用OpenCV和Python解决立体校正问题

Question

    import cv2
    import glob
    import argparse
    import math
    from numpy import genfromtxt
    import matplotlib.pyplot as plt
    import numpy as np
    import os.path
    from scipy import ndimage
    import os


    left = cv2.imread('D:/input image 1.jpg', cv2.IMREAD_UNCHANGED)
    right = cv2.imread('D:/input image 2.jpg', cv2.IMREAD_UNCHANGED)
    #left = (left/256).astype('uint8')
    #right = (right/256).astype('uint8')


    cameraMatrix1 = np.array([[1485.8503101355045, 0, 641.0072474534551], [0, 1486.8249802291273, 454.1981417235667], [0, 0, 1]])
    cameraMatrix2 = np.array([[1472.34425902698, 0, 656.7358738783742], [0, 1473.184475795988, 441.016803589085], [0, 0, 1]])
    distCoeffs1 = np.array([-0.09236217303671054, 0.15801009565677457, 0.0020679941868083445, -0.0023435708660260184, 0.04491629603683055])
    distCoeffs2 = np.array([-0.09949068652688753, 0.22953391558591676, 0.0016749995113326907, -0.0015940937703328348, -0.13603886268508916])
    rotationMatrix = np.array([[0.9999169807005986, 0.0026862926847088424, -0.012602203704541104],[-0.002633967055223802, 0.9999878496600472, 0.0041668633079119935],[0.012613243997904163, -0.004133323588458492, 0.9999119069757908]])
    transVector = np.array([29.96389633009774, 0.5883268401189343, -5.0370190999346365])
    essentialMatrix = np.array([[-0.005846632380824811, 5.0345261532342365, 0.6092635826971343], [-5.4145428656773165, 0.11031957194242471, -29.897779179091888], [-0.6672019134164675, 29.96195184048419, 0.1322696748639909]])
    fundMatrix = np.array([[4.567507458136527e-08, -3.930495370357416e-05, 0.010750771532659317], [4.227537878312907e-05, -8.607826196991683e-07, 0.3201405456504413], [-0.010999824926761303, -0.3182113833954986, 1]])


    flags = cv2.CALIB_ZERO_DISPARITY
    image_size = left.shape[::-1]

    R1, R2, P1, P2, Q, roi1, roi2 = cv2.stereoRectify(cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2, image_size, rotationMatrix, transVector, flags = flags)

    leftmapX, leftmapY = cv2.initUndistortRectifyMap(cameraMatrix1, distCoeffs1, R1, P1, image_size, cv2.CV_32FC1)
    rightmapX, rightmapY = cv2.initUndistortRectifyMap(cameraMatrix2, distCoeffs2, R2, P2, image_size, cv2.CV_32FC1)

    left_remap = cv2.remap(left, leftmapX, leftmapY, cv2.INTER_LANCZOS4)
    right_remap = cv2.remap(right, leftmapX, rightmapY, cv2.INTER_LANCZOS4)


    # For some reason, the images get rotated upside down after remapping, and I have to invert them back
    left_remap = ndimage.rotate(left_remap,180)
    right_remap = ndimage.rotate(right_remap,180)

    for line in range(0, int(right_remap.shape[0] / 20)):
        left_remap[line * 20, :] = 0
        right_remap[line * 20, :] = 0

    cv2.namedWindow('output images', cv2.WINDOW_NORMAL)
    cv2.imshow('output images', np.hstack([left_remap, right_remap]))
    cv2.waitKey(0)
    cv2.destroyAllWindows()

我正在对水平和垂直方向上的5个不同的镜头进行校正（即，我需要所有的公共点在所有5个镜头的图像中完全相同的位置）。当我用我的两个镜片测试立体背诵时，这两个镜片水平放置的比垂直放置的要远得多，cv2.StereoConvertive总是把它们当作垂直分开来解释。我希望能够告诉函数水平地解释它。我在这里看到过一些类似的问题，但在任何地方都找不到有用的答复。你知道吗

编辑：为了便于复制，我在代码中添加了所有失真系数和校准矩阵。这些值是通过使用大量的校准图像和一个函数cv2.stereoCalibrate获得的，但是不可能上传所有这些图像和用于系数提取的代码。你知道吗

output image

input image 1

input image 2

clarifying the issue