import datasets import pandas as pd _CITATION = """\ @InProceedings{huggingface:dataset, title = {people-tracking-dataset}, author = {TrainingDataPro}, year = {2023} } """ _DESCRIPTION = """\ The dataset comprises of annotated video frames from positioned in a public space camera. The tracking of each individual in the camera's view has been achieved using the rectangle tool in the Computer Vision Annotation Tool (CVAT). """ _NAME = 'people-tracking-dataset' _HOMEPAGE = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}" _LICENSE = "" _DATA = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}/resolve/main/data/" class PeopleTrackingDataset(datasets.GeneratorBasedBuilder): """Small sample of image-text pairs""" def _info(self): return datasets.DatasetInfo( description=_DESCRIPTION, features=datasets.Features({ 'image_id': datasets.Value('int32'), 'image': datasets.Image(), 'mask': datasets.Image(), 'annotations': datasets.Value('string') }), supervised_keys=None, homepage=_HOMEPAGE, citation=_CITATION, ) def _split_generators(self, dl_manager): images = dl_manager.download(f"{_DATA}images.tar.gz") masks = dl_manager.download(f"{_DATA}boxes.tar.gz") annotations = dl_manager.download(f"{_DATA}{_NAME}.csv") images = dl_manager.iter_archive(images) masks = dl_manager.iter_archive(masks) return [ datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={ "images": images, 'masks': masks, 'annotations': annotations }), ] def _generate_examples(self, images, masks, annotations): annotations_df = pd.read_csv(annotations) for idx, ((image_path, image), (mask_path, mask)) in enumerate(zip(images, masks)): yield idx, { 'image_id': annotations_df.loc[ annotations_df['image_name'] == image_path] ['image_id'].values[0], "image": { "path": image_path, "bytes": image.read() }, "mask": { "path": mask_path, "bytes": mask.read() }, 'annotations': annotations_df.loc[ annotations_df['image_name'] == image_path] ['annotations'].values[0] }