深度学习图像处理目标检测图像分割计算机视觉 10--通用场景图像分割

    from Stitcher import Stitcher
    import cv2
    
    # 读取拼接图片
    imageA = cv2.imread("left_01.png")
    imageB = cv2.imread("right_01.png")
    
    # 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()
    
    
    import numpy as np
    import 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]))
            # 将图片B传入result图片最左端
            result[0:imageB.shape[0], 0:imageB.shape[1]] = imageB
    
            # 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 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.DescriptorMatcher_create("BruteForce")
      
            # 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