一、待搜索圖
二、測試集
三、new_similarity_compare.py
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# -*- encoding=utf-8 -*-from image_similarity_function import *import osimport shutil# 融合相似度閾值threshold1 = 0.70# 最終相似度較高判斷閾值threshold2 = 0.95# 融合函數(shù)計(jì)算圖片相似度def calc_image_similarity(img1_path, img2_path): """ :param img1_path: filepath+filename :param img2_path: filepath+filename :return: 圖片最終相似度 """ 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)) # 如果三種算法的相似度最大的那個(gè)大于0.7，則相似度取最大，否則，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__': # 搜索文件夾 filepath = r'D:\Dataset\cityscapes\leftImg8bit\val\frankfurt' #待查找文件夾 searchpath = r'C:\Users\Administrator\Desktop\cityscapes_paper' # 相似圖片存放路徑 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 # 比較 kk = calc_image_similarity(img1_path, img2_path) try: if kk >= threshold2: # 將兩張照片同時(shí)拷貝到指定目錄 shutil.copy(img2_path, os.path.join(newfilepath, srcfilename[:-4] + "_" + filename)) except Exception as e: # print(e) pass |
四、image_similarity_function.py
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# -*- encoding=utf-8 -*-# 導(dǎo)入包import cv2from functools import reducefrom PIL import Image# 計(jì)算兩個(gè)圖片相似度函數(shù)ORB算法def ORB_img_similarity(img1_path, img2_path): """ :param img1_path: 圖片1路徑 :param img2_path: 圖片2路徑 :return: 圖片相似度 """ try: # 讀取圖片 img1 = cv2.imread(img1_path, cv2.IMREAD_GRAYSCALE) img2 = cv2.imread(img2_path, cv2.IMREAD_GRAYSCALE) # 初始化ORB檢測器 orb = cv2.ORB_create() kp1, des1 = orb.detectAndCompute(img1, None) kp2, des2 = orb.detectAndCompute(img2, None) # 提取并計(jì)算特征點(diǎn) bf = cv2.BFMatcher(cv2.NORM_HAMMING) # knn篩選結(jié)果 matches = bf.knnMatch(des1, trainDescriptors=des2, k=2) # 查看最大匹配點(diǎn)數(shù)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'# 計(jì)算圖片的局部哈希值--pHashdef phash(img): """ :param img: 圖片 :return: 返回圖片的局部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 < avg else 1, img.getdata())), 0) return hash_value# 計(jì)算兩個(gè)圖片相似度函數(shù)局部敏感哈希算法def phash_img_similarity(img1_path, img2_path): """ :param img1_path: 圖片1路徑 :param img2_path: 圖片2路徑 :return: 圖片相似度 """ # 讀取圖片 img1 = Image.open(img1_path) img2 = Image.open(img2_path) # 計(jì)算漢明距離 distance = bin(phash(img1) ^ phash(img2)).count('1') similary = 1 - distance / max(len(bin(phash(img1))), len(bin(phash(img1)))) return similary# 直方圖計(jì)算圖片相似度算法def make_regalur_image(img, size=(256, 256)): """我們有必要把所有的圖片都統(tǒng)一到特別的規(guī)格，在這里我選擇是的256x256的分辨率。""" return img.resize(size).convert('RGB')def hist_similar(lh, rh): assert len(lh) == len(rh) return sum(1 - (0 if l == r else float(abs(l - r)) / max(l, r)) for l, r in zip(lh, rh)) / len(lh)def calc_similar(li, ri): return sum(hist_similar(l.histogram(), r.histogram()) for l, r in zip(split_image(li), split_image(ri))) / 16.0def calc_similar_by_path(lf, rf): li, ri = make_regalur_image(Image.open(lf)), make_regalur_image(Image.open(rf)) return calc_similar(li, ri)def split_image(img, part_size=(64, 64)): w, h = img.size pw, ph = part_size assert w % pw == h % ph == 0 return [img.crop((i, j, i + pw, j + ph)).copy() for i in range(0, w, pw) \ for j in range(0, h, ph)] |
五、結(jié)果
到此這篇關(guān)于Python圖片檢索之以圖搜圖的文章就介紹到這了,更多相關(guān)Python以圖搜圖內(nèi)容請(qǐng)搜索服務(wù)器之家以前的文章或繼續(xù)瀏覽下面的相關(guān)文章希望大家以后多多支持服務(wù)器之家！
原文鏈接：https://blog.csdn.net/weixin_43723625/article/details/117298412
