Python cv2.stereoCalibrate() Examples
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code examples of cv2.stereoCalibrate().
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Example #1
Source File: stereo_camera_calibration.py From stereoDepth with Apache License 2.0 | 8 votes |
def stereo_calibrate(left_file, right_file, left_dir, left_prefix, right_dir, right_prefix, image_format, save_file, square_size, width=9, height=6): """ Stereo calibration and rectification """ objp, leftp, rightp = load_image_points(left_dir, left_prefix, right_dir, right_prefix, image_format, square_size, width, height) K1, D1 = load_coefficients(left_file) K2, D2 = load_coefficients(right_file) flag = 0 # flag |= cv2.CALIB_FIX_INTRINSIC flag |= cv2.CALIB_USE_INTRINSIC_GUESS ret, K1, D1, K2, D2, R, T, E, F = cv2.stereoCalibrate(objp, leftp, rightp, K1, D1, K2, D2, image_size) print("Stereo calibration rms: ", ret) R1, R2, P1, P2, Q, roi_left, roi_right = cv2.stereoRectify(K1, D1, K2, D2, image_size, R, T, flags=cv2.CALIB_ZERO_DISPARITY, alpha=0.9) save_stereo_coefficients(save_file, K1, D1, K2, D2, R, T, E, F, R1, R2, P1, P2, Q)
Example #2
Source File: calibration.py From StereoVision with GNU General Public License v3.0 | 4 votes |
def calibrate_cameras(self): """Calibrate cameras based on found chessboard corners.""" criteria = (cv2.TERM_CRITERIA_MAX_ITER + cv2.TERM_CRITERIA_EPS, 100, 1e-5) flags = (cv2.CALIB_FIX_ASPECT_RATIO + cv2.CALIB_ZERO_TANGENT_DIST + cv2.CALIB_SAME_FOCAL_LENGTH) calib = StereoCalibration() (calib.cam_mats["left"], calib.dist_coefs["left"], calib.cam_mats["right"], calib.dist_coefs["right"], calib.rot_mat, calib.trans_vec, calib.e_mat, calib.f_mat) = cv2.stereoCalibrate(self.object_points, self.image_points["left"], self.image_points["right"], self.image_size, calib.cam_mats["left"], calib.dist_coefs["left"], calib.cam_mats["right"], calib.dist_coefs["right"], calib.rot_mat, calib.trans_vec, calib.e_mat, calib.f_mat, criteria=criteria, flags=flags)[1:] (calib.rect_trans["left"], calib.rect_trans["right"], calib.proj_mats["left"], calib.proj_mats["right"], calib.disp_to_depth_mat, calib.valid_boxes["left"], calib.valid_boxes["right"]) = cv2.stereoRectify(calib.cam_mats["left"], calib.dist_coefs["left"], calib.cam_mats["right"], calib.dist_coefs["right"], self.image_size, calib.rot_mat, calib.trans_vec, flags=0) for side in ("left", "right"): (calib.undistortion_map[side], calib.rectification_map[side]) = cv2.initUndistortRectifyMap( calib.cam_mats[side], calib.dist_coefs[side], calib.rect_trans[side], calib.proj_mats[side], self.image_size, cv2.CV_32FC1) # This is replaced because my results were always bad. Estimates are # taken from the OpenCV samples. width, height = self.image_size focal_length = 0.8 * width calib.disp_to_depth_mat = np.float32([[1, 0, 0, -0.5 * width], [0, -1, 0, 0.5 * height], [0, 0, 0, -focal_length], [0, 0, 1, 0]]) return calib
Example #3
Source File: calibration_utils.py From depthai with MIT License | 3 votes |
def stereo_calibrate(self): """Calibrate camera and construct Homography.""" # init camera calibrations rt, self.M1, self.d1, self.r1, self.t1 = cv2.calibrateCamera( self.objpoints, self.imgpoints_l, self.img_shape, None, None) rt, self.M2, self.d2, self.r2, self.t2 = cv2.calibrateCamera( self.objpoints, self.imgpoints_r, self.img_shape, None, None) # config flags = 0 #flags |= cv2.CALIB_FIX_ASPECT_RATIO flags |= cv2.CALIB_USE_INTRINSIC_GUESS #flags |= cv2.CALIB_SAME_FOCAL_LENGTH #flags |= cv2.CALIB_ZERO_TANGENT_DIST flags |= cv2.CALIB_RATIONAL_MODEL #flags |= cv2.CALIB_FIX_K1 #flags |= cv2.CALIB_FIX_K2 #flags |= cv2.CALIB_FIX_K3 #flags |= cv2.CALIB_FIX_K4 #flags |= cv2.CALIB_FIX_K5 #flags |= cv2.CALIB_FIX_K6 stereocalib_criteria = (cv2.TERM_CRITERIA_COUNT + cv2.TERM_CRITERIA_EPS, 100, 1e-5) # stereo calibration procedure ret, self.M1, self.d1, self.M2, self.d2, R, T, E, F = cv2.stereoCalibrate( self.objpoints, self.imgpoints_l, self.imgpoints_r, self.M1, self.d1, self.M2, self.d2, self.img_shape, criteria=stereocalib_criteria, flags=flags) assert ret < 1.0, "[ERROR] Calibration RMS error < 1.0 (%i). Re-try image capture." % (ret) print("[OK] Calibration successful w/ RMS error=" + str(ret)) # construct Homography plane_depth = 40000000.0 # arbitrary plane depth #TODO: Need to understand effect of plane_depth. Why does this improve some boards' cals? n = np.array([[0.0], [0.0], [-1.0]]) d_inv = 1.0 / plane_depth H = (R - d_inv * np.dot(T, n.transpose())) self.H = np.dot(self.M2, np.dot(H, np.linalg.inv(self.M1))) self.H /= self.H[2, 2] # rectify Homography for right camera disparity = (self.M1[0, 0] * T[0] / plane_depth) self.H[0, 2] -= disparity self.H = self.H.astype(np.float32) print("Rectifying Homography...") print(self.H)