大蟒蛇python教程共享OpenCV实战之图像拼接的示例代码

目录

• 背景
• 实现步骤
• 一、读取文件
• 二、单应性矩阵计算
• 三、图像拼接
• 总结

img1 = cv2.imread('map1.png')  img2 = cv2.imread('map2.png')    img1 = cv2.resize(img1, (640, 480))  img2 = cv2.resize(img2, (640, 480))    input = np.hstack((img1, img2))  cv2.imshow('input', input)  cv2.waitkey(0)

def get_homo(img1, img2):      # 1实现      sift = cv2.xfeatures2d.sift_create()      # 2实现      k1, p1 = sift.detectandcompute(img1, none)      k2, p2 = sift.detectandcompute(img2, none)      # 3实现      bf = cv2.bfmatcher()      # 4实现      matches = bf.knnmatch(p1, p2, k=2)      # 5实现      good = []      for m1, m2 in matches:          if m1.distance < 0.8 * m2.distance:              good.append(m1)      # 6实现      if len(good) > 8:          img1_pts = []          img2_pts = []          for m in good:              img1_pts.append(k1[m.queryidx].pt)              img2_pts.append(k2[m.trainidx].pt)          img1_pts = np.float32(img1_pts).reshape(-1, 1, 2)          img2_pts = np.float32(img2_pts).reshape(-1, 1, 2)          h, mask = cv2.findhomography(img1_pts, img2_pts, cv2.ransac, 5.0)          return h      else:          print('piints is not enough 8!')          exit()

def stitch_img(img1, img2, h):      # 1实现      h1, w1 = img1.shape[:2]      h2, w2 = img2.shape[:2]      img1_point = np.float32([[0,0], [0,h1], [w1,h1], [w1,0]]).reshape(-1, 1, 2)      img2_point = np.float32([[0,0], [0,h2], [w2,h2], [w2,0]]).reshape(-1, 1, 2)      # 2实现      img1_trans = cv2.perspectivetransform(img1_point, h)      # 将img1变换后的角点与img2原来的角点做拼接      result_point = np.concatenate((img2_point, img1_trans), axis=0)      # 获得拼接后图像x,y的最小值      [x_min, y_min] = np.int32(result_point.min(axis=0).ravel()-0.5)      # 获得拼接后图像x,y的最大值      [x_max, y_max] = np.int32(result_point.max(axis=0).ravel()+0.5)      # 平移距离      trans_dist = [-x_min, -y_min]      # 构建一个齐次平移矩阵      trans_array = np.array([[1, 0, trans_dist[0]],                              [0, 1, trans_dist[1]],                              [0, 0, 1]])      # 平移和单应性变换      res_img = cv2.warpperspective(img1, trans_array.dot(h), (x_max-x_min, y_max-y_min))      # 3实现      res_img[trans_dist[1]:trans_dist[1]+h2,              trans_dist[0]:trans_dist[0]+w2] = img2      return res_img    h = get_homo(img1, img2)  res_img = stitch_img(img1, img2, h)  # 4实现  cv2.imshow('result', res_img)  cv2.waitkey(0)