Opencv实现程序-部分8

from os.path import join from os import walk import numpy as np import cv2 from matplotlib import pyplot as plt from sys import argv folder = './database' query = cv2.imread(join(folder, "catt_seed.jpg")) # plt.imshow(query) # plt.show() files = [] images = [] descriptors = [] # 在文件夹folder下的所有文件 /* 开发不易，整理也不易，如需要详细的说明文档和程序，以及完整的数据集，训练好的模型，或者进一步开发， 可加作者新联系方式咨询，WX：Q3101759565，QQ：3101759565 */ # print(descriptors) # sift特征 cv2.xfeatures2d.SIFT_create() sift = cv2.xfeatures2d.SIFT_create() # 计算特征/特征描述符 query_kp, query_ds query_kp, query_ds = sift.detectAndCompute(query, None) FLANN_INDEX_KDTREE = 0 # 建立dict1: index_params index_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5) # 建立dict2: search_params search_params = dict(checks = 50) # 进行FlannBasedMatcher cv2.FlannBasedMatcher flann = cv2.FlannBasedMatcher(index_params, search_params) # minimum number of matches MIN_MATCH_COUNT = 5 potential_culprits = {} # 所有的npy文件 for d in descriptors: # print("--------- analyzing %s for matches ------------" % d) # flann.knnMatch函数 # query_ds:输入图像query描述符 # np.load(join(folder, d): 保存的配对图像的描述符 np.load函数读取.py文件 matches = flann.knnMatch(query_ds, np.load(join(folder, d)), k=2) good = [] for m, n in matches: if m.distance < 0.7 * n.distance: good.append(m) if len(good) > MIN_MATCH_COUNT: # print("%s is a match! (%d)" % (d, len(good))) else: print("%s is not a match" % d) # potential_culprits是字典 # 保存数据库中对应的图像： 与query的len(good) potential_culprits[d] = len(good) max_matches = 0 potential_suspect = None potential_suspect_show = None # potential_culprits.iteritems()： 是字典的迭代器 # culprit：是对应的key matches是对应的value for culprit, matches in potential_culprits.items(): if matches > max_matches: max_matches = matches # 获得最大的匹配数 potential_suspect = culprit potential_suspect_show = potential_suspect # potential_suspect为npy文件 # potential_suspect.replace("npy", "") # 进一步 potential_suspect.replace("npy", "").upper() print("potential suspect is %s" % potential_suspect.replace("npy", "").upper()) plt.imshow(query) plt.show() if potential_suspect_show != None: plt.imshow(cv2.imread(join(folder, potential_suspect_show.replace("npy", "jpg")))) plt.show()