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| import os | |
| import random | |
| from PIL import Image | |
| import torch | |
| import numpy as np | |
| from torchvision import transforms | |
| from sklearn.model_selection import train_test_split | |
| class SaliencyDataset(torch.utils.data.Dataset): | |
| def __init__(self, split, img_size=512, val_split_ratio=0.05, subset_ratio=None): | |
| self.img_size = img_size | |
| self.split = split | |
| self.image_dir, self.mask_dir = self.set_directories(split) | |
| all_images = [f for f in os.listdir(self.image_dir) if f.endswith('.jpg')] | |
| if split in ['train', 'valid']: | |
| train_imgs, val_imgs = train_test_split(all_images, test_size=val_split_ratio, random_state=42) | |
| self.images = train_imgs if split == 'train' else val_imgs | |
| else: | |
| self.images = all_images | |
| if subset_ratio: # subsampling | |
| total_samples = len(self.images) | |
| indices = np.random.choice(total_samples, int(total_samples * subset_ratio), replace=False) | |
| self.images = [self.images[i] for i in indices] | |
| self.img_resize = transforms.Resize((img_size, img_size), interpolation=transforms.InterpolationMode.BILINEAR) | |
| self.mask_resize = transforms.Resize((img_size, img_size), interpolation=transforms.InterpolationMode.NEAREST) | |
| self.normalize = transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)) | |
| def __len__(self): | |
| return len(self.images) | |
| def __getitem__(self, idx): | |
| img_filename = self.images[idx] | |
| img_path = os.path.join(self.image_dir, img_filename) | |
| mask_filename = img_filename.replace('.jpg', '.png') | |
| mask_path = os.path.join(self.mask_dir, mask_filename) | |
| img = Image.open(img_path).convert('RGB') | |
| mask = Image.open(mask_path) | |
| if mask.mode != 'L': mask = mask.convert('L') | |
| mask = mask.point(lambda p: 255 if p > 128 else 0) | |
| img, mask = self.img_resize(img), self.mask_resize(mask) | |
| if self.split == 'train': | |
| img, mask = self.apply_augmentations(img, mask) | |
| img = transforms.ToTensor()(img) | |
| img = self.normalize(img) | |
| mask = transforms.ToTensor()(mask).squeeze(0) | |
| return img, mask | |
| def apply_augmentations(self, img, mask): | |
| if random.random() > 0.5: # horizontal flip | |
| img = transforms.functional.hflip(img) | |
| mask = transforms.functional.hflip(mask) | |
| if random.random() > 0.5: # random resized crop | |
| resized_crop = transforms.RandomResizedCrop(self.img_size, scale=(0.8, 1.0)) | |
| i, j, h, w = resized_crop.get_params(img, scale=(0.8, 1.0), ratio=(3/4, 4/3)) | |
| img = transforms.functional.resized_crop( | |
| img, i, j, h, w, (self.img_size, self.img_size), | |
| interpolation=transforms.InterpolationMode.BILINEAR | |
| ) | |
| mask = transforms.functional.resized_crop( | |
| mask, i, j, h, w, (self.img_size, self.img_size), | |
| interpolation=transforms.InterpolationMode.NEAREST | |
| ) | |
| if random.random() > 0.5: # color jitter | |
| color_jitter = transforms.ColorJitter(brightness=0.3, contrast=0.3, saturation=0.2, hue=0.05) | |
| img = color_jitter(img) | |
| return img, mask | |
| class DUTSDataset(SaliencyDataset): | |
| def set_directories(self, split): | |
| train_or_test = 'train' if split in ['train', 'valid'] else 'test' | |
| image_dir = f'/data/duts_{train_or_test}_data/images' | |
| mask_dir = f'/data/duts_{train_or_test}_data/masks' | |
| return image_dir, mask_dir | |
| class MSRADataset(SaliencyDataset): | |
| def set_directories(self, split): | |
| image_dir = '/data/msra_data/images' | |
| mask_dir = '/data/msra_data/masks' | |
| return image_dir, mask_dir | |
| class PASCALSDataset(SaliencyDataset): | |
| def set_directories(self, split): | |
| image_dir = '/data/pascals_data/images' | |
| mask_dir = '/data/pascals_data/masks' | |
| return image_dir, mask_dir |