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"""
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This module contains the code for a dataset class called FaceMaskDataset, which is used to process and
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load image data related to face masks. The dataset class inherits from the PyTorch Dataset class and
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provides methods for data augmentation, getting items from the dataset, and determining the length of the
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dataset. The module also includes imports for necessary libraries such as json, random, pathlib, torch,
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PIL, and transformers.
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"""
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import json
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import random
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from pathlib import Path
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import torch
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from PIL import Image
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from torch.utils.data import Dataset
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from torchvision import transforms
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from transformers import CLIPImageProcessor
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class FaceMaskDataset(Dataset):
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"""
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FaceMaskDataset is a custom dataset for face mask images.
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Args:
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img_size (int): The size of the input images.
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drop_ratio (float, optional): The ratio of dropped pixels during data augmentation. Defaults to 0.1.
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data_meta_paths (list, optional): The paths to the metadata files containing image paths and labels. Defaults to ["./data/HDTF_meta.json"].
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sample_margin (int, optional): The margin for sampling regions in the image. Defaults to 30.
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Attributes:
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img_size (int): The size of the input images.
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drop_ratio (float): The ratio of dropped pixels during data augmentation.
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data_meta_paths (list): The paths to the metadata files containing image paths and labels.
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sample_margin (int): The margin for sampling regions in the image.
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processor (CLIPImageProcessor): The image processor for preprocessing images.
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transform (transforms.Compose): The image augmentation transform.
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"""
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def __init__(
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self,
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img_size,
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drop_ratio=0.1,
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data_meta_paths=None,
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sample_margin=30,
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):
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super().__init__()
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self.img_size = img_size
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self.sample_margin = sample_margin
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vid_meta = []
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for data_meta_path in data_meta_paths:
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with open(data_meta_path, "r", encoding="utf-8") as f:
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vid_meta.extend(json.load(f))
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self.vid_meta = vid_meta
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self.length = len(self.vid_meta)
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self.clip_image_processor = CLIPImageProcessor()
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self.transform = transforms.Compose(
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[
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transforms.Resize(self.img_size),
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transforms.ToTensor(),
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transforms.Normalize([0.5], [0.5]),
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]
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)
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self.cond_transform = transforms.Compose(
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[
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transforms.Resize(self.img_size),
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transforms.ToTensor(),
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]
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)
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self.drop_ratio = drop_ratio
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def augmentation(self, image, transform, state=None):
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"""
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Apply data augmentation to the input image.
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Args:
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image (PIL.Image): The input image.
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transform (torchvision.transforms.Compose): The data augmentation transforms.
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state (dict, optional): The random state for reproducibility. Defaults to None.
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Returns:
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PIL.Image: The augmented image.
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"""
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if state is not None:
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torch.set_rng_state(state)
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return transform(image)
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def __getitem__(self, index):
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video_meta = self.vid_meta[index]
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video_path = video_meta["image_path"]
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mask_path = video_meta["mask_path"]
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face_emb_path = video_meta["face_emb"]
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video_frames = sorted(Path(video_path).iterdir())
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video_length = len(video_frames)
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margin = min(self.sample_margin, video_length)
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ref_img_idx = random.randint(0, video_length - 1)
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if ref_img_idx + margin < video_length:
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tgt_img_idx = random.randint(
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ref_img_idx + margin, video_length - 1)
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elif ref_img_idx - margin > 0:
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tgt_img_idx = random.randint(0, ref_img_idx - margin)
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else:
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tgt_img_idx = random.randint(0, video_length - 1)
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ref_img_pil = Image.open(video_frames[ref_img_idx])
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tgt_img_pil = Image.open(video_frames[tgt_img_idx])
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tgt_mask_pil = Image.open(mask_path)
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assert ref_img_pil is not None, "Fail to load reference image."
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assert tgt_img_pil is not None, "Fail to load target image."
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assert tgt_mask_pil is not None, "Fail to load target mask."
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state = torch.get_rng_state()
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tgt_img = self.augmentation(tgt_img_pil, self.transform, state)
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tgt_mask_img = self.augmentation(
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tgt_mask_pil, self.cond_transform, state)
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tgt_mask_img = tgt_mask_img.repeat(3, 1, 1)
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ref_img_vae = self.augmentation(
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ref_img_pil, self.transform, state)
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face_emb = torch.load(face_emb_path)
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sample = {
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"video_dir": video_path,
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"img": tgt_img,
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"tgt_mask": tgt_mask_img,
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"ref_img": ref_img_vae,
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"face_emb": face_emb,
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}
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return sample
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def __len__(self):
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return len(self.vid_meta)
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if __name__ == "__main__":
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data = FaceMaskDataset(img_size=(512, 512))
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train_dataloader = torch.utils.data.DataLoader(
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data, batch_size=4, shuffle=True, num_workers=1
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)
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for step, batch in enumerate(train_dataloader):
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print(batch["tgt_mask"].shape)
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break
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