我想做类似于 this question 的事情,但用于 stereoCalibrate()
而不是 calibrateCamera()
。也就是说,计算立体相机校准的重投影误差。
我的简化示例如下所示:
import cv2
import numpy as np
def calibrate_stereo(w, h, objpoints, imgpoints_l, imgpoints_r):
stereocalib_criteria = (cv2.TERM_CRITERIA_COUNT + cv2.TERM_CRITERIA_EPS , 1000, 1e-6)
retval, A1, D1, A2, D2, R, T, E, F = cv2.stereoCalibrate(objpoints,imgpoints_l, imgpoints_r,None,None,None,None, (w,h), flags=0, criteria=stereocalib_criteria)
return (retval, (A1,D1,A2,D2, R, T, E, F))
def calc_rms_stereo(objectpoints, imgpoints_l, imgpoints_r, A1, D1, A2, D2, R, T):
tot_error = 0
total_points = 0
for i, objpoints in enumerate(objectpoints):
# calculate world <-> cam1 transformation
_, rvec_l, tvec_l,_ = cv2.solvePnPRansac(objpoints, imgpoints_l[i], A1, D1)
# compute reprojection error for cam1
rp_l, _ = cv2.projectPoints(objpoints, rvec_l, tvec_l, A1, D1)
tot_error += np.sum(np.square(np.float64(imgpoints_l[i] - rp_l)))
total_points += len(objpoints)
# calculate world <-> cam2 transformation
rvec_r, tvec_r = cv2.composeRT(rvec_l,tvec_l,cv2.Rodrigues(R)[0],T)[:2]
# compute reprojection error for cam2
rp_r,_ = cv2.projectPoints(objpoints, rvec_r, tvec_r, A2, D2)
tot_error += np.square(imgpoints_r[i] - rp_r).sum()
total_points += len(objpoints)
mean_error = np.sqrt(tot_error/total_points)
return mean_error
if __name__ == "__main__":
# omitted: reading values for w,h, objectPoints, imgpoints_l, imgpoints_r from file (format as expected by the OpenCV functions)
# [...]
rms, (A1,D1,A2,D2,R,T,_,_) = calibrate_stereo(w, h, objectpoints, imgpoints_l, imgpoints_r)
print("RMS (stereo calib): {}".format(rms))
rms_2 = calc_rms_stereo(objectpoints, imgpoints_l, imgpoints_r, A1, D1, A2, D2, R, T)
print("RMS (custom calculation):", rms_2)
示例输出:
RMS (stereo calib): 0.14342257926694932
RMS (custom calculation): 0.356273345751
据我所知,stereoCalibrate()
源代码中的计算与我的非常相似。我错过了什么?
Ubuntu 上的 OpenCV 3.3.0
最佳答案
我在基于 OpenCV 实现的自定义立体校准算法后解决了这个问题。
cv2.stereoCalibrate()
里面计算的重投影误差的区别|我的自定义计算源于外部参数的不同值 rvec_l
和 tvec_l
.这些向量描述了左摄像头与每个图像的校准图案之间的旋转和平移。 cv2.solvePnpRansac()
仅根据左图像的重投影误差产生优化值,而在 cv2.stereoCalibrate()
中这些值与 R
一起优化和 T
基于每个立体对的两个图像中的重投影误差。
如果想要精确复制 cv2.stereoCalibrate()
返回的 RMS 值, 必须修改 cv::stereoCalibrate()
的 C/C++ 源代码也返回优化的外部参数(cv::calibrateCamera()
已经为单目校准做了这件事)。
关于python - OpenCV + Python : Calculate stereo reprojection error,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/47329154/