SSIM对比图片相似度

Thu, 02 Oct 2025 00:00:00 +0000

对比图片相似度, 相似度越接近1 表示相似度越高,

import cv2
import numpy as np
from scipy.ndimage import gaussian_filter

def load_image_grayscale(image_path):
 img = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)
 if img is None:
 raise ValueError(f"无法读取图像: {image_path}")
 img = img.astype(np.float64) / 255.0
 
 return img

def compute_statistics(img1, img2, window_size=11, sigma=1.5):
 mu1 = gaussian_filter(img1, sigma=sigma)
 mu2 = gaussian_filter(img2, sigma=sigma)
 
 sigma1_sq = gaussian_filter(img1 ** 2, sigma=sigma) - mu1 ** 2
 sigma2_sq = gaussian_filter(img2 ** 2, sigma=sigma) - mu2 ** 2
 
 
 
 sigma12 = gaussian_filter(img1 * img2, sigma=sigma) - mu1 * mu2
 
 
 return mu1, mu2, sigma1_sq, sigma2_sq, sigma12

def calculate_ssim(img1, img2, window_size=11, sigma=1.5, K1=0.01, K2=0.03, L=1.0):
 mu1, mu2, sigma1_sq, sigma2_sq, sigma12 = compute_statistics(img1, img2, window_size, sigma)
 
 C1 = (K1 * L) ** 2
 C2 = (K2 * L) ** 2
 
 luminance = (2 * mu1 * mu2 + C1) / (mu1 ** 2 + mu2 ** 2 + C1)
 
 print(f"luminance: {np.mean(luminance):.4f}")
 
 # 确保方差值非负
 sigma1_sq = np.maximum(sigma1_sq, 0)
 sigma2_sq = np.maximum(sigma2_sq, 0)
 
 contrast = (2 * np.sqrt(sigma1_sq) * np.sqrt(sigma2_sq) + C2) / (sigma1_sq + sigma2_sq + C2)
 print(f"contrast: {np.mean(contrast):.4f}")
 structure = (sigma12 + C2 / 2) / (np.sqrt(sigma1_sq) * np.sqrt(sigma2_sq) + C2 / 2)
 print(f"structure: {np.mean(structure):.4f}")
 
 ssim_map = luminance * contrast * structure
 return np.mean(ssim_map)

def ssim_index(image1_path, image2_path, window_size=11, sigma=1.5, K1=0.01, K2=0.03):
 img1 = load_image_grayscale(image1_path)
 img2 = load_image_grayscale(image2_path)
 
 if img1.shape != img2.shape:
 raise ValueError("输入的两幅图像必须具有相同的尺寸")
 
 ssim = calculate_ssim(img1, img2, window_size, sigma, K1, K2, L=1.0)
 return ssim

# 示例使用
if __name__ == "__main__":
 image1_path = '/Users/pkl/Downloads/3.png'
 image2_path = '/Users/pkl/Downloads/3_副本.png'
 
 try:
 ssim_value = ssim_index(image1_path, image2_path)
 print(f"两张图像的SSIM值为: {ssim_value:.4f}")
 except Exception as e:
 print(f"计算SSIM时发生错误: {e}")

输出结果>

❯ /opt/anaconda3/bin/python "SSIM结构相似性值计算.py"
luminance: 0.9946
contrast: 0.9917
structure: 0.9869
两张图像的SSIM值为: 0.9831

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SSIM对比图片相似度