chore: import upstream snapshot with attribution

modules/flann/misc/python/pyopencv_flann.hpp

@@ -0,0 +1,119 @@
+#ifdef HAVE_OPENCV_FLANN
+typedef cvflann::flann_distance_t cvflann_flann_distance_t;
+typedef cvflann::flann_algorithm_t cvflann_flann_algorithm_t;
+
+template<>
+PyObject* pyopencv_from(const cvflann_flann_algorithm_t& value)
+{
+    return PyInt_FromLong(int(value));
+}
+
+template<>
+PyObject* pyopencv_from(const cvflann_flann_distance_t& value)
+{
+    return PyInt_FromLong(int(value));
+}
+
+template<>
+bool pyopencv_to(PyObject *o, cv::flann::IndexParams& p, const ArgInfo& info)
+{
+    if (!o || o == Py_None)
+    {
+        return true;
+    }
+
+    if(!PyDict_Check(o))
+    {
+        failmsg("Argument '%s' is not a dictionary", info.name);
+        return false;
+    }
+
+    PyObject* key_obj = NULL;
+    PyObject* value_obj = NULL;
+    Py_ssize_t key_pos = 0;
+
+    while(PyDict_Next(o, &key_pos, &key_obj, &value_obj))
+    {
+        // get key
+        std::string key;
+        if (!getUnicodeString(key_obj, key))
+        {
+            failmsg("Key at pos %lld is not a string", static_cast<int64_t>(key_pos));
+            return false;
+        }
+        // key_arg_info.name is bound to key lifetime
+        const ArgInfo key_arg_info(key.c_str(), false);
+
+        // get value
+        if (isBool(value_obj))
+        {
+            npy_bool npy_value = NPY_FALSE;
+            if (PyArray_BoolConverter(value_obj, &npy_value) >= 0)
+            {
+                p.setBool(key, npy_value == NPY_TRUE);
+                continue;
+            }
+            PyErr_Clear();
+        }
+
+        int int_value = 0;
+        if (pyopencv_to(value_obj, int_value, key_arg_info))
+        {
+            if (key == "algorithm")
+            {
+                p.setAlgorithm(int_value);
+            }
+            else
+            {
+                p.setInt(key, int_value);
+            }
+            continue;
+        }
+        PyErr_Clear();
+
+        double flt_value = 0.0;
+        if (pyopencv_to(value_obj, flt_value, key_arg_info))
+        {
+            if (key == "eps")
+            {
+                p.setFloat(key, static_cast<float>(flt_value));
+            }
+            else
+            {
+                p.setDouble(key, flt_value);
+            }
+            continue;
+        }
+        PyErr_Clear();
+
+        std::string str_value;
+        if (getUnicodeString(value_obj, str_value))
+        {
+            p.setString(key, str_value);
+            continue;
+        }
+        PyErr_Clear();
+        // All conversions are failed
+        failmsg("Failed to parse IndexParam with key '%s'. "
+                "Supported types: [bool, int, float, str]", key.c_str());
+        return false;
+
+    }
+    return true;
+}
+
+template<>
+bool pyopencv_to(PyObject* obj, cv::flann::SearchParams & value, const ArgInfo& info)
+{
+    return pyopencv_to<cv::flann::IndexParams>(obj, value, info);
+}
+
+template<>
+bool pyopencv_to(PyObject *o, cvflann::flann_distance_t& dist, const ArgInfo& info)
+{
+    int d = (int)dist;
+    bool ok = pyopencv_to(o, d, info);
+    dist = (cvflann::flann_distance_t)d;
+    return ok;
+}
+#endif

modules/flann/misc/python/test/test_flann_based_matcher.py

@@ -0,0 +1,30 @@
+#!/usr/bin/env python
+# Python 2/3 compatibility
+from __future__ import print_function
+
+import cv2
+import numpy as np
+
+from tests_common import NewOpenCVTests
+
+
+class FlannBasedMatcher(NewOpenCVTests):
+    def test_all_parameters_can_be_passed(self):
+        img1 = self.get_sample("samples/data/right01.jpg")
+        img2 = self.get_sample("samples/data/right02.jpg")
+
+        orb = cv2.ORB.create()
+
+        kp1, des1 = orb.detectAndCompute(img1, None)
+        kp2, des2 = orb.detectAndCompute(img2, None)
+        FLANN_INDEX_KDTREE = 1
+        index_param = dict(algorithm=FLANN_INDEX_KDTREE, trees=4)
+        search_param = dict(checks=32, sorted=True, eps=0.5,
+                            explore_all_trees=False)
+        matcher = cv2.FlannBasedMatcher(index_param, search_param)
+        matches = matcher.knnMatch(np.float32(des1), np.float32(des2), k=2)
+        self.assertGreater(len(matches), 0)
+
+
+if __name__ == '__main__':
+    NewOpenCVTests.bootstrap()