# coding:utf-8

 """

 单应性匹配：

     两幅图像中的一幅 出现投影畸变的时候，他们还能彼此匹配

 """

 import cv2

 import numpy as np

 # 最小匹配数量设为10个， 大于这个数量从中筛选出10个最好的

 MIN_MATCH_COUNT = 10

 # 读入两幅图片 图片中有相同部分

 img1 = cv2.imread("../data/logo1.png", cv2.IMREAD_GRAYSCALE)

 img2 = cv2.imread("../data/album1.png", cv2.IMREAD_GRAYSCALE)

 # 获取sift特征检测器

 sift = cv2.xfeatures2d.SIFT_create()

 # 检测关键点 计算描述符

 kp1, des1 = sift.detectAndCompute(img1, None)

 kp2, des2 = sift.detectAndCompute(img2, None)

 # kdtree建立索引方式的常量参数

 FLANN_INDEX_KDTREE = 0

 index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)

 search_params = dict(checks=50) # checks指定索引树要被遍历的次数

 flann = cv2.FlannBasedMatcher(index_params, search_params)

 # 进行匹配搜索

 matches = flann.knnMatch(des1, des2, k=2)

 # 寻找距离近的放入good列表

 good = []

 for m, n in matches:

     if m.distance < 0.7 * n.distance:

         good.append(m)

 # 如果足够多  就筛选

 if len(good) > MIN_MATCH_COUNT:

     # 通过距离近的描述符 找到两幅图片的关键点

     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)

     # 单应性匹配图关键点匹配线。。不懂啥意思

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

     matchesMask = mask.ravel().tolist()

     h, w = img1.shape

     # 计算第二张图相对于第一张图的畸变

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

     dst = cv2.perspectiveTransform(pts, M)

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

 else:

     matchesMask = None

 draw_params = dict(

     matchColor=(0, 255, 0),

     singlePointColor=None,

     matchesMask=matchesMask,

     flags=2

 )

 img3 = cv2.drawMatches(img1, kp1, img2, kp2, good, None, **draw_params)

 cv2.imshow("", img3)

 cv2.waitKey()