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