在Opencv中将图像与深度图对齐

img = cv2.imread('image.jpg')
depth = cv2.imread('depth.jpg', cv2.IMREAD_UNCHANGED)

# 进行相机标定
retval, cameraMatrix, distCoeffs, rvecs, tvecs = cv2.calibrateCamera(objPoints, imgPoints, imageSize, None, None)

# 矫正图像
undistorted = cv2.undistort(img, cameraMatrix, distCoeffs)

depth_gray = cv2.cvtColor(depth, cv2.COLOR_BGR2GRAY)

sift = cv2.xfeatures2d.SIFT_create()

# 提取图像和深度图像中的关键点和描述符
keypoints1, descriptors1 = sift.detectAndCompute(undistorted, None)
keypoints2, descriptors2 = sift.detectAndCompute(depth_gray, None)

# 使用BFMatcher进行特征匹配
bf = cv2.BFMatcher()
matches = bf.knnMatch(descriptors1, descriptors2, k=2)

# 应用Ratio Test来筛选匹配点
good_matches = []
for m, n in matches:
    if m.distance < 0.75 * n.distance:
        good_matches.append(m)

# 可视化匹配结果
matched_img = cv2.drawMatches(undistorted, keypoints1, depth_gray, keypoints2, good_matches, None, flags=2)