基于OpenCV的灰度图的图片相似度计算

RGB图转化为灰度图
256度灰（图片内颜色种类不超过256中，不然会失真）
RGB → L

import os

import numpy as np
from skimage.metrics import structural_similarity as ssim
from PIL import Image

def calculate_ssim(original_path, modified_path):
    # 打开原始图片和修改后的图片
    original = Image.open(original_path).convert("L")
    modified = Image.open(modified_path).convert("L")
    original.save("hahiahi.png")
    # 将图像转为 NumPy 数组
    original_array = np.array(original)
    modified_array = np.array(modified)

    # 计算 SSIM
    similarity_index, _ = ssim(original_array, modified_array, full=True)

    return similarity_index


def compare_images_in_directories(original_directory, modified_directory):
    # 获取原始目录和修改目录下的所有文件名
    original_files = os.listdir(original_directory)
    modified_files = os.listdir(modified_directory)
    similarities = []  # 用于存储相似度的数组
    i = 0
    # 遍历两个目录下的同名文件进行对比
    for filename in set(original_files).intersection(modified_files):
        i += 1
        original_path = os.path.join(original_directory, filename)
        modified_path = os.path.join(modified_directory, filename)

        # 调用计算相似度函数
        similarity_index = calculate_ssim(original_path, modified_path)
        similarities.append((filename, similarity_index))  # 存储文件名和相似度的元组
        # print(f"Similarity index for {filename}: {similarity_index}")
    return similarities, i


# 输入原始目录、修改目录和输出目录
original_directory = r"D:/AutoTest/PythonProject/img/testcase_img/PecExplorer/"
modified_directory = r"D:/AutoTest/PythonProject/img/standard_img/PecExplorer/"


# 调用函数进行处理
similarities, i = compare_images_in_directories(original_directory, modified_directory)

for i,j in similarities:
    print(j)

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51