在 Python 中使用 OpenCV 中的鱼眼相机捕获的点不失真的正确方法是什么？

回答 Q1：您没有正确使用map_1和map_2。cv2.fisheye.initUndistortRectifyMap函数生成的map应该是目标图像的像素位置到源图像的像素位置的映射，即dst(x,y)=src(mapx(x,y),mapy (x,y))。请参阅OpenCV 中的重映射。在代码中，map_1用于 x 方向像素映射，map_2用于 y 方向像素映射。例如， (X_undistorted, Y_undistorted)是未失真图像中的像素位置。map_1[Y_undistorted, X_undistorted]告诉你这个像素应该在哪里映射到扭曲图像中的x坐标，而map_2会给你相应的y坐标。因此，map_1和map_2对于从失真图像构建未失真图像很有用，并不真正适合逆向过程。remapped_points = []for corner in corners2:    remapped_points.append(              (map_1[int(corner[0][1]), int(corner[0][0])], map_2[int(corner[0][1]), int(corner[0][0])]))此代码查找角的未失真像素位置是不正确的。您将需要使用undistortPoints函数。回答 Q2：映射和不失真是不同的。您可以将映射视为基于未失真图像中的像素位置与像素图构建未失真图像，而未失真是使用镜头失真模型使用原始像素位置找到未失真像素位置。为了在未失真的图像中找到角点的正确像素位置。您需要使用新估计的 K 将未失真点的归一化坐标转换回像素坐标，在您的情况下，它是final_K，因为未失真的图像可以被视为由具有 final_K 的相机拍摄而没有失真（有小缩放效果）。这是修改后的 undistort 函数：def undistort_list_of_points(point_list, in_K, in_d, in_K_new):    K = np.asarray(in_K)    d = np.asarray(in_d)    # Input can be list of bbox coords, poly coords, etc.    # TODO -- Check if point behind camera?    points_2d = np.asarray(point_list)    points_2d = points_2d[:, 0:2].astype('float32')    points2d_undist = np.empty_like(points_2d)    points_2d = np.expand_dims(points_2d, axis=1)    result = np.squeeze(cv2.fisheye.undistortPoints(points_2d, K, d))    K_new = np.asarray(in_K_new)    fx = K_new[0, 0]    fy = K_new[1, 1]    cx = K_new[0, 2]    cy = K_new[1, 2]    for i, (px, py) in enumerate(result):        points2d_undist[i, 0] = px * fx + cx        points2d_undist[i, 1] = py * fy + cy    return points2d_undist这是我做同样事情的代码。import cv2import numpy as npimport matplotlib.pyplot as pltK = np.asarray([[556.3834638575809,0,955.3259939726225],[0,556.2366649196925,547.3011305411478],[0,0,1]])D = np.asarray([[-0.05165940570900624],[0.0031093602070252167],[-0.0034036648250202746],[0.0003390345044343793]])print("K:\n", K)print("D:\n", D.ravel())# read image and get the original image on the leftimage_path = "sample.jpg"image = cv2.imread(image_path)image = image[:, :image.shape[1]//2, :]image_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)fig = plt.figure()plt.imshow(image_gray, "gray")H_in, W_in = image_gray.shapeprint("Grayscale Image Dimension:\n", (W_in, H_in))scale_factor = 1.0 balance = 1.0img_dim_out =(int(W_in*scale_factor), int(H_in*scale_factor))if scale_factor != 1.0:    K_out = K*scale_factor    K_out[2,2] = 1.0K_new = cv2.fisheye.estimateNewCameraMatrixForUndistortRectify(K_out, D, img_dim_out, np.eye(3), balance=balance)print("Newly estimated K:\n", K_new)map1, map2 = cv2.fisheye.initUndistortRectifyMap(K, D, np.eye(3), K_new, img_dim_out, cv2.CV_32FC1)print("Rectify Map1 Dimension:\n", map1.shape)print("Rectify Map2 Dimension:\n", map2.shape)undistorted_image_gray = cv2.remap(image_gray, map1, map2, interpolation=cv2.INTER_LINEAR, borderMode=cv2.BORDER_CONSTANT)fig = plt.figure()plt.imshow(undistorted_image_gray, "gray")  ret, corners = cv2.findChessboardCorners(image_gray, (6,8),cv2.CALIB_CB_ADAPTIVE_THRESH+cv2.CALIB_CB_FAST_CHECK+cv2.CALIB_CB_NORMALIZE_IMAGE)corners_subpix = cv2.cornerSubPix(image_gray, corners, (3,3), (-1,-1), (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.1))undistorted_corners = cv2.fisheye.undistortPoints(corners_subpix, K, D)undistorted_corners = undistorted_corners.reshape(-1,2)fx = K_new[0,0]fy = K_new[1,1]cx = K_new[0,2]cy = K_new[1,2]undistorted_corners_pixel = np.zeros_like(undistorted_corners)for i, (x, y) in enumerate(undistorted_corners):    px = x*fx + cx    py = y*fy + cy    undistorted_corners_pixel[i,0] = px    undistorted_corners_pixel[i,1] = py    undistorted_image_show = cv2.cvtColor(undistorted_image_gray, cv2.COLOR_GRAY2BGR)for corner in undistorted_corners_pixel:    image_corners = cv2.circle(np.zeros_like(undistorted_image_show), (int(corner[0]),int(corner[1])), 15, [0, 255, 0], -1)    undistorted_image_show = cv2.add(undistorted_image_show, image_corners)fig = plt.figure()plt.imshow(undistorted_image_show, "gray")

在 Python 中使用 OpenCV 中的鱼眼相机捕获的点不失真的正确方法是什么？

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