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README.md

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-# coding=utf-8
-# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""WIDER FACE dataset."""
-
-import os
-
-import datasets
-
-
-_HOMEPAGE = "http://shuoyang1213.me/WIDERFACE/"
-
-_LICENSE = "Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)"
-
-_CITATION = """\
-@inproceedings{yang2016wider,
-    Author = {Yang, Shuo and Luo, Ping and Loy, Chen Change and Tang, Xiaoou},
-    Booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
-    Title = {WIDER FACE: A Face Detection Benchmark},
-    Year = {2016}}
-"""
-
-_DESCRIPTION = """\
-WIDER FACE dataset is a face detection benchmark dataset, of which images are
-selected from the publicly available WIDER dataset. We choose 32,203 images and
-label 393,703 faces with a high degree of variability in scale, pose and
-occlusion as depicted in the sample images. WIDER FACE dataset is organized
-based on 61 event classes. For each event class, we randomly select 40%/10%/50%
-data as training, validation and testing sets. We adopt the same evaluation
-metric employed in the PASCAL VOC dataset. Similar to MALF and Caltech datasets,
-we do not release bounding box ground truth for the test images. Users are
-required to submit final prediction files, which we shall proceed to evaluate.
-"""
-
-
-_REPO = "https://huggingface.co/datasets/wider_face/resolve/main/data"
-_URLS = {
-    "train": f"{_REPO}/WIDER_train.zip",
-    "validation": f"{_REPO}/WIDER_val.zip",
-    "test": f"{_REPO}/WIDER_test.zip",
-    "annot": f"{_REPO}/wider_face_split.zip",
-}
-
-
-class WiderFace(datasets.GeneratorBasedBuilder):
-    """WIDER FACE dataset."""
-
-    VERSION = datasets.Version("1.0.0")
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "image": datasets.Image(),
-                    "faces": datasets.Sequence(
-                        {
-                            "bbox": datasets.Sequence(datasets.Value("float32"), length=4),
-                            "blur": datasets.ClassLabel(names=["clear", "normal", "heavy"]),
-                            "expression": datasets.ClassLabel(names=["typical", "exaggerate"]),
-                            "illumination": datasets.ClassLabel(names=["normal", "exaggerate "]),
-                            "occlusion": datasets.ClassLabel(names=["no", "partial", "heavy"]),
-                            "pose": datasets.ClassLabel(names=["typical", "atypical"]),
-                            "invalid": datasets.Value("bool"),
-                        }
-                    ),
-                }
-            ),
-            supervised_keys=None,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        data_dir = dl_manager.download_and_extract(_URLS)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "split": "train",
-                    "data_dir": data_dir["train"],
-                    "annot_dir": data_dir["annot"],
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "split": "test",
-                    "data_dir": data_dir["test"],
-                    "annot_dir": data_dir["annot"],
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "split": "val",
-                    "data_dir": data_dir["validation"],
-                    "annot_dir": data_dir["annot"],
-                },
-            ),
-        ]
-
-    def _generate_examples(self, split, data_dir, annot_dir):
-        image_dir = os.path.join(data_dir, "WIDER_" + split, "images")
-        annot_fname = "wider_face_test_filelist.txt" if split == "test" else f"wider_face_{split}_bbx_gt.txt"
-        with open(os.path.join(annot_dir, "wider_face_split", annot_fname), "r", encoding="utf-8") as f:
-            idx = 0
-            while True:
-                line = f.readline()
-                line = line.rstrip()
-                if not line.endswith(".jpg"):
-                    break
-                image_file_path = os.path.join(image_dir, line)
-                faces = []
-                if split != "test":
-                    # Read number of bounding boxes
-                    nbboxes = int(f.readline())
-                    # Cases with 0 bounding boxes, still have one line with all zeros.
-                    # So we have to read it and discard it.
-                    if nbboxes == 0:
-                        f.readline()
-                    else:
-                        for _ in range(nbboxes):
-                            line = f.readline()
-                            line = line.rstrip()
-                            line_split = line.split()
-                            assert len(line_split) == 10, f"Cannot parse line: {line_split}"
-                            line_parsed = [int(n) for n in line_split]
-                            (
-                                xmin,
-                                ymin,
-                                wbox,
-                                hbox,
-                                blur,
-                                expression,
-                                illumination,
-                                invalid,
-                                occlusion,
-                                pose,
-                            ) = line_parsed
-                            faces.append(
-                                {
-                                    "bbox": [xmin, ymin, wbox, hbox],
-                                    "blur": blur,
-                                    "expression": expression,
-                                    "illumination": illumination,
-                                    "occlusion": occlusion,
-                                    "pose": pose,
-                                    "invalid": invalid,
-                                }
-                            )
-                yield idx, {"image": image_file_path, "faces": faces}
-                idx += 1