Python图片检索之以图搜图

 # -*- encoding=utf-8 -*- from image_similarity_function import * import os import shutil # 融合相似度阈值 threshold1 = 0.70 # 最终相似度较高判断阈值 threshold2 = 0.95 # 融合函数计算图片相似度 def calc_image_similarity(img1_path, img2_path): """ :param img1_path: filepath+filename :param img2_path: filepath+filename :return: 图片最终相似度 """ similary_ORB = float(ORB_img_similarity(img1_path, img2_path)) similary_phash = float(phash_img_similarity(img1_path, img2_path)) similary_hist = float(calc_similar_by_path(img1_path, img2_path)) # 如果三种算法的相似度最大的那个大于0.7，则相似度取最大，否则，取最小。 max_three_similarity = max(similary_ORB, similary_phash, similary_hist) min_three_similarity = min(similary_ORB, similary_phash, similary_hist) if max_three_similarity > threshold1: result = max_three_similarity else: result = min_three_similarity return round(result, 3) if __name__ == '__main__': # 搜索文件夹 filepath = r'D:\Dataset\cityscapes\leftImg8bit\val\frankfurt' #待查找文件夹 searchpath = r'C:\Users\Administrator\Desktop\cityscapes_pa<p style="color:transparent">来源gao!%daima.com搞$代*!码网</p>per' # 相似图片存放路径 newfilepath = r'C:\Users\Administrator\Desktop\result' for parent, dirnames, filenames in os.walk(searchpath): for srcfilename in filenames: img1_path = searchpath +"\\"+ srcfilename for parent, dirnames, filenames in os.walk(filepath): for i, filename in enumerate(filenames): print("{}/{}: {} , {} ".format(i+1, len(filenames), srcfilename,filename)) img2_path = filepath + "\\" + filename # 比较 kk = calc_image_similarity(img1_path, img2_path) try: if kk >= threshold2: # 将两张照片同时拷贝到指定目录 shutil.copy(img2_path, os.path.join(newfilepath, srcfilename[:-4] + "_" + filename)) except Exception as e: # print(e) pass 

 # -*- encoding=utf-8 -*- # 导入包 import cv2 from functools import reduce from PIL import Image # 计算两个图片相似度函数ORB算法 def ORB_img_similarity(img1_path, img2_path): """ :param img1_path: 图片1路径 :param img2_path: 图片2路径 :return: 图片相似度 """ try: # 读取图片 img1 = cv2.imread(img1_path, cv2.IMREAD_GRAYSCALE) img2 = cv2.imread(img2_path, cv2.IMREAD_GRAYSCALE) # 初始化ORB检测器 orb = cv2.ORB_create() kp1, des1 = orb.detectAndCompute(img1, None) kp2, des2 = orb.detectAndCompute(img2, None) # 提取并计算特征点 bf = cv2.BFMatcher(cv2.NORM_HAMMING) # knn筛选结果 matches = bf.knnMatch(des1, trainDescriptors=des2, k=2) # 查看最大匹配点数目 good = [m for (m, n) in matches if m.distance <0.75 * n.distance] similary = len(good) / len(matches) return similary except: return '0' # 计算图片的局部哈希值--pHash def phash(img): """ :param img: 图片 :return: 返回图片的局部hash值 """ img = img.resize((8, 8), Image.ANTIALIAS).convert('L') avg = reduce(lambda x, y: x + y, img.getdata()) / 64. hash_value = reduce(lambda x, y: x | (y[1] << y[0]), enumerate(map(lambda i: 0 if i