为什么Epilolar geometry/sfm在python opencv中没有给出正确的值

我试图找到两个图像之间的旋转/平移

对于最简单的情况，我使用两个完全相同的图像，并检查它是否提供0个平移和旋转（单位矩阵）

然而，它并没有给出我所期望的结果。为什么

利用ORB特征和十个匹配特征来寻找本质矩阵和R/t。结果是（它们是相同的图像）：

我所期望的是：

t = [[0, 0, 0]]
r = [[1, 0, 0], [0, 1, 0], [0, 0, 0]]

为什么它不会给出奇怪的结果

    orb = cv2.ORB_create()
    img1 = self.img1
    img2 = self.img2

    gray2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)
    gray1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)

    kpts1, descs1 = orb.detectAndCompute(gray1, None)
    kpts2, descs2 = orb.detectAndCompute(gray2, None)

    bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
    matches = bf.match(descs1, descs2)
    dmatches = sorted(matches, key=lambda x: x.distance)

    src_pts = np.float32([kpts1[m.queryIdx].pt for m in dmatches]).reshape(-1, 1, 2)
    src_pts = src_pts[0:10]
    dst_pts = np.float32([kpts2[m.trainIdx].pt for m in dmatches]).reshape(-1, 1, 2)
    dst_pts = dst_pts[0:10]

    K = np.array([[842.102288, 0., 263.697271],
                  [0., 833.300569, 536.024168],
                  [0., 0., 1.]])

    E, mask2 = cv2.findEssentialMat(src_pts, dst_pts, K, cv2.RANSAC, 0.999, 1.0);
    points, R, t, mask = cv2.recoverPose(E, src_pts, dst_pts)

测试图像（图像1=图像2=img）

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有时我们需要测试以找到问题，因此如果您将链接放到图像中，这将非常有用

但是，有几件事你应该知道：

• 在OpenCV上，默认情况下，使用ORB检测器检测的最大关键点数等于500个关键点
• 通过计算基本矩阵，您可以得到由掩码表示的内联值和离群值，但根据您的源代码，您没有将其考虑在内，这会影响恢复姿势的结果
• Recoverpose还需要摄像机矩阵

因此，作为第一步，我要求您在等待图像发布时测试此代码

    orb = cv2.ORB_create(nfeatures = 2000)
    img1 = self.img1
    img2 = self.img2

    gray2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)
    gray1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)

    kpts1, descs1 = orb.detectAndCompute(gray1, None)
    kpts2, descs2 = orb.detectAndCompute(gray2, None)

    bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
    matches = bf.match(descs1, descs2)
    dmatches = sorted(matches, key=lambda x: x.distance)

    src_pts = np.float32([kpts1[m.queryIdx].pt for m in dmatches]).reshape(-1, 1, 2)
    dst_pts = np.float32([kpts2[m.trainIdx].pt for m in dmatches]).reshape(-1, 1, 2)

    K = np.array([[842.102288, 0., 263.697271],
                  [0., 833.300569, 536.024168],
                  [0., 0., 1.]])

    E, inliers_mask_E = cv2.findEssentialMat(src_pts, dst_pts, K method = cv2.RANSAC, prob = 0.999, threshold = 1.0);
    points, R, t, inliers_mask_RP = cv2.recoverPose(E, src_pts, dst_pts, cameraMatrix = K, mask = inliers_mask_E)

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谢谢你的回复，我尝试了你的代码，但是它仍然给出了奇怪的结果。旋转矩阵是恒等式（我所期望的，正确的mat），但平移仍然很奇怪（-0.57,0.57，-0.57）！

    orb = cv2.ORB_create(nfeatures = 2000)
    img1 = self.img1
    img2 = self.img2

    gray2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)
    gray1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)

    kpts1, descs1 = orb.detectAndCompute(gray1, None)
    kpts2, descs2 = orb.detectAndCompute(gray2, None)

    bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
    matches = bf.match(descs1, descs2)
    dmatches = sorted(matches, key=lambda x: x.distance)

    src_pts = np.float32([kpts1[m.queryIdx].pt for m in dmatches]).reshape(-1, 1, 2)
    dst_pts = np.float32([kpts2[m.trainIdx].pt for m in dmatches]).reshape(-1, 1, 2)

    K = np.array([[842.102288, 0., 263.697271],
                  [0., 833.300569, 536.024168],
                  [0., 0., 1.]])

    E, inliers_mask_E = cv2.findEssentialMat(src_pts, dst_pts, K method = cv2.RANSAC, prob = 0.999, threshold = 1.0);
    points, R, t, inliers_mask_RP = cv2.recoverPose(E, src_pts, dst_pts, cameraMatrix = K, mask = inliers_mask_E)