无法在两个图像之间找到足够的匹配来拼接它们

Question

我试图将两张图片拼接在一起，但我没有这样做，因为该节目没有检测到两张图片之间足够的匹配

以下是代码：

import numpy as np
import imutils
import cv2

class Stitcher:
    def __init__(self):
        self.isv3 = imutils.is_cv3()

    def stitch(self, images, ratio=0.75, reprojThresh=5.0,
        showMatches=False):
        (imageB, imageA) = images
        (kpsA, featuresA) = self.detectAndDescribe(imageA)
        (kpsB, featuresB) = self.detectAndDescribe(imageB)

        M = self.matchKeypoints(kpsA, kpsB,
            featuresA, featuresB, ratio, reprojThresh)

        if M is None:
            return None

        (matches, H, status) = M
        result = cv2.warpPerspective(imageA, H,
            (imageA.shape[1] + imageB.shape[1], imageA.shape[0]))

        result[0:imageB.shape[0], 0:imageB.shape[1]] = imageB
        if showMatches:
            vis = self.drawMatches(imageA, imageB, kpsA, kpsB, matches,
                status)
            return (result, vis)
        return result

    def detectAndDescribe(self, image):
        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
        if self.isv3:
            descriptor = cv2.xfeatures2d.SIFT_create()
            (kps, features) = descriptor.detectAndCompute(image, None)


        else:
            detector = cv2.FeatureDetector_create("SIFT")
            kps = detector.detect(gray)

            # extract features from the image
            extractor = cv2.DescriptorExtractor_create("SIFT")
            (kps, features) = extractor.compute(gray, kps)

        # convert the keypoints from KeyPoint objects to NumPy
        # arrays
        kps = np.float32([kp.pt for kp in kps])

        # return a tuple of keypoints and features
        return (kps, features)

    def matchKeypoints(self, kpsA, kpsB, featuresA, featuresB,
        ratio, reprojThresh):
        # compute the raw matches and initialize the list of actual
        # matches
        matcher = cv2.DescriptorMatcher_create("BruteForce")
        rawMatches = matcher.knnMatch(featuresA, featuresB, 2)
        matches = []

        # loop over the raw matches
        for m in rawMatches:
            # ensure the distance is within a certain ratio of each
            # other (i.e. Lowe's ratio test)
            if len(m) == 2 and m[0].distance < m[1].distance * ratio:
                matches.append((m[0].trainIdx, m[0].queryIdx))

        # computing a homography requires at least 4 matches
        if len(matches) > 4:
            # construct the two sets of points
            ptsA = np.float32([kpsA[i] for (_, i) in matches])
            ptsB = np.float32([kpsB[i] for (i, _) in matches])

            # compute the homography between the two sets of points
            (H, status) = cv2.findHomography(ptsA, ptsB, cv2.RANSAC,
                reprojThresh)

            # return the matches along with the homograpy matrix
            # and status of each matched point
            return (matches, H, status)

        # otherwise, no homograpy could be computed
        return None

    def drawMatches(self, imageA, imageB, kpsA, kpsB, matches, status):
        # initialize the output visualization image
        (hA, wA) = imageA.shape[:2]
        (hB, wB) = imageB.shape[:2]
        vis = np.zeros((max(hA, hB), wA + wB, 3), dtype="uint8")
        vis[0:hA, 0:wA] = imageA
        vis[0:hB, wA:] = imageB

        # loop over the matches
        for ((trainIdx, queryIdx), s) in zip(matches, status):
            # only process the match if the keypoint was successfully
            # matched
            if s == 1:
                # draw the match
                ptA = (int(kpsA[queryIdx][0]), int(kpsA[queryIdx][1]))
                ptB = (int(kpsB[trainIdx][0]) + wA, int(kpsB[trainIdx][1]))
                cv2.line(vis, ptA, ptB, (0, 255, 0), 1)

        # return the visualization
        return vis

以下是原始图片：

图片A：

图片B：

匹配点：

拼接的结果：

结果无处可寻，如果由于两张图片之间没有足够的匹配点，我可以纠正它。