前言 工作上的项目应用了surf计算特征。对surf进行初步的了解。
本篇参考链接：https://blog.csdn.net/u012483097/article/details/105369239/

1. 主要流程图
   
2. 灰度化图片

   img_path1 = r"../left.jpg"
   img_path2 = r"../right.jpg"
   img1 = cv2.imread(img_path1)
   img2 = cv2.imread(img_path2)
   # 转化为灰度图
   img1_gray = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
   img2_gray = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)
   

3. surf 计算

   surf = cv2.xfeatures2d.SURF_create()
   surf.create(1000)
   # 计算img1， img2 图像的关键点和匹配点
   kp1, des1 = surf.detectAndCompute(img1_gray, None)
   kp2, des2 = surf.detectAndCompute(img2_gray, None)
   

4. 特征选择与匹配

   '''
       BFMatcher.knnMatch() 暴力匹配  FlannBasedMatcher.knnMatch() flann匹配
       return : 这里的matches返回的是DMatch对象，DMatch有4个属性
       distance: 两个匹配点的距离，距离越短，匹配程度越高
       trainIdx: 训练描述符的索引
       queryIdx: 查询描述符的索引
       imgIdx: 训练图片的索引
       '''
       # ======================brute match============================#
       # 选择最优点
       goods = []
       img1points = []
       img2points = []
       if usemode == 'b':
           bf = cv2.BFMatcher()
           matches = bf.knnMatch(des1, des2, k=2)
       # ======================brute match end============================#
   
       # ======================knn match============================#
       elif usemode == 'k':
           kf = cv2.FlannBasedMatcher()
           matches = kf.knnMatch(des1, des2, 2)
       # ======================knn match end============================#
       # 特征匹配
       temp = cv2.drawMatches(img1, kp1, img2, kp2, goods, None)
       if len(goods) != 0:
           for i in goods:
               # queryIdx 表示查询点的位置 trainIdx表示匹配训练的点
               img1points.append(kp1[i.queryIdx].pt)
               img2points.append(kp2[i.trainIdx].pt)
   

5. 获取最优单应性变换矩阵

   src_pts = np.float32([m for m in img1points])
   dst_pts = np.float32([m for m in img2points])
   # 在目标平面找需要匹配平面的最优单应性变换矩阵
   H, _ = cv2.findHomography(dst_pts, src_pts, cv2.LMEDS)
   

6. 透视变换

   img_warpPerspective = cv2.warpPerspective(img_right, H,
                                                 (img_left.shape[1] + img_right.shape[1], min(img_left.shape[0],img_left.shape[0])))
   

7. 图片拼接

   # 直接将左右拼接起来
   w = img_left.shape[1]
   img_warpPerspective[:, 0:w] = img_left
   

8. 8. 结果

   左图
   右图