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 <>`_ 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 ('' if compressed) ├── WIDER_train ('' if compressed) ├── WIDER_val ('' if compressed) └── WIDER_test ('' 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 <>`_ is required. """ BASE_FOLDER = "widerface" FILE_LIST = [ # File ID MD5 Hash Filename ("15hGDLhsx8bLgLcIRD5DhYt5iBxnjNF1M", "3fedf70df600953d25982bcd13d91ba2", ""), ("1GUCogbp16PMGa39thoMMeWxp7Rp5oM8Q", "dfa7d7e790efa35df3788964cf0bbaea", ""), ("1HIfDbVEWKmsYKJZm4lchTBDLW5N7dY5T", "e5d8f4248ed24c334bbd12f49c29dd40", ""), ] ANNOTATIONS_FILE = ( "", "0e3767bcf0e326556d407bf5bff5d27c", "", ) 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: 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 =[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(): print("Files already downloaded and verified") 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] )


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