Datasets:
Tasks:
Object Detection
Size Categories:
1K<n<10K
Source Datasets:
original
Tags:
other-object-detection
| # from https://github.com/huggingface/datasets/blob/master/templates/new_dataset_script.py | |
| import numpy as np | |
| import datasets | |
| from PIL import Image | |
| _DESCRIPTION = """ | |
| This dataset contains photos of rivers on which there may be waste. The waste items are annotated | |
| through bounding boxes, and are assigned to one of the 4 following categories: plastic bottle, plastic bag, | |
| another plastic waste, or non-plastic waste. Note that some photos may not contain any waste. | |
| """ | |
| _HOMEPAGE = "" | |
| _LICENSE = "" | |
| _URL = "https://storage.googleapis.com/kili-datasets-public/plastic-in-river/<VERSION>/" | |
| _URLS = { | |
| "train_images": f"{_URL}train/images.tar.gz", | |
| "train_annotations": f"{_URL}train/annotations.tar.gz", | |
| "validation_images": f"{_URL}validation/images.tar.gz", | |
| "validation_annotations": f"{_URL}validation/annotations.tar.gz", | |
| "test_images": f"{_URL}test/images.tar.gz", | |
| "test_annotations": f"{_URL}test/annotations.tar.gz" | |
| } | |
| class PlasticInRiver(datasets.GeneratorBasedBuilder): | |
| """Download script for the Plastic In River dataset""" | |
| VERSION = datasets.Version("1.3.0") | |
| def _info(self): | |
| features = datasets.Features( | |
| { | |
| "image": datasets.Image(), | |
| "litter": datasets.Sequence( | |
| { | |
| "label": datasets.ClassLabel(num_classes=4, names=["PLASTIC_BAG", "PLASTIC_BOTTLE", "OTHER_PLASTIC_WASTE", "NOT_PLASTIC_WASTE"]), | |
| "bbox": datasets.Sequence(datasets.Value("float32"), length=4), | |
| } | |
| ) | |
| } | |
| ) | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, | |
| features=features, | |
| license=_LICENSE, | |
| ) | |
| def _split_generators(self, dl_manager): | |
| urls = {k: v.replace("<VERSION>", f"v{str(self.VERSION)}") for k, v in _URLS.items()} | |
| downloaded_files = dl_manager.download(urls) | |
| return [ | |
| datasets.SplitGenerator( | |
| name=split, | |
| gen_kwargs={ | |
| "image_files": dl_manager.iter_archive(downloaded_files[f"{split_name}_images"]), | |
| "annotations_files": dl_manager.iter_archive(downloaded_files[f"{split_name}_annotations"]), | |
| "split": split_name, | |
| }, | |
| ) for split, split_name in [ | |
| (datasets.Split.TRAIN, "train"), | |
| (datasets.Split.TEST, "test"), | |
| (datasets.Split.VALIDATION, "validation"), | |
| ] | |
| ] | |
| def _generate_examples(self, image_files, annotations_files, split): | |
| for idx, (image_file, annotations_file) in enumerate(zip(image_files, annotations_files)): | |
| image_array = np.array(Image.open(image_file[1])) | |
| data = { | |
| "image": image_array, | |
| "litter": [] | |
| } | |
| for l in annotations_file[1].readlines(): | |
| numbers = l.decode("utf-8").split(" ") | |
| data["litter"].append({ | |
| "label": int(numbers[0]), | |
| "bbox": [float(n) for n in numbers[1:]] | |
| }) | |
| yield idx, data | |