# im1 和 im2 都为灰度图像,uint8 类型 # method 1 diff = im1 - im2 mse = np.mean(np.square(diff)) psnr = 10 * np.log10(255 * 255 / mse) # method 2 psnr = skimage.measure.compare_psnr(im1, im2, 255) compare_psnr(im_true, im_test, data_range=None) 函数原型可见此处 ...
log10(255 * 255 / mse) # method 2 psnr = skimage.measure.compare_psnr(im1, im2, 255) compare_psnr(im_true, im_test, data_range=None) 函数原型可见此处 针对超光谱图像,我们需要针对不同波段分别计算 PSNR,然后取平均值,这个指标称为 MPSNR。 2. SSIM (Structural SIMilarity) 结构相似性 ...
compare_psnr 函数可能是你记错了或者从某个特定的教程、示例代码中看到的。在 scikit-image 库中,计算 PSNR(峰值信噪比)通常使用的是 peak_signal_noise_ratio 函数。这个函数位于 skimage.metrics 模块中。 你可以使用以下代码来导入并使用该函数: python from skimage.metrics import peak_signal_noise_ratio as ...
from skimage.measure import compare_ssim as ssim from skimage.measure import compare_psnr from skimage.measure import compare_lpips import torch def get_ssim(img1, img2): """ 计算两个图像的SSIM指标 """ return ssim(img1, img2) def get_psnr(img1, img2): """ 计算两个图像的PSNR指标 "...
# im1 和 im2 都为灰度图像,uint8 类型ssim=skimage.measure.compare_ssim(im1,im2,data_range=255) compare_ssim(X, Y, win_size=None, gradient=False, data_range=None, multichannel=False, gaussian_weights=False, full=False, **kwargs) 函数原型可见此处 ...
psnr = skimage.measure.compare_psnr(im1, im2, 255) 1. 2. 3. 4. 5. 6. 7. 8. 9. compare_psnr(im_true, im_test, data_range=None) 函数原型可见此处 针对超光谱图像,我们需要针对不同波段分别计算 PSNR,然后取平均值,这个指标称为 MPSNR。
安装skimage 库 pip install scikit-image 1. skimage 库也提供了相关的计算实现. skimage.measure.compare_psnr from skimage.metrics import structural_similarity PSNR = peak_signal_noise_ratio(img1, img2) 1. 2. 完示例代码 import cv2 from skimage.metrics import mean_squared_error ...
import argparse import os import cv2 import pandas as pd from skimage.measure import compare_ssim from skimage.measure import compare_psnr parser = argparse.ArgumentParser(description='image_eval') parser.add_argument('--orig_path',help='path to orig image dataset', default='orig/') parser.add...
# im1 和 im2 都为灰度图像,uint8 类型ssim = skimage.measure.compare_ssim(im1, im2, data_range=255) compare_ssim(X, Y, win_size=None, gradient=False, data_range=None, multichannel=False, gaussian_weights=False, full=False, **kwargs) 函数原型可见此处 ...
from skimage.measure import compare_ssim import argparse import imutils import cv2 # 2. Construct the argument parse and parse the arguments # ap = argparse.ArgumentParser() # ap.add_argument("-f", "--first", required=True, help="Directory of the image that will be compared") # ap.add...