自学教程：OpenCV全景图像拼接的实现示例

import numpy as npimport cv2 class Stitcher:     #拼接函数    def stitch(self, images, ratio=0.75, reprojThresh=4.0,showMatches=False):        #获取输入图片        (imageB, imageA) = images        #检测A、B图片的SIFT关键特征点，并计算特征描述子        (kpsA, featuresA) = self.detectAndDescribe(imageA)        (kpsB, featuresB) = self.detectAndDescribe(imageB)         # 匹配两张图片的所有特征点，返回匹配结果        M = self.matchKeypoints(kpsA, kpsB, featuresA, featuresB, ratio, reprojThresh)         # 如果返回结果为空，没有匹配成功的特征点，退出算法        if M is None:            return None         # 否则，提取匹配结果        # H是3x3视角变换矩阵              (matches, H, status) = M        # 将图片A进行视角变换，result是变换后图片        result = cv2.warpPerspective(imageA, H, (imageA.shape[1] + imageB.shape[1], imageA.shape[0]))        self.cv_show('result', result)        # 将图片B传入result图片最左端        result[0:imageB.shape[0], 0:imageB.shape[1]] = imageB        self.cv_show('result', result)        # 检测是否需要显示图片匹配        if showMatches:            # 生成匹配图片            vis = self.drawMatches(imageA, imageB, kpsA, kpsB, matches, status)            # 返回结果            return (result, vis)         # 返回匹配结果        return result    def cv_show(self,name,img):        cv2.imshow(name, img)        cv2.waitKey(0)        cv2.destroyAllWindows()     def detectAndDescribe(self, image):        # 将彩色图片转换成灰度图        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)         # 建立SIFT生成器        descriptor = cv2.xfeatures2d.SIFT_create()        # 检测SIFT特征点，并计算描述子        (kps, features) = descriptor.detectAndCompute(image, None)         # 将结果转换成NumPy数组        kps = np.float32([kp.pt for kp in kps])         # 返回特征点集，及对应的描述特征        return (kps, features)     def matchKeypoints(self, kpsA, kpsB, featuresA, featuresB, ratio, reprojThresh):        # 建立暴力匹配器        matcher = cv2.BFMatcher()          # 使用KNN检测来自A、B图的SIFT特征匹配对，K=2        rawMatches = matcher.knnMatch(featuresA, featuresB, 2)         matches = []        for m in rawMatches:            # 当最近距离跟次近距离的比值小于ratio值时，保留此匹配对            if len(m) == 2 and m[0].distance < m[1].distance * ratio:            # 存储两个点在featuresA, featuresB中的索引值                matches.append((m[0].trainIdx, m[0].queryIdx))         # 当筛选后的匹配对大于4时，计算视角变换矩阵        if len(matches) > 4:            # 获取匹配对的点坐标            ptsA = np.float32([kpsA[i] for (_, i) in matches])            ptsB = np.float32([kpsB[i] for (i, _) in matches])             # 计算视角变换矩阵            (H, status) = cv2.findHomography(ptsA, ptsB, cv2.RANSAC, reprojThresh)             # 返回结果            return (matches, H, status)         # 如果匹配对小于4时，返回None        return None     def drawMatches(self, imageA, imageB, kpsA, kpsB, matches, status):        # 初始化可视化图片，将A、B图左右连接到一起        (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         # 联合遍历，画出匹配对        for ((trainIdx, queryIdx), s) in zip(matches, status):            # 当点对匹配成功时，画到可视化图上            if s == 1:                # 画出匹配对                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)         # 返回可视化结果        return vis

from Stitcher import Stitcherimport cv2 # 读取拼接图片imageA = cv2.imread("left_01.jpg")imageB = cv2.imread("right_01.jpg") # 把图片拼接成全景图stitcher = Stitcher()(result, vis) = stitcher.stitch([imageA, imageB], showMatches=True) # 显示所有图片cv2.imshow("Image A", imageA)cv2.imshow("Image B", imageB)cv2.imshow("Keypoint Matches", vis)cv2.imshow("Result", result)cv2.waitKey(0)cv2.destroyAllWindows()

pip install --user opencv-contrib-python==3.3.0.10