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calib3d: fix Rodrigues CV_32F and CV_64F type mismatch in projectPoints #25824 Fixes #25318 ### Pull Request Readiness Checklist See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request - [x] I agree to contribute to the project under Apache 2 License. - [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV - [x] The PR is proposed to the proper branch - [x] There is a reference to the original bug report and related work - [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable Patch to opencv_extra has the same branch name. - [ ] The feature is well documented and sample code can be built with the project CMake
89 lines
3.2 KiB
Python
89 lines
3.2 KiB
Python
#!/usr/bin/env python
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'''
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camera calibration for distorted images with chess board samples
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reads distorted images, calculates the calibration and write undistorted images
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'''
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# Python 2/3 compatibility
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from __future__ import print_function
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import numpy as np
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import cv2 as cv
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from tests_common import NewOpenCVTests
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class calibration_test(NewOpenCVTests):
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def test_calibration(self):
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img_names = []
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for i in range(1, 15):
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if i < 10:
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img_names.append('samples/data/left0{}.jpg'.format(str(i)))
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elif i != 10:
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img_names.append('samples/data/left{}.jpg'.format(str(i)))
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square_size = 1.0
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pattern_size = (9, 6)
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pattern_points = np.zeros((np.prod(pattern_size), 3), np.float32)
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pattern_points[:, :2] = np.indices(pattern_size).T.reshape(-1, 2)
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pattern_points *= square_size
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obj_points = []
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img_points = []
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h, w = 0, 0
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for fn in img_names:
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img = self.get_sample(fn, 0)
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if img is None:
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continue
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h, w = img.shape[:2]
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found, corners = cv.findChessboardCorners(img, pattern_size)
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if found:
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term = (cv.TERM_CRITERIA_EPS + cv.TERM_CRITERIA_COUNT, 30, 0.1)
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cv.cornerSubPix(img, corners, (5, 5), (-1, -1), term)
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if not found:
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continue
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img_points.append(corners.reshape(-1, 2))
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obj_points.append(pattern_points)
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# calculate camera distortion
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rms, camera_matrix, dist_coefs, _rvecs, _tvecs = cv.calibrateCamera(obj_points, img_points, (w, h), None, None, flags = 0)
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eps = 0.01
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normCamEps = 10.0
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normDistEps = 0.05
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cameraMatrixTest = [[ 532.80992189, 0., 342.4952186 ],
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[ 0., 532.93346422, 233.8879292 ],
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[ 0., 0., 1. ]]
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distCoeffsTest = [ -2.81325576e-01, 2.91130406e-02,
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1.21234330e-03, -1.40825372e-04, 1.54865844e-01]
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self.assertLess(abs(rms - 0.196334638034), eps)
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self.assertLess(cv.norm(camera_matrix - cameraMatrixTest, cv.NORM_L1), normCamEps)
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self.assertLess(cv.norm(dist_coefs - distCoeffsTest, cv.NORM_L1), normDistEps)
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def test_projectPoints(self):
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objectPoints = np.array([[181.24588 , 87.80361 , 11.421074],
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[ 87.17948 , 184.75563 , 37.223446],
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[ 22.558456, 45.495266, 246.05797 ]], dtype=np.float32)
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rvec = np.array([[ 0.9357548 , -0.28316498, 0.21019171],
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[ 0.30293274, 0.9505806 , -0.06803132],
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[-0.18054008, 0.12733458, 0.9752903 ]], dtype=np.float32)
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tvec = np.array([ 69.32692 , 17.602057, 135.77672 ], dtype=np.float32)
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cameraMatrix = np.array([[214.0047 , 26.98735 , 253.37799 ],
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[189.8172 , 10.038101, 18.862494],
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[114.07123 , 200.87277 , 194.56332 ]], dtype=np.float32)
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distCoeffs = distCoeffs = np.zeros((4, 1), dtype=np.float32)
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imagePoints, jacobian = cv.projectPoints(objectPoints, rvec, tvec, cameraMatrix, distCoeffs)
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self.assertTrue(imagePoints is not None)
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self.assertTrue(jacobian is not None)
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if __name__ == '__main__':
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NewOpenCVTests.bootstrap()
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