使用OpenCV和Python进行立体校正的问题

Question

我正在使用4个离散摄像头（相同类型），但目前只需要一对（cam1和cam2）来制作立体相机架。目的是校准立体成像，并获取有关场景的3D信息。我在Visual Studio 2017中使用Python 3.6和OpenCV 3.4.3。
我拍摄了28张棋盘校准图案的图像，并按标准OpenCV程序分别对相机进行了校准。由于校准数据似乎很好，畸变校正也很好，因此下一步是图像的矫正。
这就是事情变得奇怪的地方。我花了过去三周的时间阅读了很多资料，并尝试了很多方法，但结果总是不理想。我使用了cv2.stereoCalibrate（还尝试了不同的标志，如不同主题所建议的），cv2.stereoRectify（也使用了不同的alpha值），cv2.initUndistortRectifyMap和cv2.remap进行实际重映射图像（方法1）。但结果总是不尽如人意。

我最近成功获取了经过cv2.uncalibratedRectification修正的图像。因此，我没有使用匹配点（因为SURF和SIFT是非免费的...），而是采用了稍微不同的方法。我的28张校准图像中的校准图案边缘被用作输入点。这个方法效果很好，但矫正后的图像看起来并不完美。

以下是我的图像（这些不是校准图像），以便您能够想象我在谈论什么：
原始左右图像
畸变校正图像
使用方法1进行矫正，alpha=1
使用方法1进行矫正，alpha=0
未校准矫正，目前获得的最佳结果

有人能给我提示一下，我的方法1使用有什么问题吗？我看到了很多类似问题的帖子，但是在评论中找不到解决方案。或者这是OpenCV的一个bug吗？

还有人有什么想法可以改进未校准的矫正吗？

这里是相关调用的代码片段：

# imports
import numpy as np
import cv2
import glob
import argparse
import sys
import os

# size calib array
numEdgeX = 10
numEdgeY = 7

# preface
exitCode = 0

# get directories
pathDir = str(os.path.dirname(os.path.realpath(__file__)))
pathDir = pathDir[:-17]
pathCalib = pathDir + "\\CalibData" + "\\chess"

try:
# define pair
p = 1
cal_path = pathCalib + "\\pair" + str(p)

images_right = glob.glob(cal_path + '\RIGHT/*.bmp')
images_left = glob.glob(cal_path + '\LEFT/*.bmp')
images_left.sort()
images_right.sort()

# termination criteria
criteria = (cv2.TermCriteria_EPS +
                    cv2.TermCriteria_MAX_ITER, 30, 0.001)
criteria_cal = (cv2.TermCriteria_EPS +
                    cv2.TermCriteria_MAX_ITER, 30, 1e-5)

# prepare object points, like (0,0,0); (1,0,0); ...; (6,5,0)
objp = np.zeros((numEdgeX*numEdgeY, 3), np.float32)
objp[:, :2] = np.mgrid[0:numEdgeX, 0:numEdgeY].T.reshape(-1, 2)

objpoints = []     # 3d points in real world space
imgpoints_l = []   # 2d points in image plane for calibration
imgpoints_r = []   # 2d points in image plane for calibration

for i, fname in enumerate(images_right):
        print(str(i+1) + " out of " + str(len(images_right)))
        img_l = cv2.imread(images_left[i])
        img_r = cv2.imread(images_right[i])

        # convert to cv2
        img_l = cv2.cvtColor(img_l, cv2.COLOR_BGR2GRAY)
        img_r = cv2.cvtColor(img_r, cv2.COLOR_BGR2GRAY)

        # find the chess board corners
        ret_l, corners_l = cv2.findChessboardCorners(img_l, (numEdgeX, numEdgeY), None)
        ret_r, corners_r = cv2.findChessboardCorners(img_r, (numEdgeX, numEdgeY), None)

        objpoints.append(objp)

        if ret_l is True:
            print("image " + str(i+1) + "left - io")
            rt = cv2.cornerSubPix(img_l, corners_l, (11, 11),
                                  (-1, -1), criteria)
            imgpoints_l.append(corners_l)

        if ret_r is True:
            print("image " + str(i+1) + "right - io")
            rt = cv2.cornerSubPix(img_r, corners_r, (11, 11),
                                  (-1, -1), criteria)
            imgpoints_r.append(corners_r)

        # get shape
        img_shape = img_l.shape[::-1]


### CALIBRATION ###
# calibrate left camera
rt, M1, d1, r1, t1 = cv2.calibrateCamera(
objpoints, imgpoints_l, img_shape, None, None)

# calibrate right camera
rt, M2, d2, r2, t2 = cv2.calibrateCamera(
objpoints, imgpoints_r, img_shape, None, None)

# stereo calibration
flags = (cv2.CALIB_FIX_K5 + cv2.CALIB_FIX_K6)

stereocalib_criteria = (cv2.TERM_CRITERIA_MAX_ITER +
                    cv2.TERM_CRITERIA_EPS, 100, 1e-5)

#flags = 0
#flags = cv2.CALIB_USE_INTRINSIC_GUESS
#flags = cv2.CALIB_FIX_PRINCIPAL_POINT
#flags = cv2.CALIB_FIX_ASPECT_RATIO
#flags = cv2.CALIB_ZERO_TANGENT_DIST
#flags = cv2.CALIB_FIX_INTRINSIC   
#flags = cv2.CALIB_FIX_FOCAL_LENGTH
#flags = cv2.CALIB_FIX_K1...6
#flags = cv2.CALIB_RATIONAL_MODEL
#flags = cv2.CALIB_THIN_PRISM_MODEL
#flags = cv2.CALIB_SAME_FOCAL_LENGTH
#flags = cv2.CALIB_FIX_S1_S2_S3_S4

flags = (cv2.CALIB_FIX_PRINCIPAL_POINT | cv2.CALIB_FIX_ASPECT_RATIO | cv2.CALIB_FIX_FOCAL_LENGTH |
         cv2.CALIB_FIX_INTRINSIC | cv2.CALIB_FIX_K3 | cv2.CALIB_FIX_K4 | cv2.CALIB_FIX_K5 |
         cv2.CALIB_FIX_K6)

T = np.zeros((3, 1), dtype=np.float64)
R = np.eye(3, dtype=np.float64)

ret, M1, d1, M2, d2, R, T, E, F = cv2.stereoCalibrate(
        objpoints, imgpoints_l,
        imgpoints_r, M1, d1, M2,
        d2, img_shape,
        criteria = stereocalib_criteria,
        flags=flags)

# get new optimal camera matrix
newCamMtx1, roi1 = cv2.getOptimalNewCameraMatrix(M1, d1, img_shape, 0, img_shape)
newCamMtx2, roi2 = cv2.getOptimalNewCameraMatrix(M2, d2, img_shape, 0, img_shape)

# rectification and undistortion maps which can be used directly to correct the stereo pair
(rectification_l, rectification_r, projection_l,
    projection_r, disparityToDepthMap, ROI_l, ROI_r) = cv2.stereoRectify(
        M1, d1, M2, d2, img_shape, R, T,
        None, None, None, None, None,
        #cv2.CALIB_ZERO_DISPARITY,                  # principal points of each camera have the same pixel coordinates in rect views
        alpha=0)                                   # alpha=1 no pixels lost, alpha=0 pixels lost

leftMapX, leftMapY = cv2.initUndistortRectifyMap(
    M1, d1, rectification_l, projection_l,
    img_shape, cv2.CV_32FC1)
rightMapX, rightMapY = cv2.initUndistortRectifyMap(
    M2, d2, rectification_r, projection_r,
    img_shape, cv2.CV_32FC1)

### UNCALIBRATED RECTIFICATION ###
imgpoints_l_undis = []
imgpoints_r_undis = []

for i, fname in enumerate(images_right):
    img_l = cv2.imread(images_left[i])
    img_r = cv2.imread(images_right[i])

    # convert to cv2
    img_l = cv2.cvtColor(img_l, cv2.COLOR_BGR2GRAY)
    img_r = cv2.cvtColor(img_r, cv2.COLOR_BGR2GRAY)

    # undistort
    img_l_undis = cv2.undistort(img_l, M1, d1, None, newCamMtx1)
    img_r_undis = cv2.undistort(img_r, M2, d2, None, newCamMtx2)

    # find the chess board corners in undistorted image
    ret_l_undis, corners_l_undis = cv2.findChessboardCorners(img_l_undis, (numEdgeX, numEdgeY), None)
    ret_r_undis, corners_r_undis = cv2.findChessboardCorners(img_r_undis, (numEdgeX, numEdgeY), None)

    if ret_l_undis is True:
        rt = cv2.cornerSubPix(img_l_undis, corners_l_undis, (11, 11), (-1, -1), criteria)
        for j in range(0, len(rt)):
            x = rt[j][0,:]
            imgpoints_l_undis.append(x)

        if ret_r_undis is True:
            rt = cv2.cornerSubPix(img_r_undis, corners_r_undis, (11, 11), (-1, -1), criteria)
            for j in range(0, len(rt)):
                x = rt[j][0,:]
                imgpoints_r_undis.append(x)

# convert to np array
imgpoints_l_undis = np.array(imgpoints_l_undis)
imgpoints_r_undis = np.array(imgpoints_r_undis)

# compute rectification uncalibrated
ret, uncRectMtx1, uncRectMtx2 = cv2.stereoRectifyUncalibrated(imgpoints_l_undis, imgpoints_r_undis, F, img_shape)

### REMAPPING ###
# load images and convert to cv2 format
img_l = cv2.imread(images_left[0])
img_l = cv2.cvtColor(img_l, cv2.COLOR_BGR2GRAY)
img_l_undis = cv2.undistort(img_l, M1, d1, None, newCamMtx1)
img_r = cv2.imread(images_right[0])
img_r = cv2.cvtColor(img_r, cv2.COLOR_BGR2GRAY)
img_r_undis = cv2.undistort(img_r, M2, d2, None, newCamMtx2)

# remap
imglCalRect = cv2.remap(img_l, leftMapX, leftMapY, cv2.INTER_LINEAR)
imgrCalRect = cv2.remap(img_r, rightMapX, rightMapY, cv2.INTER_LINEAR)
numpyHorizontalCalibRect = np.hstack((imglCalRect, imgrCalRect))

# warp for uncalibrated rectification
imglUncalRect = cv2.warpPerspective(img_l_undis, uncRectMtx1, img_shape)
imgrUncalRect = cv2.warpPerspective(img_r_undis, uncRectMtx2, img_shape)
numpyHorizontalUncalibRect = np.hstack((imglUncalRect, imgrUncalRect))    

### SHOW RESULTS ###
# calculate point arrays for epipolar lines
lineThickness = 5
lineColor = [0, 255, 0]
numLines = 20
interv = round(img_shape[0] / numLines)
x1 = np.zeros((numLines, 1))
y1 = np.zeros((numLines, 1))
x2 = np.full((numLines, 1), (3*img_shape[1]))
y2 = np.zeros((numLines, 1))
for jj in range(0, numLines):
    y1[jj] = jj * interv
y2 = y1

for jj in range(0, numLines):
        cv2.line(numpyHorizontalCalibRect, (x1[jj], y1[jj]), (x2[jj], y2[jj]),
                 lineColor, lineThickness)
        cv2.line(numpyHorizontalUncalibRect, (x1[jj], y1[jj]), (x2[jj], y2[jj]),
                 lineColor, lineThickness)
cv2.namedWindow("calibRect", cv2.WINDOW_NORMAL)
cv2.namedWindow("uncalibRect", cv2.WINDOW_NORMAL)
cv2.imshow("calibRect", numpyHorizontalCalibRect)
cv2.imshow("uncalibRect", numpyHorizontalUncalibRect)
cv2.waitKey()


except (IOError, ValueError):
print("An I/O error or a ValueError occurred")
except:
print("An unexpected error occurred")
raise

谢谢！

- Guido

Answer 1

完成了！问题在于OpenCV将我的图像解释为垂直立体系统，而我只是将其视为水平的。

- Guido

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你是如何在OpenCV的stereoRectify函数中指定水平立体标志的？ - Moiz Sajid