| import datasets | |
| import pandas as pd | |
| _CITATION = """\ | |
| @InProceedings{huggingface:dataset, | |
| title = {pose_estimation}, | |
| author = {TrainingDataPro}, | |
| year = {2023} | |
| } | |
| """ | |
| _DESCRIPTION = """\ | |
| The dataset is primarly intended to dentify and predict the positions of major | |
| joints of a human body in an image. It consists of people's photographs with | |
| body part labeled with keypoints. | |
| """ | |
| _NAME = 'pose_estimation' | |
| _HOMEPAGE = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}" | |
| _LICENSE = "cc-by-nc-nd-4.0" | |
| _DATA = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}/resolve/main/data/" | |
| class PoseEstimation(datasets.GeneratorBasedBuilder): | |
| def _info(self): | |
| return datasets.DatasetInfo(description=_DESCRIPTION, | |
| features=datasets.Features({ | |
| 'image_id': datasets.Value('uint32'), | |
| 'image': datasets.Image(), | |
| 'mask': datasets.Image(), | |
| 'shapes': datasets.Value('string') | |
| }), | |
| supervised_keys=None, | |
| homepage=_HOMEPAGE, | |
| citation=_CITATION, | |
| license=_LICENSE) | |
| def _split_generators(self, dl_manager): | |
| images = dl_manager.download(f"{_DATA}images.tar.gz") | |
| masks = dl_manager.download(f"{_DATA}masks.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, sep=',') | |
| for idx, ((image_path, image), | |
| (mask_path, mask)) in enumerate(zip(images, masks)): | |
| file_name = int(image_path.split('.')[0].split('/')[-1]) | |
| yield idx, { | |
| 'image_id': | |
| annotations_df.loc[annotations_df['image_id'] == file_name] | |
| ['image_id'].values[0], | |
| "image": { | |
| "path": image_path, | |
| "bytes": image.read() | |
| }, | |
| "mask": { | |
| "path": mask_path, | |
| "bytes": mask.read() | |
| }, | |
| 'shapes': | |
| annotations_df.loc[annotations_df['image_id'] == file_name] | |
| ['shapes'].values[0], | |
| } | |