Created
June 13, 2020 08:59
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# Normalize for Esential Matrix calaculation | |
pts_l_norm = cv2.undistortPoints(np.expand_dims(pts_l, axis=1), cameraMatrix=K_l, distCoeffs=None) | |
pts_r_norm = cv2.undistortPoints(np.expand_dims(pts_r, axis=1), cameraMatrix=K_r, distCoeffs=None) | |
E, mask = cv2.findEssentialMat(pts_l_norm, pts_r_norm, focal=1.0, pp=(0., 0.), method=cv2.RANSAC, prob=0.999, threshold=3.0) | |
points, R, t, mask = cv2.recoverPose(E, pts_l_norm, pts_r_norm) | |
M_r = np.hstack((R, t)) | |
M_l = np.hstack((np.eye(3, 3), np.zeros((3, 1)))) | |
P_l = np.dot(K_l, M_l) | |
P_r = np.dot(K_r, M_r) | |
point_4d_hom = cv2.triangulatePoints(P_l, P_r, np.expand_dims(pts_l, axis=1), np.expand_dims(pts_r, axis=1)) | |
point_4d = point_4d_hom / np.tile(point_4d_hom[-1, :], (4, 1)) | |
point_3d = point_4d[:3, :].T |
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