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198 lines
8.1 KiB
198 lines
8.1 KiB
5 months ago
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import os
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from os.path import abspath, expanduser
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from pathlib import Path
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from typing import Any, Callable, Dict, List, Optional, Tuple, Union
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import torch
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from PIL import Image
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from .utils import download_and_extract_archive, download_file_from_google_drive, extract_archive, verify_str_arg
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from .vision import VisionDataset
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class WIDERFace(VisionDataset):
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"""`WIDERFace <http://shuoyang1213.me/WIDERFACE/>`_ Dataset.
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Args:
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root (str or ``pathlib.Path``): Root directory where images and annotations are downloaded to.
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Expects the following folder structure if download=False:
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.. code::
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<root>
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└── widerface
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├── wider_face_split ('wider_face_split.zip' if compressed)
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├── WIDER_train ('WIDER_train.zip' if compressed)
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├── WIDER_val ('WIDER_val.zip' if compressed)
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└── WIDER_test ('WIDER_test.zip' if compressed)
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split (string): The dataset split to use. One of {``train``, ``val``, ``test``}.
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Defaults to ``train``.
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transform (callable, optional): A function/transform that takes in a PIL image
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and returns a transformed version. E.g, ``transforms.RandomCrop``
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target_transform (callable, optional): A function/transform that takes in the
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target and transforms it.
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download (bool, optional): If true, downloads the dataset from the internet and
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puts it in root directory. If dataset is already downloaded, it is not
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downloaded again.
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.. warning::
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To download the dataset `gdown <https://github.com/wkentaro/gdown>`_ is required.
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"""
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BASE_FOLDER = "widerface"
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FILE_LIST = [
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# File ID MD5 Hash Filename
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("15hGDLhsx8bLgLcIRD5DhYt5iBxnjNF1M", "3fedf70df600953d25982bcd13d91ba2", "WIDER_train.zip"),
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("1GUCogbp16PMGa39thoMMeWxp7Rp5oM8Q", "dfa7d7e790efa35df3788964cf0bbaea", "WIDER_val.zip"),
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("1HIfDbVEWKmsYKJZm4lchTBDLW5N7dY5T", "e5d8f4248ed24c334bbd12f49c29dd40", "WIDER_test.zip"),
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]
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ANNOTATIONS_FILE = (
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"http://shuoyang1213.me/WIDERFACE/support/bbx_annotation/wider_face_split.zip",
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"0e3767bcf0e326556d407bf5bff5d27c",
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"wider_face_split.zip",
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)
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def __init__(
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self,
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root: Union[str, Path],
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split: str = "train",
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transform: Optional[Callable] = None,
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target_transform: Optional[Callable] = None,
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download: bool = False,
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) -> None:
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super().__init__(
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root=os.path.join(root, self.BASE_FOLDER), transform=transform, target_transform=target_transform
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)
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# check arguments
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self.split = verify_str_arg(split, "split", ("train", "val", "test"))
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if download:
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self.download()
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if not self._check_integrity():
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raise RuntimeError("Dataset not found or corrupted. You can use download=True to download and prepare it")
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self.img_info: List[Dict[str, Union[str, Dict[str, torch.Tensor]]]] = []
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if self.split in ("train", "val"):
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self.parse_train_val_annotations_file()
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else:
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self.parse_test_annotations_file()
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def __getitem__(self, index: int) -> Tuple[Any, Any]:
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"""
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Args:
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index (int): Index
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Returns:
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tuple: (image, target) where target is a dict of annotations for all faces in the image.
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target=None for the test split.
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"""
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# stay consistent with other datasets and return a PIL Image
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img = Image.open(self.img_info[index]["img_path"])
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if self.transform is not None:
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img = self.transform(img)
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target = None if self.split == "test" else self.img_info[index]["annotations"]
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if self.target_transform is not None:
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target = self.target_transform(target)
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return img, target
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def __len__(self) -> int:
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return len(self.img_info)
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def extra_repr(self) -> str:
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lines = ["Split: {split}"]
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return "\n".join(lines).format(**self.__dict__)
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def parse_train_val_annotations_file(self) -> None:
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filename = "wider_face_train_bbx_gt.txt" if self.split == "train" else "wider_face_val_bbx_gt.txt"
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filepath = os.path.join(self.root, "wider_face_split", filename)
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with open(filepath) as f:
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lines = f.readlines()
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file_name_line, num_boxes_line, box_annotation_line = True, False, False
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num_boxes, box_counter = 0, 0
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labels = []
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for line in lines:
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line = line.rstrip()
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if file_name_line:
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img_path = os.path.join(self.root, "WIDER_" + self.split, "images", line)
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img_path = abspath(expanduser(img_path))
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file_name_line = False
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num_boxes_line = True
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elif num_boxes_line:
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num_boxes = int(line)
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num_boxes_line = False
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box_annotation_line = True
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elif box_annotation_line:
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box_counter += 1
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line_split = line.split(" ")
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line_values = [int(x) for x in line_split]
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labels.append(line_values)
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if box_counter >= num_boxes:
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box_annotation_line = False
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file_name_line = True
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labels_tensor = torch.tensor(labels)
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self.img_info.append(
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{
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"img_path": img_path,
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"annotations": {
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"bbox": labels_tensor[:, 0:4].clone(), # x, y, width, height
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"blur": labels_tensor[:, 4].clone(),
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"expression": labels_tensor[:, 5].clone(),
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"illumination": labels_tensor[:, 6].clone(),
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"occlusion": labels_tensor[:, 7].clone(),
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"pose": labels_tensor[:, 8].clone(),
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"invalid": labels_tensor[:, 9].clone(),
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},
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}
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)
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box_counter = 0
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labels.clear()
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else:
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raise RuntimeError(f"Error parsing annotation file {filepath}")
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def parse_test_annotations_file(self) -> None:
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filepath = os.path.join(self.root, "wider_face_split", "wider_face_test_filelist.txt")
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filepath = abspath(expanduser(filepath))
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with open(filepath) as f:
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lines = f.readlines()
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for line in lines:
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line = line.rstrip()
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img_path = os.path.join(self.root, "WIDER_test", "images", line)
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img_path = abspath(expanduser(img_path))
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self.img_info.append({"img_path": img_path})
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def _check_integrity(self) -> bool:
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# Allow original archive to be deleted (zip). Only need the extracted images
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all_files = self.FILE_LIST.copy()
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all_files.append(self.ANNOTATIONS_FILE)
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for (_, md5, filename) in all_files:
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file, ext = os.path.splitext(filename)
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extracted_dir = os.path.join(self.root, file)
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if not os.path.exists(extracted_dir):
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return False
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return True
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def download(self) -> None:
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if self._check_integrity():
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print("Files already downloaded and verified")
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return
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# download and extract image data
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for (file_id, md5, filename) in self.FILE_LIST:
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download_file_from_google_drive(file_id, self.root, filename, md5)
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filepath = os.path.join(self.root, filename)
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extract_archive(filepath)
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# download and extract annotation files
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download_and_extract_archive(
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url=self.ANNOTATIONS_FILE[0], download_root=self.root, md5=self.ANNOTATIONS_FILE[1]
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)
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