import cv2 import numpy as np import torch # This script is adapted from the following repository: https://github.com/JingyunLiang/SwinIR def calculate_psnr(img1, img2, test_y_channel=False): """Calculate PSNR (Peak Signal-to-Noise Ratio). Ref: https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio Args: img1 (ndarray): Images with range [0, 255]. img2 (ndarray): Images with range [0, 255]. test_y_channel (bool): Test on Y channel of YCbCr. Default: False. Returns: float: psnr result. """ assert img1.shape == img2.shape, (f'Image shapes are differnet: {img1.shape}, {img2.shape}.') assert img1.shape[2] == 3 img1 = img1.astype(np.float64) img2 = img2.astype(np.float64) if test_y_channel: img1 = to_y_channel(img1) img2 = to_y_channel(img2) mse = np.mean((img1 - img2) ** 2) if mse == 0: return float('inf') return 20. * np.log10(255. / np.sqrt(mse)) def _ssim(img1, img2): """Calculate SSIM (structural similarity) for one channel images. It is called by func:`calculate_ssim`. Args: img1 (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 img1 = img1.astype(np.float64) img2 = img2.astype(np.float64) kernel = cv2.getGaussianKernel(11, 1.5) window = np.outer(kernel, kernel.transpose()) mu1 = cv2.filter2D(img1, -1, window)[5:-5, 5:-5] 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(img1 ** 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(img1 * 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 calculate_ssim(img1, img2, test_y_channel=False): """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: img1 (ndarray): Images with range [0, 255]. img2 (ndarray): Images with range [0, 255]. test_y_channel (bool): Test on Y channel of YCbCr. Default: False. Returns: float: ssim result. """ assert img1.shape == img2.shape, (f'Image shapes are differnet: {img1.shape}, {img2.shape}.') assert img1.shape[2] == 3 img1 = img1.astype(np.float64) img2 = img2.astype(np.float64) if test_y_channel: img1 = to_y_channel(img1) img2 = to_y_channel(img2) ssims = [] for i in range(img1.shape[2]): ssims.append(_ssim(img1[..., i], img2[..., i])) return np.array(ssims).mean() def to_y_channel(img): """Change to Y channel of YCbCr. Args: img (ndarray): Images with range [0, 255]. Returns: (ndarray): Images with range [0, 255] (float type) without round. """ img = img.astype(np.float32) / 255. if img.ndim == 3 and img.shape[2] == 3: img = bgr2ycbcr(img, y_only=True) img = img[..., None] return img * 255. def _convert_input_type_range(img): """Convert the type and range of the input image. It converts the input image to np.float32 type and range of [0, 1]. It is mainly used for pre-processing the input image in colorspace convertion functions such as rgb2ycbcr and ycbcr2rgb. Args: img (ndarray): The input image. It accepts: 1. np.uint8 type with range [0, 255]; 2. np.float32 type with range [0, 1]. Returns: (ndarray): The converted image with type of np.float32 and range of [0, 1]. """ img_type = img.dtype img = img.astype(np.float32) if img_type == np.float32: pass elif img_type == np.uint8: img /= 255. else: raise TypeError('The img type should be np.float32 or np.uint8, ' f'but got {img_type}') return img def _convert_output_type_range(img, dst_type): """Convert the type and range of the image according to dst_type. It converts the image to desired type and range. If `dst_type` is np.uint8, images will be converted to np.uint8 type with range [0, 255]. If `dst_type` is np.float32, it converts the image to np.float32 type with range [0, 1]. It is mainly used for post-processing images in colorspace convertion functions such as rgb2ycbcr and ycbcr2rgb. Args: img (ndarray): The image to be converted with np.float32 type and range [0, 255]. dst_type (np.uint8 | np.float32): If dst_type is np.uint8, it converts the image to np.uint8 type with range [0, 255]. If dst_type is np.float32, it converts the image to np.float32 type with range [0, 1]. Returns: (ndarray): The converted image with desired type and range. """ if dst_type not in (np.uint8, np.float32): raise TypeError('The dst_type should be np.float32 or np.uint8, ' f'but got {dst_type}') if dst_type == np.uint8: img = img.round() else: img /= 255. return img.astype(dst_type) def bgr2ycbcr(img, y_only=False): """Convert a BGR image to YCbCr image. The bgr version of rgb2ycbcr. It implements the ITU-R BT.601 conversion for standard-definition television. See more details in https://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion. It differs from a similar function in cv2.cvtColor: `BGR <-> YCrCb`. In OpenCV, it implements a JPEG conversion. See more details in https://en.wikipedia.org/wiki/YCbCr#JPEG_conversion. Args: img (ndarray): The input image. It accepts: 1. np.uint8 type with range [0, 255]; 2. np.float32 type with range [0, 1]. y_only (bool): Whether to only return Y channel. Default: False. Returns: ndarray: The converted YCbCr image. The output image has the same type and range as input image. """ img_type = img.dtype img = _convert_input_type_range(img) if y_only: out_img = np.dot(img, [24.966, 128.553, 65.481]) + 16.0 else: out_img = np.matmul( img, [[24.966, 112.0, -18.214], [128.553, -74.203, -93.786], [65.481, -37.797, 112.0]]) + [16, 128, 128] out_img = _convert_output_type_range(out_img, img_type) return out_img