chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
@@ -0,0 +1,172 @@
|
||||
Create Calibration Pattern {#tutorial_camera_calibration_pattern}
|
||||
==========================
|
||||
|
||||
@tableofcontents
|
||||
|
||||
@next_tutorial{tutorial_camera_calibration_square_chess}
|
||||
|
||||
| | |
|
||||
| -: | :- |
|
||||
| Authors | Laurent Berger, Alexander Panov, Alexander Smorkalov |
|
||||
| Compatibility | OpenCV > 4.12 |
|
||||
|
||||
|
||||
The tutorial describes all pattern supported by OpenCV for camera(s) calibration and pose estimation
|
||||
with their strength, pitfalls and practical recommendations.
|
||||
|
||||
What is calibration pattern? why I need it?
|
||||
-------------------------------------------
|
||||
|
||||
The flat printable pattern may be used:
|
||||
|
||||
1. For camera intrinsics (internal parameters) calibration. See @ref tutorial_camera_calibration.
|
||||
2. For stereo or multi-camera system extrinsics (external parameters: rotation and translation
|
||||
of each camera) calibration. See cv::stereoCalibrate for details.
|
||||
3. Camera pose registration relative to well known point in 3d world. See multiview calibration
|
||||
tutorial in OpenCV 5.x.
|
||||
|
||||
Pattern Types
|
||||
-------------
|
||||
|
||||
**Chessboard**. Classic calibration pattern of black and white squares. The all calibration algorithms
|
||||
use internal chessboard corners as features. See cv::findChessboardCorners and cv::cornerSubPix to
|
||||
detect the board and refine corners coordinates with sub-pixel accuracy. The board size is defined
|
||||
as amount of internal corners, but not amount of black or white squares. Also pay attention, that
|
||||
the board with even size is symmetric. If board has even amount of corners by one of direction then
|
||||
its pose is defined up to 180 degrees (2 solutions). It the board is square with size N x N then its
|
||||
pose is defined up to 90 degrees (4 solutions). The last two cases are not suitable for calibration.
|
||||
Example code to generate features coordinates for calibration (object points):
|
||||
```
|
||||
std::vector<cv::Point3f> objectPoints;
|
||||
for (int i = 0; i < boardSize.height; ++i) {
|
||||
for (int j = 0; j < boardSize.width; ++j) {
|
||||
objectPoints.push_back(Point3f(j*squareSize, i*squareSize, 0));
|
||||
}
|
||||
}
|
||||
```
|
||||
Printable chessboard pattern: https://github.com/opencv/opencv/blob/4.x/doc/pattern.png
|
||||
(9x6 chessboard, page width: 210 mm, page height: 297 mm (A4))
|
||||
|
||||
**Circles Grid**. The circles grid is symmetric or asymmetric (each even row shifted) grid of black
|
||||
circles on a white background or vice verse. See cv::findCirclesGrid function to detect the board
|
||||
with OpenCV. The detector produces sub-pixel coordinates of the circle centers and does not require
|
||||
additional refinement. The board size is defined as amount of circles in grid by x and y axis.
|
||||
In case of asymmetric grid the shifted rows are taken into account too. The board is suitable for
|
||||
intrinsics calibration. Symmetric grids suffer from the same issue as chessboard pattern with even
|
||||
size. It's pose is defined up to 180 degrees.
|
||||
Example code to generate features coordinates for calibration with symmetric grid (object points):
|
||||
```
|
||||
std::vector<cv::Point3f> objectPoints;
|
||||
for (int i = 0; i < boardSize.height; ++i) {
|
||||
for (int j = 0; j < boardSize.width; ++j) {
|
||||
objectPoints.push_back(Point3f(j*squareSize, i*squareSize, 0));
|
||||
}
|
||||
}
|
||||
```
|
||||
Example code to generate features coordinates for calibration with asymmetric grid (object points):
|
||||
```
|
||||
std::vector<cv::Point3f> objectPoints;
|
||||
for (int i = 0; i < boardSize.height; i++) {
|
||||
for (int j = 0; j < boardSize.width; j++) {
|
||||
objectPoints.push_back(Point3f((2 * j + i % 2)*squareSize, i*squareSize, 0));
|
||||
}
|
||||
}
|
||||
```
|
||||
Printable asymmetric circles grid pattern: https://github.com/opencv/opencv/blob/4.x/doc/acircles_pattern.png
|
||||
(11x4 asymmetric circles grid, page width: 210 mm, page height: 297 mm (A4))
|
||||
|
||||
**ChAruco board**. Chessboard unreached with ArUco markers. Each internal corner of the board is
|
||||
described by 2 neighborhood ArUco markers that makes it unique. The board size is defined in number
|
||||
of units, but not internal corners. ChAruco board of size N x M is equivalent to chessboard pattern
|
||||
of size N-1 x M-1. OpenCV provides `cv::aruco::CharucoDetector` class for the board detection.
|
||||
The detector algorithm finds ArUco markers first and them "assembles" the board using knowledge
|
||||
about ArUco pairs. In opposite to the previous pattern partially occluded board may be used as all
|
||||
corners are labeled. The board is rotation invariant, but set of ArUco markers and their order
|
||||
should be known to detector apriori. It cannot detect ChAruco board with predefined size and random
|
||||
set of markers.
|
||||
Example code to generate features coordinates for calibration (object points) for board size in units:
|
||||
```
|
||||
std::vector<cv::Point3f> objectPoints;
|
||||
for (int i = 0; i < boardSize.height-1; ++i) {
|
||||
for (int j = 0; j < boardSize.width-1; ++j) {
|
||||
objectPoints.push_back(Point3f(j*squareSize, i*squareSize, 0));
|
||||
}
|
||||
}
|
||||
```
|
||||
Printable ChAruco board pattern: https://github.com/opencv/opencv/blob/4.x/doc/charuco_board_pattern.png
|
||||
(7X5 ChAruco board, square size: 30 mm, marker size: 15 mm, ArUco dict: DICT_5X5_100, page width:
|
||||
210 mm, page height: 297 mm (A4))
|
||||
|
||||
Create Your Own Pattern
|
||||
-----------------------
|
||||
|
||||
In case if ready pattern does not satisfy your requirements, you can generate your own. OpenCV
|
||||
provides generate_pattern.py tool in `apps/pattern-tools` of source repository or your binary
|
||||
distribution. The only requirement is Python 3.
|
||||
|
||||
Examples:
|
||||
|
||||
create a checkerboard pattern in file chessboard.svg with 9 rows, 6 columns and a square size of 20mm:
|
||||
|
||||
python generate_pattern.py -o chessboard.svg --rows 9 --columns 6 --type checkerboard --square_size 20
|
||||
|
||||
create a circle board pattern in file circleboard.svg with 7 rows, 5 columns and a radius of 15 mm:
|
||||
|
||||
python generate_pattern.py -o circleboard.svg --rows 7 --columns 5 --type circles --square_size 15
|
||||
|
||||
create a circle board pattern in file acircleboard.svg with 7 rows, 5 columns and a square size of
|
||||
10mm and less spacing between circle:
|
||||
|
||||
python generate_pattern.py -o acircleboard.svg --rows 7 --columns 5 --type acircles --square_size 10 --radius_rate 2
|
||||
|
||||
create a radon checkerboard for findChessboardCornersSB() with markers in (7 4), (7 5), (8 5) cells:
|
||||
|
||||
python generate_pattern.py -o radon_checkerboard.svg --rows 10 --columns 15 --type radon_checkerboard -s 12.1 -m 7 4 7 5 8 5
|
||||
|
||||
create a ChAruco board pattern in charuco_board.svg with 7 rows, 5 columns, square size 30 mm, aruco
|
||||
marker size 15 mm and using DICT_5X5_100 as dictionary for aruco markers (it contains in DICT_ARUCO.json file):
|
||||
|
||||
python generate_pattern.py -o charuco_board.svg --rows 7 --columns 5 -T charuco_board --square_size 30 --marker_size 15 -f DICT_5X5_100.json.gz
|
||||
|
||||
If you want to change the measurement units, use the -u option (e.g. mm, inches, px, m)
|
||||
|
||||
If you want to change the page size, use the -w (width) and -h (height) options
|
||||
|
||||
If you want to use your own dictionary for the ChAruco board, specify the name of your dictionary
|
||||
file. For example:
|
||||
|
||||
python generate_pattern.py -o charuco_board.svg --rows 7 --columns 5 -T charuco_board -f my_dictionary.json
|
||||
|
||||
You can generate your dictionary in the file my_dictionary.json with 30 markers and a marker size of
|
||||
5 bits using the utility provided in `samples/cpp/aruco_dict_utils.cpp`.
|
||||
|
||||
bin/example_cpp_aruco_dict_utils.exe my_dict.json -nMarkers=30 -markerSize=5
|
||||
|
||||
Pattern Size
|
||||
------------
|
||||
|
||||
Pattern is defined by it's physical board size, element (square or circle) physical size and amount
|
||||
of elements. Factors that affect calibration quality:
|
||||
|
||||
- **Amount of features**. Most of OpenCV functions that work with detected patterns use optimization
|
||||
or some random consensus strategies inside. More features on board means more points for optimization
|
||||
and better estimation quality. Calibration process requires several images. It means that in most
|
||||
of cases lower amount of pattern features may be compensated by higher amount frames.
|
||||
|
||||
- **Element size**. The physical size of elements depends on the distance and size in pixels.
|
||||
Each detector defines some minimal size for reliable detection. For circles grid it's circle
|
||||
radius, for chessboard it's square size, for ChAruco board it's ArUco marker element size.
|
||||
General recommendation: larger elements (in frame pixels) reduces detection uncertainty.
|
||||
|
||||
- **Board size**. The board should be fully visible, sharp and reliably detected by OpenCV algorithms.
|
||||
So, the board size should satisfy previous items, if it's used with typical target distance.
|
||||
Usually larger board is better, but smaller boards allow to calibrate corners better.
|
||||
|
||||
Generic Recommendations
|
||||
-----------------------
|
||||
|
||||
1. The final pattern should be as flat as possible. It improves calibration accuracy.
|
||||
2. Glance pattern is worse than matte. Blinks and shadows on glance surface degrades board detection
|
||||
significantly.
|
||||
3. Most of detection algorithms expect white (black) border around the markers. Please do not cut
|
||||
them or cover them.
|
||||
Reference in New Issue
Block a user