我试图使用OpenCV的“unsistort points”方法来取消图像的某些点,但没有成功。
这些将是我的失调系数
optic_camera_matrix: [[710.52285, 0.0, 882.14702],
[0.0, 713.9636, 638.8421],
[0.0, 0.0, 1.0]],
distorsion_coeffs: [[-0.4176419401669212,
0.15978235598732332,
-8.299875092923166e-05,
-0.001784191694247801,
-0.027396621999692457]],
即使我能够对整个图像不失真,为了优化相机处理时间,如果我不改变角点(图像的红点):
distorted_border_points = np.array([[[584,1415],
[576,457],
[1956,415],
[1996,1422],
[1261,242],
[1281,1594]]],np.float32)
undistorted_points = cv2.undistortPoints(distorted_border_points, optic_camera_matrix, distorsion_coeffs)
我得到了这个回报:
[[[ -6.40190065e-01 1.66883194e+00]
[ -4.87006754e-01 -2.88225353e-01]
[ -1.82562262e-01 3.74070629e-02]
[ -5.28450182e-04 -3.51850584e-04]
[ 8.09574544e-01 -8.40054870e-01]
[ -5.28259724e-02 -1.22379906e-01]]]
如果绘制,它们不会在矩形中对齐,就像在第一张图像中一样。
我相信失真系数计算得很好(因为失真对第一张图像起作用),但在这里我附上了凸轮的代码
import glob
import cv2
import numpy as np
import os
import json
import numpy as np
directory = os.path.dirname(__file__)
def get_optic_calibration_parameters(device,config_folder=None):
if config_folder is None:
optic_calibration_path = directory + '/../config/' + \
device + '/optic_calibration.json'
else:
optic_calibration_path = config_folder + device + '/optic_calibration.json'
if not os.path.exists(optic_calibration_path):
os.makedirs(optic_calibration_path[:-22])
with open(optic_calibration_path) as optic_calibration_file:
optic_calibration = json.load(optic_calibration_file)
optic_camera_matrix = optic_calibration['optic_camera_matrix']
distorsion_coeffs = optic_calibration['distorsion_coeffs']
optic_resolution = optic_calibration['optic_resolution']
return optic_camera_matrix, distorsion_coeffs, optic_resolution
def _save_calibration_parameters(camera_matrix, distorsion_coeffs, optic_resolution, device, config_folder=None):
if config_folder is None:
optic_calibration_path = directory + '/../config/' + \
device + '/optic_calibration.json'
else:
optic_calibration_path = config_folder + device + '/optic_calibration.json'
if not os.path.exists(optic_calibration_path):
os.makedirs(optic_calibration_path[:-22])
optic_calibration_parameters = {'optic_camera_matrix': camera_matrix.tolist(),
'distorsion_coeffs': distorsion_coeffs.tolist(),
'optic_resolution': optic_resolution}
with open(optic_calibration_path, 'wb') as optic_calibration_file:
json.dump(optic_calibration_parameters, optic_calibration_file)
return
def _get_image_points(plot=False, dim=(4, 5), input_dir='calibration_samples',extension='jp'):
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
objp = np.zeros((dim[0] * dim[1], 3), np.float32)
objp[:, :2] = np.mgrid[0:dim[1], 0:dim[0]].T.reshape(-1, 2)
# Arrays to store object points and image points from all the images.
objpoints = [] # 3d point in real world space
imgpoints = [] # 2d points in image plane.
images = glob.glob(input_dir + '*.'+extension+'*')
for fname in images:
img = cv2.imread(fname)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Find the chess board corners
ret, corners = cv2.findChessboardCorners(gray, (dim[1], dim[0]), None)
# If found, add object points, image points (after refining them)
if ret == True:
objpoints.append(objp)
corners2 = cv2.cornerSubPix(
gray, corners, (11, 11), (-1, -1), criteria)
imgpoints.append(corners2)
# Draw and display the corners
if plot:
img = cv2.drawChessboardCorners(
img, (dim[1], dim[0]), corners2, ret)
cv2.imshow('img', img)
cv2.waitKey(500)
resolution = (img.shape[1], img.shape[0])
return imgpoints, objpoints, resolution
def calibrate_camera(optic_resolution, imgpoints, objpoints, device, config_folder=None):
_, camera_matrix, distorsion_coeffs, _, _ = cv2.calibrateCamera(
objpoints, imgpoints, optic_resolution, None, None)
_save_calibration_parameters(
camera_matrix, distorsion_coeffs, optic_resolution, device, config_folder=config_folder)
return
要执行该功能,我加载了不同的图像:
imgpoints, objpoints, optic_resolution = _get_image_points(plot=False, dim=(4,5), input_dir=calibration_samples)
_show_N_chessborders(N=3, dim=(4,5), input_dir=calibration_samples)
calibrate_camera(optic_resolution, imgpoints, objpoints,device, config_folder=config_folder)
这就是我存储json配置文件的方式
如果anyoce可以帮助我解决问题,我将不胜感激。谢谢!
如果我理解正确的话,getOptimalNewCameraMatrix
函数仅适用于在不对整个图像进行分类时不想要黑色边的情况(请参阅this问题),而不是在不对各个点进行分类时。此外,似乎P
中的R
和undistortPoints
仅用于立体视觉事物。
我会简化它,然后说:
undistorted_points = cv2.undistortPoints(np.array(points), optical_camera_matrix, d)
其中optical_camera_matrix
是直接来自calibrateCamera
函数的矩阵。只要确保points
是一个1xN or Nx1 2-channel
阵列。
更新:
我意识到问题是什么。秘密是关于这个website的判决。
此函数还对projectPoints()执行反向转换
据我理解(如果我错了请纠正我),未失真的点被归一化。为了将它们放回像素单元中,只需执行以下操作(伪代码):
for each point in undistorted_points:
point = point * focal_length + boresight
我希望这有帮助!