forked from mrq/DL-Art-School
148 lines
4.6 KiB
Python
148 lines
4.6 KiB
Python
'''
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calculate the PSNR and SSIM.
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same as MATLAB's results
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'''
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import os
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import math
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import numpy as np
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import cv2
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import glob
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def main():
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# Configurations
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# GT - Ground-truth;
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# Gen: Generated / Restored / Recovered images
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folder_GT = '/mnt/SSD/xtwang/BasicSR_datasets/val_set5/Set5'
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folder_Gen = '/home/xtwang/Projects/BasicSR/results/RRDB_PSNR_x4/set5'
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crop_border = 4
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suffix = '' # suffix for Gen images
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test_Y = False # True: test Y channel only; False: test RGB channels
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PSNR_all = []
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SSIM_all = []
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img_list = sorted(glob.glob(folder_GT + '/*'))
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if test_Y:
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print('Testing Y channel.')
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else:
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print('Testing RGB channels.')
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for i, img_path in enumerate(img_list):
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base_name = os.path.splitext(os.path.basename(img_path))[0]
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im_GT = cv2.imread(img_path) / 255.
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im_Gen = cv2.imread(os.path.join(folder_Gen, base_name + suffix + '.png')) / 255.
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if test_Y and im_GT.shape[2] == 3: # evaluate on Y channel in YCbCr color space
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im_GT_in = bgr2ycbcr(im_GT)
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im_Gen_in = bgr2ycbcr(im_Gen)
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else:
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im_GT_in = im_GT
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im_Gen_in = im_Gen
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# crop borders
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if im_GT_in.ndim == 3:
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cropped_GT = im_GT_in[crop_border:-crop_border, crop_border:-crop_border, :]
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cropped_Gen = im_Gen_in[crop_border:-crop_border, crop_border:-crop_border, :]
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elif im_GT_in.ndim == 2:
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cropped_GT = im_GT_in[crop_border:-crop_border, crop_border:-crop_border]
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cropped_Gen = im_Gen_in[crop_border:-crop_border, crop_border:-crop_border]
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else:
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raise ValueError('Wrong image dimension: {}. Should be 2 or 3.'.format(im_GT_in.ndim))
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# calculate PSNR and SSIM
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PSNR = calculate_psnr(cropped_GT * 255, cropped_Gen * 255)
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SSIM = calculate_ssim(cropped_GT * 255, cropped_Gen * 255)
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print('{:3d} - {:25}. \tPSNR: {:.6f} dB, \tSSIM: {:.6f}'.format(
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i + 1, base_name, PSNR, SSIM))
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PSNR_all.append(PSNR)
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SSIM_all.append(SSIM)
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print('Average: PSNR: {:.6f} dB, SSIM: {:.6f}'.format(
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sum(PSNR_all) / len(PSNR_all),
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sum(SSIM_all) / len(SSIM_all)))
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def calculate_psnr(img1, img2):
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# img1 and img2 have range [0, 255]
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img1 = img1.astype(np.float64)
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img2 = img2.astype(np.float64)
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mse = np.mean((img1 - img2)**2)
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if mse == 0:
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return float('inf')
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return 20 * math.log10(255.0 / math.sqrt(mse))
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def ssim(img1, img2):
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C1 = (0.01 * 255)**2
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C2 = (0.03 * 255)**2
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img1 = img1.astype(np.float64)
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img2 = img2.astype(np.float64)
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kernel = cv2.getGaussianKernel(11, 1.5)
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window = np.outer(kernel, kernel.transpose())
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mu1 = cv2.filter2D(img1, -1, window)[5:-5, 5:-5] # valid
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mu2 = cv2.filter2D(img2, -1, window)[5:-5, 5:-5]
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mu1_sq = mu1**2
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mu2_sq = mu2**2
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mu1_mu2 = mu1 * mu2
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sigma1_sq = cv2.filter2D(img1**2, -1, window)[5:-5, 5:-5] - mu1_sq
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sigma2_sq = cv2.filter2D(img2**2, -1, window)[5:-5, 5:-5] - mu2_sq
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sigma12 = cv2.filter2D(img1 * img2, -1, window)[5:-5, 5:-5] - mu1_mu2
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ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) *
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(sigma1_sq + sigma2_sq + C2))
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return ssim_map.mean()
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def calculate_ssim(img1, img2):
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'''calculate SSIM
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the same outputs as MATLAB's
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img1, img2: [0, 255]
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'''
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if not img1.shape == img2.shape:
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raise ValueError('Input images must have the same dimensions.')
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if img1.ndim == 2:
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return ssim(img1, img2)
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elif img1.ndim == 3:
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if img1.shape[2] == 3:
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ssims = []
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for i in range(3):
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ssims.append(ssim(img1, img2))
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return np.array(ssims).mean()
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elif img1.shape[2] == 1:
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return ssim(np.squeeze(img1), np.squeeze(img2))
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else:
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raise ValueError('Wrong input image dimensions.')
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def bgr2ycbcr(img, only_y=True):
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'''same as matlab rgb2ycbcr
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only_y: only return Y channel
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Input:
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uint8, [0, 255]
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float, [0, 1]
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'''
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in_img_type = img.dtype
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img.astype(np.float32)
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if in_img_type != np.uint8:
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img *= 255.
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# convert
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if only_y:
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rlt = np.dot(img, [24.966, 128.553, 65.481]) / 255.0 + 16.0
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else:
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rlt = np.matmul(img, [[24.966, 112.0, -18.214], [128.553, -74.203, -93.786],
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[65.481, -37.797, 112.0]]) / 255.0 + [16, 128, 128]
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if in_img_type == np.uint8:
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rlt = rlt.round()
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else:
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rlt /= 255.
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return rlt.astype(in_img_type)
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if __name__ == '__main__':
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main()
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