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 |
|
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 |
|
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 |
|
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
