import cv2 import math import random import numpy as np import os.path as osp from scipy.io import loadmat import torch import torch.utils.data as data from torchvision.transforms.functional import (adjust_brightness, adjust_contrast, adjust_hue, adjust_saturation, normalize) from basicsr.data import gaussian_kernels as gaussian_kernels from basicsr.data.transforms import augment from basicsr.data.data_util import paths_from_folder from basicsr.utils import FileClient, get_root_logger, imfrombytes, img2tensor from basicsr.utils.registry import DATASET_REGISTRY @DATASET_REGISTRY.register() class FFHQBlindJointDataset(data.Dataset): def __init__(self, opt): super(FFHQBlindJointDataset, self).__init__() logger = get_root_logger() self.opt = opt # file client (io backend) self.file_client = None self.io_backend_opt = opt['io_backend'] self.gt_folder = opt['dataroot_gt'] self.gt_size = opt.get('gt_size', 512) self.in_size = opt.get('in_size', 512) assert self.gt_size >= self.in_size, 'Wrong setting.' self.mean = opt.get('mean', [0.5, 0.5, 0.5]) self.std = opt.get('std', [0.5, 0.5, 0.5]) self.component_path = opt.get('component_path', None) self.latent_gt_path = opt.get('latent_gt_path', None) if self.component_path is not None: self.crop_components = True self.components_dict = torch.load(self.component_path) self.eye_enlarge_ratio = opt.get('eye_enlarge_ratio', 1.4) self.nose_enlarge_ratio = opt.get('nose_enlarge_ratio', 1.1) self.mouth_enlarge_ratio = opt.get('mouth_enlarge_ratio', 1.3) else: self.crop_components = False if self.latent_gt_path is not None: self.load_latent_gt = True self.latent_gt_dict = torch.load(self.latent_gt_path) else: self.load_latent_gt = False if self.io_backend_opt['type'] == 'lmdb': self.io_backend_opt['db_paths'] = self.gt_folder if not self.gt_folder.endswith('.lmdb'): raise ValueError("'dataroot_gt' should end with '.lmdb', "f'but received {self.gt_folder}') with open(osp.join(self.gt_folder, 'meta_info.txt')) as fin: self.paths = [line.split('.')[0] for line in fin] else: self.paths = paths_from_folder(self.gt_folder) # perform corrupt self.use_corrupt = opt.get('use_corrupt', True) self.use_motion_kernel = False # self.use_motion_kernel = opt.get('use_motion_kernel', True) if self.use_motion_kernel: self.motion_kernel_prob = opt.get('motion_kernel_prob', 0.001) motion_kernel_path = opt.get('motion_kernel_path', 'basicsr/data/motion-blur-kernels-32.pth') self.motion_kernels = torch.load(motion_kernel_path) if self.use_corrupt: # degradation configurations self.blur_kernel_size = self.opt['blur_kernel_size'] self.kernel_list = self.opt['kernel_list'] self.kernel_prob = self.opt['kernel_prob'] # Small degradation self.blur_sigma = self.opt['blur_sigma'] self.downsample_range = self.opt['downsample_range'] self.noise_range = self.opt['noise_range'] self.jpeg_range = self.opt['jpeg_range'] # Large degradation self.blur_sigma_large = self.opt['blur_sigma_large'] self.downsample_range_large = self.opt['downsample_range_large'] self.noise_range_large = self.opt['noise_range_large'] self.jpeg_range_large = self.opt['jpeg_range_large'] # print logger.info(f'Blur: blur_kernel_size {self.blur_kernel_size}, sigma: [{", ".join(map(str, self.blur_sigma))}]') logger.info(f'Downsample: downsample_range [{", ".join(map(str, self.downsample_range))}]') logger.info(f'Noise: [{", ".join(map(str, self.noise_range))}]') logger.info(f'JPEG compression: [{", ".join(map(str, self.jpeg_range))}]') # color jitter self.color_jitter_prob = opt.get('color_jitter_prob', None) self.color_jitter_pt_prob = opt.get('color_jitter_pt_prob', None) self.color_jitter_shift = opt.get('color_jitter_shift', 20) if self.color_jitter_prob is not None: logger.info(f'Use random color jitter. Prob: {self.color_jitter_prob}, shift: {self.color_jitter_shift}') # to gray self.gray_prob = opt.get('gray_prob', 0.0) if self.gray_prob is not None: logger.info(f'Use random gray. Prob: {self.gray_prob}') self.color_jitter_shift /= 255. @staticmethod def color_jitter(img, shift): """jitter color: randomly jitter the RGB values, in numpy formats""" jitter_val = np.random.uniform(-shift, shift, 3).astype(np.float32) img = img + jitter_val img = np.clip(img, 0, 1) return img @staticmethod def color_jitter_pt(img, brightness, contrast, saturation, hue): """jitter color: randomly jitter the brightness, contrast, saturation, and hue, in torch Tensor formats""" fn_idx = torch.randperm(4) for fn_id in fn_idx: if fn_id == 0 and brightness is not None: brightness_factor = torch.tensor(1.0).uniform_(brightness[0], brightness[1]).item() img = adjust_brightness(img, brightness_factor) if fn_id == 1 and contrast is not None: contrast_factor = torch.tensor(1.0).uniform_(contrast[0], contrast[1]).item() img = adjust_contrast(img, contrast_factor) if fn_id == 2 and saturation is not None: saturation_factor = torch.tensor(1.0).uniform_(saturation[0], saturation[1]).item() img = adjust_saturation(img, saturation_factor) if fn_id == 3 and hue is not None: hue_factor = torch.tensor(1.0).uniform_(hue[0], hue[1]).item() img = adjust_hue(img, hue_factor) return img def get_component_locations(self, name, status): components_bbox = self.components_dict[name] if status[0]: # hflip # exchange right and left eye tmp = components_bbox['left_eye'] components_bbox['left_eye'] = components_bbox['right_eye'] components_bbox['right_eye'] = tmp # modify the width coordinate components_bbox['left_eye'][0] = self.gt_size - components_bbox['left_eye'][0] components_bbox['right_eye'][0] = self.gt_size - components_bbox['right_eye'][0] components_bbox['nose'][0] = self.gt_size - components_bbox['nose'][0] components_bbox['mouth'][0] = self.gt_size - components_bbox['mouth'][0] locations_gt = {} locations_in = {} for part in ['left_eye', 'right_eye', 'nose', 'mouth']: mean = components_bbox[part][0:2] half_len = components_bbox[part][2] if 'eye' in part: half_len *= self.eye_enlarge_ratio elif part == 'nose': half_len *= self.nose_enlarge_ratio elif part == 'mouth': half_len *= self.mouth_enlarge_ratio loc = np.hstack((mean - half_len + 1, mean + half_len)) loc = torch.from_numpy(loc).float() locations_gt[part] = loc loc_in = loc/(self.gt_size//self.in_size) locations_in[part] = loc_in return locations_gt, locations_in def __getitem__(self, index): if self.file_client is None: self.file_client = FileClient(self.io_backend_opt.pop('type'), **self.io_backend_opt) # load gt image gt_path = self.paths[index] name = osp.basename(gt_path)[:-4] img_bytes = self.file_client.get(gt_path) img_gt = imfrombytes(img_bytes, float32=True) # random horizontal flip img_gt, status = augment(img_gt, hflip=self.opt['use_hflip'], rotation=False, return_status=True) if self.load_latent_gt: if status[0]: latent_gt = self.latent_gt_dict['hflip'][name] else: latent_gt = self.latent_gt_dict['orig'][name] if self.crop_components: locations_gt, locations_in = self.get_component_locations(name, status) # generate in image img_in = img_gt if self.use_corrupt: # motion blur if self.use_motion_kernel and random.random() < self.motion_kernel_prob: m_i = random.randint(0,31) k = self.motion_kernels[f'{m_i:02d}'] img_in = cv2.filter2D(img_in,-1,k) # gaussian blur kernel = gaussian_kernels.random_mixed_kernels( self.kernel_list, self.kernel_prob, self.blur_kernel_size, self.blur_sigma, self.blur_sigma, [-math.pi, math.pi], noise_range=None) img_in = cv2.filter2D(img_in, -1, kernel) # downsample scale = np.random.uniform(self.downsample_range[0], self.downsample_range[1]) img_in = cv2.resize(img_in, (int(self.gt_size // scale), int(self.gt_size // scale)), interpolation=cv2.INTER_LINEAR) # noise if self.noise_range is not None: noise_sigma = np.random.uniform(self.noise_range[0] / 255., self.noise_range[1] / 255.) noise = np.float32(np.random.randn(*(img_in.shape))) * noise_sigma img_in = img_in + noise img_in = np.clip(img_in, 0, 1) # jpeg if self.jpeg_range is not None: jpeg_p = np.random.uniform(self.jpeg_range[0], self.jpeg_range[1]) encode_param = [int(cv2.IMWRITE_JPEG_QUALITY), jpeg_p] _, encimg = cv2.imencode('.jpg', img_in * 255., encode_param) img_in = np.float32(cv2.imdecode(encimg, 1)) / 255. # resize to in_size img_in = cv2.resize(img_in, (self.in_size, self.in_size), interpolation=cv2.INTER_LINEAR) # generate in_large with large degradation img_in_large = img_gt if self.use_corrupt: # motion blur if self.use_motion_kernel and random.random() < self.motion_kernel_prob: m_i = random.randint(0,31) k = self.motion_kernels[f'{m_i:02d}'] img_in_large = cv2.filter2D(img_in_large,-1,k) # gaussian blur kernel = gaussian_kernels.random_mixed_kernels( self.kernel_list, self.kernel_prob, self.blur_kernel_size, self.blur_sigma_large, self.blur_sigma_large, [-math.pi, math.pi], noise_range=None) img_in_large = cv2.filter2D(img_in_large, -1, kernel) # downsample scale = np.random.uniform(self.downsample_range_large[0], self.downsample_range_large[1]) img_in_large = cv2.resize(img_in_large, (int(self.gt_size // scale), int(self.gt_size // scale)), interpolation=cv2.INTER_LINEAR) # noise if self.noise_range_large is not None: noise_sigma = np.random.uniform(self.noise_range_large[0] / 255., self.noise_range_large[1] / 255.) noise = np.float32(np.random.randn(*(img_in_large.shape))) * noise_sigma img_in_large = img_in_large + noise img_in_large = np.clip(img_in_large, 0, 1) # jpeg if self.jpeg_range_large is not None: jpeg_p = np.random.uniform(self.jpeg_range_large[0], self.jpeg_range_large[1]) encode_param = [int(cv2.IMWRITE_JPEG_QUALITY), jpeg_p] _, encimg = cv2.imencode('.jpg', img_in_large * 255., encode_param) img_in_large = np.float32(cv2.imdecode(encimg, 1)) / 255. # resize to in_size img_in_large = cv2.resize(img_in_large, (self.in_size, self.in_size), interpolation=cv2.INTER_LINEAR) # random color jitter (only for lq) if self.color_jitter_prob is not None and (np.random.uniform() < self.color_jitter_prob): img_in = self.color_jitter(img_in, self.color_jitter_shift) img_in_large = self.color_jitter(img_in_large, self.color_jitter_shift) # random to gray (only for lq) if self.gray_prob and np.random.uniform() < self.gray_prob: img_in = cv2.cvtColor(img_in, cv2.COLOR_BGR2GRAY) img_in = np.tile(img_in[:, :, None], [1, 1, 3]) img_in_large = cv2.cvtColor(img_in_large, cv2.COLOR_BGR2GRAY) img_in_large = np.tile(img_in_large[:, :, None], [1, 1, 3]) # BGR to RGB, HWC to CHW, numpy to tensor img_in, img_in_large, img_gt = img2tensor([img_in, img_in_large, img_gt], bgr2rgb=True, float32=True) # random color jitter (pytorch version) (only for lq) if self.color_jitter_pt_prob is not None and (np.random.uniform() < self.color_jitter_pt_prob): brightness = self.opt.get('brightness', (0.5, 1.5)) contrast = self.opt.get('contrast', (0.5, 1.5)) saturation = self.opt.get('saturation', (0, 1.5)) hue = self.opt.get('hue', (-0.1, 0.1)) img_in = self.color_jitter_pt(img_in, brightness, contrast, saturation, hue) img_in_large = self.color_jitter_pt(img_in_large, brightness, contrast, saturation, hue) # round and clip img_in = np.clip((img_in * 255.0).round(), 0, 255) / 255. img_in_large = np.clip((img_in_large * 255.0).round(), 0, 255) / 255. # Set vgg range_norm=True if use the normalization here # normalize normalize(img_in, self.mean, self.std, inplace=True) normalize(img_in_large, self.mean, self.std, inplace=True) normalize(img_gt, self.mean, self.std, inplace=True) return_dict = {'in': img_in, 'in_large_de': img_in_large, 'gt': img_gt, 'gt_path': gt_path} if self.crop_components: return_dict['locations_in'] = locations_in return_dict['locations_gt'] = locations_gt if self.load_latent_gt: return_dict['latent_gt'] = latent_gt return return_dict def __len__(self): return len(self.paths)