import cv2 import numpy as np import torch import torch.nn.functional as F from r_basicsr.metrics.metric_util import reorder_image, to_y_channel from r_basicsr.utils.color_util import rgb2ycbcr_pt from r_basicsr.utils.registry import METRIC_REGISTRY @METRIC_REGISTRY.register() def calculate_psnr(img, img2, crop_border, input_order='HWC', test_y_channel=False, **kwargs): """Calculate PSNR (Peak Signal-to-Noise Ratio). Ref: https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio Args: img (ndarray): Images with range [0, 255]. img2 (ndarray): Images with range [0, 255]. crop_border (int): Cropped pixels in each edge of an image. These pixels are not involved in the calculation. input_order (str): Whether the input order is 'HWC' or 'CHW'. Default: 'HWC'. test_y_channel (bool): Test on Y channel of YCbCr. Default: False. Returns: float: PSNR result. """ assert img.shape == img2.shape, (f'Image shapes are different: {img.shape}, {img2.shape}.') if input_order not in ['HWC', 'CHW']: raise ValueError(f'Wrong input_order {input_order}. Supported input_orders are "HWC" and "CHW"') img = reorder_image(img, input_order=input_order) img2 = reorder_image(img2, input_order=input_order) if crop_border != 0: img = img[crop_border:-crop_border, crop_border:-crop_border, ...] img2 = img2[crop_border:-crop_border, crop_border:-crop_border, ...] if test_y_channel: img = to_y_channel(img) img2 = to_y_channel(img2) img = img.astype(np.float64) img2 = img2.astype(np.float64) mse = np.mean((img - img2)**2) if mse == 0: return float('inf') return 10. * np.log10(255. * 255. / mse) @METRIC_REGISTRY.register() def calculate_psnr_pt(img, img2, crop_border, test_y_channel=False, **kwargs): """Calculate PSNR (Peak Signal-to-Noise Ratio) (PyTorch version). Ref: https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio Args: img (Tensor): Images with range [0, 1], shape (n, 3/1, h, w). img2 (Tensor): Images with range [0, 1], shape (n, 3/1, h, w). crop_border (int): Cropped pixels in each edge of an image. These pixels are not involved in the calculation. test_y_channel (bool): Test on Y channel of YCbCr. Default: False. Returns: float: PSNR result. """ assert img.shape == img2.shape, (f'Image shapes are different: {img.shape}, {img2.shape}.') if crop_border != 0: img = img[:, :, crop_border:-crop_border, crop_border:-crop_border] img2 = img2[:, :, crop_border:-crop_border, crop_border:-crop_border] if test_y_channel: img = rgb2ycbcr_pt(img, y_only=True) img2 = rgb2ycbcr_pt(img2, y_only=True) img = img.to(torch.float64) img2 = img2.to(torch.float64) mse = torch.mean((img - img2)**2, dim=[1, 2, 3]) return 10. * torch.log10(1. / (mse + 1e-8)) @METRIC_REGISTRY.register() def calculate_ssim(img, img2, crop_border, input_order='HWC', test_y_channel=False, **kwargs): """Calculate SSIM (structural similarity). Ref: Image quality assessment: From error visibility to structural similarity The results are the same as that of the official released MATLAB code in https://ece.uwaterloo.ca/~z70wang/research/ssim/. For three-channel images, SSIM is calculated for each channel and then averaged. Args: img (ndarray): Images with range [0, 255]. img2 (ndarray): Images with range [0, 255]. crop_border (int): Cropped pixels in each edge of an image. These pixels are not involved in the calculation. input_order (str): Whether the input order is 'HWC' or 'CHW'. Default: 'HWC'. test_y_channel (bool): Test on Y channel of YCbCr. Default: False. Returns: float: SSIM result. """ assert img.shape == img2.shape, (f'Image shapes are different: {img.shape}, {img2.shape}.') if input_order not in ['HWC', 'CHW']: raise ValueError(f'Wrong input_order {input_order}. Supported input_orders are "HWC" and "CHW"') img = reorder_image(img, input_order=input_order) img2 = reorder_image(img2, input_order=input_order) if crop_border != 0: img = img[crop_border:-crop_border, crop_border:-crop_border, ...] img2 = img2[crop_border:-crop_border, crop_border:-crop_border, ...] if test_y_channel: img = to_y_channel(img) img2 = to_y_channel(img2) img = img.astype(np.float64) img2 = img2.astype(np.float64) ssims = [] for i in range(img.shape[2]): ssims.append(_ssim(img[..., i], img2[..., i])) return np.array(ssims).mean() @METRIC_REGISTRY.register() def calculate_ssim_pt(img, img2, crop_border, test_y_channel=False, **kwargs): """Calculate SSIM (structural similarity) (PyTorch version). Ref: Image quality assessment: From error visibility to structural similarity The results are the same as that of the official released MATLAB code in https://ece.uwaterloo.ca/~z70wang/research/ssim/. For three-channel images, SSIM is calculated for each channel and then averaged. Args: img (Tensor): Images with range [0, 1], shape (n, 3/1, h, w). img2 (Tensor): Images with range [0, 1], shape (n, 3/1, h, w). crop_border (int): Cropped pixels in each edge of an image. These pixels are not involved in the calculation. test_y_channel (bool): Test on Y channel of YCbCr. Default: False. Returns: float: SSIM result. """ assert img.shape == img2.shape, (f'Image shapes are different: {img.shape}, {img2.shape}.') if crop_border != 0: img = img[:, :, crop_border:-crop_border, crop_border:-crop_border] img2 = img2[:, :, crop_border:-crop_border, crop_border:-crop_border] if test_y_channel: img = rgb2ycbcr_pt(img, y_only=True) img2 = rgb2ycbcr_pt(img2, y_only=True) img = img.to(torch.float64) img2 = img2.to(torch.float64) ssim = _ssim_pth(img * 255., img2 * 255.) return ssim def _ssim(img, img2): """Calculate SSIM (structural similarity) for one channel images. It is called by func:`calculate_ssim`. Args: img (ndarray): Images with range [0, 255] with order 'HWC'. img2 (ndarray): Images with range [0, 255] with order 'HWC'. Returns: float: SSIM result. """ c1 = (0.01 * 255)**2 c2 = (0.03 * 255)**2 kernel = cv2.getGaussianKernel(11, 1.5) window = np.outer(kernel, kernel.transpose()) mu1 = cv2.filter2D(img, -1, window)[5:-5, 5:-5] # valid mode for window size 11 mu2 = cv2.filter2D(img2, -1, window)[5:-5, 5:-5] mu1_sq = mu1**2 mu2_sq = mu2**2 mu1_mu2 = mu1 * mu2 sigma1_sq = cv2.filter2D(img**2, -1, window)[5:-5, 5:-5] - mu1_sq sigma2_sq = cv2.filter2D(img2**2, -1, window)[5:-5, 5:-5] - mu2_sq sigma12 = cv2.filter2D(img * img2, -1, window)[5:-5, 5:-5] - mu1_mu2 ssim_map = ((2 * mu1_mu2 + c1) * (2 * sigma12 + c2)) / ((mu1_sq + mu2_sq + c1) * (sigma1_sq + sigma2_sq + c2)) return ssim_map.mean() def _ssim_pth(img, img2): """Calculate SSIM (structural similarity) (PyTorch version). It is called by func:`calculate_ssim_pt`. Args: img (Tensor): Images with range [0, 1], shape (n, 3/1, h, w). img2 (Tensor): Images with range [0, 1], shape (n, 3/1, h, w). Returns: float: SSIM result. """ c1 = (0.01 * 255)**2 c2 = (0.03 * 255)**2 kernel = cv2.getGaussianKernel(11, 1.5) window = np.outer(kernel, kernel.transpose()) window = torch.from_numpy(window).view(1, 1, 11, 11).expand(img.size(1), 1, 11, 11).to(img.dtype).to(img.device) mu1 = F.conv2d(img, window, stride=1, padding=0, groups=img.shape[1]) # valid mode mu2 = F.conv2d(img2, window, stride=1, padding=0, groups=img2.shape[1]) # valid mode mu1_sq = mu1.pow(2) mu2_sq = mu2.pow(2) mu1_mu2 = mu1 * mu2 sigma1_sq = F.conv2d(img * img, window, stride=1, padding=0, groups=img.shape[1]) - mu1_sq sigma2_sq = F.conv2d(img2 * img2, window, stride=1, padding=0, groups=img.shape[1]) - mu2_sq sigma12 = F.conv2d(img * img2, window, stride=1, padding=0, groups=img.shape[1]) - mu1_mu2 cs_map = (2 * sigma12 + c2) / (sigma1_sq + sigma2_sq + c2) ssim_map = ((2 * mu1_mu2 + c1) / (mu1_sq + mu2_sq + c1)) * cs_map return ssim_map.mean([1, 2, 3])