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