| import datasets |
| import pandas as pd |
|
|
| _CITATION = """\ |
| @InProceedings{huggingface:dataset, |
| title = {plantations_segmentation}, |
| author = {TrainingDataPro}, |
| year = {2023} |
| } |
| """ |
|
|
| _DESCRIPTION = """\ |
| The images consist of aerial photography of agricultural plantations with crops |
| such as cabbage and zucchini. The dataset addresses agricultural tasks such as |
| plant detection and counting, health assessment, and irrigation planning. |
| The dataset consists of plantations' photographs with object and class |
| segmentation of cabbage. |
| """ |
| _NAME = 'plantations_segmentation' |
|
|
| _HOMEPAGE = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}" |
|
|
| _LICENSE = "" |
|
|
| _DATA = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}/resolve/main/data/" |
|
|
|
|
| class PlantationsSegmentation(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(), |
| 'class_segmentation': datasets.Image(), |
| 'object_segmentation': datasets.Image(), |
| 'shapes': datasets.Value('large_string') |
| }), |
| supervised_keys=None, |
| homepage=_HOMEPAGE, |
| citation=_CITATION, |
| ) |
|
|
| def _split_generators(self, dl_manager): |
| images = dl_manager.download(f"{_DATA}images.tar.gz") |
| class_segmentation_masks = dl_manager.download( |
| f"{_DATA}class_segmentation.tar.gz") |
| object_segmentation_masks = dl_manager.download( |
| f"{_DATA}object_segmentation.tar.gz") |
| annotations = dl_manager.download(f"{_DATA}{_NAME}.csv") |
| images = dl_manager.iter_archive(images) |
| class_segmentation_masks = dl_manager.iter_archive( |
| class_segmentation_masks) |
| object_segmentation_masks = dl_manager.iter_archive( |
| object_segmentation_masks) |
| return [ |
| datasets.SplitGenerator( |
| name=datasets.Split.TRAIN, |
| gen_kwargs={ |
| "images": images, |
| 'class_segmentation_masks': class_segmentation_masks, |
| 'object_segmentation_masks': object_segmentation_masks, |
| 'annotations': annotations |
| }), |
| ] |
|
|
| def _generate_examples(self, images, class_segmentation_masks, |
| object_segmentation_masks, annotations): |
| annotations_df = pd.read_csv(annotations) |
|
|
| for idx, ((image_path, image), (class_segmentation_path, |
| class_segmentation), |
| (object_segmentation_path, |
| object_segmentation)) in enumerate( |
| zip(images, class_segmentation_masks, |
| object_segmentation_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() |
| }, |
| "class_segmentation": { |
| "path": class_segmentation_path, |
| "bytes": class_segmentation.read() |
| }, |
| "object_segmentation": { |
| "path": object_segmentation_path, |
| "bytes": object_segmentation.read() |
| }, |
| 'shapes': |
| annotations_df.loc[annotations_df['image_name'] == |
| image_path]['shapes'].values[0][:500] + |
| '...' |
| } |
|
|
