Python图片检索之以图搜图

发布时间: 2022-06-20 本文共3504个字,预计阅读时间需要9分钟

一、待搜索图

二、测试集

三、new_similarity_compare.py

# -*- 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_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

四、image_similarity_function.py

# -*- 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 < 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))) / 16.0


def 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、结果

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