自学教程：Python图片检索之以图搜图

# -*- encoding=utf-8 -*-from image_similarity_function import *import osimport 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_paper'    # 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 cv2from functools import reducefrom 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--pHashdef 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 < avg else 1, img.getdata())),                        0)    return hash_value# 计算两个图片相似度函数局部敏感哈希算法def phash_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    """    # 读取图片    img1 = Image.open(img1_path)    img2 = Image.open(img2_path)    # 计算汉明距离    distance = bin(phash(img1) ^ phash(img2)).count('1')    similary = 1 - distance / max(len(bin(phash(img1))), len(bin(phash(img1))))    return similary# 直方图计算图片相似度算法def make_regalur_image(img, size=(256, 256)):    """我们有必要把所有的图片都统一到特别的规格，在这里我选择是的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)]