OpenCV从入门到精通实战(三)——全景图像拼接

        # 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.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