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testdata.py

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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.
+"""PP4AV dataset."""
+
+import os
+import glob as glob
+from tqdm import tqdm
+from pathlib import Path
+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/testdata/resolve/main/data"
+_URLS = {
+    "fisheye": f"{_REPO}/fisheye.zip",
+    "annot": f"{_REPO}/annotations.zip",
+}
+
+IMG_EXT = ['png', 'jpeg', 'jpg']
+
+
+class TestData(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)
+                        }
+                    ),
+                    "plates": datasets.Sequence(
+                        {
+                            "bbox": datasets.Sequence(datasets.Value("float32"), length=4)
+                        }
+                    ),
+                }
+            ),
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _generate_examples(self, data_dir, annot_dir):
+        image_dir = os.path.join(data_dir, "fisheye")
+        anno_dir = os.path.join(data_dir, "annotations")
+        files = []
+        for file_type in IMG_EXT:
+            files.extend(list(Path(image_dir).glob(f'**/*.{file_type}')))
+            
+        for image_path in tqdm(files):
+            img_relative_path = image_path.relative_to(image_dir)
+            gt_pah = (Path(label_dir) / src_img_relative_path).with_suffix('.txt')
+            
+            
+            
+            yield idx, {"image": image_file_path, "faces": faces, "plates": plates}
+        
+        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