请解释下面代码的作用。" 图像匹配 " def picmatch_go(mode,pic): MIN_MATCH_COUNT = 10 img1 = mode img2 = pic img1 = cv2.pyrDown(img1) img2 = cv2.pyrDown(img2)

初始化SIFT描述符

sift = cv2.SIFT_create()

基于SIFT找到关键点和描述符

kp1, des1 = sift.detectAndCompute(img1, None) kp2, des2 = sift.detectAndCompute(img2, None)

设置FLANN匹配器参数，定义FLANN匹配器，使用 KNN 算法实现匹配

FLANN_INDEX_KDTREE = 0 index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5) search_params = dict(checks=100) flann = cv2.FlannBasedMatcher(index_params, search_params) matches = flann.knnMatch(des1, des2, k=2)

去除错误匹配

good = [] for m, n in matches: if m.distance < 0.8 * n.distance: good.append(m)

单应性

if len(good) > MIN_MATCH_COUNT:

改变数组的表现形式，不改变数据内容，数据内容是每个关键点的坐标位置

src_pts = np.float32([kp1[m.queryIdx].pt for m in good]).reshape(-1, 1, 2) dst_pts = np.float32([kp2[m.trainIdx].pt for m in good]).reshape(-1, 1, 2)

findHomography 函数是计算变换矩阵

参数cv2.RANSAC是使用RANSAC算法寻找一个最佳单应性矩阵H，即返回值M

返回值：M 为变换矩阵，mask是掩模

M, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0) matchesMask = mask.ravel().tolist()

pts是图像img1的四个顶点

h, w, c = img1.shape pts = np.float32([[0, 0], [0, h - 1], [w - 1, h - 1], [w - 1, 0]]).reshape(-1, 1, 2)

计算变换后的四个顶点坐标位置

dst = cv2.perspectiveTransform(pts, M)

根据四个顶点坐标位置在img2图像画出变换后的边框

img2 = cv2.polylines(img2, [np.int32(dst)], True, (0, 0, 255), 3, cv2.LINE_AA)

校正后的图像

imgOut = cv2.warpPerspective(img2, M, (img1.shape[1], img1.shape[0]),

flags=cv2.INTER_LINEAR + cv2.WARP_INVERSE_MAP)

a = "Y" else: print("Not enough matches are found - %d/%d" % (len(good), MIN_MATCH_COUNT)) matchesMask = None a = "N" return img2 , a 请用中文回答，能否在此函数上完善，输出匹配图像与模板图像的偏移角度（以角度而不是弧度为单位）？