bool _useHarrisDetector, double _k )
|
||||
: nfeatures(_nfeatures), qualityLevel(_qualityLevel), minDistance(_minDistance),
|
||||
blockSize(_blockSize), gradSize(_gradientSize), useHarrisDetector(_useHarrisDetector), k(_k)
|
||||
{
|
||||
}
|
||||
|
||||
void read( const FileNode& fn) CV_OVERRIDE
|
||||
{
|
||||
// if node is empty, keep previous value
|
||||
if (!fn["nfeatures"].empty())
|
||||
fn["nfeatures"] >> nfeatures;
|
||||
if (!fn["qualityLevel"].empty())
|
||||
fn["qualityLevel"] >> qualityLevel;
|
||||
if (!fn["minDistance"].empty())
|
||||
fn["minDistance"] >> minDistance;
|
||||
if (!fn["blockSize"].empty())
|
||||
fn["blockSize"] >> blockSize;
|
||||
if (!fn["gradSize"].empty())
|
||||
fn["gradSize"] >> gradSize;
|
||||
if (!fn["useHarrisDetector"].empty())
|
||||
fn["useHarrisDetector"] >> useHarrisDetector;
|
||||
if (!fn["k"].empty())
|
||||
fn["k"] >> k;
|
||||
}
|
||||
void write( FileStorage& fs) const CV_OVERRIDE
|
||||
{
|
||||
if(fs.isOpened())
|
||||
{
|
||||
fs << "name" << getDefaultName();
|
||||
fs << "nfeatures" << nfeatures;
|
||||
fs << "qualityLevel" << qualityLevel;
|
||||
fs << "minDistance" << minDistance;
|
||||
fs << "blockSize" << blockSize;
|
||||
fs << "gradSize" << gradSize;
|
||||
fs << "useHarrisDetector" << useHarrisDetector;
|
||||
fs << "k" << k;
|
||||
}
|
||||
}
|
||||
|
||||
void setMaxFeatures(int maxFeatures) CV_OVERRIDE { nfeatures = maxFeatures; }
|
||||
int getMaxFeatures() const CV_OVERRIDE { return nfeatures; }
|
||||
|
||||
void setQualityLevel(double qlevel) CV_OVERRIDE { qualityLevel = qlevel; }
|
||||
double getQualityLevel() const CV_OVERRIDE { return qualityLevel; }
|
||||
|
||||
void setMinDistance(double minDistance_) CV_OVERRIDE { minDistance = minDistance_; }
|
||||
double getMinDistance() const CV_OVERRIDE { return minDistance; }
|
||||
|
||||
void setBlockSize(int blockSize_) CV_OVERRIDE { blockSize = blockSize_; }
|
||||
int getBlockSize() const CV_OVERRIDE { return blockSize; }
|
||||
|
||||
void setGradientSize(int gradientSize_) CV_OVERRIDE { gradSize = gradientSize_; }
|
||||
int getGradientSize() CV_OVERRIDE { return gradSize; }
|
||||
|
||||
void setHarrisDetector(bool val) CV_OVERRIDE { useHarrisDetector = val; }
|
||||
bool getHarrisDetector() const CV_OVERRIDE { return useHarrisDetector; }
|
||||
|
||||
void setK(double k_) CV_OVERRIDE { k = k_; }
|
||||
double getK() const CV_OVERRIDE { return k; }
|
||||
|
||||
void detect( InputArray _image, std::vector<KeyPoint>& keypoints, InputArray _mask ) CV_OVERRIDE
|
||||
{
|
||||
CV_INSTRUMENT_REGION();
|
||||
|
||||
if(_image.empty())
|
||||
{
|
||||
keypoints.clear();
|
||||
return;
|
||||
}
|
||||
|
||||
std::vector<Point2f> corners;
|
||||
std::vector<float> cornersQuality;
|
||||
|
||||
if (_image.isUMat())
|
||||
{
|
||||
UMat ugrayImage;
|
||||
if( _image.type() != CV_8U )
|
||||
cvtColor( _image, ugrayImage, COLOR_BGR2GRAY );
|
||||
else
|
||||
ugrayImage = _image.getUMat();
|
||||
|
||||
goodFeaturesToTrack( ugrayImage, corners, nfeatures, qualityLevel, minDistance, _mask,
|
||||
cornersQuality, blockSize, gradSize, useHarrisDetector, k );
|
||||
}
|
||||
else
|
||||
{
|
||||
Mat image = _image.getMat(), grayImage = image;
|
||||
if( image.type() != CV_8U )
|
||||
cvtColor( image, grayImage, COLOR_BGR2GRAY );
|
||||
|
||||
goodFeaturesToTrack( grayImage, corners, nfeatures, qualityLevel, minDistance, _mask,
|
||||
cornersQuality, blockSize, gradSize, useHarrisDetector, k );
|
||||
}
|
||||
|
||||
CV_Assert(corners.size() == cornersQuality.size());
|
||||
|
||||
keypoints.resize(corners.size());
|
||||
for (size_t i = 0; i < corners.size(); i++)
|
||||
keypoints[i] = KeyPoint(corners[i], (float)blockSize, -1, cornersQuality[i]);
|
||||
|
||||
}
|
||||
|
||||
int nfeatures;
|
||||
double qualityLevel;
|
||||
double minDistance;
|
||||
int blockSize;
|
||||
int gradSize;
|
||||
bool useHarrisDetector;
|
||||
double k;
|
||||
};
|
||||
|
||||
|
||||
Ptr<GFTTDetector> GFTTDetector::create( int _nfeatures, double _qualityLevel,
|
||||
double _minDistance, int _blockSize, int _gradientSize,
|
||||
bool _useHarrisDetector, double _k )
|
||||
{
|
||||
return makePtr<GFTTDetector_Impl>(_nfeatures, _qualityLevel,
|
||||
_minDistance, _blockSize, _gradientSize, _useHarrisDetector, _k);
|
||||
}
|
||||
|
||||
Ptr<GFTTDetector> GFTTDetector::create( int _nfeatures, double _qualityLevel,
|
||||
double _minDistance, int _blockSize,
|
||||
bool _useHarrisDetector, double _k )
|
||||
{
|
||||
return makePtr<GFTTDetector_Impl>(_nfeatures, _qualityLevel,
|
||||
_minDistance, _blockSize, 3, _useHarrisDetector, _k);
|
||||
}
|
||||
|
||||
String GFTTDetector::getDefaultName() const
|
||||
{
|
||||
return (Feature2D::getDefaultName() + ".GFTTDetector");
|
||||
}
|
||||
|
||||
}
|
||||
@@ -0,0 +1,161 @@
|
||||
/*M///////////////////////////////////////////////////////////////////////////////////////
|
||||
//
|
||||
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
|
||||
//
|
||||
// By downloading, copying, installing or using the software you agree to this license.
|
||||
// If you do not agree to this license, do not download, install,
|
||||
// copy or use the software.
|
||||
//
|
||||
//
|
||||
// License Agreement
|
||||
// For Open Source Computer Vision Library
|
||||
//
|
||||
// Copyright (C) 2017, Intel Corporation, all rights reserved.
|
||||
// Third party copyrights are property of their respective owners.
|
||||
//
|
||||
// Redistribution and use in source and binary forms, with or without modification,
|
||||
// are permitted provided that the following conditions are met:
|
||||
//
|
||||
// * Redistribution's of source code must retain the above copyright notice,
|
||||
// this list of conditions and the following disclaimer.
|
||||
//
|
||||
// * Redistribution's in binary form must reproduce the above copyright notice,
|
||||
// this list of conditions and the following disclaimer in the documentation
|
||||
// and/or other materials provided with the distribution.
|
||||
//
|
||||
// * The name of the copyright holders may not be used to endorse or promote products
|
||||
// derived from this software without specific prior written permission.
|
||||
//
|
||||
// This software is provided by the copyright holders and contributors "as is" and
|
||||
// any express or implied warranties, including, but not limited to, the implied
|
||||
// warranties of merchantability and fitness for a particular purpose are disclaimed.
|
||||
// In no event shall the Intel Corporation or contributors be liable for any direct,
|
||||
// indirect, incidental, special, exemplary, or consequential damages
|
||||
// (including, but not limited to, procurement of substitute goods or services;
|
||||
// loss of use, data, or profits; or business interruption) however caused
|
||||
// and on any theory of liability, whether in contract, strict liability,
|
||||
// or tort (including negligence or otherwise) arising in any way out of
|
||||
// the use of this software, even if advised of the possibility of such damage.
|
||||
//
|
||||
//M*/
|
||||
|
||||
#ifndef OPENCV_FEATURES2D_HAL_REPLACEMENT_HPP
|
||||
#define OPENCV_FEATURES2D_HAL_REPLACEMENT_HPP
|
||||
|
||||
#include "opencv2/core/hal/interface.h"
|
||||
|
||||
#if defined(__clang__) // clang or MSVC clang
|
||||
#pragma clang diagnostic push
|
||||
#pragma clang diagnostic ignored "-Wunused-parameter"
|
||||
#elif defined(_MSC_VER)
|
||||
#pragma warning(push)
|
||||
#pragma warning(disable : 4100)
|
||||
#elif defined(__GNUC__)
|
||||
#pragma GCC diagnostic push
|
||||
#pragma GCC diagnostic ignored "-Wunused-parameter"
|
||||
#endif
|
||||
|
||||
//! @addtogroup features2d_hal_interface
|
||||
//! @note Define your functions to override default implementations:
|
||||
//! @code
|
||||
//! #undef hal_add8u
|
||||
//! #define hal_add8u my_add8u
|
||||
//! @endcode
|
||||
//! @{
|
||||
/**
|
||||
@brief Detects corners using the FAST algorithm, returns mask.
|
||||
@param src_data Source image data
|
||||
@param src_step Source image step
|
||||
@param dst_data Destination mask data
|
||||
@param dst_step Destination mask step
|
||||
@param width Source image width
|
||||
@param height Source image height
|
||||
@param type FAST type
|
||||
*/
|
||||
inline int hal_ni_FAST_dense(const uchar* src_data, size_t src_step, uchar* dst_data, size_t dst_step, int width, int height, cv::FastFeatureDetector::DetectorType type) { return CV_HAL_ERROR_NOT_IMPLEMENTED; }
|
||||
|
||||
//! @cond IGNORED
|
||||
#define cv_hal_FAST_dense hal_ni_FAST_dense
|
||||
//! @endcond
|
||||
|
||||
/**
|
||||
@brief Non-maximum suppression for FAST_9_16.
|
||||
@param src_data,src_step Source mask
|
||||
@param dst_data,dst_step Destination mask after NMS
|
||||
@param width,height Source mask dimensions
|
||||
*/
|
||||
inline int hal_ni_FAST_NMS(const uchar* src_data, size_t src_step, uchar* dst_data, size_t dst_step, int width, int height) { return CV_HAL_ERROR_NOT_IMPLEMENTED; }
|
||||
|
||||
//! @cond IGNORED
|
||||
#define cv_hal_FAST_NMS hal_ni_FAST_NMS
|
||||
//! @endcond
|
||||
|
||||
/**
|
||||
@brief Detects corners using the FAST algorithm.
|
||||
@param src_data Source image data
|
||||
@param src_step Source image step
|
||||
@param width Source image width
|
||||
@param height Source image height
|
||||
@param keypoints_data Pointer to keypoints
|
||||
@param keypoints_count Count of keypoints
|
||||
@param threshold Threshold for keypoint
|
||||
@param nonmax_suppression Indicates if make nonmaxima suppression or not.
|
||||
@param type FAST type
|
||||
*/
|
||||
inline int hal_ni_FAST(const uchar* src_data, size_t src_step, int width, int height, uchar* keypoints_data, size_t* keypoints_count, int threshold, bool nonmax_suppression, int /*cv::FastFeatureDetector::DetectorType*/ type) { return CV_HAL_ERROR_NOT_IMPLEMENTED; }
|
||||
|
||||
/**
|
||||
@brief Detects corners using the FAST algorithm.
|
||||
@param src_data Source image data
|
||||
@param src_step Source image step
|
||||
@param width Source image width
|
||||
@param height Source image height
|
||||
@param keypoints_data Pointer to keypoints
|
||||
@param keypoints_count Count of keypoints
|
||||
@param threshold Threshold for keypoint
|
||||
@param nonmax_suppression Indicates if make nonmaxima suppression or not.
|
||||
@param type FAST type
|
||||
@param realloc_func function for reallocation
|
||||
*/
|
||||
inline int hal_ni_FASTv2(const uchar* src_data, size_t src_step, int width, int height, void** keypoints_data, size_t* keypoints_count, int threshold, bool nonmax_suppression, int /*cv::FastFeatureDetector::DetectorType*/ type, void* (*realloc_func)(void*, size_t)) { return CV_HAL_ERROR_NOT_IMPLEMENTED; }
|
||||
|
||||
//! @cond IGNORED
|
||||
#define cv_hal_FAST hal_ni_FAST
|
||||
#define cv_hal_FASTv2 hal_ni_FASTv2
|
||||
//! @endcond
|
||||
|
||||
//! @}
|
||||
|
||||
|
||||
#if defined(__clang__)
|
||||
#pragma clang diagnostic pop
|
||||
#elif defined(_MSC_VER)
|
||||
#pragma warning(pop)
|
||||
#elif defined(__GNUC__)
|
||||
#pragma GCC diagnostic pop
|
||||
#endif
|
||||
|
||||
#include "custom_hal.hpp"
|
||||
|
||||
//! @cond IGNORED
|
||||
#define CALL_HAL_RET(name, fun, retval, ...) \
|
||||
int res = __CV_EXPAND(fun(__VA_ARGS__, &retval)); \
|
||||
if (res == CV_HAL_ERROR_OK) \
|
||||
return retval; \
|
||||
else if (res != CV_HAL_ERROR_NOT_IMPLEMENTED) \
|
||||
CV_Error_(cv::Error::StsInternal, \
|
||||
("HAL implementation " CVAUX_STR(name) " ==> " CVAUX_STR(fun) " returned %d (0x%08x)", res, res));
|
||||
|
||||
|
||||
#define CALL_HAL(name, fun, ...) \
|
||||
{ \
|
||||
int res = __CV_EXPAND(fun(__VA_ARGS__)); \
|
||||
if (res == CV_HAL_ERROR_OK) \
|
||||
return; \
|
||||
else if (res != CV_HAL_ERROR_NOT_IMPLEMENTED) \
|
||||
CV_Error_(cv::Error::StsInternal, \
|
||||
("HAL implementation " CVAUX_STR(name) " ==> " CVAUX_STR(fun) " returned %d (0x%08x)", res, res)); \
|
||||
}
|
||||
//! @endcond
|
||||
|
||||
#endif
|
||||
@@ -0,0 +1,273 @@
|
||||
/*M///////////////////////////////////////////////////////////////////////////////////////
|
||||
//
|
||||
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
|
||||
//
|
||||
// By downloading, copying, installing or using the software you agree to this license.
|
||||
// If you do not agree to this license, do not download, install,
|
||||
// copy or use the software.
|
||||
//
|
||||
//
|
||||
// License Agreement
|
||||
// For Open Source Computer Vision Library
|
||||
//
|
||||
// Copyright (C) 2008, Willow Garage Inc., all rights reserved.
|
||||
// Third party copyrights are property of their respective owners.
|
||||
//
|
||||
// Redistribution and use in source and binary forms, with or without modification,
|
||||
// are permitted provided that the following conditions are met:
|
||||
//
|
||||
// * Redistribution's of source code must retain the above copyright notice,
|
||||
// this list of conditions and the following disclaimer.
|
||||
//
|
||||
// * Redistribution's in binary form must reproduce the above copyright notice,
|
||||
// this list of conditions and the following disclaimer in the documentation
|
||||
// and/or other materials provided with the distribution.
|
||||
//
|
||||
// * The name of Intel Corporation may not be used to endorse or promote products
|
||||
// derived from this software without specific prior written permission.
|
||||
//
|
||||
// This software is provided by the copyright holders and contributors "as is" and
|
||||
// any express or implied warranties, including, but not limited to, the implied
|
||||
// warranties of merchantability and fitness for a particular purpose are disclaimed.
|
||||
// In no event shall the Intel Corporation or contributors be liable for any direct,
|
||||
// indirect, incidental, special, exemplary, or consequential damages
|
||||
// (including, but not limited to, procurement of substitute goods or services;
|
||||
// loss of use, data, or profits; or business interruption) however caused
|
||||
// and on any theory of liability, whether in contract, strict liability,
|
||||
// or tort (including negligence or otherwise) arising in any way out of
|
||||
// the use of this software, even if advised of the possibility of such damage.
|
||||
//
|
||||
//M*/
|
||||
|
||||
/*
|
||||
OpenCV wrapper of reference implementation of
|
||||
[1] KAZE Features. Pablo F. Alcantarilla, Adrien Bartoli and Andrew J. Davison.
|
||||
In European Conference on Computer Vision (ECCV), Fiorenze, Italy, October 2012
|
||||
http://www.robesafe.com/personal/pablo.alcantarilla/papers/Alcantarilla12eccv.pdf
|
||||
@author Eugene Khvedchenya <ekhvedchenya@gmail.com>
|
||||
*/
|
||||
|
||||
#include "precomp.hpp"
|
||||
#include "kaze/KAZEFeatures.h"
|
||||
|
||||
namespace cv
|
||||
{
|
||||
|
||||
class KAZE_Impl CV_FINAL : public KAZE
|
||||
{
|
||||
public:
|
||||
KAZE_Impl(bool _extended, bool _upright, float _threshold, int _octaves,
|
||||
int _sublevels, KAZE::DiffusivityType _diffusivity)
|
||||
: extended(_extended)
|
||||
, upright(_upright)
|
||||
, threshold(_threshold)
|
||||
, octaves(_octaves)
|
||||
, sublevels(_sublevels)
|
||||
, diffusivity(_diffusivity)
|
||||
{
|
||||
}
|
||||
|
||||
virtual ~KAZE_Impl() CV_OVERRIDE {}
|
||||
|
||||
void setExtended(bool extended_) CV_OVERRIDE { extended = extended_; }
|
||||
bool getExtended() const CV_OVERRIDE { return extended; }
|
||||
|
||||
void setUpright(bool upright_) CV_OVERRIDE { upright = upright_; }
|
||||
bool getUpright() const CV_OVERRIDE { return upright; }
|
||||
|
||||
void setThreshold(double threshold_) CV_OVERRIDE { threshold = (float)threshold_; }
|
||||
double getThreshold() const CV_OVERRIDE { return threshold; }
|
||||
|
||||
void setNOctaves(int octaves_) CV_OVERRIDE { octaves = octaves_; }
|
||||
int getNOctaves() const CV_OVERRIDE { return octaves; }
|
||||
|
||||
void setNOctaveLayers(int octaveLayers_) CV_OVERRIDE { sublevels = octaveLayers_; }
|
||||
int getNOctaveLayers() const CV_OVERRIDE { return sublevels; }
|
||||
|
||||
void setDiffusivity(KAZE::DiffusivityType diff_) CV_OVERRIDE{ diffusivity = diff_; }
|
||||
KAZE::DiffusivityType getDiffusivity() const CV_OVERRIDE{ return diffusivity; }
|
||||
|
||||
// returns the descriptor size in bytes
|
||||
int descriptorSize() const CV_OVERRIDE
|
||||
{
|
||||
return extended ? 128 : 64;
|
||||
}
|
||||
|
||||
// returns the descriptor type
|
||||
int descriptorType() const CV_OVERRIDE
|
||||
{
|
||||
return CV_32F;
|
||||
}
|
||||
|
||||
// returns the default norm type
|
||||
int defaultNorm() const CV_OVERRIDE
|
||||
{
|
||||
return NORM_L2;
|
||||
}
|
||||
|
||||
void detectAndCompute(InputArray image, InputArray mask,
|
||||
std::vector<KeyPoint>& keypoints,
|
||||
OutputArray descriptors,
|
||||
bool useProvidedKeypoints) CV_OVERRIDE
|
||||
{
|
||||
CV_INSTRUMENT_REGION();
|
||||
|
||||
#ifdef HAVE_OPENCL
|
||||
bool use_opencl = ocl::useOpenCL() && upright
|
||||
&& !ocl::Device::getDefault().hostUnifiedMemory();
|
||||
if (use_opencl)
|
||||
{
|
||||
UMat img_gray = image.getUMat();
|
||||
if (img_gray.channels() > 1)
|
||||
{
|
||||
UMat tmp;
|
||||
cvtColor(img_gray, tmp, COLOR_BGR2GRAY);
|
||||
img_gray = tmp;
|
||||
}
|
||||
|
||||
UMat img1_32;
|
||||
int depth = img_gray.depth();
|
||||
if ( depth == CV_32F )
|
||||
img1_32 = img_gray;
|
||||
else if ( depth == CV_8U )
|
||||
img_gray.convertTo(img1_32, CV_32F, 1.0 / 255.0, 0);
|
||||
else if ( depth == CV_16U )
|
||||
img_gray.convertTo(img1_32, CV_32F, 1.0 / 65535.0, 0);
|
||||
|
||||
CV_Assert( ! img1_32.empty() );
|
||||
|
||||
KAZEOptions options;
|
||||
options.img_width = img1_32.cols;
|
||||
options.img_height = img1_32.rows;
|
||||
options.extended = extended;
|
||||
options.upright = upright;
|
||||
options.dthreshold = threshold;
|
||||
options.omax = octaves;
|
||||
options.nsublevels = sublevels;
|
||||
options.diffusivity = diffusivity;
|
||||
|
||||
KAZEFeatures impl(options);
|
||||
UTPyramid uPyr;
|
||||
impl.Create_Nonlinear_Scale_Space_UMat(img1_32, uPyr);
|
||||
|
||||
if (!useProvidedKeypoints)
|
||||
{
|
||||
impl.Feature_Detection_UMat(uPyr, keypoints);
|
||||
}
|
||||
|
||||
if (!mask.empty())
|
||||
{
|
||||
cv::KeyPointsFilter::runByPixelsMask(keypoints, mask.getMat());
|
||||
}
|
||||
|
||||
if( descriptors.needed() )
|
||||
{
|
||||
impl.Compute_Descriptors_UMat(keypoints, descriptors, uPyr);
|
||||
|
||||
CV_Assert((!descriptors.empty() || descriptors.cols() == descriptorSize()));
|
||||
CV_Assert((!descriptors.empty() || (descriptors.type() == descriptorType())));
|
||||
}
|
||||
return;
|
||||
}
|
||||
#endif
|
||||
|
||||
// CPU path
|
||||
cv::Mat img = image.getMat();
|
||||
if (img.channels() > 1)
|
||||
cvtColor(image, img, COLOR_BGR2GRAY);
|
||||
|
||||
Mat img1_32;
|
||||
if ( img.depth() == CV_32F )
|
||||
img1_32 = img;
|
||||
else if ( img.depth() == CV_8U )
|
||||
img.convertTo(img1_32, CV_32F, 1.0 / 255.0, 0);
|
||||
else if ( img.depth() == CV_16U )
|
||||
img.convertTo(img1_32, CV_32F, 1.0 / 65535.0, 0);
|
||||
|
||||
CV_Assert( ! img1_32.empty() );
|
||||
|
||||
KAZEOptions options;
|
||||
options.img_width = img.cols;
|
||||
options.img_height = img.rows;
|
||||
options.extended = extended;
|
||||
options.upright = upright;
|
||||
options.dthreshold = threshold;
|
||||
options.omax = octaves;
|
||||
options.nsublevels = sublevels;
|
||||
options.diffusivity = diffusivity;
|
||||
|
||||
KAZEFeatures impl(options);
|
||||
impl.Create_Nonlinear_Scale_Space(img1_32);
|
||||
|
||||
if (!useProvidedKeypoints)
|
||||
{
|
||||
impl.Feature_Detection(keypoints);
|
||||
}
|
||||
|
||||
if (!mask.empty())
|
||||
{
|
||||
cv::KeyPointsFilter::runByPixelsMask(keypoints, mask.getMat());
|
||||
}
|
||||
|
||||
if( descriptors.needed() )
|
||||
{
|
||||
Mat desc;
|
||||
impl.Feature_Description(keypoints, desc);
|
||||
desc.copyTo(descriptors);
|
||||
|
||||
CV_Assert((!desc.rows || desc.cols == descriptorSize()));
|
||||
CV_Assert((!desc.rows || (desc.type() == descriptorType())));
|
||||
}
|
||||
}
|
||||
|
||||
void write(FileStorage& fs) const CV_OVERRIDE
|
||||
{
|
||||
writeFormat(fs);
|
||||
fs << "name" << getDefaultName();
|
||||
fs << "extended" << (int)extended;
|
||||
fs << "upright" << (int)upright;
|
||||
fs << "threshold" << threshold;
|
||||
fs << "octaves" << octaves;
|
||||
fs << "sublevels" << sublevels;
|
||||
fs << "diffusivity" << diffusivity;
|
||||
}
|
||||
|
||||
void read(const FileNode& fn) CV_OVERRIDE
|
||||
{
|
||||
// if node is empty, keep previous value
|
||||
if (!fn["extended"].empty())
|
||||
extended = (int)fn["extended"] != 0;
|
||||
if (!fn["upright"].empty())
|
||||
upright = (int)fn["upright"] != 0;
|
||||
if (!fn["threshold"].empty())
|
||||
threshold = (float)fn["threshold"];
|
||||
if (!fn["octaves"].empty())
|
||||
octaves = (int)fn["octaves"];
|
||||
if (!fn["sublevels"].empty())
|
||||
sublevels = (int)fn["sublevels"];
|
||||
if (!fn["diffusivity"].empty())
|
||||
diffusivity = static_cast<KAZE::DiffusivityType>((int)fn["diffusivity"]);
|
||||
}
|
||||
|
||||
bool extended;
|
||||
bool upright;
|
||||
float threshold;
|
||||
int octaves;
|
||||
int sublevels;
|
||||
KAZE::DiffusivityType diffusivity;
|
||||
};
|
||||
|
||||
Ptr<KAZE> KAZE::create(bool extended, bool upright,
|
||||
float threshold,
|
||||
int octaves, int sublevels,
|
||||
KAZE::DiffusivityType diffusivity)
|
||||
{
|
||||
return makePtr<KAZE_Impl>(extended, upright, threshold, octaves, sublevels, diffusivity);
|
||||
}
|
||||
|
||||
String KAZE::getDefaultName() const
|
||||
{
|
||||
return (Feature2D::getDefaultName() + ".KAZE");
|
||||
}
|
||||
|
||||
}
|
||||
@@ -0,0 +1,65 @@
|
||||
/**
|
||||
* @file AKAZEConfig.h
|
||||
* @brief AKAZE configuration file
|
||||
* @date Feb 23, 2014
|
||||
* @author Pablo F. Alcantarilla, Jesus Nuevo
|
||||
*/
|
||||
|
||||
#ifndef __OPENCV_FEATURES_2D_AKAZE_CONFIG_H__
|
||||
#define __OPENCV_FEATURES_2D_AKAZE_CONFIG_H__
|
||||
|
||||
namespace cv
|
||||
{
|
||||
/* ************************************************************************* */
|
||||
/// AKAZE configuration options structure
|
||||
struct AKAZEOptions {
|
||||
|
||||
AKAZEOptions()
|
||||
: omax(4)
|
||||
, nsublevels(4)
|
||||
, img_width(0)
|
||||
, img_height(0)
|
||||
, soffset(1.6f)
|
||||
, derivative_factor(1.5f)
|
||||
, sderivatives(1.0)
|
||||
, diffusivity(KAZE::DIFF_PM_G2)
|
||||
|
||||
, dthreshold(0.001f)
|
||||
, min_dthreshold(0.00001f)
|
||||
|
||||
, descriptor(AKAZE::DESCRIPTOR_MLDB)
|
||||
, descriptor_size(0)
|
||||
, descriptor_channels(3)
|
||||
, descriptor_pattern_size(10)
|
||||
|
||||
, kcontrast(0.001f)
|
||||
, kcontrast_percentile(0.7f)
|
||||
, kcontrast_nbins(300)
|
||||
{
|
||||
}
|
||||
|
||||
int omax; ///< Maximum octave evolution of the image 2^sigma (coarsest scale sigma units)
|
||||
int nsublevels; ///< Default number of sublevels per scale level
|
||||
int img_width; ///< Width of the input image
|
||||
int img_height; ///< Height of the input image
|
||||
float soffset; ///< Base scale offset (sigma units)
|
||||
float derivative_factor; ///< Factor for the multiscale derivatives
|
||||
float sderivatives; ///< Smoothing factor for the derivatives
|
||||
KAZE::DiffusivityType diffusivity; ///< Diffusivity type
|
||||
|
||||
float dthreshold; ///< Detector response threshold to accept point
|
||||
float min_dthreshold; ///< Minimum detector threshold to accept a point
|
||||
|
||||
AKAZE::DescriptorType descriptor; ///< Type of descriptor
|
||||
int descriptor_size; ///< Size of the descriptor in bits. 0->Full size
|
||||
int descriptor_channels; ///< Number of channels in the descriptor (1, 2, 3)
|
||||
int descriptor_pattern_size; ///< Actual patch size is 2*pattern_size*point.scale
|
||||
|
||||
float kcontrast; ///< The contrast factor parameter
|
||||
float kcontrast_percentile; ///< Percentile level for the contrast factor
|
||||
int kcontrast_nbins; ///< Number of bins for the contrast factor histogram
|
||||
};
|
||||
|
||||
}
|
||||
|
||||
#endif
|
||||
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,121 @@
|
||||
/**
|
||||
* @file AKAZE.h
|
||||
* @brief Main class for detecting and computing binary descriptors in an
|
||||
* accelerated nonlinear scale space
|
||||
* @date Mar 27, 2013
|
||||
* @author Pablo F. Alcantarilla, Jesus Nuevo
|
||||
*/
|
||||
|
||||
#ifndef __OPENCV_FEATURES_2D_AKAZE_FEATURES_H__
|
||||
#define __OPENCV_FEATURES_2D_AKAZE_FEATURES_H__
|
||||
|
||||
/* ************************************************************************* */
|
||||
// Includes
|
||||
#include "AKAZEConfig.h"
|
||||
|
||||
namespace cv
|
||||
{
|
||||
|
||||
/// A-KAZE nonlinear diffusion filtering evolution
|
||||
template <typename MatType>
|
||||
struct Evolution
|
||||
{
|
||||
Evolution() {
|
||||
etime = 0.0f;
|
||||
esigma = 0.0f;
|
||||
octave = 0;
|
||||
sublevel = 0;
|
||||
sigma_size = 0;
|
||||
octave_ratio = 0.0f;
|
||||
border = 0;
|
||||
}
|
||||
|
||||
template <typename T>
|
||||
explicit Evolution(const Evolution<T> &other) {
|
||||
size = other.size;
|
||||
etime = other.etime;
|
||||
esigma = other.esigma;
|
||||
octave = other.octave;
|
||||
sublevel = other.sublevel;
|
||||
sigma_size = other.sigma_size;
|
||||
octave_ratio = other.octave_ratio;
|
||||
border = other.border;
|
||||
|
||||
other.Lx.copyTo(Lx);
|
||||
other.Ly.copyTo(Ly);
|
||||
other.Lt.copyTo(Lt);
|
||||
other.Lsmooth.copyTo(Lsmooth);
|
||||
other.Ldet.copyTo(Ldet);
|
||||
}
|
||||
|
||||
MatType Lx, Ly; ///< First order spatial derivatives
|
||||
MatType Lt; ///< Evolution image
|
||||
MatType Lsmooth; ///< Smoothed image, used only for computing determinant, released afterwards
|
||||
MatType Ldet; ///< Detector response
|
||||
|
||||
Size size; ///< Size of the layer
|
||||
float etime; ///< Evolution time
|
||||
float esigma; ///< Evolution sigma. For linear diffusion t = sigma^2 / 2
|
||||
int octave; ///< Image octave
|
||||
int sublevel; ///< Image sublevel in each octave
|
||||
int sigma_size; ///< Integer esigma. For computing the feature detector responses
|
||||
float octave_ratio; ///< Scaling ratio of this octave. ratio = 2^octave
|
||||
int border; ///< Width of border where descriptors cannot be computed
|
||||
};
|
||||
|
||||
typedef Evolution<Mat> MEvolution;
|
||||
typedef Evolution<UMat> UEvolution;
|
||||
typedef std::vector<MEvolution> Pyramid;
|
||||
typedef std::vector<UEvolution> UMatPyramid;
|
||||
|
||||
/* ************************************************************************* */
|
||||
// AKAZE Class Declaration
|
||||
class AKAZEFeatures {
|
||||
|
||||
private:
|
||||
|
||||
AKAZEOptions options_; ///< Configuration options for AKAZE
|
||||
Pyramid evolution_; ///< Vector of nonlinear diffusion evolution
|
||||
|
||||
/// FED parameters
|
||||
int ncycles_; ///< Number of cycles
|
||||
bool reordering_; ///< Flag for reordering time steps
|
||||
std::vector<std::vector<float > > tsteps_; ///< Vector of FED dynamic time steps
|
||||
std::vector<int> nsteps_; ///< Vector of number of steps per cycle
|
||||
|
||||
/// Matrices for the M-LDB descriptor computation
|
||||
cv::Mat descriptorSamples_; // List of positions in the grids to sample LDB bits from.
|
||||
cv::Mat descriptorBits_;
|
||||
cv::Mat bitMask_;
|
||||
|
||||
/// Scale Space methods
|
||||
void Allocate_Memory_Evolution();
|
||||
void Find_Scale_Space_Extrema(std::vector<Mat>& keypoints_by_layers);
|
||||
void Do_Subpixel_Refinement(std::vector<Mat>& keypoints_by_layers,
|
||||
std::vector<KeyPoint>& kpts);
|
||||
|
||||
public:
|
||||
void GetEvolutionPyramid(UMatPyramid& uPyr);
|
||||
void Create_Nonlinear_Scale_Space_UMat(InputArray img, UMatPyramid& uPyr);
|
||||
void Feature_Detection_UMat(UMatPyramid& uPyr, std::vector<cv::KeyPoint>& kpts);
|
||||
void Compute_Descriptors_UMat(std::vector<cv::KeyPoint>& kpts, OutputArray desc, UMatPyramid& uPyr);
|
||||
|
||||
/// Feature description methods
|
||||
void Compute_Keypoints_Orientation(std::vector<cv::KeyPoint>& kpts) const;
|
||||
|
||||
public:
|
||||
/// Constructor with input arguments
|
||||
AKAZEFeatures(const AKAZEOptions& options);
|
||||
void Create_Nonlinear_Scale_Space(InputArray img);
|
||||
void Feature_Detection(std::vector<cv::KeyPoint>& kpts);
|
||||
void Compute_Descriptors(std::vector<cv::KeyPoint>& kpts, OutputArray desc);
|
||||
};
|
||||
|
||||
/* ************************************************************************* */
|
||||
/// Inline functions
|
||||
void generateDescriptorSubsample(cv::Mat& sampleList, cv::Mat& comparisons,
|
||||
int nbits, int pattern_size, int nchannels);
|
||||
|
||||
}
|
||||
|
||||
#endif
|
||||
@@ -0,0 +1,56 @@
|
||||
/**
|
||||
* @file KAZEConfig.h
|
||||
* @brief Configuration file
|
||||
* @date Dec 27, 2011
|
||||
* @author Pablo F. Alcantarilla
|
||||
*/
|
||||
|
||||
#ifndef __OPENCV_FEATURES_2D_KAZE_CONFIG_H__
|
||||
#define __OPENCV_FEATURES_2D_KAZE_CONFIG_H__
|
||||
|
||||
// OpenCV Includes
|
||||
#include "../precomp.hpp"
|
||||
#include <opencv2/features2d.hpp>
|
||||
|
||||
namespace cv
|
||||
{
|
||||
//*************************************************************************************
|
||||
|
||||
struct KAZEOptions {
|
||||
|
||||
KAZEOptions()
|
||||
: diffusivity(KAZE::DIFF_PM_G2)
|
||||
|
||||
, soffset(1.60f)
|
||||
, omax(4)
|
||||
, nsublevels(4)
|
||||
, img_width(0)
|
||||
, img_height(0)
|
||||
, sderivatives(1.0f)
|
||||
, dthreshold(0.001f)
|
||||
, kcontrast(0.01f)
|
||||
, kcontrast_percentille(0.7f)
|
||||
, kcontrast_bins(300)
|
||||
, upright(false)
|
||||
, extended(false)
|
||||
{
|
||||
}
|
||||
|
||||
KAZE::DiffusivityType diffusivity;
|
||||
float soffset;
|
||||
int omax;
|
||||
int nsublevels;
|
||||
int img_width;
|
||||
int img_height;
|
||||
float sderivatives;
|
||||
float dthreshold;
|
||||
float kcontrast;
|
||||
float kcontrast_percentille;
|
||||
int kcontrast_bins;
|
||||
bool upright;
|
||||
bool extended;
|
||||
};
|
||||
|
||||
}
|
||||
|
||||
#endif
|
||||
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,70 @@
|
||||
|
||||
/**
|
||||
* @file KAZE.h
|
||||
* @brief Main program for detecting and computing descriptors in a nonlinear
|
||||
* scale space
|
||||
* @date Jan 21, 2012
|
||||
* @author Pablo F. Alcantarilla
|
||||
*/
|
||||
|
||||
#ifndef __OPENCV_FEATURES_2D_KAZE_FEATURES_H__
|
||||
#define __OPENCV_FEATURES_2D_KAZE_FEATURES_H__
|
||||
|
||||
/* ************************************************************************* */
|
||||
// Includes
|
||||
#include "KAZEConfig.h"
|
||||
#include "nldiffusion_functions.h"
|
||||
#include "fed.h"
|
||||
#include "TEvolution.h"
|
||||
|
||||
namespace cv
|
||||
{
|
||||
|
||||
/* ************************************************************************* */
|
||||
// KAZE Class Declaration
|
||||
class KAZEFeatures
|
||||
{
|
||||
private:
|
||||
|
||||
/// Parameters of the Nonlinear diffusion class
|
||||
KAZEOptions options_; ///< Configuration options for KAZE
|
||||
TPyramid evolution_; ///< Vector of nonlinear diffusion evolution
|
||||
|
||||
/// Vector of keypoint vectors for finding extrema in multiple threads
|
||||
std::vector<std::vector<cv::KeyPoint> > kpts_par_;
|
||||
|
||||
/// FED parameters
|
||||
int ncycles_; ///< Number of cycles
|
||||
bool reordering_; ///< Flag for reordering time steps
|
||||
std::vector<std::vector<float > > tsteps_; ///< Vector of FED dynamic time steps
|
||||
std::vector<int> nsteps_; ///< Vector of number of steps per cycle
|
||||
|
||||
public:
|
||||
|
||||
/// Constructor
|
||||
KAZEFeatures(KAZEOptions& options);
|
||||
|
||||
/// Public methods for KAZE interface
|
||||
void Allocate_Memory_Evolution(void);
|
||||
int Create_Nonlinear_Scale_Space(const cv::Mat& img);
|
||||
void Feature_Detection(std::vector<cv::KeyPoint>& kpts);
|
||||
void Feature_Description(std::vector<cv::KeyPoint>& kpts, cv::Mat& desc);
|
||||
static void Compute_Main_Orientation(cv::KeyPoint& kpt, const TPyramid& evolution_, const KAZEOptions& options);
|
||||
|
||||
#ifdef HAVE_OPENCL
|
||||
void Create_Nonlinear_Scale_Space_UMat(InputArray img, UTPyramid& uPyr);
|
||||
void Feature_Detection_UMat(UTPyramid& uPyr, std::vector<cv::KeyPoint>& kpts);
|
||||
void Compute_Descriptors_UMat(std::vector<cv::KeyPoint>& kpts, OutputArray desc, UTPyramid& uPyr);
|
||||
#endif
|
||||
|
||||
/// Feature Detection Methods
|
||||
void Compute_KContrast(const cv::Mat& img, const float& kper);
|
||||
void Compute_Multiscale_Derivatives(void);
|
||||
void Compute_Detector_Response(void);
|
||||
void Determinant_Hessian(std::vector<cv::KeyPoint>& kpts);
|
||||
void Do_Subpixel_Refinement(std::vector<cv::KeyPoint>& kpts);
|
||||
};
|
||||
|
||||
}
|
||||
|
||||
#endif
|
||||
@@ -0,0 +1,5 @@
|
||||
This folder contains both KAZE and AKAZE sources.
|
||||
For license details, please refer to the following files:
|
||||
|
||||
- KAZE: LICENSE.KAZE
|
||||
- AKAZE: LICENSE.AKAZE
|
||||
@@ -0,0 +1,26 @@
|
||||
Copyright (c) 2014, Pablo Fernandez Alcantarilla, Jesus Nuevo
|
||||
All Rights Reserved
|
||||
|
||||
Redistribution and use in source and binary forms, with or without modification,
|
||||
are permitted provided that the following conditions are met:
|
||||
|
||||
* Redistributions of source code must retain the above copyright notice,
|
||||
this list of conditions and the following disclaimer.
|
||||
|
||||
* Redistributions in binary form must reproduce the above copyright notice,
|
||||
this list of conditions and the following disclaimer in the documentation
|
||||
and/or other materials provided with the distribution.
|
||||
|
||||
* Neither the name of the copyright holders nor the names of its contributors
|
||||
may be used to endorse or promote products derived from this software without
|
||||
specific prior written permission.
|
||||
|
||||
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY
|
||||
EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
|
||||
OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT
|
||||
SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
|
||||
INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED
|
||||
TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR
|
||||
BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
|
||||
CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY
|
||||
WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
|
||||
@@ -0,0 +1,26 @@
|
||||
Copyright (c) 2012, Pablo Fernández Alcantarilla
|
||||
All Rights Reserved
|
||||
|
||||
Redistribution and use in source and binary forms, with or without modification,
|
||||
are permitted provided that the following conditions are met:
|
||||
|
||||
* Redistributions of source code must retain the above copyright notice,
|
||||
this list of conditions and the following disclaimer.
|
||||
|
||||
* Redistributions in binary form must reproduce the above copyright notice,
|
||||
this list of conditions and the following disclaimer in the documentation
|
||||
and/or other materials provided with the distribution.
|
||||
|
||||
* Neither the name of the copyright holders nor the names of its contributors
|
||||
may be used to endorse or promote products derived from this software without
|
||||
specific prior written permission.
|
||||
|
||||
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY
|
||||
EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
|
||||
OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT
|
||||
SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
|
||||
INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED
|
||||
TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR
|
||||
BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
|
||||
CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY
|
||||
WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
|
||||
@@ -0,0 +1,47 @@
|
||||
/**
|
||||
* @file TEvolution.h
|
||||
* @brief Header file with the declaration of the TEvolution struct
|
||||
* @date Jun 02, 2014
|
||||
* @author Pablo F. Alcantarilla
|
||||
*/
|
||||
|
||||
#ifndef __OPENCV_FEATURES_2D_TEVOLUTION_H__
|
||||
#define __OPENCV_FEATURES_2D_TEVOLUTION_H__
|
||||
|
||||
namespace cv
|
||||
{
|
||||
|
||||
/* ************************************************************************* */
|
||||
/// KAZE/A-KAZE nonlinear diffusion filtering evolution
|
||||
template <typename MatType>
|
||||
struct TEvolutionT
|
||||
{
|
||||
TEvolutionT() {
|
||||
etime = 0.0f;
|
||||
esigma = 0.0f;
|
||||
octave = 0;
|
||||
sublevel = 0;
|
||||
sigma_size = 0;
|
||||
}
|
||||
|
||||
MatType Lx, Ly; ///< First order spatial derivatives
|
||||
MatType Lxx, Lxy, Lyy; ///< Second order spatial derivatives
|
||||
MatType Lt; ///< Evolution image
|
||||
MatType Lsmooth; ///< Smoothed image
|
||||
MatType Ldet; ///< Detector response
|
||||
|
||||
float etime; ///< Evolution time
|
||||
float esigma; ///< Evolution sigma. For linear diffusion t = sigma^2 / 2
|
||||
int octave; ///< Image octave
|
||||
int sublevel; ///< Image sublevel in each octave
|
||||
int sigma_size; ///< Integer esigma. For computing the feature detector responses
|
||||
};
|
||||
|
||||
typedef TEvolutionT<Mat> TEvolution;
|
||||
typedef TEvolutionT<UMat> UTEvolution;
|
||||
typedef std::vector<TEvolution> TPyramid;
|
||||
typedef std::vector<UTEvolution> UTPyramid;
|
||||
|
||||
}
|
||||
|
||||
#endif
|
||||
@@ -0,0 +1,218 @@
|
||||
// Internal header: shared nonlinear diffusion utilities used by both
|
||||
// AKAZEFeatures.cpp and KAZEFeatures.cpp.
|
||||
// Not part of the public API.
|
||||
|
||||
#ifndef __OPENCV_FEATURES_2D_AKAZE_DIFFUSION_HPP__
|
||||
#define __OPENCV_FEATURES_2D_AKAZE_DIFFUSION_HPP__
|
||||
|
||||
#include "../precomp.hpp"
|
||||
#include "nldiffusion_functions.h"
|
||||
#ifdef HAVE_OPENCL
|
||||
#include "opencl_kernels_features2d.hpp"
|
||||
#endif
|
||||
|
||||
namespace cv
|
||||
{
|
||||
|
||||
static inline void
|
||||
nld_step_scalar_one_lane(const Mat& Lt, const Mat& Lf, Mat& Lstep, float step_size, int row_begin, int row_end)
|
||||
{
|
||||
CV_INSTRUMENT_REGION();
|
||||
|
||||
Lstep.create(Lt.size(), Lt.type());
|
||||
const int cols = Lt.cols - 2;
|
||||
int row = row_begin;
|
||||
|
||||
const float *lt_a, *lt_c, *lt_b;
|
||||
const float *lf_a, *lf_c, *lf_b;
|
||||
float *dst;
|
||||
float step_r = 0.f;
|
||||
|
||||
if (row == 0) {
|
||||
lt_c = Lt.ptr<float>(0) + 1;
|
||||
lf_c = Lf.ptr<float>(0) + 1;
|
||||
lt_b = Lt.ptr<float>(1) + 1;
|
||||
lf_b = Lf.ptr<float>(1) + 1;
|
||||
|
||||
dst = Lstep.ptr<float>(0);
|
||||
dst[0] = 0.0f;
|
||||
++dst;
|
||||
|
||||
for (int j = 0; j < cols; j++) {
|
||||
step_r = (lf_c[j] + lf_c[j + 1])*(lt_c[j + 1] - lt_c[j]) +
|
||||
(lf_c[j] + lf_c[j - 1])*(lt_c[j - 1] - lt_c[j]) +
|
||||
(lf_c[j] + lf_b[j ])*(lt_b[j ] - lt_c[j]);
|
||||
dst[j] = step_r * step_size;
|
||||
}
|
||||
|
||||
dst[cols] = 0.0f;
|
||||
++row;
|
||||
}
|
||||
|
||||
int middle_end = std::min(Lt.rows - 1, row_end);
|
||||
for (; row < middle_end; ++row)
|
||||
{
|
||||
lt_a = Lt.ptr<float>(row - 1);
|
||||
lf_a = Lf.ptr<float>(row - 1);
|
||||
lt_c = Lt.ptr<float>(row );
|
||||
lf_c = Lf.ptr<float>(row );
|
||||
lt_b = Lt.ptr<float>(row + 1);
|
||||
lf_b = Lf.ptr<float>(row + 1);
|
||||
dst = Lstep.ptr<float>(row);
|
||||
|
||||
step_r = (lf_c[0] + lf_c[1])*(lt_c[1] - lt_c[0]) +
|
||||
(lf_c[0] + lf_b[0])*(lt_b[0] - lt_c[0]) +
|
||||
(lf_c[0] + lf_a[0])*(lt_a[0] - lt_c[0]);
|
||||
dst[0] = step_r * step_size;
|
||||
|
||||
lt_a++; lt_c++; lt_b++;
|
||||
lf_a++; lf_c++; lf_b++;
|
||||
dst++;
|
||||
|
||||
for (int j = 0; j < cols; j++)
|
||||
{
|
||||
step_r = (lf_c[j] + lf_c[j + 1])*(lt_c[j + 1] - lt_c[j]) +
|
||||
(lf_c[j] + lf_c[j - 1])*(lt_c[j - 1] - lt_c[j]) +
|
||||
(lf_c[j] + lf_b[j ])*(lt_b[j ] - lt_c[j]) +
|
||||
(lf_c[j] + lf_a[j ])*(lt_a[j ] - lt_c[j]);
|
||||
dst[j] = step_r * step_size;
|
||||
}
|
||||
|
||||
step_r = (lf_c[cols] + lf_c[cols - 1])*(lt_c[cols - 1] - lt_c[cols]) +
|
||||
(lf_c[cols] + lf_b[cols ])*(lt_b[cols ] - lt_c[cols]) +
|
||||
(lf_c[cols] + lf_a[cols ])*(lt_a[cols ] - lt_c[cols]);
|
||||
dst[cols] = step_r * step_size;
|
||||
}
|
||||
|
||||
if (row_end == Lt.rows) {
|
||||
lt_a = Lt.ptr<float>(row - 1) + 1;
|
||||
lf_a = Lf.ptr<float>(row - 1) + 1;
|
||||
lt_c = Lt.ptr<float>(row ) + 1;
|
||||
lf_c = Lf.ptr<float>(row ) + 1;
|
||||
|
||||
dst = Lstep.ptr<float>(row);
|
||||
dst[0] = 0.0f;
|
||||
++dst;
|
||||
|
||||
for (int j = 0; j < cols; j++) {
|
||||
step_r = (lf_c[j] + lf_c[j + 1])*(lt_c[j + 1] - lt_c[j]) +
|
||||
(lf_c[j] + lf_c[j - 1])*(lt_c[j - 1] - lt_c[j]) +
|
||||
(lf_c[j] + lf_a[j ])*(lt_a[j ] - lt_c[j]);
|
||||
dst[j] = step_r * step_size;
|
||||
}
|
||||
|
||||
dst[cols] = 0.0f;
|
||||
}
|
||||
}
|
||||
|
||||
class NonLinearScalarDiffusionStep : public ParallelLoopBody
|
||||
{
|
||||
public:
|
||||
NonLinearScalarDiffusionStep(const Mat& Lt, const Mat& Lf, Mat& Lstep, float step_size)
|
||||
: Lt_(&Lt), Lf_(&Lf), Lstep_(&Lstep), step_size_(step_size)
|
||||
{}
|
||||
|
||||
void operator()(const Range& range) const CV_OVERRIDE
|
||||
{
|
||||
nld_step_scalar_one_lane(*Lt_, *Lf_, *Lstep_, step_size_, range.start, range.end);
|
||||
}
|
||||
|
||||
private:
|
||||
const Mat* Lt_;
|
||||
const Mat* Lf_;
|
||||
Mat* Lstep_;
|
||||
float step_size_;
|
||||
};
|
||||
|
||||
#ifdef HAVE_OPENCL
|
||||
static inline bool
|
||||
ocl_non_linear_diffusion_step(InputArray Lt_, InputArray Lf_, OutputArray Lstep_, float step_size)
|
||||
{
|
||||
if(!Lt_.isContinuous())
|
||||
return false;
|
||||
|
||||
UMat Lt = Lt_.getUMat();
|
||||
UMat Lf = Lf_.getUMat();
|
||||
UMat Lstep = Lstep_.getUMat();
|
||||
|
||||
size_t globalSize[] = {(size_t)Lt.cols, (size_t)Lt.rows};
|
||||
|
||||
ocl::Kernel ker("AKAZE_nld_step_scalar", ocl::features2d::akaze_oclsrc);
|
||||
if( ker.empty() )
|
||||
return false;
|
||||
|
||||
return ker.args(
|
||||
ocl::KernelArg::ReadOnly(Lt),
|
||||
ocl::KernelArg::PtrReadOnly(Lf),
|
||||
ocl::KernelArg::PtrWriteOnly(Lstep),
|
||||
step_size).run(2, globalSize, 0, false);
|
||||
}
|
||||
|
||||
static inline bool
|
||||
ocl_pm_g2(InputArray Lx_, InputArray Ly_, OutputArray Lflow_, float kcontrast)
|
||||
{
|
||||
UMat Lx = Lx_.getUMat();
|
||||
UMat Ly = Ly_.getUMat();
|
||||
UMat Lflow = Lflow_.getUMat();
|
||||
|
||||
int total = Lx.rows * Lx.cols;
|
||||
size_t globalSize[] = {(size_t)total};
|
||||
|
||||
ocl::Kernel ker("AKAZE_pm_g2", ocl::features2d::akaze_oclsrc);
|
||||
if( ker.empty() )
|
||||
return false;
|
||||
|
||||
return ker.args(
|
||||
ocl::KernelArg::PtrReadOnly(Lx),
|
||||
ocl::KernelArg::PtrReadOnly(Ly),
|
||||
ocl::KernelArg::PtrWriteOnly(Lflow),
|
||||
kcontrast, total).run(1, globalSize, 0, false);
|
||||
}
|
||||
#endif // HAVE_OPENCL
|
||||
|
||||
static inline void
|
||||
non_linear_diffusion_step(InputArray Lt_, InputArray Lf_, OutputArray Lstep_, float step_size)
|
||||
{
|
||||
CV_INSTRUMENT_REGION();
|
||||
|
||||
Lstep_.create(Lt_.size(), Lt_.type());
|
||||
|
||||
CV_OCL_RUN(Lt_.isUMat() && Lf_.isUMat() && Lstep_.isUMat(),
|
||||
ocl_non_linear_diffusion_step(Lt_, Lf_, Lstep_, step_size));
|
||||
|
||||
Mat Lt = Lt_.getMat();
|
||||
Mat Lf = Lf_.getMat();
|
||||
Mat Lstep = Lstep_.getMat();
|
||||
parallel_for_(Range(0, Lt.rows), NonLinearScalarDiffusionStep(Lt, Lf, Lstep, step_size));
|
||||
}
|
||||
|
||||
static inline void
|
||||
compute_diffusivity(InputArray Lx, InputArray Ly, OutputArray Lflow, float kcontrast, KAZE::DiffusivityType diffusivity)
|
||||
{
|
||||
CV_INSTRUMENT_REGION();
|
||||
|
||||
Lflow.create(Lx.size(), Lx.type());
|
||||
|
||||
switch (diffusivity) {
|
||||
case KAZE::DIFF_PM_G1:
|
||||
pm_g1(Lx, Ly, Lflow, kcontrast);
|
||||
break;
|
||||
case KAZE::DIFF_PM_G2:
|
||||
CV_OCL_RUN(Lx.isUMat() && Ly.isUMat() && Lflow.isUMat(), ocl_pm_g2(Lx, Ly, Lflow, kcontrast));
|
||||
pm_g2(Lx, Ly, Lflow, kcontrast);
|
||||
break;
|
||||
case KAZE::DIFF_WEICKERT:
|
||||
weickert_diffusivity(Lx, Ly, Lflow, kcontrast);
|
||||
break;
|
||||
case KAZE::DIFF_CHARBONNIER:
|
||||
charbonnier_diffusivity(Lx, Ly, Lflow, kcontrast);
|
||||
break;
|
||||
default:
|
||||
CV_Error_(Error::StsError, ("Diffusivity is not supported: %d", static_cast<int>(diffusivity)));
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
} // namespace cv
|
||||
|
||||
#endif // __OPENCV_FEATURES_2D_AKAZE_DIFFUSION_HPP__
|
||||
@@ -0,0 +1,192 @@
|
||||
//=============================================================================
|
||||
//
|
||||
// fed.cpp
|
||||
// Authors: Pablo F. Alcantarilla (1), Jesus Nuevo (2)
|
||||
// Institutions: Georgia Institute of Technology (1)
|
||||
// TrueVision Solutions (2)
|
||||
// Date: 15/09/2013
|
||||
// Email: pablofdezalc@gmail.com
|
||||
//
|
||||
// AKAZE Features Copyright 2013, Pablo F. Alcantarilla, Jesus Nuevo
|
||||
// All Rights Reserved
|
||||
// See LICENSE for the license information
|
||||
//=============================================================================
|
||||
|
||||
/**
|
||||
* @file fed.cpp
|
||||
* @brief Functions for performing Fast Explicit Diffusion and building the
|
||||
* nonlinear scale space
|
||||
* @date Sep 15, 2013
|
||||
* @author Pablo F. Alcantarilla, Jesus Nuevo
|
||||
* @note This code is derived from FED/FJ library from Grewenig et al.,
|
||||
* The FED/FJ library allows solving more advanced problems
|
||||
* Please look at the following papers for more information about FED:
|
||||
* [1] S. Grewenig, J. Weickert, C. Schroers, A. Bruhn. Cyclic Schemes for
|
||||
* PDE-Based Image Analysis. Technical Report No. 327, Department of Mathematics,
|
||||
* Saarland University, Saarbrücken, Germany, March 2013
|
||||
* [2] S. Grewenig, J. Weickert, A. Bruhn. From box filtering to fast explicit diffusion.
|
||||
* DAGM, 2010
|
||||
*
|
||||
*/
|
||||
#include "../precomp.hpp"
|
||||
#include "fed.h"
|
||||
|
||||
using namespace std;
|
||||
|
||||
//*************************************************************************************
|
||||
//*************************************************************************************
|
||||
|
||||
/**
|
||||
* @brief This function allocates an array of the least number of time steps such
|
||||
* that a certain stopping time for the whole process can be obtained and fills
|
||||
* it with the respective FED time step sizes for one cycle
|
||||
* The function returns the number of time steps per cycle or 0 on failure
|
||||
* @param T Desired process stopping time
|
||||
* @param M Desired number of cycles
|
||||
* @param tau_max Stability limit for the explicit scheme
|
||||
* @param reordering Reordering flag
|
||||
* @param tau The vector with the dynamic step sizes
|
||||
*/
|
||||
int fed_tau_by_process_time(const float& T, const int& M, const float& tau_max,
|
||||
const bool& reordering, std::vector<float>& tau) {
|
||||
// All cycles have the same fraction of the stopping time
|
||||
return fed_tau_by_cycle_time(T/(float)M,tau_max,reordering,tau);
|
||||
}
|
||||
|
||||
//*************************************************************************************
|
||||
//*************************************************************************************
|
||||
|
||||
/**
|
||||
* @brief This function allocates an array of the least number of time steps such
|
||||
* that a certain stopping time for the whole process can be obtained and fills it
|
||||
* it with the respective FED time step sizes for one cycle
|
||||
* The function returns the number of time steps per cycle or 0 on failure
|
||||
* @param t Desired cycle stopping time
|
||||
* @param tau_max Stability limit for the explicit scheme
|
||||
* @param reordering Reordering flag
|
||||
* @param tau The vector with the dynamic step sizes
|
||||
*/
|
||||
int fed_tau_by_cycle_time(const float& t, const float& tau_max,
|
||||
const bool& reordering, std::vector<float> &tau) {
|
||||
int n = 0; // Number of time steps
|
||||
float scale = 0.0; // Ratio of t we search to maximal t
|
||||
|
||||
// Compute necessary number of time steps
|
||||
n = cvCeil(sqrtf(3.0f*t/tau_max+0.25f)-0.5f-1.0e-8f);
|
||||
scale = 3.0f*t/(tau_max*(float)(n*(n+1)));
|
||||
|
||||
// Call internal FED time step creation routine
|
||||
return fed_tau_internal(n,scale,tau_max,reordering,tau);
|
||||
}
|
||||
|
||||
//*************************************************************************************
|
||||
//*************************************************************************************
|
||||
|
||||
/**
|
||||
* @brief This function allocates an array of time steps and fills it with FED
|
||||
* time step sizes
|
||||
* The function returns the number of time steps per cycle or 0 on failure
|
||||
* @param n Number of internal steps
|
||||
* @param scale Ratio of t we search to maximal t
|
||||
* @param tau_max Stability limit for the explicit scheme
|
||||
* @param reordering Reordering flag
|
||||
* @param tau The vector with the dynamic step sizes
|
||||
*/
|
||||
int fed_tau_internal(const int& n, const float& scale, const float& tau_max,
|
||||
const bool& reordering, std::vector<float> &tau) {
|
||||
float c = 0.0, d = 0.0; // Time savers
|
||||
vector<float> tauh; // Helper vector for unsorted taus
|
||||
|
||||
if (n <= 0) {
|
||||
return 0;
|
||||
}
|
||||
|
||||
// Allocate memory for the time step size
|
||||
tau = vector<float>(n);
|
||||
|
||||
if (reordering) {
|
||||
tauh = vector<float>(n);
|
||||
}
|
||||
|
||||
// Compute time saver
|
||||
c = 1.0f / (4.0f * (float)n + 2.0f);
|
||||
d = scale * tau_max / 2.0f;
|
||||
|
||||
// Set up originally ordered tau vector
|
||||
for (int k = 0; k < n; ++k) {
|
||||
float h = cosf((float)CV_PI * (2.0f * (float)k + 1.0f) * c);
|
||||
|
||||
if (reordering) {
|
||||
tauh[k] = d / (h * h);
|
||||
}
|
||||
else {
|
||||
tau[k] = d / (h * h);
|
||||
}
|
||||
}
|
||||
|
||||
// Permute list of time steps according to chosen reordering function
|
||||
int kappa = 0, prime = 0;
|
||||
|
||||
if (reordering == true) {
|
||||
// Choose kappa cycle with k = n/2
|
||||
// This is a heuristic. We can use Leja ordering instead!!
|
||||
kappa = n / 2;
|
||||
|
||||
// Get modulus for permutation
|
||||
prime = n + 1;
|
||||
|
||||
while (!fed_is_prime_internal(prime)) {
|
||||
prime++;
|
||||
}
|
||||
|
||||
// Perform permutation
|
||||
for (int k = 0, l = 0; l < n; ++k, ++l) {
|
||||
int index = 0;
|
||||
while ((index = ((k+1)*kappa) % prime - 1) >= n) {
|
||||
k++;
|
||||
}
|
||||
|
||||
tau[l] = tauh[index];
|
||||
}
|
||||
}
|
||||
|
||||
return n;
|
||||
}
|
||||
|
||||
//*************************************************************************************
|
||||
//*************************************************************************************
|
||||
|
||||
/**
|
||||
* @brief This function checks if a number is prime or not
|
||||
* @param number Number to check if it is prime or not
|
||||
* @return true if the number is prime
|
||||
*/
|
||||
bool fed_is_prime_internal(const int& number) {
|
||||
bool is_prime = false;
|
||||
|
||||
if (number <= 1) {
|
||||
return false;
|
||||
}
|
||||
else if (number == 1 || number == 2 || number == 3 || number == 5 || number == 7) {
|
||||
return true;
|
||||
}
|
||||
else if ((number % 2) == 0 || (number % 3) == 0 || (number % 5) == 0 || (number % 7) == 0) {
|
||||
return false;
|
||||
}
|
||||
else {
|
||||
is_prime = true;
|
||||
int upperLimit = (int)sqrt(1.0f + number);
|
||||
int divisor = 11;
|
||||
|
||||
while (divisor <= upperLimit ) {
|
||||
if (number % divisor == 0)
|
||||
{
|
||||
is_prime = false;
|
||||
}
|
||||
|
||||
divisor +=2;
|
||||
}
|
||||
|
||||
return is_prime;
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,25 @@
|
||||
#ifndef __OPENCV_FEATURES_2D_FED_H__
|
||||
#define __OPENCV_FEATURES_2D_FED_H__
|
||||
|
||||
//******************************************************************************
|
||||
//******************************************************************************
|
||||
|
||||
// Includes
|
||||
#include <vector>
|
||||
|
||||
//*************************************************************************************
|
||||
//*************************************************************************************
|
||||
|
||||
// Declaration of functions
|
||||
int fed_tau_by_process_time(const float& T, const int& M, const float& tau_max,
|
||||
const bool& reordering, std::vector<float>& tau);
|
||||
int fed_tau_by_cycle_time(const float& t, const float& tau_max,
|
||||
const bool& reordering, std::vector<float> &tau) ;
|
||||
int fed_tau_internal(const int& n, const float& scale, const float& tau_max,
|
||||
const bool& reordering, std::vector<float> &tau);
|
||||
bool fed_is_prime_internal(const int& number);
|
||||
|
||||
//*************************************************************************************
|
||||
//*************************************************************************************
|
||||
|
||||
#endif // __OPENCV_FEATURES_2D_FED_H__
|
||||
@@ -0,0 +1,542 @@
|
||||
//=============================================================================
|
||||
//
|
||||
// nldiffusion_functions.cpp
|
||||
// Author: Pablo F. Alcantarilla
|
||||
// Institution: University d'Auvergne
|
||||
// Address: Clermont Ferrand, France
|
||||
// Date: 27/12/2011
|
||||
// Email: pablofdezalc@gmail.com
|
||||
//
|
||||
// KAZE Features Copyright 2012, Pablo F. Alcantarilla
|
||||
// All Rights Reserved
|
||||
// See LICENSE for the license information
|
||||
//=============================================================================
|
||||
|
||||
/**
|
||||
* @file nldiffusion_functions.cpp
|
||||
* @brief Functions for non-linear diffusion applications:
|
||||
* 2D Gaussian Derivatives
|
||||
* Perona and Malik conductivity equations
|
||||
* Perona and Malik evolution
|
||||
* @date Dec 27, 2011
|
||||
* @author Pablo F. Alcantarilla
|
||||
*/
|
||||
|
||||
#include "../precomp.hpp"
|
||||
#include "nldiffusion_functions.h"
|
||||
#include <iostream>
|
||||
|
||||
// Namespaces
|
||||
|
||||
/* ************************************************************************* */
|
||||
|
||||
namespace cv
|
||||
{
|
||||
using namespace std;
|
||||
|
||||
/* ************************************************************************* */
|
||||
/**
|
||||
* @brief This function smoothes an image with a Gaussian kernel
|
||||
* @param src Input image
|
||||
* @param dst Output image
|
||||
* @param ksize_x Kernel size in X-direction (horizontal)
|
||||
* @param ksize_y Kernel size in Y-direction (vertical)
|
||||
* @param sigma Kernel standard deviation
|
||||
*/
|
||||
void gaussian_2D_convolution(const cv::Mat& src, cv::Mat& dst, int ksize_x, int ksize_y, float sigma) {
|
||||
|
||||
int ksize_x_ = 0, ksize_y_ = 0;
|
||||
|
||||
// Compute an appropriate kernel size according to the specified sigma
|
||||
if (sigma > ksize_x || sigma > ksize_y || ksize_x == 0 || ksize_y == 0) {
|
||||
ksize_x_ = cvCeil(2.0f*(1.0f + (sigma - 0.8f) / (0.3f)));
|
||||
ksize_y_ = ksize_x_;
|
||||
}
|
||||
|
||||
// The kernel size must be and odd number
|
||||
if ((ksize_x_ % 2) == 0) {
|
||||
ksize_x_ += 1;
|
||||
}
|
||||
|
||||
if ((ksize_y_ % 2) == 0) {
|
||||
ksize_y_ += 1;
|
||||
}
|
||||
|
||||
// Perform the Gaussian Smoothing with border replication
|
||||
GaussianBlur(src, dst, Size(ksize_x_, ksize_y_), sigma, sigma, BORDER_REPLICATE);
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
/**
|
||||
* @brief This function computes image derivatives with Scharr kernel
|
||||
* @param src Input image
|
||||
* @param dst Output image
|
||||
* @param xorder Derivative order in X-direction (horizontal)
|
||||
* @param yorder Derivative order in Y-direction (vertical)
|
||||
* @note Scharr operator approximates better rotation invariance than
|
||||
* other stencils such as Sobel. See Weickert and Scharr,
|
||||
* A Scheme for Coherence-Enhancing Diffusion Filtering with Optimized Rotation Invariance,
|
||||
* Journal of Visual Communication and Image Representation 2002
|
||||
*/
|
||||
void image_derivatives_scharr(const cv::Mat& src, cv::Mat& dst, int xorder, int yorder) {
|
||||
Scharr(src, dst, CV_32F, xorder, yorder, 1.0, 0, BORDER_DEFAULT);
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
/**
|
||||
* @brief This function computes the Perona and Malik conductivity coefficient g1
|
||||
* g1 = exp(-|dL|^2/k^2)
|
||||
* @param _Lx First order image derivative in X-direction (horizontal)
|
||||
* @param _Ly First order image derivative in Y-direction (vertical)
|
||||
* @param _dst Output image
|
||||
* @param k Contrast factor parameter
|
||||
*/
|
||||
void pm_g1(InputArray _Lx, InputArray _Ly, OutputArray _dst, float k) {
|
||||
_dst.create(_Lx.size(), _Lx.type());
|
||||
Mat Lx = _Lx.getMat();
|
||||
Mat Ly = _Ly.getMat();
|
||||
Mat dst = _dst.getMat();
|
||||
|
||||
Size sz = Lx.size();
|
||||
float inv_k = 1.0f / (k*k);
|
||||
for (int y = 0; y < sz.height; y++) {
|
||||
|
||||
const float* Lx_row = Lx.ptr<float>(y);
|
||||
const float* Ly_row = Ly.ptr<float>(y);
|
||||
float* dst_row = dst.ptr<float>(y);
|
||||
|
||||
for (int x = 0; x < sz.width; x++) {
|
||||
dst_row[x] = (-inv_k*(Lx_row[x]*Lx_row[x] + Ly_row[x]*Ly_row[x]));
|
||||
}
|
||||
}
|
||||
|
||||
exp(dst, dst);
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
/**
|
||||
* @brief This function computes the Perona and Malik conductivity coefficient g2
|
||||
* g2 = 1 / (1 + dL^2 / k^2)
|
||||
* @param _Lx First order image derivative in X-direction (horizontal)
|
||||
* @param _Ly First order image derivative in Y-direction (vertical)
|
||||
* @param _dst Output image
|
||||
* @param k Contrast factor parameter
|
||||
*/
|
||||
void pm_g2(InputArray _Lx, InputArray _Ly, OutputArray _dst, float k) {
|
||||
CV_INSTRUMENT_REGION();
|
||||
|
||||
_dst.create(_Lx.size(), _Lx.type());
|
||||
Mat Lx = _Lx.getMat();
|
||||
Mat Ly = _Ly.getMat();
|
||||
Mat dst = _dst.getMat();
|
||||
|
||||
Size sz = Lx.size();
|
||||
dst.create(sz, Lx.type());
|
||||
float k2inv = 1.0f / (k * k);
|
||||
|
||||
for(int y = 0; y < sz.height; y++) {
|
||||
const float *Lx_row = Lx.ptr<float>(y);
|
||||
const float *Ly_row = Ly.ptr<float>(y);
|
||||
float* dst_row = dst.ptr<float>(y);
|
||||
for(int x = 0; x < sz.width; x++) {
|
||||
dst_row[x] = 1.0f / (1.0f + ((Lx_row[x] * Lx_row[x] + Ly_row[x] * Ly_row[x]) * k2inv));
|
||||
}
|
||||
}
|
||||
}
|
||||
/* ************************************************************************* */
|
||||
/**
|
||||
* @brief This function computes Weickert conductivity coefficient gw
|
||||
* @param _Lx First order image derivative in X-direction (horizontal)
|
||||
* @param _Ly First order image derivative in Y-direction (vertical)
|
||||
* @param _dst Output image
|
||||
* @param k Contrast factor parameter
|
||||
* @note For more information check the following paper: J. Weickert
|
||||
* Applications of nonlinear diffusion in image processing and computer vision,
|
||||
* Proceedings of Algorithmy 2000
|
||||
*/
|
||||
void weickert_diffusivity(InputArray _Lx, InputArray _Ly, OutputArray _dst, float k) {
|
||||
_dst.create(_Lx.size(), _Lx.type());
|
||||
Mat Lx = _Lx.getMat();
|
||||
Mat Ly = _Ly.getMat();
|
||||
Mat dst = _dst.getMat();
|
||||
|
||||
Size sz = Lx.size();
|
||||
float inv_k = 1.0f / (k*k);
|
||||
for (int y = 0; y < sz.height; y++) {
|
||||
|
||||
const float* Lx_row = Lx.ptr<float>(y);
|
||||
const float* Ly_row = Ly.ptr<float>(y);
|
||||
float* dst_row = dst.ptr<float>(y);
|
||||
|
||||
for (int x = 0; x < sz.width; x++) {
|
||||
float dL = inv_k*(Lx_row[x]*Lx_row[x] + Ly_row[x]*Ly_row[x]);
|
||||
dst_row[x] = -3.315f/(dL*dL*dL*dL);
|
||||
}
|
||||
}
|
||||
|
||||
exp(dst, dst);
|
||||
dst = 1.0 - dst;
|
||||
}
|
||||
|
||||
|
||||
/* ************************************************************************* */
|
||||
/**
|
||||
* @brief This function computes Charbonnier conductivity coefficient gc
|
||||
* gc = 1 / sqrt(1 + dL^2 / k^2)
|
||||
* @param _Lx First order image derivative in X-direction (horizontal)
|
||||
* @param _Ly First order image derivative in Y-direction (vertical)
|
||||
* @param _dst Output image
|
||||
* @param k Contrast factor parameter
|
||||
* @note For more information check the following paper: J. Weickert
|
||||
* Applications of nonlinear diffusion in image processing and computer vision,
|
||||
* Proceedings of Algorithmy 2000
|
||||
*/
|
||||
void charbonnier_diffusivity(InputArray _Lx, InputArray _Ly, OutputArray _dst, float k) {
|
||||
_dst.create(_Lx.size(), _Lx.type());
|
||||
Mat Lx = _Lx.getMat();
|
||||
Mat Ly = _Ly.getMat();
|
||||
Mat dst = _dst.getMat();
|
||||
|
||||
Size sz = Lx.size();
|
||||
float inv_k = 1.0f / (k*k);
|
||||
for (int y = 0; y < sz.height; y++) {
|
||||
|
||||
const float* Lx_row = Lx.ptr<float>(y);
|
||||
const float* Ly_row = Ly.ptr<float>(y);
|
||||
float* dst_row = dst.ptr<float>(y);
|
||||
|
||||
for (int x = 0; x < sz.width; x++) {
|
||||
float den = sqrt(1.0f+inv_k*(Lx_row[x]*Lx_row[x] + Ly_row[x]*Ly_row[x]));
|
||||
dst_row[x] = 1.0f / den;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
/* ************************************************************************* */
|
||||
/**
|
||||
* @brief This function computes a good empirical value for the k contrast factor
|
||||
* given an input image, the percentile (0-1), the gradient scale and the number of
|
||||
* bins in the histogram
|
||||
* @param img Input image
|
||||
* @param perc Percentile of the image gradient histogram (0-1)
|
||||
* @param gscale Scale for computing the image gradient histogram
|
||||
* @param nbins Number of histogram bins
|
||||
* @param ksize_x Kernel size in X-direction (horizontal) for the Gaussian smoothing kernel
|
||||
* @param ksize_y Kernel size in Y-direction (vertical) for the Gaussian smoothing kernel
|
||||
* @return k contrast factor
|
||||
*/
|
||||
float compute_k_percentile(const cv::Mat& img, float perc, float gscale, int nbins, int ksize_x, int ksize_y) {
|
||||
CV_INSTRUMENT_REGION();
|
||||
|
||||
int nbin = 0, nelements = 0, nthreshold = 0, k = 0;
|
||||
float kperc = 0.0, modg = 0.0;
|
||||
float npoints = 0.0;
|
||||
float hmax = 0.0;
|
||||
|
||||
// Create the array for the histogram
|
||||
std::vector<int> hist(nbins, 0);
|
||||
|
||||
// Create the matrices
|
||||
Mat gaussian = Mat::zeros(img.rows, img.cols, CV_32F);
|
||||
Mat Lx = Mat::zeros(img.rows, img.cols, CV_32F);
|
||||
Mat Ly = Mat::zeros(img.rows, img.cols, CV_32F);
|
||||
|
||||
// Perform the Gaussian convolution
|
||||
gaussian_2D_convolution(img, gaussian, ksize_x, ksize_y, gscale);
|
||||
|
||||
// Compute the Gaussian derivatives Lx and Ly
|
||||
Scharr(gaussian, Lx, CV_32F, 1, 0, 1, 0, cv::BORDER_DEFAULT);
|
||||
Scharr(gaussian, Ly, CV_32F, 0, 1, 1, 0, cv::BORDER_DEFAULT);
|
||||
|
||||
// Skip the borders for computing the histogram
|
||||
for (int i = 1; i < gaussian.rows - 1; i++) {
|
||||
const float *lx = Lx.ptr<float>(i);
|
||||
const float *ly = Ly.ptr<float>(i);
|
||||
for (int j = 1; j < gaussian.cols - 1; j++) {
|
||||
modg = lx[j]*lx[j] + ly[j]*ly[j];
|
||||
|
||||
// Get the maximum
|
||||
if (modg > hmax) {
|
||||
hmax = modg;
|
||||
}
|
||||
}
|
||||
}
|
||||
hmax = sqrt(hmax);
|
||||
// Skip the borders for computing the histogram
|
||||
for (int i = 1; i < gaussian.rows - 1; i++) {
|
||||
const float *lx = Lx.ptr<float>(i);
|
||||
const float *ly = Ly.ptr<float>(i);
|
||||
for (int j = 1; j < gaussian.cols - 1; j++) {
|
||||
modg = lx[j]*lx[j] + ly[j]*ly[j];
|
||||
|
||||
// Find the correspondent bin
|
||||
if (modg != 0.0) {
|
||||
nbin = (int)floor(nbins*(sqrt(modg) / hmax));
|
||||
|
||||
if (nbin == nbins) {
|
||||
nbin--;
|
||||
}
|
||||
|
||||
hist[nbin]++;
|
||||
npoints++;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Now find the perc of the histogram percentile
|
||||
nthreshold = (int)(npoints*perc);
|
||||
|
||||
for (k = 0; nelements < nthreshold && k < nbins; k++) {
|
||||
nelements = nelements + hist[k];
|
||||
}
|
||||
|
||||
if (nelements < nthreshold) {
|
||||
kperc = 0.03f;
|
||||
}
|
||||
else {
|
||||
kperc = hmax*((float)(k) / (float)nbins);
|
||||
}
|
||||
|
||||
return kperc;
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
/**
|
||||
* @brief This function computes Scharr image derivatives
|
||||
* @param src Input image
|
||||
* @param dst Output image
|
||||
* @param xorder Derivative order in X-direction (horizontal)
|
||||
* @param yorder Derivative order in Y-direction (vertical)
|
||||
* @param scale Scale factor for the derivative size
|
||||
*/
|
||||
void compute_scharr_derivatives(const cv::Mat& src, cv::Mat& dst, int xorder, int yorder, int scale) {
|
||||
Mat kx, ky;
|
||||
compute_derivative_kernels(kx, ky, xorder, yorder, scale);
|
||||
sepFilter2D(src, dst, CV_32F, kx, ky);
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
/**
|
||||
* @brief Compute derivative kernels for sizes different than 3
|
||||
* @param _kx Horizontal kernel ues
|
||||
* @param _ky Vertical kernel values
|
||||
* @param dx Derivative order in X-direction (horizontal)
|
||||
* @param dy Derivative order in Y-direction (vertical)
|
||||
* @param scale Scale factor or derivative size
|
||||
*/
|
||||
void compute_derivative_kernels(cv::OutputArray _kx, cv::OutputArray _ky, int dx, int dy, int scale) {
|
||||
CV_INSTRUMENT_REGION();
|
||||
|
||||
int ksize = 3 + 2 * (scale - 1);
|
||||
|
||||
// The standard Scharr kernel
|
||||
if (scale == 1) {
|
||||
getDerivKernels(_kx, _ky, dx, dy, 0, true, CV_32F);
|
||||
return;
|
||||
}
|
||||
|
||||
_kx.create(ksize, 1, CV_32F, -1, true);
|
||||
_ky.create(ksize, 1, CV_32F, -1, true);
|
||||
Mat kx = _kx.getMat();
|
||||
Mat ky = _ky.getMat();
|
||||
std::vector<float> kerI;
|
||||
|
||||
float w = 10.0f / 3.0f;
|
||||
float norm = 1.0f / (2.0f*scale*(w + 2.0f));
|
||||
|
||||
for (int k = 0; k < 2; k++) {
|
||||
Mat* kernel = k == 0 ? &kx : &ky;
|
||||
int order = k == 0 ? dx : dy;
|
||||
kerI.assign(ksize, 0.0f);
|
||||
|
||||
if (order == 0) {
|
||||
kerI[0] = norm, kerI[ksize / 2] = w*norm, kerI[ksize - 1] = norm;
|
||||
}
|
||||
else if (order == 1) {
|
||||
kerI[0] = -1, kerI[ksize / 2] = 0, kerI[ksize - 1] = 1;
|
||||
}
|
||||
|
||||
Mat temp(kernel->rows, kernel->cols, CV_32F, &kerI[0]);
|
||||
temp.copyTo(*kernel);
|
||||
}
|
||||
}
|
||||
|
||||
class Nld_Step_Scalar_Invoker : public cv::ParallelLoopBody
|
||||
{
|
||||
public:
|
||||
Nld_Step_Scalar_Invoker(cv::Mat& Ld, const cv::Mat& c, cv::Mat& Lstep, float _stepsize)
|
||||
: _Ld(&Ld)
|
||||
, _c(&c)
|
||||
, _Lstep(&Lstep)
|
||||
, stepsize(_stepsize)
|
||||
{
|
||||
}
|
||||
|
||||
virtual ~Nld_Step_Scalar_Invoker()
|
||||
{
|
||||
|
||||
}
|
||||
|
||||
void operator()(const cv::Range& range) const CV_OVERRIDE
|
||||
{
|
||||
cv::Mat& Ld = *_Ld;
|
||||
const cv::Mat& c = *_c;
|
||||
cv::Mat& Lstep = *_Lstep;
|
||||
|
||||
for (int i = range.start; i < range.end; i++)
|
||||
{
|
||||
const float *c_prev = c.ptr<float>(i - 1);
|
||||
const float *c_curr = c.ptr<float>(i);
|
||||
const float *c_next = c.ptr<float>(i + 1);
|
||||
const float *ld_prev = Ld.ptr<float>(i - 1);
|
||||
const float *ld_curr = Ld.ptr<float>(i);
|
||||
const float *ld_next = Ld.ptr<float>(i + 1);
|
||||
|
||||
float *dst = Lstep.ptr<float>(i);
|
||||
|
||||
for (int j = 1; j < Lstep.cols - 1; j++)
|
||||
{
|
||||
float xpos = (c_curr[j] + c_curr[j+1])*(ld_curr[j+1] - ld_curr[j]);
|
||||
float xneg = (c_curr[j-1] + c_curr[j]) *(ld_curr[j] - ld_curr[j-1]);
|
||||
float ypos = (c_curr[j] + c_next[j]) *(ld_next[j] - ld_curr[j]);
|
||||
float yneg = (c_prev[j] + c_curr[j]) *(ld_curr[j] - ld_prev[j]);
|
||||
dst[j] = 0.5f*stepsize*(xpos - xneg + ypos - yneg);
|
||||
}
|
||||
}
|
||||
}
|
||||
private:
|
||||
cv::Mat * _Ld;
|
||||
const cv::Mat * _c;
|
||||
cv::Mat * _Lstep;
|
||||
float stepsize;
|
||||
};
|
||||
|
||||
/* ************************************************************************* */
|
||||
/**
|
||||
* @brief This function performs a scalar non-linear diffusion step
|
||||
* @param Ld Output image in the evolution
|
||||
* @param c Conductivity image
|
||||
* @param Lstep Previous image in the evolution
|
||||
* @param stepsize The step size in time units
|
||||
* @note Forward Euler Scheme 3x3 stencil
|
||||
* The function c is a scalar value that depends on the gradient norm
|
||||
* dL_by_ds = d(c dL_by_dx)_by_dx + d(c dL_by_dy)_by_dy
|
||||
*/
|
||||
void nld_step_scalar(cv::Mat& Ld, const cv::Mat& c, cv::Mat& Lstep, float stepsize) {
|
||||
CV_INSTRUMENT_REGION();
|
||||
|
||||
cv::parallel_for_(cv::Range(1, Lstep.rows - 1), Nld_Step_Scalar_Invoker(Ld, c, Lstep, stepsize), (double)Ld.total()/(1 << 16));
|
||||
|
||||
float xneg, xpos, yneg, ypos;
|
||||
float* dst = Lstep.ptr<float>(0);
|
||||
const float* cprv = NULL;
|
||||
const float* ccur = c.ptr<float>(0);
|
||||
const float* cnxt = c.ptr<float>(1);
|
||||
const float* ldprv = NULL;
|
||||
const float* ldcur = Ld.ptr<float>(0);
|
||||
const float* ldnxt = Ld.ptr<float>(1);
|
||||
for (int j = 1; j < Lstep.cols - 1; j++) {
|
||||
xpos = (ccur[j] + ccur[j+1]) * (ldcur[j+1] - ldcur[j]);
|
||||
xneg = (ccur[j-1] + ccur[j]) * (ldcur[j] - ldcur[j-1]);
|
||||
ypos = (ccur[j] + cnxt[j]) * (ldnxt[j] - ldcur[j]);
|
||||
dst[j] = 0.5f*stepsize*(xpos - xneg + ypos);
|
||||
}
|
||||
|
||||
dst = Lstep.ptr<float>(Lstep.rows - 1);
|
||||
ccur = c.ptr<float>(Lstep.rows - 1);
|
||||
cprv = c.ptr<float>(Lstep.rows - 2);
|
||||
ldcur = Ld.ptr<float>(Lstep.rows - 1);
|
||||
ldprv = Ld.ptr<float>(Lstep.rows - 2);
|
||||
|
||||
for (int j = 1; j < Lstep.cols - 1; j++) {
|
||||
xpos = (ccur[j] + ccur[j+1]) * (ldcur[j+1] - ldcur[j]);
|
||||
xneg = (ccur[j-1] + ccur[j]) * (ldcur[j] - ldcur[j-1]);
|
||||
yneg = (cprv[j] + ccur[j]) * (ldcur[j] - ldprv[j]);
|
||||
dst[j] = 0.5f*stepsize*(xpos - xneg - yneg);
|
||||
}
|
||||
|
||||
ccur = c.ptr<float>(1);
|
||||
ldcur = Ld.ptr<float>(1);
|
||||
cprv = c.ptr<float>(0);
|
||||
ldprv = Ld.ptr<float>(0);
|
||||
|
||||
int r0 = Lstep.cols - 1;
|
||||
int r1 = Lstep.cols - 2;
|
||||
|
||||
for (int i = 1; i < Lstep.rows - 1; i++) {
|
||||
cnxt = c.ptr<float>(i + 1);
|
||||
ldnxt = Ld.ptr<float>(i + 1);
|
||||
dst = Lstep.ptr<float>(i);
|
||||
|
||||
xpos = (ccur[0] + ccur[1]) * (ldcur[1] - ldcur[0]);
|
||||
ypos = (ccur[0] + cnxt[0]) * (ldnxt[0] - ldcur[0]);
|
||||
yneg = (cprv[0] + ccur[0]) * (ldcur[0] - ldprv[0]);
|
||||
dst[0] = 0.5f*stepsize*(xpos + ypos - yneg);
|
||||
|
||||
xneg = (ccur[r1] + ccur[r0]) * (ldcur[r0] - ldcur[r1]);
|
||||
ypos = (ccur[r0] + cnxt[r0]) * (ldnxt[r0] - ldcur[r0]);
|
||||
yneg = (cprv[r0] + ccur[r0]) * (ldcur[r0] - ldprv[r0]);
|
||||
dst[r0] = 0.5f*stepsize*(-xneg + ypos - yneg);
|
||||
|
||||
cprv = ccur;
|
||||
ccur = cnxt;
|
||||
ldprv = ldcur;
|
||||
ldcur = ldnxt;
|
||||
}
|
||||
Ld += Lstep;
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
/**
|
||||
* @brief This function downsamples the input image using OpenCV resize
|
||||
* @param src Input image to be downsampled
|
||||
* @param dst Output image with half of the resolution of the input image
|
||||
*/
|
||||
void halfsample_image(const cv::Mat& src, cv::Mat& dst) {
|
||||
// Make sure the destination image is of the right size
|
||||
CV_Assert(src.cols / 2 == dst.cols);
|
||||
CV_Assert(src.rows / 2 == dst.rows);
|
||||
resize(src, dst, dst.size(), 0, 0, cv::INTER_AREA);
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
/**
|
||||
* @brief This function checks if a given pixel is a maximum in a local neighbourhood
|
||||
* @param img Input image where we will perform the maximum search
|
||||
* @param dsize Half size of the neighbourhood
|
||||
* @param value Response value at (x,y) position
|
||||
* @param row Image row coordinate
|
||||
* @param col Image column coordinate
|
||||
* @param same_img Flag to indicate if the image value at (x,y) is in the input image
|
||||
* @return 1->is maximum, 0->otherwise
|
||||
*/
|
||||
bool check_maximum_neighbourhood(const cv::Mat& img, int dsize, float value, int row, int col, bool same_img) {
|
||||
|
||||
bool response = true;
|
||||
|
||||
for (int i = row - dsize; i <= row + dsize; i++) {
|
||||
for (int j = col - dsize; j <= col + dsize; j++) {
|
||||
if (i >= 0 && i < img.rows && j >= 0 && j < img.cols) {
|
||||
if (same_img == true) {
|
||||
if (i != row || j != col) {
|
||||
if ((*(img.ptr<float>(i)+j)) > value) {
|
||||
response = false;
|
||||
return response;
|
||||
}
|
||||
}
|
||||
}
|
||||
else {
|
||||
if ((*(img.ptr<float>(i)+j)) > value) {
|
||||
response = false;
|
||||
return response;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return response;
|
||||
}
|
||||
|
||||
}
|
||||
@@ -0,0 +1,47 @@
|
||||
/**
|
||||
* @file nldiffusion_functions.h
|
||||
* @brief Functions for non-linear diffusion applications:
|
||||
* 2D Gaussian Derivatives
|
||||
* Perona and Malik conductivity equations
|
||||
* Perona and Malik evolution
|
||||
* @date Dec 27, 2011
|
||||
* @author Pablo F. Alcantarilla
|
||||
*/
|
||||
|
||||
#ifndef __OPENCV_FEATURES_2D_NLDIFFUSION_FUNCTIONS_H__
|
||||
#define __OPENCV_FEATURES_2D_NLDIFFUSION_FUNCTIONS_H__
|
||||
|
||||
/* ************************************************************************* */
|
||||
// Declaration of functions
|
||||
|
||||
namespace cv
|
||||
{
|
||||
|
||||
// Gaussian 2D convolution
|
||||
void gaussian_2D_convolution(const cv::Mat& src, cv::Mat& dst, int ksize_x, int ksize_y, float sigma);
|
||||
|
||||
// Diffusivity functions
|
||||
void pm_g1(InputArray Lx, InputArray Ly, OutputArray dst, float k);
|
||||
void pm_g2(InputArray Lx, InputArray Ly, OutputArray dst, float k);
|
||||
void weickert_diffusivity(InputArray Lx, InputArray Ly, OutputArray dst, float k);
|
||||
void charbonnier_diffusivity(InputArray Lx, InputArray Ly, OutputArray dst, float k);
|
||||
|
||||
float compute_k_percentile(const cv::Mat& img, float perc, float gscale, int nbins, int ksize_x, int ksize_y);
|
||||
|
||||
// Image derivatives
|
||||
void compute_scharr_derivatives(const cv::Mat& src, cv::Mat& dst, int xorder, int yorder, int scale);
|
||||
void compute_derivative_kernels(cv::OutputArray _kx, cv::OutputArray _ky, int dx, int dy, int scale);
|
||||
void image_derivatives_scharr(const cv::Mat& src, cv::Mat& dst, int xorder, int yorder);
|
||||
|
||||
// Nonlinear diffusion filtering scalar step
|
||||
void nld_step_scalar(cv::Mat& Ld, const cv::Mat& c, cv::Mat& Lstep, float stepsize);
|
||||
|
||||
// For non-maxima suppression
|
||||
bool check_maximum_neighbourhood(const cv::Mat& img, int dsize, float value, int row, int col, bool same_img);
|
||||
|
||||
// Image downsampling
|
||||
void halfsample_image(const cv::Mat& src, cv::Mat& dst);
|
||||
|
||||
}
|
||||
|
||||
#endif
|
||||
@@ -0,0 +1,42 @@
|
||||
#ifndef __OPENCV_FEATURES_2D_KAZE_UTILS_H__
|
||||
#define __OPENCV_FEATURES_2D_KAZE_UTILS_H__
|
||||
|
||||
/* ************************************************************************* */
|
||||
/**
|
||||
* @brief This function computes the value of a 2D Gaussian function
|
||||
* @param x X Position
|
||||
* @param y Y Position
|
||||
* @param sigma Standard Deviation
|
||||
*/
|
||||
inline float gaussian(float x, float y, float sigma) {
|
||||
return expf(-(x*x + y*y) / (2.0f*sigma*sigma));
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
/**
|
||||
* @brief This function checks descriptor limits
|
||||
* @param x X Position
|
||||
* @param y Y Position
|
||||
* @param width Image width
|
||||
* @param height Image height
|
||||
*/
|
||||
inline void checkDescriptorLimits(int &x, int &y, int width, int height) {
|
||||
|
||||
if (x < 0) {
|
||||
x = 0;
|
||||
}
|
||||
|
||||
if (y < 0) {
|
||||
y = 0;
|
||||
}
|
||||
|
||||
if (x > width - 1) {
|
||||
x = width - 1;
|
||||
}
|
||||
|
||||
if (y > height - 1) {
|
||||
y = height - 1;
|
||||
}
|
||||
}
|
||||
|
||||
#endif
|
||||
@@ -0,0 +1,293 @@
|
||||
/*M///////////////////////////////////////////////////////////////////////////////////////
|
||||
//
|
||||
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
|
||||
//
|
||||
// By downloading, copying, installing or using the software you agree to this license.
|
||||
// If you do not agree to this license, do not download, install,
|
||||
// copy or use the software.
|
||||
//
|
||||
//
|
||||
// License Agreement
|
||||
// For Open Source Computer Vision Library
|
||||
//
|
||||
// Copyright (C) 2008, Willow Garage Inc., all rights reserved.
|
||||
// Third party copyrights are property of their respective owners.
|
||||
//
|
||||
// Redistribution and use in source and binary forms, with or without modification,
|
||||
// are permitted provided that the following conditions are met:
|
||||
//
|
||||
// * Redistribution's of source code must retain the above copyright notice,
|
||||
// this list of conditions and the following disclaimer.
|
||||
//
|
||||
// * Redistribution's in binary form must reproduce the above copyright notice,
|
||||
// this list of conditions and the following disclaimer in the documentation
|
||||
// and/or other materials provided with the distribution.
|
||||
//
|
||||
// * The name of Intel Corporation may not be used to endorse or promote products
|
||||
// derived from this software without specific prior written permission.
|
||||
//
|
||||
// This software is provided by the copyright holders and contributors "as is" and
|
||||
// any express or implied warranties, including, but not limited to, the implied
|
||||
// warranties of merchantability and fitness for a particular purpose are disclaimed.
|
||||
// In no event shall the Intel Corporation or contributors be liable for any direct,
|
||||
// indirect, incidental, special, exemplary, or consequential damages
|
||||
// (including, but not limited to, procurement of substitute goods or services;
|
||||
// loss of use, data, or profits; or business interruption) however caused
|
||||
// and on any theory of liability, whether in contract, strict liability,
|
||||
// or tort (including negligence or otherwise) arising in any way out of
|
||||
// the use of this software, even if advised of the possibility of such damage.
|
||||
//
|
||||
//M*/
|
||||
|
||||
#include "precomp.hpp"
|
||||
|
||||
namespace cv
|
||||
{
|
||||
|
||||
struct KeypointResponseGreaterThanOrEqualToThreshold
|
||||
{
|
||||
KeypointResponseGreaterThanOrEqualToThreshold(float _value) :
|
||||
value(_value)
|
||||
{
|
||||
}
|
||||
inline bool operator()(const KeyPoint& kpt) const
|
||||
{
|
||||
return kpt.response >= value;
|
||||
}
|
||||
float value;
|
||||
};
|
||||
|
||||
struct KeypointResponseGreater
|
||||
{
|
||||
inline bool operator()(const KeyPoint& kp1, const KeyPoint& kp2) const
|
||||
{
|
||||
return kp1.response > kp2.response;
|
||||
}
|
||||
};
|
||||
|
||||
// takes keypoints and culls them by the response
|
||||
void KeyPointsFilter::retainBest(std::vector<KeyPoint>& keypoints, int n_points)
|
||||
{
|
||||
//this is only necessary if the keypoints size is greater than the number of desired points.
|
||||
if( n_points >= 0 && keypoints.size() > (size_t)n_points )
|
||||
{
|
||||
if (n_points==0)
|
||||
{
|
||||
keypoints.clear();
|
||||
return;
|
||||
}
|
||||
//first use nth element to partition the keypoints into the best and worst.
|
||||
std::nth_element(keypoints.begin(), keypoints.begin() + n_points - 1, keypoints.end(), KeypointResponseGreater());
|
||||
//this is the boundary response, and in the case of FAST may be ambiguous
|
||||
float ambiguous_response = keypoints[n_points - 1].response;
|
||||
//use std::partition to grab all of the keypoints with the boundary response.
|
||||
std::vector<KeyPoint>::const_iterator new_end =
|
||||
std::partition(keypoints.begin() + n_points, keypoints.end(),
|
||||
KeypointResponseGreaterThanOrEqualToThreshold(ambiguous_response));
|
||||
//resize the keypoints, given this new end point. nth_element and partition reordered the points inplace
|
||||
keypoints.resize(new_end - keypoints.begin());
|
||||
}
|
||||
}
|
||||
|
||||
struct RoiPredicate
|
||||
{
|
||||
RoiPredicate( const Rect& _r ) : r(_r)
|
||||
{}
|
||||
|
||||
bool operator()( const KeyPoint& keyPt ) const
|
||||
{
|
||||
// workaround for https://github.com/opencv/opencv/issues/26016
|
||||
// To keep its behaviour, keyPt.pt casts to Point_<int>.
|
||||
return !r.contains( Point_<int>(keyPt.pt) );
|
||||
}
|
||||
|
||||
Rect r;
|
||||
};
|
||||
|
||||
void KeyPointsFilter::runByImageBorder( std::vector<KeyPoint>& keypoints, Size imageSize, int borderSize )
|
||||
{
|
||||
if( borderSize > 0)
|
||||
{
|
||||
if (imageSize.height <= borderSize * 2 || imageSize.width <= borderSize * 2)
|
||||
keypoints.clear();
|
||||
else
|
||||
keypoints.erase( std::remove_if(keypoints.begin(), keypoints.end(),
|
||||
RoiPredicate(Rect(Point(borderSize, borderSize),
|
||||
Point(imageSize.width - borderSize, imageSize.height - borderSize)))),
|
||||
keypoints.end() );
|
||||
}
|
||||
}
|
||||
|
||||
struct SizePredicate
|
||||
{
|
||||
SizePredicate( float _minSize, float _maxSize ) : minSize(_minSize), maxSize(_maxSize)
|
||||
{}
|
||||
|
||||
bool operator()( const KeyPoint& keyPt ) const
|
||||
{
|
||||
float size = keyPt.size;
|
||||
return (size < minSize) || (size > maxSize);
|
||||
}
|
||||
|
||||
float minSize, maxSize;
|
||||
};
|
||||
|
||||
void KeyPointsFilter::runByKeypointSize( std::vector<KeyPoint>& keypoints, float minSize, float maxSize )
|
||||
{
|
||||
CV_Assert( minSize >= 0 );
|
||||
CV_Assert( maxSize >= 0);
|
||||
CV_Assert( minSize <= maxSize );
|
||||
|
||||
keypoints.erase( std::remove_if(keypoints.begin(), keypoints.end(), SizePredicate(minSize, maxSize)),
|
||||
keypoints.end() );
|
||||
}
|
||||
|
||||
class MaskPredicate
|
||||
{
|
||||
public:
|
||||
MaskPredicate( const Mat& _mask ) : mask(_mask) {}
|
||||
bool operator() (const KeyPoint& key_pt) const
|
||||
{
|
||||
return mask.at<uchar>( (int)(key_pt.pt.y + 0.5f), (int)(key_pt.pt.x + 0.5f) ) == 0;
|
||||
}
|
||||
MaskPredicate& operator=(const MaskPredicate&) = delete;
|
||||
// To avoid -Wdeprecated-copy warning, copy constructor is needed.
|
||||
MaskPredicate(const MaskPredicate&) = default;
|
||||
|
||||
private:
|
||||
const Mat mask;
|
||||
};
|
||||
|
||||
void KeyPointsFilter::runByPixelsMask( std::vector<KeyPoint>& keypoints, const Mat& mask )
|
||||
{
|
||||
CV_INSTRUMENT_REGION();
|
||||
|
||||
if( mask.empty() )
|
||||
return;
|
||||
|
||||
keypoints.erase(std::remove_if(keypoints.begin(), keypoints.end(), MaskPredicate(mask)), keypoints.end());
|
||||
}
|
||||
/*
|
||||
* Remove objects from some image and a vector by mask for pixels of this image
|
||||
*/
|
||||
template <typename T>
|
||||
void runByPixelsMask2(std::vector<KeyPoint> &keypoints, std::vector<T> &removeFrom, const Mat &mask)
|
||||
{
|
||||
if (mask.empty())
|
||||
return;
|
||||
|
||||
MaskPredicate maskPredicate(mask);
|
||||
removeFrom.erase(std::remove_if(removeFrom.begin(), removeFrom.end(),
|
||||
[&](const T &x)
|
||||
{
|
||||
auto index = &x - &removeFrom.front();
|
||||
return maskPredicate(keypoints[index]);
|
||||
}),
|
||||
removeFrom.end());
|
||||
keypoints.erase(std::remove_if(keypoints.begin(), keypoints.end(), maskPredicate), keypoints.end());
|
||||
}
|
||||
void KeyPointsFilter::runByPixelsMask2VectorPoint(std::vector<KeyPoint> &keypoints, std::vector<std::vector<Point> > &removeFrom, const Mat &mask)
|
||||
{
|
||||
runByPixelsMask2(keypoints, removeFrom, mask);
|
||||
}
|
||||
|
||||
struct KeyPoint_LessThan
|
||||
{
|
||||
KeyPoint_LessThan(const std::vector<KeyPoint>& _kp) : kp(&_kp) {}
|
||||
bool operator()(int i, int j) const
|
||||
{
|
||||
const KeyPoint& kp1 = (*kp)[i];
|
||||
const KeyPoint& kp2 = (*kp)[j];
|
||||
if( kp1.pt.x != kp2.pt.x )
|
||||
return kp1.pt.x < kp2.pt.x;
|
||||
if( kp1.pt.y != kp2.pt.y )
|
||||
return kp1.pt.y < kp2.pt.y;
|
||||
if( kp1.size != kp2.size )
|
||||
return kp1.size > kp2.size;
|
||||
if( kp1.angle != kp2.angle )
|
||||
return kp1.angle < kp2.angle;
|
||||
if( kp1.response != kp2.response )
|
||||
return kp1.response > kp2.response;
|
||||
if( kp1.octave != kp2.octave )
|
||||
return kp1.octave > kp2.octave;
|
||||
if( kp1.class_id != kp2.class_id )
|
||||
return kp1.class_id > kp2.class_id;
|
||||
|
||||
return i < j;
|
||||
}
|
||||
const std::vector<KeyPoint>* kp;
|
||||
};
|
||||
|
||||
void KeyPointsFilter::removeDuplicated( std::vector<KeyPoint>& keypoints )
|
||||
{
|
||||
int i, j, n = (int)keypoints.size();
|
||||
AutoBuffer<int> kpidx(n);
|
||||
AutoBuffer<uchar> mask(n, (uchar)1);
|
||||
|
||||
for( i = 0; i < n; i++ )
|
||||
kpidx[i] = i;
|
||||
std::sort(kpidx.begin(), kpidx.end(), KeyPoint_LessThan(keypoints));
|
||||
for( i = 1, j = 0; i < n; i++ )
|
||||
{
|
||||
KeyPoint& kp1 = keypoints[kpidx[i]];
|
||||
KeyPoint& kp2 = keypoints[kpidx[j]];
|
||||
if( kp1.pt.x != kp2.pt.x || kp1.pt.y != kp2.pt.y ||
|
||||
kp1.size != kp2.size || kp1.angle != kp2.angle )
|
||||
j = i;
|
||||
else
|
||||
mask[kpidx[i]] = 0;
|
||||
}
|
||||
|
||||
for( i = j = 0; i < n; i++ )
|
||||
{
|
||||
if( mask[i] )
|
||||
{
|
||||
if( i != j )
|
||||
keypoints[j] = keypoints[i];
|
||||
j++;
|
||||
}
|
||||
}
|
||||
keypoints.resize(j);
|
||||
}
|
||||
|
||||
struct KeyPoint12_LessThan
|
||||
{
|
||||
bool operator()(const KeyPoint &kp1, const KeyPoint &kp2) const
|
||||
{
|
||||
if( kp1.pt.x != kp2.pt.x )
|
||||
return kp1.pt.x < kp2.pt.x;
|
||||
if( kp1.pt.y != kp2.pt.y )
|
||||
return kp1.pt.y < kp2.pt.y;
|
||||
if( kp1.size != kp2.size )
|
||||
return kp1.size > kp2.size;
|
||||
if( kp1.angle != kp2.angle )
|
||||
return kp1.angle < kp2.angle;
|
||||
if( kp1.response != kp2.response )
|
||||
return kp1.response > kp2.response;
|
||||
if( kp1.octave != kp2.octave )
|
||||
return kp1.octave > kp2.octave;
|
||||
return kp1.class_id > kp2.class_id;
|
||||
}
|
||||
};
|
||||
|
||||
void KeyPointsFilter::removeDuplicatedSorted( std::vector<KeyPoint>& keypoints )
|
||||
{
|
||||
int i, j, n = (int)keypoints.size();
|
||||
|
||||
if (n < 2) return;
|
||||
|
||||
std::sort(keypoints.begin(), keypoints.end(), KeyPoint12_LessThan());
|
||||
|
||||
for( i = 0, j = 1; j < n; ++j )
|
||||
{
|
||||
const KeyPoint& kp1 = keypoints[i];
|
||||
const KeyPoint& kp2 = keypoints[j];
|
||||
if( kp1.pt.x != kp2.pt.x || kp1.pt.y != kp2.pt.y ||
|
||||
kp1.size != kp2.size || kp1.angle != kp2.angle ) {
|
||||
keypoints[++i] = keypoints[j];
|
||||
}
|
||||
}
|
||||
keypoints.resize(i + 1);
|
||||
}
|
||||
|
||||
}
|
||||
@@ -0,0 +1,52 @@
|
||||
/*M///////////////////////////////////////////////////////////////////////////////////////
|
||||
//
|
||||
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
|
||||
//
|
||||
// By downloading, copying, installing or using the software you agree to this license.
|
||||
// If you do not agree to this license, do not download, install,
|
||||
// copy or use the software.
|
||||
//
|
||||
//
|
||||
// License Agreement
|
||||
// For Open Source Computer Vision Library
|
||||
//
|
||||
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
|
||||
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
|
||||
// Copyright (C) 2015, Itseez Inc., all rights reserved.
|
||||
// Third party copyrights are property of their respective owners.
|
||||
//
|
||||
// Redistribution and use in source and binary forms, with or without modification,
|
||||
// are permitted provided that the following conditions are met:
|
||||
//
|
||||
// * Redistribution's of source code must retain the above copyright notice,
|
||||
// this list of conditions and the following disclaimer.
|
||||
//
|
||||
// * Redistribution's in binary form must reproduce the above copyright notice,
|
||||
// this list of conditions and the following disclaimer in the documentation
|
||||
// and/or other materials provided with the distribution.
|
||||
//
|
||||
// * The name of the copyright holders may not be used to endorse or promote products
|
||||
// derived from this software without specific prior written permission.
|
||||
//
|
||||
// This software is provided by the copyright holders and contributors "as is" and
|
||||
// any express or implied warranties, including, but not limited to, the implied
|
||||
// warranties of merchantability and fitness for a particular purpose are disclaimed.
|
||||
// In no event shall the Intel Corporation or contributors be liable for any direct,
|
||||
// indirect, incidental, special, exemplary, or consequential damages
|
||||
// (including, but not limited to, procurement of substitute goods or services;
|
||||
// loss of use, data, or profits; or business interruption) however caused
|
||||
// and on any theory of liability, whether in contract, strict liability,
|
||||
// or tort (including negligence or otherwise) arising in any way out of
|
||||
// the use of this software, even if advised of the possibility of such damage.
|
||||
//
|
||||
//M*/
|
||||
|
||||
//
|
||||
// Library initialization file
|
||||
//
|
||||
|
||||
#include "precomp.hpp"
|
||||
|
||||
IPP_INITIALIZER_AUTO
|
||||
|
||||
/* End of file. */
|
||||
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,646 @@
|
||||
// This file is part of OpenCV project.
|
||||
// It is subject to the license terms in the LICENSE file found in the top-level directory
|
||||
// of this distribution and at http://opencv.org/license.html
|
||||
|
||||
|
||||
/**
|
||||
* @brief This function computes the Perona and Malik conductivity coefficient g2
|
||||
* g2 = 1 / (1 + dL^2 / k^2)
|
||||
* @param lx First order image derivative in X-direction (horizontal)
|
||||
* @param ly First order image derivative in Y-direction (vertical)
|
||||
* @param dst Output image
|
||||
* @param k Contrast factor parameter
|
||||
*/
|
||||
__kernel void
|
||||
AKAZE_pm_g2(__global const float* lx, __global const float* ly, __global float* dst,
|
||||
float k, int size)
|
||||
{
|
||||
int i = get_global_id(0);
|
||||
// OpenCV plays with dimensions so we need explicit check for this
|
||||
if (!(i < size))
|
||||
{
|
||||
return;
|
||||
}
|
||||
|
||||
const float k2inv = 1.0f / (k * k);
|
||||
dst[i] = 1.0f / (1.0f + ((lx[i] * lx[i] + ly[i] * ly[i]) * k2inv));
|
||||
}
|
||||
|
||||
__kernel void
|
||||
AKAZE_nld_step_scalar(__global const float* lt, int lt_step, int lt_offset, int rows, int cols,
|
||||
__global const float* lf, __global float* dst, float step_size)
|
||||
{
|
||||
/* The labeling scheme for this five star stencil:
|
||||
[ a ]
|
||||
[ -1 c +1 ]
|
||||
[ b ]
|
||||
*/
|
||||
// column-first indexing
|
||||
int i = get_global_id(1);
|
||||
int j = get_global_id(0);
|
||||
|
||||
// OpenCV plays with dimensions so we need explicit check for this
|
||||
if (!(i < rows && j < cols))
|
||||
{
|
||||
return;
|
||||
}
|
||||
|
||||
// get row indexes
|
||||
int a = (i - 1) * cols;
|
||||
int c = (i ) * cols;
|
||||
int b = (i + 1) * cols;
|
||||
// compute stencil
|
||||
float res = 0.0f;
|
||||
if (i == 0) // first rows
|
||||
{
|
||||
if (j == 0 || j == (cols - 1))
|
||||
{
|
||||
res = 0.0f;
|
||||
} else
|
||||
{
|
||||
res = (lf[c + j] + lf[c + j + 1])*(lt[c + j + 1] - lt[c + j]) +
|
||||
(lf[c + j] + lf[c + j - 1])*(lt[c + j - 1] - lt[c + j]) +
|
||||
(lf[c + j] + lf[b + j ])*(lt[b + j ] - lt[c + j]);
|
||||
}
|
||||
} else if (i == (rows - 1)) // last row
|
||||
{
|
||||
if (j == 0 || j == (cols - 1))
|
||||
{
|
||||
res = 0.0f;
|
||||
} else
|
||||
{
|
||||
res = (lf[c + j] + lf[c + j + 1])*(lt[c + j + 1] - lt[c + j]) +
|
||||
(lf[c + j] + lf[c + j - 1])*(lt[c + j - 1] - lt[c + j]) +
|
||||
(lf[c + j] + lf[a + j ])*(lt[a + j ] - lt[c + j]);
|
||||
}
|
||||
} else // inner rows
|
||||
{
|
||||
if (j == 0) // first column
|
||||
{
|
||||
res = (lf[c + 0] + lf[c + 1])*(lt[c + 1] - lt[c + 0]) +
|
||||
(lf[c + 0] + lf[b + 0])*(lt[b + 0] - lt[c + 0]) +
|
||||
(lf[c + 0] + lf[a + 0])*(lt[a + 0] - lt[c + 0]);
|
||||
} else if (j == (cols - 1)) // last column
|
||||
{
|
||||
res = (lf[c + j] + lf[c + j - 1])*(lt[c + j - 1] - lt[c + j]) +
|
||||
(lf[c + j] + lf[b + j ])*(lt[b + j ] - lt[c + j]) +
|
||||
(lf[c + j] + lf[a + j ])*(lt[a + j ] - lt[c + j]);
|
||||
} else // inner stencil
|
||||
{
|
||||
res = (lf[c + j] + lf[c + j + 1])*(lt[c + j + 1] - lt[c + j]) +
|
||||
(lf[c + j] + lf[c + j - 1])*(lt[c + j - 1] - lt[c + j]) +
|
||||
(lf[c + j] + lf[b + j ])*(lt[b + j ] - lt[c + j]) +
|
||||
(lf[c + j] + lf[a + j ])*(lt[a + j ] - lt[c + j]);
|
||||
}
|
||||
}
|
||||
|
||||
dst[c + j] = res * step_size;
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Compute determinant from hessians
|
||||
* @details Compute Ldet by (Lxx.mul(Lyy) - Lxy.mul(Lxy)) * sigma
|
||||
*
|
||||
* @param lxx spatial derivates
|
||||
* @param lxy spatial derivates
|
||||
* @param lyy spatial derivates
|
||||
* @param dst output determinant
|
||||
* @param sigma determinant will be scaled by this sigma
|
||||
*/
|
||||
__kernel void
|
||||
AKAZE_compute_determinant(__global const float* lxx, __global const float* lxy, __global const float* lyy,
|
||||
__global float* dst, float sigma, int size)
|
||||
{
|
||||
int i = get_global_id(0);
|
||||
// OpenCV plays with dimensions so we need explicit check for this
|
||||
if (!(i < size))
|
||||
{
|
||||
return;
|
||||
}
|
||||
|
||||
dst[i] = (lxx[i] * lyy[i] - lxy[i] * lxy[i]) * sigma;
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Find scale space extrema in 3x3 neighborhood
|
||||
* @details Detects local maxima in Hessian response within 3x3 neighborhood
|
||||
*
|
||||
* @param ldet Hessian determinant response
|
||||
* @param rows image height
|
||||
* @param cols image width
|
||||
* @param threshold detector response threshold
|
||||
* @param border border to ignore
|
||||
* @param keypoint_mask output binary mask of detected keypoints
|
||||
*/
|
||||
__kernel void
|
||||
AKAZE_find_extrema_same_scale(__global const float* ldet, int rows, int cols,
|
||||
float threshold, int border, __global uchar* keypoint_mask)
|
||||
{
|
||||
int x = get_global_id(0);
|
||||
int y = get_global_id(1);
|
||||
|
||||
// Check bounds
|
||||
if (x < border || x >= cols - border || y < border || y >= rows - border)
|
||||
{
|
||||
return;
|
||||
}
|
||||
|
||||
int idx = y * cols + x;
|
||||
float value = ldet[idx];
|
||||
|
||||
// Filter by threshold
|
||||
if (value <= threshold)
|
||||
{
|
||||
keypoint_mask[idx] = 0;
|
||||
return;
|
||||
}
|
||||
|
||||
// 3x3 local maxima check
|
||||
// Check horizontal neighbors
|
||||
if (value <= ldet[idx - 1] || value <= ldet[idx + 1])
|
||||
{
|
||||
keypoint_mask[idx] = 0;
|
||||
return;
|
||||
}
|
||||
|
||||
// Check vertical neighbors
|
||||
int prev_row = (y - 1) * cols + x;
|
||||
int next_row = (y + 1) * cols + x;
|
||||
if (value <= ldet[prev_row] || value <= ldet[next_row])
|
||||
{
|
||||
keypoint_mask[idx] = 0;
|
||||
return;
|
||||
}
|
||||
|
||||
// Check diagonal neighbors
|
||||
if (value <= ldet[prev_row - 1] || value <= ldet[prev_row + 1] ||
|
||||
value <= ldet[next_row - 1] || value <= ldet[next_row + 1])
|
||||
{
|
||||
keypoint_mask[idx] = 0;
|
||||
return;
|
||||
}
|
||||
|
||||
// This is a local maximum
|
||||
keypoint_mask[idx] = 1;
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Cross-scale non-maximum suppression (lower scale filtering)
|
||||
* @details For each keypoint in current level, project to lower level and suppress if weaker
|
||||
*
|
||||
* @param keypoints_current keypoint mask for current scale level
|
||||
* @param keypoints_lower keypoint mask for lower scale level
|
||||
* @param ldet_current Hessian response for current level
|
||||
* @param ldet_lower Hessian response for lower level
|
||||
* @param rows image height
|
||||
* @param cols image width
|
||||
* @param diff_ratio ratio to project from current to lower level
|
||||
* @param search_radius search radius in lower level
|
||||
*/
|
||||
__kernel void
|
||||
AKAZE_cross_scale_filter_lower(__global const uchar* keypoints_current,
|
||||
__global uchar* keypoints_lower,
|
||||
__global const float* ldet_current,
|
||||
__global const float* ldet_lower,
|
||||
int rows, int cols,
|
||||
int lower_rows, int lower_cols,
|
||||
int diff_ratio, int search_radius)
|
||||
{
|
||||
int x = get_global_id(0);
|
||||
int y = get_global_id(1);
|
||||
|
||||
if (x >= cols || y >= rows)
|
||||
return;
|
||||
|
||||
int idx = y * cols + x;
|
||||
|
||||
// Only process keypoints in current level
|
||||
if (keypoints_current[idx] == 0)
|
||||
return;
|
||||
|
||||
// Project to lower scale level
|
||||
int p_x = x * diff_ratio;
|
||||
int p_y = y * diff_ratio;
|
||||
|
||||
// Search for neighbor in lower level within radius
|
||||
int radius_sq = search_radius * search_radius;
|
||||
int found = 0;
|
||||
int neighbor_idx = -1;
|
||||
|
||||
// Brute force search within radius
|
||||
for (int dy = -search_radius; dy <= search_radius; dy++)
|
||||
{
|
||||
for (int dx = -search_radius; dx <= search_radius; dx++)
|
||||
{
|
||||
int nx = p_x + dx;
|
||||
int ny = p_y + dy;
|
||||
|
||||
if (nx >= 0 && nx < lower_cols && ny >= 0 && ny < lower_rows)
|
||||
{
|
||||
int nidx = ny * lower_cols + nx;
|
||||
if (keypoints_lower[nidx] == 1)
|
||||
{
|
||||
if (dx * dx + dy * dy <= radius_sq)
|
||||
{
|
||||
found = 1;
|
||||
neighbor_idx = nidx;
|
||||
break;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
if (found) break;
|
||||
}
|
||||
|
||||
// If neighbor found and current response is higher, suppress neighbor
|
||||
if (found && ldet_current[idx] > ldet_lower[neighbor_idx])
|
||||
{
|
||||
keypoints_lower[neighbor_idx] = 0;
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Cross-scale non-maximum suppression (upper scale filtering)
|
||||
* @details For each keypoint in current level, project to upper level and suppress if weaker
|
||||
*
|
||||
* @param keypoints_current keypoint mask for current scale level
|
||||
* @param keypoints_upper keypoint mask for upper scale level
|
||||
* @param ldet_current Hessian response for current level
|
||||
* @param ldet_upper Hessian response for upper level
|
||||
* @param rows image height
|
||||
* @param cols image width
|
||||
* @param diff_ratio ratio to project from current to upper level
|
||||
* @param search_radius search radius in upper level
|
||||
*/
|
||||
__kernel void
|
||||
AKAZE_cross_scale_filter_upper(__global const uchar* keypoints_current,
|
||||
__global uchar* keypoints_upper,
|
||||
__global const float* ldet_current,
|
||||
__global const float* ldet_upper,
|
||||
int rows, int cols,
|
||||
int upper_rows, int upper_cols,
|
||||
int diff_ratio, int search_radius)
|
||||
{
|
||||
int x = get_global_id(0);
|
||||
int y = get_global_id(1);
|
||||
|
||||
if (x >= cols || y >= rows)
|
||||
return;
|
||||
|
||||
int idx = y * cols + x;
|
||||
|
||||
// Only process keypoints in current level
|
||||
if (keypoints_current[idx] == 0)
|
||||
return;
|
||||
|
||||
// Project to upper scale level
|
||||
int p_x = x / diff_ratio;
|
||||
int p_y = y / diff_ratio;
|
||||
|
||||
// Search for neighbor in upper level within radius
|
||||
int radius_sq = search_radius * search_radius;
|
||||
int found = 0;
|
||||
int neighbor_idx = -1;
|
||||
|
||||
// Brute force search within radius
|
||||
for (int dy = -search_radius; dy <= search_radius; dy++)
|
||||
{
|
||||
for (int dx = -search_radius; dx <= search_radius; dx++)
|
||||
{
|
||||
int nx = p_x + dx;
|
||||
int ny = p_y + dy;
|
||||
|
||||
if (nx >= 0 && nx < upper_cols && ny >= 0 && ny < upper_rows)
|
||||
{
|
||||
int nidx = ny * upper_cols + nx;
|
||||
if (keypoints_upper[nidx] == 1)
|
||||
{
|
||||
if (dx * dx + dy * dy <= radius_sq)
|
||||
{
|
||||
found = 1;
|
||||
neighbor_idx = nidx;
|
||||
break;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
if (found) break;
|
||||
}
|
||||
|
||||
// If neighbor found and current response is higher, suppress neighbor
|
||||
if (found && ldet_current[idx] > ldet_upper[neighbor_idx])
|
||||
{
|
||||
keypoints_upper[neighbor_idx] = 0;
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Combined subpixel refinement and orientation for single level
|
||||
* @details Processes subpixel refinement and orientation together to eliminate intermediate transfers
|
||||
*
|
||||
* @param keypoints keypoint mask for this level
|
||||
* @param ldet Hessian response for this level
|
||||
* @param Lx gradient in x direction for this level
|
||||
* @param Ly gradient in y direction for this level
|
||||
* @param rows image height
|
||||
* @param cols image width
|
||||
* @param octave_ratio scale ratio for this level
|
||||
* @param esigma evolution sigma for this level
|
||||
* @param octave octave number for this level
|
||||
* @param level evolution level index
|
||||
* @param output_count atomic counter for number of refined keypoints
|
||||
* @param output_x output x coordinates
|
||||
* @param output_y output y coordinates
|
||||
* @param output_size output keypoint sizes
|
||||
* @param output_response output keypoint responses
|
||||
* @param output_octave output octave numbers
|
||||
* @param output_class_id output level indices
|
||||
* @param output_angle output orientation angles
|
||||
* @param max_output maximum number of keypoints that can be output
|
||||
*/
|
||||
__kernel void
|
||||
AKAZE_subpixel_refinement_orientation(__global const uchar* keypoints,
|
||||
__global const float* ldet,
|
||||
__global const float* Lx,
|
||||
__global const float* Ly,
|
||||
int rows, int cols,
|
||||
float octave_ratio, float esigma, int octave, int level,
|
||||
__global int* output_count,
|
||||
__global float* output_x,
|
||||
__global float* output_y,
|
||||
__global float* output_size,
|
||||
__global float* output_response,
|
||||
__global int* output_octave,
|
||||
__global int* output_class_id,
|
||||
__global float* output_angle,
|
||||
int max_output)
|
||||
{
|
||||
int x = get_global_id(0);
|
||||
int y = get_global_id(1);
|
||||
|
||||
if (x >= cols || y >= rows)
|
||||
return;
|
||||
|
||||
int idx = y * cols + x;
|
||||
|
||||
// Only process keypoints
|
||||
if (keypoints[idx] == 0)
|
||||
return;
|
||||
|
||||
// Compute gradient for subpixel refinement
|
||||
float Dx = 0.5f * (ldet[idx + 1] - ldet[idx - 1]);
|
||||
float Dy = 0.5f * (ldet[idx + cols] - ldet[idx - cols]);
|
||||
|
||||
// Compute Hessian
|
||||
float Dxx = ldet[idx + 1] + ldet[idx - 1] - 2.0f * ldet[idx];
|
||||
float Dyy = ldet[idx + cols] + ldet[idx - cols] - 2.0f * ldet[idx];
|
||||
float Dxy = 0.25f * (ldet[idx + cols + 1] + ldet[idx - cols - 1] -
|
||||
ldet[idx - cols + 1] - ldet[idx + cols - 1]);
|
||||
|
||||
// Solve 2x2 linear system
|
||||
float det = Dxx * Dyy - Dxy * Dxy;
|
||||
|
||||
if (fabs(det) < 1e-10f)
|
||||
return;
|
||||
|
||||
float inv_det = 1.0f / det;
|
||||
float dx = (-Dyy * Dx + Dxy * Dy) * inv_det;
|
||||
float dy = (Dxy * Dx - Dxx * Dy) * inv_det;
|
||||
|
||||
if (fabs(dx) > 1.0f || fabs(dy) > 1.0f)
|
||||
return;
|
||||
|
||||
int out_idx = atomic_inc(output_count);
|
||||
|
||||
if (out_idx >= max_output)
|
||||
return;
|
||||
|
||||
float refined_x = x * octave_ratio + dx * octave_ratio + 0.5f * (octave_ratio - 1.0f);
|
||||
float refined_y = y * octave_ratio + dy * octave_ratio + 0.5f * (octave_ratio - 1.0f);
|
||||
|
||||
output_x[out_idx] = refined_x;
|
||||
output_y[out_idx] = refined_y;
|
||||
output_size[out_idx] = esigma * 3.0f; // derivative_factor(1.5) * 2.0(diameter) = 3.0
|
||||
output_response[out_idx] = ldet[idx];
|
||||
output_octave[out_idx] = octave;
|
||||
output_class_id[out_idx] = level;
|
||||
|
||||
// Compute orientation using gradient histogram
|
||||
float scale_f = 0.5f * output_size[out_idx] / octave_ratio;
|
||||
int scale = (int)(scale_f + 0.5f);
|
||||
float x0_f = refined_x / octave_ratio;
|
||||
int x0 = (int)(x0_f + (x0_f >= 0 ? 0.5f : -0.5f));
|
||||
float y0_f = refined_y / octave_ratio;
|
||||
int y0 = (int)(y0_f + (y0_f >= 0 ? 0.5f : -0.5f));
|
||||
|
||||
float histogram[36];
|
||||
for (int b = 0; b < 36; b++)
|
||||
histogram[b] = 0.0f;
|
||||
|
||||
const int radius = 6 * scale;
|
||||
const int radius_sq = radius * radius;
|
||||
|
||||
for (int dy = -radius; dy <= radius; dy++) {
|
||||
for (int dx = -radius; dx <= radius; dx++) {
|
||||
if (dx * dx + dy * dy > radius_sq)
|
||||
continue;
|
||||
|
||||
int sx = x0 + dx;
|
||||
int sy = y0 + dy;
|
||||
|
||||
if (sx < 0 || sx >= cols || sy < 0 || sy >= rows)
|
||||
continue;
|
||||
|
||||
int sidx = sy * cols + sx;
|
||||
float gx = Lx[sidx];
|
||||
float gy = Ly[sidx];
|
||||
|
||||
float magnitude = sqrt(gx * gx + gy * gy);
|
||||
float angle = atan2(gy, gx);
|
||||
if (angle < 0.0f)
|
||||
angle += 2.0f * M_PI_F;
|
||||
|
||||
float dist = sqrt((float)(dx * dx + dy * dy));
|
||||
float weight = exp(-dist * dist / (2.0f * scale * scale));
|
||||
|
||||
int bin = (int)(angle * 36 / (2.0f * M_PI_F));
|
||||
bin = bin % 36;
|
||||
histogram[bin] += magnitude * weight;
|
||||
}
|
||||
}
|
||||
|
||||
float max_hist = 0.0f;
|
||||
int max_bin = 0;
|
||||
for (int b = 0; b < 36; b++) {
|
||||
if (histogram[b] > max_hist) {
|
||||
max_hist = histogram[b];
|
||||
max_bin = b;
|
||||
}
|
||||
}
|
||||
|
||||
if (max_hist == 0.0f)
|
||||
{
|
||||
output_angle[out_idx] = 0.0f;
|
||||
return;
|
||||
}
|
||||
|
||||
int prev_bin = (max_bin - 1 + 36) % 36;
|
||||
int next_bin = (max_bin + 1) % 36;
|
||||
|
||||
float prev_val = histogram[prev_bin];
|
||||
float curr_val = histogram[max_bin];
|
||||
float next_val = histogram[next_bin];
|
||||
|
||||
float denom = prev_val - 2.0f * curr_val + next_val;
|
||||
float delta = (fabs(denom) > 1e-10f) ? 0.5f * (prev_val - next_val) / denom : 0.0f;
|
||||
float refined_angle = (max_bin + delta) * (2.0f * M_PI_F / 36);
|
||||
|
||||
output_angle[out_idx] = refined_angle * 180.0f / M_PI_F;
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Compute MLDB descriptor for a keypoint (single level, upright version)
|
||||
* @details Samples binary comparisons from gradient responses matching CPU implementation
|
||||
*
|
||||
* @param keypoints_x keypoint x coordinates
|
||||
* @param keypoints_y keypoint y coordinates
|
||||
* @param keypoints_size keypoint sizes
|
||||
* @param keypoints_angle keypoint orientations (unused in upright version)
|
||||
* @param num_keypoints number of keypoints
|
||||
* @param Lx gradient in x direction
|
||||
* @param Ly gradient in y direction
|
||||
* @param Lt flow response
|
||||
* @param rows image height
|
||||
* @param cols image width
|
||||
* @param octave_ratio octave ratio for this level
|
||||
* @param descriptor_size size of descriptor in bytes
|
||||
* @param output_descriptors output descriptor matrix
|
||||
*/
|
||||
__kernel void
|
||||
AKAZE_compute_mldb_descriptor_level(__global const float* keypoints_x,
|
||||
__global const float* keypoints_y,
|
||||
__global const float* keypoints_size,
|
||||
__global const float* keypoints_angle,
|
||||
int num_keypoints,
|
||||
__global const float* Lx,
|
||||
__global const float* Ly,
|
||||
__global const float* Lt,
|
||||
int rows, int cols, float octave_ratio,
|
||||
int descriptor_size,
|
||||
__global uchar* output_descriptors)
|
||||
{
|
||||
int i = get_global_id(0);
|
||||
|
||||
if (i >= num_keypoints)
|
||||
return;
|
||||
|
||||
float kpt_x = keypoints_x[i];
|
||||
float kpt_y = keypoints_y[i];
|
||||
float kpt_size = keypoints_size[i];
|
||||
|
||||
// cvRound implementation: rounds to nearest, half away from zero
|
||||
float scale_f = 0.5f * kpt_size / octave_ratio;
|
||||
int scale = (int)(scale_f + (scale_f >= 0 ? 0.5f : -0.5f));
|
||||
|
||||
float xf = kpt_x / octave_ratio;
|
||||
float yf = kpt_y / octave_ratio;
|
||||
|
||||
// Pattern size (default 10)
|
||||
const int pattern_size = 10;
|
||||
|
||||
// Sample steps for 3 grids: 2x2, 3x3, 4x4
|
||||
const int sample_step[3] = {
|
||||
pattern_size,
|
||||
(pattern_size * 2 + 2) / 3, // divUp(pattern_size * 2, 3)
|
||||
pattern_size / 2
|
||||
};
|
||||
|
||||
// Buffer for M-LDB descriptor values (max 16 cells * 3 channels)
|
||||
float values[48];
|
||||
|
||||
__global uchar* desc = &output_descriptors[i * descriptor_size];
|
||||
|
||||
// Initialize descriptor to zero
|
||||
for (int b = 0; b < descriptor_size; b++)
|
||||
desc[b] = 0;
|
||||
|
||||
int dcount1 = 0;
|
||||
|
||||
// For the three grids (2x2, 3x3, 4x4)
|
||||
for (int z = 0; z < 3; z++) {
|
||||
int dcount2 = 0;
|
||||
const int step = sample_step[z];
|
||||
|
||||
for (int i = -pattern_size; i < pattern_size; i += step) {
|
||||
for (int j = -pattern_size; j < pattern_size; j += step) {
|
||||
float di = 0.0f, dx = 0.0f, dy = 0.0f;
|
||||
int nsamples = 0;
|
||||
|
||||
// Sample within each cell
|
||||
for (int k = 0; k < step; k++) {
|
||||
for (int l = 0; l < step; l++) {
|
||||
// Get the coordinates of the sample point
|
||||
const float sample_y = yf + (l + j) * scale;
|
||||
const float sample_x = xf + (k + i) * scale;
|
||||
|
||||
// cvRound implementation for sampling coordinates
|
||||
const int y1 = (int)(sample_y + (sample_y >= 0 ? 0.5f : -0.5f));
|
||||
const int x1 = (int)(sample_x + (sample_x >= 0 ? 0.5f : -0.5f));
|
||||
|
||||
if (y1 < 0 || y1 >= rows || x1 < 0 || x1 >= cols)
|
||||
continue; // Boundaries
|
||||
|
||||
const int idx = y1 * cols + x1;
|
||||
|
||||
const float ri = Lt[idx];
|
||||
const float rx = Lx[idx];
|
||||
const float ry = Ly[idx];
|
||||
|
||||
di += ri;
|
||||
dx += rx;
|
||||
dy += ry;
|
||||
nsamples++;
|
||||
}
|
||||
}
|
||||
|
||||
if (nsamples > 0) {
|
||||
const float nsamples_inv = 1.0f / nsamples;
|
||||
di *= nsamples_inv;
|
||||
dx *= nsamples_inv;
|
||||
dy *= nsamples_inv;
|
||||
}
|
||||
|
||||
// Store values (3 channels: Lt, Lx, Ly)
|
||||
values[dcount2 * 3] = di;
|
||||
values[dcount2 * 3 + 1] = dx;
|
||||
values[dcount2 * 3 + 2] = dy;
|
||||
dcount2++;
|
||||
}
|
||||
}
|
||||
|
||||
// Do binary comparison for this grid
|
||||
const int num = (z + 2) * (z + 2);
|
||||
const int chan = 3;
|
||||
|
||||
// Apply CV_TOGGLE_FLT to handle signed floats correctly
|
||||
// This toggles the sign bit to allow correct integer comparison of floats
|
||||
int* ivalues = (int*)values;
|
||||
for (int i = 0; i < num * chan; i++) {
|
||||
ivalues[i] = ivalues[i] ^ (ivalues[i] < 0 ? 0x7fffffff : 0);
|
||||
}
|
||||
|
||||
// Match CPU comparison order: iterate Cell FIRST, then Channel
|
||||
// This produces: [Cell0-Ch0, Cell0-Ch1, Cell0-Ch2, Cell1-Ch0, Cell1-Ch1, ...]
|
||||
for (int i = 0; i < num; i++) {
|
||||
for (int j = i + 1; j < num; j++) {
|
||||
for (int pos = 0; pos < chan; pos++) {
|
||||
int ival = ivalues[chan * i + pos];
|
||||
if (ival > ivalues[chan * j + pos]) {
|
||||
desc[dcount1 / 8] |= (1 << (dcount1 % 8));
|
||||
}
|
||||
dcount1++;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,694 @@
|
||||
/*M///////////////////////////////////////////////////////////////////////////////////////
|
||||
//
|
||||
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
|
||||
//
|
||||
// By downloading, copying, installing or using the software you agree to this license.
|
||||
// If you do not agree to this license, do not download, install,
|
||||
// copy or use the software.
|
||||
//
|
||||
//
|
||||
// License Agreement
|
||||
// For Open Source Computer Vision Library
|
||||
//
|
||||
// Copyright (C) 2010-2012, Multicoreware, Inc., all rights reserved.
|
||||
// Copyright (C) 2010-2012, Advanced Micro Devices, Inc., all rights reserved.
|
||||
// Third party copyrights are property of their respective owners.
|
||||
//
|
||||
// @Authors
|
||||
// Nathan, liujun@multicorewareinc.com
|
||||
// Peng Xiao, pengxiao@outlook.com
|
||||
// Baichuan Su, baichuan@multicorewareinc.com
|
||||
//
|
||||
// Redistribution and use in source and binary forms, with or without modification,
|
||||
// are permitted provided that the following conditions are met:
|
||||
//
|
||||
// * Redistribution's of source code must retain the above copyright notice,
|
||||
// this list of conditions and the following disclaimer.
|
||||
//
|
||||
// * Redistribution's in binary form must reproduce the above copyright notice,
|
||||
// this list of conditions and the following disclaimer in the documentation
|
||||
// and/or other materials provided with the distribution.
|
||||
//
|
||||
// * The name of the copyright holders may not be used to endorse or promote products
|
||||
// derived from this software without specific prior written permission.
|
||||
//
|
||||
// This software is provided by the copyright holders and contributors "as is" and
|
||||
// any express or implied warranties, including, but not limited to, the implied
|
||||
// warranties of merchantability and fitness for a particular purpose are disclaimed.
|
||||
// In no event shall the Intel Corporation or contributors be liable for any direct,
|
||||
// indirect, incidental, special, exemplary, or consequential damages
|
||||
// (including, but not limited to, procurement of substitute goods or services;
|
||||
// loss of use, data, or profits; or business interruption) however caused
|
||||
// and on any theory of liability, whether in contract, strict liability,
|
||||
// or tort (including negligence or otherwise) arising in any way out of
|
||||
// the use of this software, even if advised of the possibility of such damage.
|
||||
//
|
||||
//M*/
|
||||
|
||||
#pragma OPENCL EXTENSION cl_khr_global_int32_base_atomics:enable
|
||||
#define MAX_FLOAT 3.40282e+038f
|
||||
|
||||
#ifndef T
|
||||
#define T float
|
||||
#endif
|
||||
|
||||
#ifndef BLOCK_SIZE
|
||||
#define BLOCK_SIZE 16
|
||||
#endif
|
||||
#ifndef MAX_DESC_LEN
|
||||
#define MAX_DESC_LEN 64
|
||||
#endif
|
||||
|
||||
#define BLOCK_SIZE_ODD (BLOCK_SIZE + 1)
|
||||
#ifndef SHARED_MEM_SZ
|
||||
# if (BLOCK_SIZE < MAX_DESC_LEN)
|
||||
# define SHARED_MEM_SZ (kercn * (BLOCK_SIZE * MAX_DESC_LEN + BLOCK_SIZE * BLOCK_SIZE))
|
||||
# else
|
||||
# define SHARED_MEM_SZ (kercn * 2 * BLOCK_SIZE_ODD * BLOCK_SIZE)
|
||||
# endif
|
||||
#endif
|
||||
|
||||
#ifndef DIST_TYPE
|
||||
#define DIST_TYPE 2
|
||||
#endif
|
||||
|
||||
// dirty fix for non-template support
|
||||
#if (DIST_TYPE == 2) // L1Dist
|
||||
# ifdef T_FLOAT
|
||||
typedef float result_type;
|
||||
# if (8 == kercn)
|
||||
typedef float8 value_type;
|
||||
# define DIST(x, y) {value_type d = fabs((x) - (y)); result += d.s0 + d.s1 + d.s2 + d.s3 + d.s4 + d.s5 + d.s6 + d.s7;}
|
||||
# elif (4 == kercn)
|
||||
typedef float4 value_type;
|
||||
# define DIST(x, y) {value_type d = fabs((x) - (y)); result += d.s0 + d.s1 + d.s2 + d.s3;}
|
||||
# else
|
||||
typedef float value_type;
|
||||
# define DIST(x, y) result += fabs((x) - (y))
|
||||
# endif
|
||||
# else
|
||||
typedef int result_type;
|
||||
# if (8 == kercn)
|
||||
typedef int8 value_type;
|
||||
# define DIST(x, y) {value_type d = abs((x) - (y)); result += d.s0 + d.s1 + d.s2 + d.s3 + d.s4 + d.s5 + d.s6 + d.s7;}
|
||||
# elif (4 == kercn)
|
||||
typedef int4 value_type;
|
||||
# define DIST(x, y) {value_type d = abs((x) - (y)); result += d.s0 + d.s1 + d.s2 + d.s3;}
|
||||
# else
|
||||
typedef int value_type;
|
||||
# define DIST(x, y) result += abs((x) - (y))
|
||||
# endif
|
||||
# endif
|
||||
# define DIST_RES(x) (x)
|
||||
#elif (DIST_TYPE == 4) // L2Dist
|
||||
typedef float result_type;
|
||||
# if (8 == kercn)
|
||||
typedef float8 value_type;
|
||||
# define DIST(x, y) {value_type d = ((x) - (y)); result += dot(d.s0123, d.s0123) + dot(d.s4567, d.s4567);}
|
||||
# elif (4 == kercn)
|
||||
typedef float4 value_type;
|
||||
# define DIST(x, y) {value_type d = ((x) - (y)); result += dot(d, d);}
|
||||
# else
|
||||
typedef float value_type;
|
||||
# define DIST(x, y) {value_type d = ((x) - (y)); result = mad(d, d, result);}
|
||||
# endif
|
||||
# define DIST_RES(x) sqrt(x)
|
||||
#elif (DIST_TYPE == 6) // Hamming
|
||||
# if (8 == kercn)
|
||||
typedef int8 value_type;
|
||||
# elif (4 == kercn)
|
||||
typedef int4 value_type;
|
||||
# else
|
||||
typedef int value_type;
|
||||
# endif
|
||||
typedef int result_type;
|
||||
# define DIST(x, y) result += popcount( (x) ^ (y) )
|
||||
# define DIST_RES(x) (x)
|
||||
#endif
|
||||
|
||||
inline result_type reduce_block(
|
||||
__local value_type *s_query,
|
||||
__local value_type *s_train,
|
||||
int lidx,
|
||||
int lidy
|
||||
)
|
||||
{
|
||||
result_type result = 0;
|
||||
#pragma unroll
|
||||
for (int j = 0 ; j < BLOCK_SIZE ; j++)
|
||||
{
|
||||
DIST(s_query[lidy * BLOCK_SIZE_ODD + j], s_train[j * BLOCK_SIZE_ODD + lidx]);
|
||||
}
|
||||
return DIST_RES(result);
|
||||
}
|
||||
|
||||
inline result_type reduce_block_match(
|
||||
__local value_type *s_query,
|
||||
__local value_type *s_train,
|
||||
int lidx,
|
||||
int lidy
|
||||
)
|
||||
{
|
||||
result_type result = 0;
|
||||
#pragma unroll
|
||||
for (int j = 0 ; j < BLOCK_SIZE ; j++)
|
||||
{
|
||||
DIST(s_query[lidy * BLOCK_SIZE_ODD + j], s_train[j * BLOCK_SIZE_ODD + lidx]);
|
||||
}
|
||||
return result;
|
||||
}
|
||||
|
||||
inline result_type reduce_multi_block(
|
||||
__local value_type *s_query,
|
||||
__local value_type *s_train,
|
||||
int block_index,
|
||||
int lidx,
|
||||
int lidy
|
||||
)
|
||||
{
|
||||
result_type result = 0;
|
||||
#pragma unroll
|
||||
for (int j = 0 ; j < BLOCK_SIZE ; j++)
|
||||
{
|
||||
DIST(s_query[lidy * MAX_DESC_LEN + block_index * BLOCK_SIZE + j], s_train[j * BLOCK_SIZE + lidx]);
|
||||
}
|
||||
return result;
|
||||
}
|
||||
|
||||
__kernel void BruteForceMatch_Match(
|
||||
__global T *query,
|
||||
__global T *train,
|
||||
__global int *bestTrainIdx,
|
||||
__global float *bestDistance,
|
||||
int query_rows,
|
||||
int query_cols,
|
||||
int train_rows,
|
||||
int train_cols,
|
||||
int step
|
||||
)
|
||||
{
|
||||
const int lidx = get_local_id(0);
|
||||
const int lidy = get_local_id(1);
|
||||
const int groupidx = get_group_id(0);
|
||||
|
||||
const int queryIdx = mad24(BLOCK_SIZE, groupidx, lidy);
|
||||
const int queryOffset = min(queryIdx, query_rows - 1) * step;
|
||||
__global TN *query_vec = (__global TN *)(query + queryOffset);
|
||||
query_cols /= kercn;
|
||||
|
||||
__local float sharebuffer[SHARED_MEM_SZ];
|
||||
__local value_type *s_query = (__local value_type *)sharebuffer;
|
||||
|
||||
#if 0 < MAX_DESC_LEN
|
||||
__local value_type *s_train = (__local value_type *)sharebuffer + BLOCK_SIZE * MAX_DESC_LEN;
|
||||
// load the query into local memory.
|
||||
#pragma unroll
|
||||
for (int i = 0; i < MAX_DESC_LEN / BLOCK_SIZE; i++)
|
||||
{
|
||||
const int loadx = mad24(BLOCK_SIZE, i, lidx);
|
||||
s_query[mad24(MAX_DESC_LEN, lidy, loadx)] = loadx < query_cols ? query_vec[loadx] : 0;
|
||||
}
|
||||
#else
|
||||
__local value_type *s_train = (__local value_type *)sharebuffer + BLOCK_SIZE_ODD * BLOCK_SIZE;
|
||||
const int s_query_i = mad24(BLOCK_SIZE_ODD, lidy, lidx);
|
||||
const int s_train_i = mad24(BLOCK_SIZE_ODD, lidx, lidy);
|
||||
#endif
|
||||
|
||||
float myBestDistance = MAX_FLOAT;
|
||||
int myBestTrainIdx = -1;
|
||||
|
||||
// loopUnrolledCached to find the best trainIdx and best distance.
|
||||
for (int t = 0, endt = (train_rows + BLOCK_SIZE - 1) / BLOCK_SIZE; t < endt; t++)
|
||||
{
|
||||
result_type result = 0;
|
||||
|
||||
const int trainOffset = min(mad24(BLOCK_SIZE, t, lidy), train_rows - 1) * step;
|
||||
__global TN *train_vec = (__global TN *)(train + trainOffset);
|
||||
#if 0 < MAX_DESC_LEN
|
||||
#pragma unroll
|
||||
for (int i = 0; i < MAX_DESC_LEN / BLOCK_SIZE; i++)
|
||||
{
|
||||
//load a BLOCK_SIZE * BLOCK_SIZE block into local train.
|
||||
const int loadx = mad24(BLOCK_SIZE, i, lidx);
|
||||
s_train[mad24(BLOCK_SIZE, lidx, lidy)] = loadx < train_cols ? train_vec[loadx] : 0;
|
||||
|
||||
//synchronize to make sure each elem for reduceIteration in share memory is written already.
|
||||
barrier(CLK_LOCAL_MEM_FENCE);
|
||||
|
||||
result += reduce_multi_block(s_query, s_train, i, lidx, lidy);
|
||||
|
||||
barrier(CLK_LOCAL_MEM_FENCE);
|
||||
}
|
||||
#else
|
||||
for (int i = 0, endq = (query_cols + BLOCK_SIZE - 1) / BLOCK_SIZE; i < endq; i++)
|
||||
{
|
||||
const int loadx = mad24(i, BLOCK_SIZE, lidx);
|
||||
//load query and train into local memory
|
||||
if (loadx < query_cols)
|
||||
{
|
||||
s_query[s_query_i] = query_vec[loadx];
|
||||
s_train[s_train_i] = train_vec[loadx];
|
||||
}
|
||||
else
|
||||
{
|
||||
s_query[s_query_i] = 0;
|
||||
s_train[s_train_i] = 0;
|
||||
}
|
||||
|
||||
barrier(CLK_LOCAL_MEM_FENCE);
|
||||
|
||||
result += reduce_block_match(s_query, s_train, lidx, lidy);
|
||||
|
||||
barrier(CLK_LOCAL_MEM_FENCE);
|
||||
}
|
||||
#endif
|
||||
result = DIST_RES(result);
|
||||
|
||||
const int trainIdx = mad24(BLOCK_SIZE, t, lidx);
|
||||
|
||||
if (queryIdx < query_rows && trainIdx < train_rows && result < myBestDistance /*&& mask(queryIdx, trainIdx)*/)
|
||||
{
|
||||
myBestDistance = result;
|
||||
myBestTrainIdx = trainIdx;
|
||||
}
|
||||
}
|
||||
|
||||
barrier(CLK_LOCAL_MEM_FENCE);
|
||||
|
||||
__local float *s_distance = (__local float *)sharebuffer;
|
||||
__local int *s_trainIdx = (__local int *)(sharebuffer + BLOCK_SIZE_ODD * BLOCK_SIZE);
|
||||
|
||||
//findBestMatch
|
||||
s_distance += lidy * BLOCK_SIZE_ODD;
|
||||
s_trainIdx += lidy * BLOCK_SIZE_ODD;
|
||||
s_distance[lidx] = myBestDistance;
|
||||
s_trainIdx[lidx] = myBestTrainIdx;
|
||||
|
||||
barrier(CLK_LOCAL_MEM_FENCE);
|
||||
|
||||
//reduce -- now all reduce implement in each threads.
|
||||
#pragma unroll
|
||||
for (int k = 0 ; k < BLOCK_SIZE; k++)
|
||||
{
|
||||
if (myBestDistance > s_distance[k])
|
||||
{
|
||||
myBestDistance = s_distance[k];
|
||||
myBestTrainIdx = s_trainIdx[k];
|
||||
}
|
||||
}
|
||||
|
||||
if (queryIdx < query_rows && lidx == 0)
|
||||
{
|
||||
bestTrainIdx[queryIdx] = myBestTrainIdx;
|
||||
bestDistance[queryIdx] = myBestDistance;
|
||||
}
|
||||
}
|
||||
|
||||
//radius_match
|
||||
__kernel void BruteForceMatch_RadiusMatch(
|
||||
__global T *query,
|
||||
__global T *train,
|
||||
float maxDistance,
|
||||
__global int *bestTrainIdx,
|
||||
__global float *bestDistance,
|
||||
__global int *nMatches,
|
||||
int query_rows,
|
||||
int query_cols,
|
||||
int train_rows,
|
||||
int train_cols,
|
||||
int bestTrainIdx_cols,
|
||||
int step,
|
||||
int ostep
|
||||
)
|
||||
{
|
||||
const int lidx = get_local_id(0);
|
||||
const int lidy = get_local_id(1);
|
||||
const int groupidx = get_group_id(0);
|
||||
const int groupidy = get_group_id(1);
|
||||
|
||||
const int queryIdx = mad24(BLOCK_SIZE, groupidy, lidy);
|
||||
const int queryOffset = min(queryIdx, query_rows - 1) * step;
|
||||
__global TN *query_vec = (__global TN *)(query + queryOffset);
|
||||
|
||||
const int trainIdx = mad24(BLOCK_SIZE, groupidx, lidx);
|
||||
const int trainOffset = min(mad24(BLOCK_SIZE, groupidx, lidy), train_rows - 1) * step;
|
||||
__global TN *train_vec = (__global TN *)(train + trainOffset);
|
||||
|
||||
query_cols /= kercn;
|
||||
|
||||
__local float sharebuffer[SHARED_MEM_SZ];
|
||||
__local value_type *s_query = (__local value_type *)sharebuffer;
|
||||
__local value_type *s_train = (__local value_type *)sharebuffer + BLOCK_SIZE_ODD * BLOCK_SIZE;
|
||||
|
||||
result_type result = 0;
|
||||
const int s_query_i = mad24(BLOCK_SIZE_ODD, lidy, lidx);
|
||||
const int s_train_i = mad24(BLOCK_SIZE_ODD, lidx, lidy);
|
||||
for (int i = 0 ; i < (query_cols + BLOCK_SIZE - 1) / BLOCK_SIZE ; ++i)
|
||||
{
|
||||
//load a BLOCK_SIZE * BLOCK_SIZE block into local train.
|
||||
const int loadx = mad24(BLOCK_SIZE, i, lidx);
|
||||
|
||||
if (loadx < query_cols)
|
||||
{
|
||||
s_query[s_query_i] = query_vec[loadx];
|
||||
s_train[s_train_i] = train_vec[loadx];
|
||||
}
|
||||
else
|
||||
{
|
||||
s_query[s_query_i] = 0;
|
||||
s_train[s_train_i] = 0;
|
||||
}
|
||||
|
||||
//synchronize to make sure each elem for reduceIteration in share memory is written already.
|
||||
barrier(CLK_LOCAL_MEM_FENCE);
|
||||
|
||||
result += reduce_block(s_query, s_train, lidx, lidy);
|
||||
|
||||
barrier(CLK_LOCAL_MEM_FENCE);
|
||||
}
|
||||
if (queryIdx < query_rows && trainIdx < train_rows && convert_float(result) < maxDistance)
|
||||
{
|
||||
int ind = atom_inc(nMatches + queryIdx);
|
||||
|
||||
if(ind < bestTrainIdx_cols)
|
||||
{
|
||||
bestTrainIdx[mad24(queryIdx, ostep, ind)] = trainIdx;
|
||||
bestDistance[mad24(queryIdx, ostep, ind)] = result;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
__kernel void BruteForceMatch_knnMatch(
|
||||
__global T *query,
|
||||
__global T *train,
|
||||
__global int2 *bestTrainIdx,
|
||||
__global float2 *bestDistance,
|
||||
int query_rows,
|
||||
int query_cols,
|
||||
int train_rows,
|
||||
int train_cols,
|
||||
int step
|
||||
)
|
||||
{
|
||||
const int lidx = get_local_id(0);
|
||||
const int lidy = get_local_id(1);
|
||||
const int groupidx = get_group_id(0);
|
||||
|
||||
const int queryIdx = mad24(BLOCK_SIZE, groupidx, lidy);
|
||||
const int queryOffset = min(queryIdx, query_rows - 1) * step;
|
||||
__global TN *query_vec = (__global TN *)(query + queryOffset);
|
||||
query_cols /= kercn;
|
||||
|
||||
__local float sharebuffer[SHARED_MEM_SZ];
|
||||
__local value_type *s_query = (__local value_type *)sharebuffer;
|
||||
|
||||
#if 0 < MAX_DESC_LEN
|
||||
__local value_type *s_train = (__local value_type *)sharebuffer + BLOCK_SIZE * MAX_DESC_LEN;
|
||||
// load the query into local memory.
|
||||
#pragma unroll
|
||||
for (int i = 0 ; i < MAX_DESC_LEN / BLOCK_SIZE; i ++)
|
||||
{
|
||||
int loadx = mad24(BLOCK_SIZE, i, lidx);
|
||||
s_query[mad24(MAX_DESC_LEN, lidy, loadx)] = loadx < query_cols ? query_vec[loadx] : 0;
|
||||
}
|
||||
#else
|
||||
__local value_type *s_train = (__local value_type *)sharebuffer + BLOCK_SIZE_ODD * BLOCK_SIZE;
|
||||
const int s_query_i = mad24(BLOCK_SIZE_ODD, lidy, lidx);
|
||||
const int s_train_i = mad24(BLOCK_SIZE_ODD, lidx, lidy);
|
||||
#endif
|
||||
|
||||
float myBestDistance1 = MAX_FLOAT;
|
||||
float myBestDistance2 = MAX_FLOAT;
|
||||
int myBestTrainIdx1 = -1;
|
||||
int myBestTrainIdx2 = -1;
|
||||
|
||||
for (int t = 0, endt = (train_rows + BLOCK_SIZE - 1) / BLOCK_SIZE; t < endt ; t++)
|
||||
{
|
||||
result_type result = 0;
|
||||
|
||||
int trainOffset = min(mad24(BLOCK_SIZE, t, lidy), train_rows - 1) * step;
|
||||
__global TN *train_vec = (__global TN *)(train + trainOffset);
|
||||
#if 0 < MAX_DESC_LEN
|
||||
#pragma unroll
|
||||
for (int i = 0 ; i < MAX_DESC_LEN / BLOCK_SIZE ; i++)
|
||||
{
|
||||
//load a BLOCK_SIZE * BLOCK_SIZE block into local train.
|
||||
const int loadx = mad24(BLOCK_SIZE, i, lidx);
|
||||
s_train[mad24(BLOCK_SIZE, lidx, lidy)] = loadx < train_cols ? train_vec[loadx] : 0;
|
||||
|
||||
//synchronize to make sure each elem for reduceIteration in share memory is written already.
|
||||
barrier(CLK_LOCAL_MEM_FENCE);
|
||||
|
||||
result += reduce_multi_block(s_query, s_train, i, lidx, lidy);
|
||||
|
||||
barrier(CLK_LOCAL_MEM_FENCE);
|
||||
}
|
||||
#else
|
||||
for (int i = 0, endq = (query_cols + BLOCK_SIZE -1) / BLOCK_SIZE; i < endq ; i++)
|
||||
{
|
||||
const int loadx = mad24(BLOCK_SIZE, i, lidx);
|
||||
//load query and train into local memory
|
||||
if (loadx < query_cols)
|
||||
{
|
||||
s_query[s_query_i] = query_vec[loadx];
|
||||
s_train[s_train_i] = train_vec[loadx];
|
||||
}
|
||||
else
|
||||
{
|
||||
s_query[s_query_i] = 0;
|
||||
s_train[s_train_i] = 0;
|
||||
}
|
||||
|
||||
barrier(CLK_LOCAL_MEM_FENCE);
|
||||
|
||||
result += reduce_block_match(s_query, s_train, lidx, lidy);
|
||||
|
||||
barrier(CLK_LOCAL_MEM_FENCE);
|
||||
}
|
||||
#endif
|
||||
result = DIST_RES(result);
|
||||
|
||||
const int trainIdx = mad24(BLOCK_SIZE, t, lidx);
|
||||
|
||||
if (queryIdx < query_rows && trainIdx < train_rows)
|
||||
{
|
||||
if (result < myBestDistance1)
|
||||
{
|
||||
myBestDistance2 = myBestDistance1;
|
||||
myBestTrainIdx2 = myBestTrainIdx1;
|
||||
myBestDistance1 = result;
|
||||
myBestTrainIdx1 = trainIdx;
|
||||
}
|
||||
else if (result < myBestDistance2)
|
||||
{
|
||||
myBestDistance2 = result;
|
||||
myBestTrainIdx2 = trainIdx;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
barrier(CLK_LOCAL_MEM_FENCE);
|
||||
|
||||
__local float *s_distance = (__local float *)sharebuffer;
|
||||
__local int *s_trainIdx = (__local int *)(sharebuffer + BLOCK_SIZE_ODD * BLOCK_SIZE);
|
||||
|
||||
// find BestMatch
|
||||
s_distance += lidy * BLOCK_SIZE_ODD;
|
||||
s_trainIdx += lidy * BLOCK_SIZE_ODD;
|
||||
s_distance[lidx] = myBestDistance1;
|
||||
s_trainIdx[lidx] = myBestTrainIdx1;
|
||||
|
||||
float bestDistance1 = MAX_FLOAT;
|
||||
float bestDistance2 = MAX_FLOAT;
|
||||
int bestTrainIdx1 = -1;
|
||||
int bestTrainIdx2 = -1;
|
||||
barrier(CLK_LOCAL_MEM_FENCE);
|
||||
|
||||
if (lidx == 0)
|
||||
{
|
||||
for (int i = 0 ; i < BLOCK_SIZE ; i++)
|
||||
{
|
||||
float val = s_distance[i];
|
||||
if (val < bestDistance1)
|
||||
{
|
||||
bestDistance2 = bestDistance1;
|
||||
bestTrainIdx2 = bestTrainIdx1;
|
||||
|
||||
bestDistance1 = val;
|
||||
bestTrainIdx1 = s_trainIdx[i];
|
||||
}
|
||||
else if (val < bestDistance2)
|
||||
{
|
||||
bestDistance2 = val;
|
||||
bestTrainIdx2 = s_trainIdx[i];
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
barrier(CLK_LOCAL_MEM_FENCE);
|
||||
|
||||
s_distance[lidx] = myBestDistance2;
|
||||
s_trainIdx[lidx] = myBestTrainIdx2;
|
||||
|
||||
barrier(CLK_LOCAL_MEM_FENCE);
|
||||
|
||||
if (lidx == 0)
|
||||
{
|
||||
for (int i = 0 ; i < BLOCK_SIZE ; i++)
|
||||
{
|
||||
float val = s_distance[i];
|
||||
|
||||
if (val < bestDistance2)
|
||||
{
|
||||
bestDistance2 = val;
|
||||
bestTrainIdx2 = s_trainIdx[i];
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
myBestDistance1 = bestDistance1;
|
||||
myBestDistance2 = bestDistance2;
|
||||
|
||||
myBestTrainIdx1 = bestTrainIdx1;
|
||||
myBestTrainIdx2 = bestTrainIdx2;
|
||||
|
||||
if (queryIdx < query_rows && lidx == 0)
|
||||
{
|
||||
bestTrainIdx[queryIdx] = (int2)(myBestTrainIdx1, myBestTrainIdx2);
|
||||
bestDistance[queryIdx] = (float2)(myBestDistance1, myBestDistance2);
|
||||
}
|
||||
}
|
||||
|
||||
#ifdef HAVE_INT64_ATOMICS
|
||||
#pragma OPENCL EXTENSION cl_khr_int64_base_atomics : enable
|
||||
|
||||
__kernel void BruteForceMatch_CrossCheckMatch(
|
||||
__global T *query,
|
||||
__global T *train,
|
||||
__global int *bestTrainIdx,
|
||||
__global float *bestDistance,
|
||||
__global ulong *revBest,
|
||||
int query_rows,
|
||||
int query_cols,
|
||||
int train_rows,
|
||||
int train_cols,
|
||||
int step
|
||||
)
|
||||
{
|
||||
const int lidx = get_local_id(0);
|
||||
const int lidy = get_local_id(1);
|
||||
const int groupidx = get_group_id(0);
|
||||
|
||||
const int queryIdx = mad24(BLOCK_SIZE, groupidx, lidy);
|
||||
const int queryOffset = min(queryIdx, query_rows - 1) * step;
|
||||
__global TN *query_vec = (__global TN *)(query + queryOffset);
|
||||
query_cols /= kercn;
|
||||
|
||||
__local float sharebuffer[SHARED_MEM_SZ];
|
||||
__local value_type *s_query = (__local value_type *)sharebuffer;
|
||||
|
||||
#if 0 < MAX_DESC_LEN
|
||||
__local value_type *s_train = (__local value_type *)sharebuffer + BLOCK_SIZE * MAX_DESC_LEN;
|
||||
#pragma unroll
|
||||
for (int i = 0; i < MAX_DESC_LEN / BLOCK_SIZE; i++)
|
||||
{
|
||||
const int loadx = mad24(BLOCK_SIZE, i, lidx);
|
||||
s_query[mad24(MAX_DESC_LEN, lidy, loadx)] = loadx < query_cols ? query_vec[loadx] : 0;
|
||||
}
|
||||
#else
|
||||
__local value_type *s_train = (__local value_type *)sharebuffer + BLOCK_SIZE_ODD * BLOCK_SIZE;
|
||||
const int s_query_i = mad24(BLOCK_SIZE_ODD, lidy, lidx);
|
||||
const int s_train_i = mad24(BLOCK_SIZE_ODD, lidx, lidy);
|
||||
#endif
|
||||
|
||||
float myBestDistance = MAX_FLOAT;
|
||||
int myBestTrainIdx = -1;
|
||||
|
||||
for (int t = 0, endt = (train_rows + BLOCK_SIZE - 1) / BLOCK_SIZE; t < endt; t++)
|
||||
{
|
||||
result_type result = 0;
|
||||
|
||||
const int trainOffset = min(mad24(BLOCK_SIZE, t, lidy), train_rows - 1) * step;
|
||||
__global TN *train_vec = (__global TN *)(train + trainOffset);
|
||||
#if 0 < MAX_DESC_LEN
|
||||
#pragma unroll
|
||||
for (int i = 0; i < MAX_DESC_LEN / BLOCK_SIZE; i++)
|
||||
{
|
||||
const int loadx = mad24(BLOCK_SIZE, i, lidx);
|
||||
s_train[mad24(BLOCK_SIZE, lidx, lidy)] = loadx < train_cols ? train_vec[loadx] : 0;
|
||||
|
||||
barrier(CLK_LOCAL_MEM_FENCE);
|
||||
|
||||
result += reduce_multi_block(s_query, s_train, i, lidx, lidy);
|
||||
|
||||
barrier(CLK_LOCAL_MEM_FENCE);
|
||||
}
|
||||
#else
|
||||
for (int i = 0, endq = (query_cols + BLOCK_SIZE - 1) / BLOCK_SIZE; i < endq; i++)
|
||||
{
|
||||
const int loadx = mad24(BLOCK_SIZE, i, lidx);
|
||||
if (loadx < query_cols)
|
||||
{
|
||||
s_query[s_query_i] = query_vec[loadx];
|
||||
s_train[s_train_i] = train_vec[loadx];
|
||||
}
|
||||
else
|
||||
{
|
||||
s_query[s_query_i] = 0;
|
||||
s_train[s_train_i] = 0;
|
||||
}
|
||||
|
||||
barrier(CLK_LOCAL_MEM_FENCE);
|
||||
|
||||
result += reduce_block_match(s_query, s_train, lidx, lidy);
|
||||
|
||||
barrier(CLK_LOCAL_MEM_FENCE);
|
||||
}
|
||||
#endif
|
||||
result = DIST_RES(result);
|
||||
|
||||
const int trainIdx = mad24(BLOCK_SIZE, t, lidx);
|
||||
|
||||
if (queryIdx < query_rows && trainIdx < train_rows)
|
||||
{
|
||||
if (result < myBestDistance)
|
||||
{
|
||||
myBestDistance = result;
|
||||
myBestTrainIdx = trainIdx;
|
||||
}
|
||||
|
||||
uint dist_bits = as_uint(result);
|
||||
ulong packed = ((ulong)dist_bits << 32) | (ulong)queryIdx;
|
||||
atom_min(&revBest[trainIdx], packed);
|
||||
}
|
||||
}
|
||||
|
||||
barrier(CLK_LOCAL_MEM_FENCE);
|
||||
|
||||
__local float *s_distance = (__local float *)sharebuffer;
|
||||
__local int *s_trainIdx = (__local int *)(sharebuffer + BLOCK_SIZE_ODD * BLOCK_SIZE);
|
||||
|
||||
s_distance += lidy * BLOCK_SIZE_ODD;
|
||||
s_trainIdx += lidy * BLOCK_SIZE_ODD;
|
||||
s_distance[lidx] = myBestDistance;
|
||||
s_trainIdx[lidx] = myBestTrainIdx;
|
||||
|
||||
barrier(CLK_LOCAL_MEM_FENCE);
|
||||
|
||||
#pragma unroll
|
||||
for (int k = 0; k < BLOCK_SIZE; k++)
|
||||
{
|
||||
if (myBestDistance > s_distance[k])
|
||||
{
|
||||
myBestDistance = s_distance[k];
|
||||
myBestTrainIdx = s_trainIdx[k];
|
||||
}
|
||||
}
|
||||
|
||||
if (queryIdx < query_rows && lidx == 0)
|
||||
{
|
||||
bestTrainIdx[queryIdx] = myBestTrainIdx;
|
||||
bestDistance[queryIdx] = myBestDistance;
|
||||
}
|
||||
}
|
||||
#endif
|
||||
@@ -0,0 +1,162 @@
|
||||
// OpenCL port of the FAST corner detector.
|
||||
// Copyright (C) 2014, Itseez Inc. See the license at http://opencv.org
|
||||
|
||||
inline int cornerScore(__global const uchar* img, int step)
|
||||
{
|
||||
int k, tofs, v = img[0], a0 = 0, b0;
|
||||
int d[16];
|
||||
#define LOAD2(idx, ofs) \
|
||||
tofs = ofs; d[idx] = (short)(v - img[tofs]); d[idx+8] = (short)(v - img[-tofs])
|
||||
LOAD2(0, 3);
|
||||
LOAD2(1, -step+3);
|
||||
LOAD2(2, -step*2+2);
|
||||
LOAD2(3, -step*3+1);
|
||||
LOAD2(4, -step*3);
|
||||
LOAD2(5, -step*3-1);
|
||||
LOAD2(6, -step*2-2);
|
||||
LOAD2(7, -step-3);
|
||||
|
||||
#pragma unroll
|
||||
for( k = 0; k < 16; k += 2 )
|
||||
{
|
||||
int a = min((int)d[(k+1)&15], (int)d[(k+2)&15]);
|
||||
a = min(a, (int)d[(k+3)&15]);
|
||||
a = min(a, (int)d[(k+4)&15]);
|
||||
a = min(a, (int)d[(k+5)&15]);
|
||||
a = min(a, (int)d[(k+6)&15]);
|
||||
a = min(a, (int)d[(k+7)&15]);
|
||||
a = min(a, (int)d[(k+8)&15]);
|
||||
a0 = max(a0, min(a, (int)d[k&15]));
|
||||
a0 = max(a0, min(a, (int)d[(k+9)&15]));
|
||||
}
|
||||
|
||||
b0 = -a0;
|
||||
#pragma unroll
|
||||
for( k = 0; k < 16; k += 2 )
|
||||
{
|
||||
int b = max((int)d[(k+1)&15], (int)d[(k+2)&15]);
|
||||
b = max(b, (int)d[(k+3)&15]);
|
||||
b = max(b, (int)d[(k+4)&15]);
|
||||
b = max(b, (int)d[(k+5)&15]);
|
||||
b = max(b, (int)d[(k+6)&15]);
|
||||
b = max(b, (int)d[(k+7)&15]);
|
||||
b = max(b, (int)d[(k+8)&15]);
|
||||
|
||||
b0 = min(b0, max(b, (int)d[k]));
|
||||
b0 = min(b0, max(b, (int)d[(k+9)&15]));
|
||||
}
|
||||
|
||||
return -b0-1;
|
||||
}
|
||||
|
||||
__kernel
|
||||
void FAST_findKeypoints(
|
||||
__global const uchar * _img, int step, int img_offset,
|
||||
int img_rows, int img_cols,
|
||||
volatile __global int* kp_loc,
|
||||
int max_keypoints, int threshold )
|
||||
{
|
||||
int j = get_global_id(0) + 3;
|
||||
int i = get_global_id(1) + 3;
|
||||
|
||||
if (i < img_rows - 3 && j < img_cols - 3)
|
||||
{
|
||||
__global const uchar* img = _img + mad24(i, step, j + img_offset);
|
||||
int v = img[0], t0 = v - threshold, t1 = v + threshold;
|
||||
int k, tofs, v0, v1;
|
||||
int m0 = 0, m1 = 0;
|
||||
|
||||
#define UPDATE_MASK(idx, ofs) \
|
||||
tofs = ofs; v0 = img[tofs]; v1 = img[-tofs]; \
|
||||
m0 |= ((v0 < t0) << idx) | ((v1 < t0) << (8 + idx)); \
|
||||
m1 |= ((v0 > t1) << idx) | ((v1 > t1) << (8 + idx))
|
||||
|
||||
UPDATE_MASK(0, 3);
|
||||
if( (m0 | m1) == 0 )
|
||||
return;
|
||||
|
||||
UPDATE_MASK(2, -step*2+2);
|
||||
UPDATE_MASK(4, -step*3);
|
||||
UPDATE_MASK(6, -step*2-2);
|
||||
|
||||
#define EVEN_MASK (1+4+16+64)
|
||||
|
||||
if( ((m0 | (m0 >> 8)) & EVEN_MASK) != EVEN_MASK &&
|
||||
((m1 | (m1 >> 8)) & EVEN_MASK) != EVEN_MASK )
|
||||
return;
|
||||
|
||||
UPDATE_MASK(1, -step+3);
|
||||
UPDATE_MASK(3, -step*3+1);
|
||||
UPDATE_MASK(5, -step*3-1);
|
||||
UPDATE_MASK(7, -step-3);
|
||||
if( ((m0 | (m0 >> 8)) & 255) != 255 &&
|
||||
((m1 | (m1 >> 8)) & 255) != 255 )
|
||||
return;
|
||||
|
||||
m0 |= m0 << 16;
|
||||
m1 |= m1 << 16;
|
||||
|
||||
#define CHECK0(i) ((m0 & (511 << i)) == (511 << i))
|
||||
#define CHECK1(i) ((m1 & (511 << i)) == (511 << i))
|
||||
|
||||
if( CHECK0(0) + CHECK0(1) + CHECK0(2) + CHECK0(3) +
|
||||
CHECK0(4) + CHECK0(5) + CHECK0(6) + CHECK0(7) +
|
||||
CHECK0(8) + CHECK0(9) + CHECK0(10) + CHECK0(11) +
|
||||
CHECK0(12) + CHECK0(13) + CHECK0(14) + CHECK0(15) +
|
||||
|
||||
CHECK1(0) + CHECK1(1) + CHECK1(2) + CHECK1(3) +
|
||||
CHECK1(4) + CHECK1(5) + CHECK1(6) + CHECK1(7) +
|
||||
CHECK1(8) + CHECK1(9) + CHECK1(10) + CHECK1(11) +
|
||||
CHECK1(12) + CHECK1(13) + CHECK1(14) + CHECK1(15) == 0 )
|
||||
return;
|
||||
|
||||
{
|
||||
int idx = atomic_inc(kp_loc);
|
||||
if( idx < max_keypoints )
|
||||
{
|
||||
kp_loc[1 + 2*idx] = j;
|
||||
kp_loc[2 + 2*idx] = i;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
///////////////////////////////////////////////////////////////////////////
|
||||
// nonmaxSupression
|
||||
|
||||
__kernel
|
||||
void FAST_nonmaxSupression(
|
||||
__global const int* kp_in, volatile __global int* kp_out,
|
||||
__global const uchar * _img, int step, int img_offset,
|
||||
int rows, int cols, int counter, int max_keypoints)
|
||||
{
|
||||
const int idx = get_global_id(0);
|
||||
|
||||
if (idx < counter)
|
||||
{
|
||||
int x = kp_in[1 + 2*idx];
|
||||
int y = kp_in[2 + 2*idx];
|
||||
__global const uchar* img = _img + mad24(y, step, x + img_offset);
|
||||
|
||||
int s = cornerScore(img, step);
|
||||
|
||||
if( (x < 4 || s > cornerScore(img-1, step)) +
|
||||
(y < 4 || s > cornerScore(img-step, step)) != 2 )
|
||||
return;
|
||||
if( (x >= cols - 4 || s > cornerScore(img+1, step)) +
|
||||
(y >= rows - 4 || s > cornerScore(img+step, step)) +
|
||||
(x < 4 || y < 4 || s > cornerScore(img-step-1, step)) +
|
||||
(x >= cols - 4 || y < 4 || s > cornerScore(img-step+1, step)) +
|
||||
(x < 4 || y >= rows - 4 || s > cornerScore(img+step-1, step)) +
|
||||
(x >= cols - 4 || y >= rows - 4 || s > cornerScore(img+step+1, step)) == 6)
|
||||
{
|
||||
int new_idx = atomic_inc(kp_out);
|
||||
if( new_idx < max_keypoints )
|
||||
{
|
||||
kp_out[1 + 3*new_idx] = x;
|
||||
kp_out[2 + 3*new_idx] = y;
|
||||
kp_out[3 + 3*new_idx] = s;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,296 @@
|
||||
// This file is part of OpenCV project.
|
||||
// It is subject to the license terms in the LICENSE file found in the top-level directory
|
||||
// of this distribution and at http://opencv.org/license.html
|
||||
|
||||
inline float gaussian(float x, float y, float sigma)
|
||||
{
|
||||
return exp(-(x*x + y*y) / (2.0f*sigma*sigma));
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Compute KAZE upright 64-dimensional descriptor
|
||||
* @details Matches CPU Get_KAZE_Upright_Descriptor_64 exactly:
|
||||
* - integer scale via round(kpt_size/2)
|
||||
* - bilinear with y1=(int)(y-0.5), y2=(int)(y+0.5) convention
|
||||
* - clamp-on-border (not skip)
|
||||
*
|
||||
* @param Lx First-order x-derivative (sigma_size-scaled), row-major float array
|
||||
* @param Ly First-order y-derivative (sigma_size-scaled), row-major float array
|
||||
* @param lx_step Row stride of Lx/Ly in float elements (= cols for continuous mat)
|
||||
* @param lx_rows Image height
|
||||
* @param lx_cols Image width
|
||||
* @param keypoints_x Keypoint x coordinates
|
||||
* @param keypoints_y Keypoint y coordinates
|
||||
* @param keypoints_size Keypoint sizes (diameter = 2*sigma)
|
||||
* @param descriptors Output descriptor matrix, shape [nkeypoints, 64]
|
||||
* @param nkeypoints Number of keypoints
|
||||
*/
|
||||
__kernel void
|
||||
KAZE_compute_upright_descriptor_64(
|
||||
__global const float* Lx,
|
||||
__global const float* Ly,
|
||||
int lx_step, int lx_rows, int lx_cols,
|
||||
__global const float* keypoints_x,
|
||||
__global const float* keypoints_y,
|
||||
__global const float* keypoints_size,
|
||||
__global float* descriptors,
|
||||
int nkeypoints)
|
||||
{
|
||||
int idx = get_global_id(0);
|
||||
if (idx >= nkeypoints)
|
||||
return;
|
||||
|
||||
const int dsize = 64;
|
||||
const int sample_step = 5;
|
||||
const int pattern_size = 12;
|
||||
|
||||
float kpt_x = keypoints_x[idx];
|
||||
float kpt_y = keypoints_y[idx];
|
||||
float kpt_sz = keypoints_size[idx];
|
||||
|
||||
int scale = (int)(kpt_sz / 2.0f + 0.5f); // cvRound equivalent
|
||||
float xf = kpt_x;
|
||||
float yf = kpt_y;
|
||||
|
||||
float dx = 0.0f, dy = 0.0f, mdx = 0.0f, mdy = 0.0f;
|
||||
float gauss_s1, gauss_s2;
|
||||
float rx, ry;
|
||||
float sample_x, sample_y;
|
||||
int x1, y1, x2, y2;
|
||||
int kx, ky, i, j, dcount = 0;
|
||||
float fx, fy;
|
||||
float res1, res2, res3, res4;
|
||||
float len = 0.0f;
|
||||
|
||||
float cx = -0.5f, cy = 0.5f;
|
||||
|
||||
i = -8;
|
||||
while (i < pattern_size)
|
||||
{
|
||||
j = -8;
|
||||
i = i - 4;
|
||||
cx += 1.0f;
|
||||
cy = -0.5f;
|
||||
|
||||
while (j < pattern_size)
|
||||
{
|
||||
dx = dy = mdx = mdy = 0.0f;
|
||||
cy += 1.0f;
|
||||
j = j - 4;
|
||||
|
||||
ky = i + sample_step;
|
||||
kx = j + sample_step;
|
||||
|
||||
float ys = yf + (float)(ky * scale);
|
||||
float xs = xf + (float)(kx * scale);
|
||||
|
||||
for (int k = i; k < i + 9; k++)
|
||||
{
|
||||
for (int l = j; l < j + 9; l++)
|
||||
{
|
||||
sample_y = (float)k * scale + yf;
|
||||
sample_x = (float)l * scale + xf;
|
||||
|
||||
gauss_s1 = gaussian(xs - sample_x, ys - sample_y, 2.5f * scale);
|
||||
|
||||
// Match CPU bilinear convention: y1=(int)(y-0.5), y2=(int)(y+0.5)
|
||||
y1 = (int)(sample_y - 0.5f);
|
||||
x1 = (int)(sample_x - 0.5f);
|
||||
y1 = clamp(y1, 0, lx_rows - 1);
|
||||
x1 = clamp(x1, 0, lx_cols - 1);
|
||||
|
||||
y2 = (int)(sample_y + 0.5f);
|
||||
x2 = (int)(sample_x + 0.5f);
|
||||
y2 = clamp(y2, 0, lx_rows - 1);
|
||||
x2 = clamp(x2, 0, lx_cols - 1);
|
||||
|
||||
fx = sample_x - (float)x1;
|
||||
fy = sample_y - (float)y1;
|
||||
|
||||
res1 = Lx[y1 * lx_step + x1];
|
||||
res2 = Lx[y1 * lx_step + x2];
|
||||
res3 = Lx[y2 * lx_step + x1];
|
||||
res4 = Lx[y2 * lx_step + x2];
|
||||
rx = (1.0f - fx)*(1.0f - fy)*res1 + fx*(1.0f - fy)*res2
|
||||
+ (1.0f - fx)*fy*res3 + fx*fy*res4;
|
||||
|
||||
res1 = Ly[y1 * lx_step + x1];
|
||||
res2 = Ly[y1 * lx_step + x2];
|
||||
res3 = Ly[y2 * lx_step + x1];
|
||||
res4 = Ly[y2 * lx_step + x2];
|
||||
ry = (1.0f - fx)*(1.0f - fy)*res1 + fx*(1.0f - fy)*res2
|
||||
+ (1.0f - fx)*fy*res3 + fx*fy*res4;
|
||||
|
||||
rx = gauss_s1 * rx;
|
||||
ry = gauss_s1 * ry;
|
||||
|
||||
dx += rx;
|
||||
dy += ry;
|
||||
mdx += fabs(rx);
|
||||
mdy += fabs(ry);
|
||||
}
|
||||
}
|
||||
|
||||
gauss_s2 = gaussian(cx - 2.0f, cy - 2.0f, 1.5f);
|
||||
|
||||
descriptors[idx * dsize + dcount++] = dx * gauss_s2;
|
||||
descriptors[idx * dsize + dcount++] = dy * gauss_s2;
|
||||
descriptors[idx * dsize + dcount++] = mdx * gauss_s2;
|
||||
descriptors[idx * dsize + dcount++] = mdy * gauss_s2;
|
||||
|
||||
len += (dx*dx + dy*dy + mdx*mdx + mdy*mdy) * gauss_s2 * gauss_s2;
|
||||
|
||||
j += 9;
|
||||
}
|
||||
i += 9;
|
||||
}
|
||||
|
||||
// L2 normalize
|
||||
len = sqrt(len);
|
||||
if (len > 1e-10f)
|
||||
{
|
||||
float len_inv = 1.0f / len;
|
||||
for (i = 0; i < dsize; i++)
|
||||
descriptors[idx * dsize + i] *= len_inv;
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Compute KAZE upright 128-dimensional descriptor (extended mode)
|
||||
* @details Matches CPU Get_KAZE_Upright_Descriptor_128: splits dx/dy by sign of the
|
||||
* cross-component for 8 values per subregion.
|
||||
*/
|
||||
__kernel void
|
||||
KAZE_compute_upright_descriptor_128(
|
||||
__global const float* Lx,
|
||||
__global const float* Ly,
|
||||
int lx_step, int lx_rows, int lx_cols,
|
||||
__global const float* keypoints_x,
|
||||
__global const float* keypoints_y,
|
||||
__global const float* keypoints_size,
|
||||
__global float* descriptors,
|
||||
int nkeypoints)
|
||||
{
|
||||
int idx = get_global_id(0);
|
||||
if (idx >= nkeypoints)
|
||||
return;
|
||||
|
||||
const int dsize = 128;
|
||||
const int sample_step = 5;
|
||||
const int pattern_size = 12;
|
||||
|
||||
float kpt_x = keypoints_x[idx];
|
||||
float kpt_y = keypoints_y[idx];
|
||||
float kpt_sz = keypoints_size[idx];
|
||||
|
||||
int scale = (int)(kpt_sz / 2.0f + 0.5f);
|
||||
float xf = kpt_x;
|
||||
float yf = kpt_y;
|
||||
|
||||
float dxp = 0.0f, dxn = 0.0f, mdxp = 0.0f, mdxn = 0.0f;
|
||||
float dyp = 0.0f, dyn = 0.0f, mdyp = 0.0f, mdyn = 0.0f;
|
||||
float gauss_s1, gauss_s2;
|
||||
float rx, ry;
|
||||
float sample_x, sample_y;
|
||||
int x1, y1, x2, y2;
|
||||
int kx, ky, i, j, dcount = 0;
|
||||
float fx, fy;
|
||||
float res1, res2, res3, res4;
|
||||
float len = 0.0f;
|
||||
|
||||
float cx = -0.5f, cy = 0.5f;
|
||||
|
||||
i = -8;
|
||||
while (i < pattern_size)
|
||||
{
|
||||
j = -8;
|
||||
i = i - 4;
|
||||
cx += 1.0f;
|
||||
cy = -0.5f;
|
||||
|
||||
while (j < pattern_size)
|
||||
{
|
||||
dxp = dxn = mdxp = mdxn = 0.0f;
|
||||
dyp = dyn = mdyp = mdyn = 0.0f;
|
||||
cy += 1.0f;
|
||||
j = j - 4;
|
||||
|
||||
ky = i + sample_step;
|
||||
kx = j + sample_step;
|
||||
|
||||
float ys = yf + (float)(ky * scale);
|
||||
float xs = xf + (float)(kx * scale);
|
||||
|
||||
for (int k = i; k < i + 9; k++)
|
||||
{
|
||||
for (int l = j; l < j + 9; l++)
|
||||
{
|
||||
sample_y = (float)k * scale + yf;
|
||||
sample_x = (float)l * scale + xf;
|
||||
|
||||
gauss_s1 = gaussian(xs - sample_x, ys - sample_y, 2.5f * scale);
|
||||
|
||||
y1 = (int)(sample_y - 0.5f);
|
||||
x1 = (int)(sample_x - 0.5f);
|
||||
y1 = clamp(y1, 0, lx_rows - 1);
|
||||
x1 = clamp(x1, 0, lx_cols - 1);
|
||||
|
||||
y2 = (int)(sample_y + 0.5f);
|
||||
x2 = (int)(sample_x + 0.5f);
|
||||
y2 = clamp(y2, 0, lx_rows - 1);
|
||||
x2 = clamp(x2, 0, lx_cols - 1);
|
||||
|
||||
fx = sample_x - (float)x1;
|
||||
fy = sample_y - (float)y1;
|
||||
|
||||
res1 = Lx[y1 * lx_step + x1];
|
||||
res2 = Lx[y1 * lx_step + x2];
|
||||
res3 = Lx[y2 * lx_step + x1];
|
||||
res4 = Lx[y2 * lx_step + x2];
|
||||
rx = (1.0f - fx)*(1.0f - fy)*res1 + fx*(1.0f - fy)*res2
|
||||
+ (1.0f - fx)*fy*res3 + fx*fy*res4;
|
||||
|
||||
res1 = Ly[y1 * lx_step + x1];
|
||||
res2 = Ly[y1 * lx_step + x2];
|
||||
res3 = Ly[y2 * lx_step + x1];
|
||||
res4 = Ly[y2 * lx_step + x2];
|
||||
ry = (1.0f - fx)*(1.0f - fy)*res1 + fx*(1.0f - fy)*res2
|
||||
+ (1.0f - fx)*fy*res3 + fx*fy*res4;
|
||||
|
||||
rx = gauss_s1 * rx;
|
||||
ry = gauss_s1 * ry;
|
||||
|
||||
if (ry >= 0.0f) { dxp += rx; mdxp += fabs(rx); }
|
||||
else { dxn += rx; mdxn += fabs(rx); }
|
||||
if (rx >= 0.0f) { dyp += ry; mdyp += fabs(ry); }
|
||||
else { dyn += ry; mdyn += fabs(ry); }
|
||||
}
|
||||
}
|
||||
|
||||
gauss_s2 = gaussian(cx - 2.0f, cy - 2.0f, 1.5f);
|
||||
|
||||
descriptors[idx * dsize + dcount++] = dxp * gauss_s2;
|
||||
descriptors[idx * dsize + dcount++] = dxn * gauss_s2;
|
||||
descriptors[idx * dsize + dcount++] = mdxp * gauss_s2;
|
||||
descriptors[idx * dsize + dcount++] = mdxn * gauss_s2;
|
||||
descriptors[idx * dsize + dcount++] = dyp * gauss_s2;
|
||||
descriptors[idx * dsize + dcount++] = dyn * gauss_s2;
|
||||
descriptors[idx * dsize + dcount++] = mdyp * gauss_s2;
|
||||
descriptors[idx * dsize + dcount++] = mdyn * gauss_s2;
|
||||
|
||||
len += (dxp*dxp + dxn*dxn + mdxp*mdxp + mdxn*mdxn
|
||||
+ dyp*dyp + dyn*dyn + mdyp*mdyp + mdyn*mdyn) * gauss_s2 * gauss_s2;
|
||||
|
||||
j += 9;
|
||||
}
|
||||
i += 9;
|
||||
}
|
||||
|
||||
len = sqrt(len);
|
||||
if (len > 1e-10f)
|
||||
{
|
||||
float len_inv = 1.0f / len;
|
||||
for (i = 0; i < dsize; i++)
|
||||
descriptors[idx * dsize + i] *= len_inv;
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,254 @@
|
||||
// OpenCL port of the ORB feature detector and descriptor extractor
|
||||
// Copyright (C) 2014, Itseez Inc. See the license at http://opencv.org
|
||||
//
|
||||
// The original code has been contributed by Peter Andreas Entschev, peter@entschev.com
|
||||
|
||||
#define LAYERINFO_SIZE 1
|
||||
#define LAYERINFO_OFS 0
|
||||
#define KEYPOINT_SIZE 3
|
||||
#define ORIENTED_KEYPOINT_SIZE 4
|
||||
#define KEYPOINT_X 0
|
||||
#define KEYPOINT_Y 1
|
||||
#define KEYPOINT_Z 2
|
||||
#define KEYPOINT_ANGLE 3
|
||||
|
||||
/////////////////////////////////////////////////////////////
|
||||
|
||||
#ifdef ORB_RESPONSES
|
||||
|
||||
__kernel void
|
||||
ORB_HarrisResponses(__global const uchar* imgbuf, int imgstep, int imgoffset0,
|
||||
__global const int* layerinfo, __global const int* keypoints,
|
||||
__global float* responses, int nkeypoints )
|
||||
{
|
||||
int idx = get_global_id(0);
|
||||
if( idx < nkeypoints )
|
||||
{
|
||||
__global const int* kpt = keypoints + idx*KEYPOINT_SIZE;
|
||||
__global const int* layer = layerinfo + kpt[KEYPOINT_Z]*LAYERINFO_SIZE;
|
||||
__global const uchar* img = imgbuf + imgoffset0 + layer[LAYERINFO_OFS] +
|
||||
(kpt[KEYPOINT_Y] - blockSize/2)*imgstep + (kpt[KEYPOINT_X] - blockSize/2);
|
||||
|
||||
int i, j;
|
||||
int a = 0, b = 0, c = 0;
|
||||
for( i = 0; i < blockSize; i++, img += imgstep-blockSize )
|
||||
{
|
||||
for( j = 0; j < blockSize; j++, img++ )
|
||||
{
|
||||
int Ix = (img[1] - img[-1])*2 + img[-imgstep+1] - img[-imgstep-1] + img[imgstep+1] - img[imgstep-1];
|
||||
int Iy = (img[imgstep] - img[-imgstep])*2 + img[imgstep-1] - img[-imgstep-1] + img[imgstep+1] - img[-imgstep+1];
|
||||
a += Ix*Ix;
|
||||
b += Iy*Iy;
|
||||
c += Ix*Iy;
|
||||
}
|
||||
}
|
||||
responses[idx] = ((float)a * b - (float)c * c - HARRIS_K * (float)(a + b) * (a + b))*scale_sq_sq;
|
||||
}
|
||||
}
|
||||
|
||||
#endif
|
||||
|
||||
/////////////////////////////////////////////////////////////
|
||||
|
||||
#ifdef ORB_ANGLES
|
||||
|
||||
#define _DBL_EPSILON 2.2204460492503131e-16f
|
||||
#define atan2_p1 (0.9997878412794807f*57.29577951308232f)
|
||||
#define atan2_p3 (-0.3258083974640975f*57.29577951308232f)
|
||||
#define atan2_p5 (0.1555786518463281f*57.29577951308232f)
|
||||
#define atan2_p7 (-0.04432655554792128f*57.29577951308232f)
|
||||
|
||||
inline float fastAtan2( float y, float x )
|
||||
{
|
||||
float ax = fabs(x), ay = fabs(y);
|
||||
float a, c, c2;
|
||||
if( ax >= ay )
|
||||
{
|
||||
c = ay/(ax + _DBL_EPSILON);
|
||||
c2 = c*c;
|
||||
a = (((atan2_p7*c2 + atan2_p5)*c2 + atan2_p3)*c2 + atan2_p1)*c;
|
||||
}
|
||||
else
|
||||
{
|
||||
c = ax/(ay + _DBL_EPSILON);
|
||||
c2 = c*c;
|
||||
a = 90.f - (((atan2_p7*c2 + atan2_p5)*c2 + atan2_p3)*c2 + atan2_p1)*c;
|
||||
}
|
||||
if( x < 0 )
|
||||
a = 180.f - a;
|
||||
if( y < 0 )
|
||||
a = 360.f - a;
|
||||
return a;
|
||||
}
|
||||
|
||||
|
||||
__kernel void
|
||||
ORB_ICAngle(__global const uchar* imgbuf, int imgstep, int imgoffset0,
|
||||
__global const int* layerinfo, __global const int* keypoints,
|
||||
__global float* responses, const __global int* u_max,
|
||||
int nkeypoints, int half_k )
|
||||
{
|
||||
int idx = get_global_id(0);
|
||||
if( idx < nkeypoints )
|
||||
{
|
||||
__global const int* kpt = keypoints + idx*KEYPOINT_SIZE;
|
||||
|
||||
__global const int* layer = layerinfo + kpt[KEYPOINT_Z]*LAYERINFO_SIZE;
|
||||
__global const uchar* center = imgbuf + imgoffset0 + layer[LAYERINFO_OFS] +
|
||||
kpt[KEYPOINT_Y]*imgstep + kpt[KEYPOINT_X];
|
||||
|
||||
int u, v, m_01 = 0, m_10 = 0;
|
||||
|
||||
// Treat the center line differently, v=0
|
||||
for( u = -half_k; u <= half_k; u++ )
|
||||
m_10 += u * center[u];
|
||||
|
||||
// Go line by line in the circular patch
|
||||
for( v = 1; v <= half_k; v++ )
|
||||
{
|
||||
// Proceed over the two lines
|
||||
int v_sum = 0;
|
||||
int d = u_max[v];
|
||||
for( u = -d; u <= d; u++ )
|
||||
{
|
||||
int val_plus = center[u + v*imgstep], val_minus = center[u - v*imgstep];
|
||||
v_sum += (val_plus - val_minus);
|
||||
m_10 += u * (val_plus + val_minus);
|
||||
}
|
||||
m_01 += v * v_sum;
|
||||
}
|
||||
|
||||
// we do not use OpenCL's atan2 intrinsic,
|
||||
// because we want to get _exactly_ the same results as the CPU version
|
||||
responses[idx] = fastAtan2((float)m_01, (float)m_10);
|
||||
}
|
||||
}
|
||||
|
||||
#endif
|
||||
|
||||
/////////////////////////////////////////////////////////////
|
||||
|
||||
#ifdef ORB_DESCRIPTORS
|
||||
|
||||
__kernel void
|
||||
ORB_computeDescriptor(__global const uchar* imgbuf, int imgstep, int imgoffset0,
|
||||
__global const int* layerinfo, __global const int* keypoints,
|
||||
__global uchar* _desc, const __global int* pattern,
|
||||
int nkeypoints, int dsize )
|
||||
{
|
||||
int idx = get_global_id(0);
|
||||
if( idx < nkeypoints )
|
||||
{
|
||||
int i;
|
||||
__global const int* kpt = keypoints + idx*ORIENTED_KEYPOINT_SIZE;
|
||||
|
||||
__global const int* layer = layerinfo + kpt[KEYPOINT_Z]*LAYERINFO_SIZE;
|
||||
__global const uchar* center = imgbuf + imgoffset0 + layer[LAYERINFO_OFS] +
|
||||
kpt[KEYPOINT_Y]*imgstep + kpt[KEYPOINT_X];
|
||||
float angle = as_float(kpt[KEYPOINT_ANGLE]);
|
||||
angle *= 0.01745329251994329547f;
|
||||
|
||||
float cosa;
|
||||
float sina = sincos(angle, &cosa);
|
||||
|
||||
__global uchar* desc = _desc + idx*dsize;
|
||||
|
||||
#define GET_VALUE(idx) \
|
||||
center[mad24(convert_int_rte(pattern[(idx)*2] * sina + pattern[(idx)*2+1] * cosa), imgstep, \
|
||||
convert_int_rte(pattern[(idx)*2] * cosa - pattern[(idx)*2+1] * sina))]
|
||||
|
||||
for( i = 0; i < dsize; i++ )
|
||||
{
|
||||
int val;
|
||||
#if WTA_K == 2
|
||||
int t0, t1;
|
||||
|
||||
t0 = GET_VALUE(0); t1 = GET_VALUE(1);
|
||||
val = t0 < t1;
|
||||
|
||||
t0 = GET_VALUE(2); t1 = GET_VALUE(3);
|
||||
val |= (t0 < t1) << 1;
|
||||
|
||||
t0 = GET_VALUE(4); t1 = GET_VALUE(5);
|
||||
val |= (t0 < t1) << 2;
|
||||
|
||||
t0 = GET_VALUE(6); t1 = GET_VALUE(7);
|
||||
val |= (t0 < t1) << 3;
|
||||
|
||||
t0 = GET_VALUE(8); t1 = GET_VALUE(9);
|
||||
val |= (t0 < t1) << 4;
|
||||
|
||||
t0 = GET_VALUE(10); t1 = GET_VALUE(11);
|
||||
val |= (t0 < t1) << 5;
|
||||
|
||||
t0 = GET_VALUE(12); t1 = GET_VALUE(13);
|
||||
val |= (t0 < t1) << 6;
|
||||
|
||||
t0 = GET_VALUE(14); t1 = GET_VALUE(15);
|
||||
val |= (t0 < t1) << 7;
|
||||
|
||||
pattern += 16*2;
|
||||
|
||||
#elif WTA_K == 3
|
||||
int t0, t1, t2;
|
||||
|
||||
t0 = GET_VALUE(0); t1 = GET_VALUE(1); t2 = GET_VALUE(2);
|
||||
val = t2 > t1 ? (t2 > t0 ? 2 : 0) : (t1 > t0);
|
||||
|
||||
t0 = GET_VALUE(3); t1 = GET_VALUE(4); t2 = GET_VALUE(5);
|
||||
val |= (t2 > t1 ? (t2 > t0 ? 2 : 0) : (t1 > t0)) << 2;
|
||||
|
||||
t0 = GET_VALUE(6); t1 = GET_VALUE(7); t2 = GET_VALUE(8);
|
||||
val |= (t2 > t1 ? (t2 > t0 ? 2 : 0) : (t1 > t0)) << 4;
|
||||
|
||||
t0 = GET_VALUE(9); t1 = GET_VALUE(10); t2 = GET_VALUE(11);
|
||||
val |= (t2 > t1 ? (t2 > t0 ? 2 : 0) : (t1 > t0)) << 6;
|
||||
|
||||
pattern += 12*2;
|
||||
|
||||
#elif WTA_K == 4
|
||||
int t0, t1, t2, t3, k;
|
||||
int a, b;
|
||||
|
||||
t0 = GET_VALUE(0); t1 = GET_VALUE(1);
|
||||
t2 = GET_VALUE(2); t3 = GET_VALUE(3);
|
||||
a = 0, b = 2;
|
||||
if( t1 > t0 ) t0 = t1, a = 1;
|
||||
if( t3 > t2 ) t2 = t3, b = 3;
|
||||
k = t0 > t2 ? a : b;
|
||||
val = k;
|
||||
|
||||
t0 = GET_VALUE(4); t1 = GET_VALUE(5);
|
||||
t2 = GET_VALUE(6); t3 = GET_VALUE(7);
|
||||
a = 0, b = 2;
|
||||
if( t1 > t0 ) t0 = t1, a = 1;
|
||||
if( t3 > t2 ) t2 = t3, b = 3;
|
||||
k = t0 > t2 ? a : b;
|
||||
val |= k << 2;
|
||||
|
||||
t0 = GET_VALUE(8); t1 = GET_VALUE(9);
|
||||
t2 = GET_VALUE(10); t3 = GET_VALUE(11);
|
||||
a = 0, b = 2;
|
||||
if( t1 > t0 ) t0 = t1, a = 1;
|
||||
if( t3 > t2 ) t2 = t3, b = 3;
|
||||
k = t0 > t2 ? a : b;
|
||||
val |= k << 4;
|
||||
|
||||
t0 = GET_VALUE(12); t1 = GET_VALUE(13);
|
||||
t2 = GET_VALUE(14); t3 = GET_VALUE(15);
|
||||
a = 0, b = 2;
|
||||
if( t1 > t0 ) t0 = t1, a = 1;
|
||||
if( t3 > t2 ) t2 = t3, b = 3;
|
||||
k = t0 > t2 ? a : b;
|
||||
val |= k << 6;
|
||||
|
||||
pattern += 16*2;
|
||||
#else
|
||||
#error "unknown/undefined WTA_K value; should be 2, 3 or 4"
|
||||
#endif
|
||||
desc[i] = (uchar)val;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
#endif
|
||||
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,56 @@
|
||||
/*M///////////////////////////////////////////////////////////////////////////////////////
|
||||
//
|
||||
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
|
||||
//
|
||||
// By downloading, copying, installing or using the software you agree to this license.
|
||||
// If you do not agree to this license, do not download, install,
|
||||
// copy or use the software.
|
||||
//
|
||||
//
|
||||
// License Agreement
|
||||
// For Open Source Computer Vision Library
|
||||
//
|
||||
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
|
||||
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
|
||||
// Third party copyrights are property of their respective owners.
|
||||
//
|
||||
// Redistribution and use in source and binary forms, with or without modification,
|
||||
// are permitted provided that the following conditions are met:
|
||||
//
|
||||
// * Redistribution's of source code must retain the above copyright notice,
|
||||
// this list of conditions and the following disclaimer.
|
||||
//
|
||||
// * Redistribution's in binary form must reproduce the above copyright notice,
|
||||
// this list of conditions and the following disclaimer in the documentation
|
||||
// and/or other materials provided with the distribution.
|
||||
//
|
||||
// * The name of the copyright holders may not be used to endorse or promote products
|
||||
// derived from this software without specific prior written permission.
|
||||
//
|
||||
// This software is provided by the copyright holders and contributors "as is" and
|
||||
// any express or implied warranties, including, but not limited to, the implied
|
||||
// warranties of merchantability and fitness for a particular purpose are disclaimed.
|
||||
// In no event shall the Intel Corporation or contributors be liable for any direct,
|
||||
// indirect, incidental, special, exemplary, or consequential damages
|
||||
// (including, but not limited to, procurement of substitute goods or services;
|
||||
// loss of use, data, or profits; or business interruption) however caused
|
||||
// and on any theory of liability, whether in contract, strict liability,
|
||||
// or tort (including negligence or otherwise) arising in any way out of
|
||||
// the use of this software, even if advised of the possibility of such damage.
|
||||
//
|
||||
//M*/
|
||||
|
||||
#ifndef __OPENCV_PRECOMP_H__
|
||||
#define __OPENCV_PRECOMP_H__
|
||||
|
||||
#include "opencv2/features2d.hpp"
|
||||
#include "opencv2/imgproc.hpp"
|
||||
|
||||
#include "opencv2/core/utility.hpp"
|
||||
#include "opencv2/core/private.hpp"
|
||||
#include "opencv2/core/ocl.hpp"
|
||||
#include "opencv2/core/hal/hal.hpp"
|
||||
|
||||
#include <algorithm>
|
||||
|
||||
#endif
|
||||
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Reference in New Issue
Block a user