主要使用 structural_similarity 算法判断两张图片的相似度。代码如下：
import numpy as np
from skimage.metrics import structural_similarity as ssim
import cv2 as cv
import os
import shutil
import sys
def compare_ssim(img1, img2):# 确保img1和img2是灰度图，如果不是，需要先转换if img1.ndim == 3:img1 = img1.mean(axis=2)if img2.ndim == 3:img2 = img2.mean(axis=2)(score, diff) = ssim(img1, img2, full=True)return score, diff# 假设 img1 和 img2 是两个 ndarray 图片
# score, diff = compare_ssim(img1, img2)
# print(f"SSIM: {score}, Diff: {diff}")def extract_number(s):return int(''.join(filter(str.isdigit, s)))
# 步骤 3: 使用 sorted() 函数进行排序
#sorted_list = sorted(string_list, key=extract_number)if __name__ == "__main__":# 原始目录 和 抽取保存目录img_folder = sys.argv[1]save_folder = sys.argv[2]os.makedirs(save_folder,exist_ok=True)img_list = os.listdir(img_folder)img_list.sort(key=extract_number)#print(img_list)SIZE = 64 #将图像缩放大小for id, imgname in enumerate(img_list):imgpath = os.path.join(img_folder, imgname)img= cv.resize(cv.imread(imgpath,cv.IMREAD_GRAYSCALE),(SIZE, SIZE))if id==0:base_img = img.copy()continueelse:score, diff = compare_ssim(base_img, img)judge = np.where(diff < 0.3, 0, 1) # 大于0.3 认为是相同的块ss = judge.sum() / (SIZE * SIZE)if ss < 0.4: # 相同块比例低于0.4 才认为图片不相似shutil.copy(imgpath, save_folder)base_img = img.copy()print("copy : {}".format(imgpath))else:continue
