Python cv2.stereoRectify() Examples

The following are 3 code examples of cv2.stereoRectify(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module cv2 , or try the search function .
Example #1
Source File: stereo_camera_calibration.py    From stereoDepth with Apache License 2.0 8 votes vote down vote up
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: utils.py    From cvcalib with Apache License 2.0 7 votes vote down vote up
def compute_stereo_rectification_maps(stereo_rig, im_size, size_factor):
    new_size = (int(im_size[1] * size_factor), int(im_size[0] * size_factor))
    rotation1, rotation2, pose1, pose2 = \
        cv2.stereoRectify(cameraMatrix1=stereo_rig.cameras[0].intrinsics.intrinsic_mat,
                          distCoeffs1=stereo_rig.cameras[0].intrinsics.distortion_coeffs,
                          cameraMatrix2=stereo_rig.cameras[1].intrinsics.intrinsic_mat,
                          distCoeffs2=stereo_rig.cameras[1].intrinsics.distortion_coeffs,
                          imageSize=(im_size[1], im_size[0]),
                          R=stereo_rig.cameras[1].extrinsics.rotation,
                          T=stereo_rig.cameras[1].extrinsics.translation,
                          flags=cv2.CALIB_ZERO_DISPARITY,
                          newImageSize=new_size
                          )[0:4]
    map1x, map1y = cv2.initUndistortRectifyMap(stereo_rig.cameras[0].intrinsics.intrinsic_mat,
                                               stereo_rig.cameras[0].intrinsics.distortion_coeffs,
                                               rotation1, pose1, new_size, cv2.CV_32FC1)
    map2x, map2y = cv2.initUndistortRectifyMap(stereo_rig.cameras[1].intrinsics.intrinsic_mat,
                                               stereo_rig.cameras[1].intrinsics.distortion_coeffs,
                                               rotation2, pose2, new_size, cv2.CV_32FC1)
    return map1x, map1y, map2x, map2y 
Example #3
Source File: calibration.py    From StereoVision with GNU General Public License v3.0 4 votes vote down vote up
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