| import datasets | |
| import pandas as pd | |
| _CITATION = """\ | |
| @InProceedings{huggingface:dataset, | |
| title = {ocr-trains-dataset}, | |
| author = {TrainingDataPro}, | |
| year = {2023} | |
| } | |
| """ | |
| _DESCRIPTION = """\ | |
| The dataset is a collection of images along with corresponding bounding box annotations | |
| that are specifically curated for **detecting pigs' heads** in images. The dataset | |
| covers different *pig breeds, sizes, and orientations*, providing a comprehensive | |
| representation of pig appearances. | |
| The pig detection dataset provides a valuable resource for researchers working on pig | |
| detection tasks. It offers a diverse collection of annotated images, allowing for | |
| comprehensive algorithm development, evaluation, and benchmarking, ultimately aiding in | |
| the development of accurate and robust models. | |
| """ | |
| _NAME = "ocr-trains-dataset" | |
| _HOMEPAGE = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}" | |
| _LICENSE = "" | |
| _DATA = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}/resolve/main/data/" | |
| class OcrTrainsDataset(datasets.GeneratorBasedBuilder): | |
| def _info(self): | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, | |
| features=datasets.Features( | |
| { | |
| "id": datasets.Value("int32"), | |
| "image": datasets.Image(), | |
| "bboxes": 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") | |
| annotations = dl_manager.download(f"{_DATA}{_NAME}.csv") | |
| images = dl_manager.iter_archive(images) | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, | |
| gen_kwargs={ | |
| "images": images, | |
| "annotations": annotations, | |
| }, | |
| ), | |
| ] | |
| def _generate_examples(self, images, annotations): | |
| annotations_df = pd.read_csv(annotations) | |
| for idx, (image_path, image) in enumerate(images): | |
| yield idx, { | |
| "id": annotations_df.loc[annotations_df["image_name"] == image_path][ | |
| "image_id" | |
| ].values[0], | |
| "image": {"path": image_path, "bytes": image.read()}, | |
| "bboxes": annotations_df["annotations"].iloc[idx], | |
| } | |