本文整理匯總了Python中cv2.stereoCalibrate方法的典型用法代碼示例。如果您正苦於以下問題：Python cv2.stereoCalibrate方法的具體用法？Python cv2.stereoCalibrate怎麽用？Python cv2.stereoCalibrate使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類cv2的用法示例。
在下文中一共展示了cv2.stereoCalibrate方法的3個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: stereo_calibrate
# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import stereoCalibrate [as 別名]
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) 示例2: calibrate_cameras
# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import stereoCalibrate [as 別名]
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 示例3: stereo_calibrate
# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import stereoCalibrate [as 別名]
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)