| import torch | |
| import os | |
| import torchvision.transforms as transforms | |
| import matplotlib.pyplot as plt | |
| import numpy as np | |
| from utils.utils import generate_mask | |
| class TrainDataset(torch.utils.data.Dataset): | |
| def __init__(self, data_path, transform = None, mults_amount = 1): | |
| self.data = os.listdir(os.path.join(data_path, 'color')) | |
| self.data_path = data_path | |
| self.transform = transform | |
| self.mults_amount = mults_amount | |
| self.ToTensor = transforms.ToTensor() | |
| def __len__(self): | |
| return len(self.data) | |
| def __getitem__(self, idx): | |
| image_name = self.data[idx] | |
| color_img = plt.imread(os.path.join(self.data_path, 'color', image_name)) | |
| if self.mults_amount > 1: | |
| mult_number = np.random.choice(range(self.mults_amount)) | |
| bw_name = image_name[:image_name.rfind('.')] + '_' + str(mult_number) + '.png' | |
| dfm_name = image_name[:image_name.rfind('.')] + '_' + str(mult_number) + '_dfm.png' | |
| else: | |
| bw_name = self.data[idx] | |
| dfm_name = os.path.splitext(self.data[idx])[0] + '0_dfm.png' | |
| bw_img = np.expand_dims(plt.imread(os.path.join(self.data_path, 'bw', bw_name)), 2) | |
| dfm_img = np.expand_dims(plt.imread(os.path.join(self.data_path, 'bw', dfm_name)), 2) | |
| bw_img = np.concatenate([bw_img, dfm_img], axis = 2) | |
| if self.transform: | |
| result = self.transform(image = color_img, mask = bw_img) | |
| color_img = result['image'] | |
| bw_img = result['mask'] | |
| dfm_img = bw_img[:, :, 1] | |
| bw_img = bw_img[:, :, 0] | |
| color_img = self.ToTensor(color_img) | |
| bw_img = self.ToTensor(bw_img) | |
| dfm_img = self.ToTensor(dfm_img) | |
| color_img = (color_img - 0.5) / 0.5 | |
| mask = generate_mask(bw_img.shape[1], bw_img.shape[2]) | |
| hint = torch.cat((color_img * mask, mask), 0) | |
| return bw_img, color_img, hint, dfm_img | |
| class FineTuningDataset(torch.utils.data.Dataset): | |
| def __init__(self, data_path, transform = None, mult_amount = 1): | |
| self.data = [x for x in os.listdir(os.path.join(data_path, 'real_manga')) if x.find('_dfm') == -1] | |
| self.color_data = [x for x in os.listdir(os.path.join(data_path, 'color'))] | |
| self.data_path = data_path | |
| self.transform = transform | |
| self.mults_amount = mult_amount | |
| np.random.shuffle(self.color_data) | |
| self.ToTensor = transforms.ToTensor() | |
| def __len__(self): | |
| return len(self.data) | |
| def __getitem__(self, idx): | |
| color_img = plt.imread(os.path.join(self.data_path, 'color', self.color_data[idx])) | |
| image_name = self.data[idx] | |
| if self.mults_amount > 1: | |
| mult_number = np.random.choice(range(self.mults_amount)) | |
| bw_name = image_name[:image_name.rfind('.')] + '_' + str(self.mults_amount) + '.png' | |
| dfm_name = image_name[:image_name.rfind('.')] + '_' + str(self.mults_amount) + '_dfm.png' | |
| else: | |
| bw_name = self.data[idx] | |
| dfm_name = os.path.splitext(self.data[idx])[0] + '_dfm.png' | |
| bw_img = np.expand_dims(plt.imread(os.path.join(self.data_path, 'real_manga', image_name)), 2) | |
| dfm_img = np.expand_dims(plt.imread(os.path.join(self.data_path, 'real_manga', dfm_name)), 2) | |
| if self.transform: | |
| result = self.transform(image = color_img) | |
| color_img = result['image'] | |
| result = self.transform(image = bw_img, mask = dfm_img) | |
| bw_img = result['image'] | |
| dfm_img = result['mask'] | |
| color_img = self.ToTensor(color_img) | |
| bw_img = self.ToTensor(bw_img) | |
| dfm_img = self.ToTensor(dfm_img) | |
| color_img = (color_img - 0.5) / 0.5 | |
| return bw_img, dfm_img, color_img | |