import cv2 import numpy as np from matplotlib import pyplot as plt

img1 = cv2.imread('1.png') img2 = cv2.imread('4.png')

gray1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY) gray1 = np.float32(gray1) dst1 = cv2.cornerHarris(gray1, 2, 3, 0.04) img1[dst1 > 0.01 * dst1.max()] = [0, 0, 255]

gray2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY) gray2 = np.float32(gray2) dst2 = cv2.cornerHarris(gray2, 2, 23, 0.04) img2[dst2 > 0.01 * dst2.max()] = [0, 0, 255]

从一幅Harris响应图像中返回角点，min_dist为分割角点和图像边界的最少像素数目

def get_harris_points(harrisim, min_dist=10, threshold=0.1): # 寻找高于阈值的候选角点 corner_threshold = harrisim.max() * threshold harrisim_t = (harrisim > corner_threshold) * 1 # 得到候选点的坐标 coords = np.array(harrisim_t.nonzero()).T # 以及它们的 Harris 响应值 candidate_values = [harrisim[c[0], c[1]] for c in coords] # 对候选点按照 Harris 响应值进行排序 index = np.argsort(candidate_values)[::-1] # 将可行点的位置保存到数组中 allowed_locations = np.zeros(harrisim.shape) allowed_locations[min_dist:-min_dist, min_dist:-min_dist] = 1 # 按照 min_distance 原则，选择最佳 Harris 点 filtered_coords = [] for i in index: if allowed_locations[coords[i, 0], coords[i, 1]] == 1: filtered_coords.append(coords[i]) allowed_locations[(coords[i, 0] - min_dist):(coords[i, 0] + min_dist), (coords[i, 1] - min_dist):(coords[i, 1] + min_dist)] = 0 return filtered_coords

对于每个返回的点，返回点周围2*wid+1个像素的值（假设选取点的min_distance > wid）

def get_descriptors(image, filtered_coords, wid=5): desc = [] for coords in filtered_coords: patch = image[coords[0] - wid:coords[0] + wid + 1, coords[1] - wid:coords[1] + wid + 1].flatten() desc.append(patch) return desc

对于第一幅图像中的每个角点描述子，使用归一化互相关，选取它在第二幅图像中的匹配角点

def match(desc1, desc2, threshold=0.5): n = len(desc1[0]) # 点对的距离 d = -np.ones((len(desc1), len(desc2))) for i in range(len(desc1)): for j in range(len(desc2)): d1 = (desc1[i] - np.mean(desc1[i])) / np.std(desc1[i]) d2 = (desc2[j] - np.mean(desc2[j])) / np.std(desc2[j]) ncc_value = sum(d1 * d2) / (n - 1) if ncc_value > threshold: d[i, j] = ncc_value ndx = np.argsort(-d) # 从大0到小排序 matchscores = ndx[:, 0] # 最大一个数的位置坐标 return matchscores

两边对称版本的match（）

def match_twosided(desc1, desc2, threshold=0.5): matches_12 = match(desc1, desc2, threshold) matches_21 = match(desc2, desc1, threshold) ndx_12 = np.where(matches_12 >= 0)[0] # 去除非对称的匹配 for n in ndx_12: if matches_21[matches_12[n]] != n: matches_12[n] = -1 return matches_12

返回将两幅图像并排拼接成的一幅新图像

def appendimages(im1, im2): row1 = im1.shape[0] row2 = im2.shape[0] if row1 < row2: im1 = np.concatenate((im1, np.zeros((row2 - row1, im1.shape[1], im1.shape[2]))), axis=0) elif row1 > row2: im2 = np.concatenate((im2, np.zeros((row1 - row2, im2.shape[1], im2.shape[2]))), axis=0) return np.concatenate((im1, im2), axis=1)

显示一幅带有连接匹配之间连线的图片

输入：im1，im2（数组图像），locs1，locs2（特征位置），matchscores（match的输出），

def plot_matches(im1, im2, locs1, locs2, matchscores): im1_rgb = cv2.cvtColor(im1, cv2.COLOR_BGR2RGB) # 将BGR顺序转换为RGB顺序 im2_rgb = cv2.cvtColor(im2, cv2.COLOR_BGR2RGB) im3 = appendimages(im1_rgb, im2_rgb) plt.imshow(im3) cols1 = im1.shape[1] for i, m in enumerate(matchscores): if m > 0: plt.plot([locs1[i][1], locs2[m][1] + cols1], [locs1[i][0], locs2[m][0]], 'c') plt.axis('off') plt.show()

wid = 9 # 比较像素点数目 filtered_coords1 = get_harris_points(dst1, wid + 1, 0.1) # 图1大于阈值的坐标 filtered_coords2 = get_harris_points(dst2, wid + 1, 0.1) # 图2大于阈值的坐标 d1 = get_descriptors(img1, filtered_coords1, wid) d2 = get_descriptors(img2, filtered_coords2, wid) matches = match_twosided(d1, d2, 0.8) # 图1的阈值点与图二哪个阈值点相关度最高，输出与图一相关性最大点的坐标 plt.figure(figsize=(30, 20)); plot_matches(img1, img2, filtered_coords1, filtered_coords2, matches) cv2.waitKey() cv2.destroyAllWindows()