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| """Dataset for filtered Kvasir-instrument and Hyper-Kvasir with bounding boxes.""" |
|
|
| import os |
| import json |
| from PIL import Image |
| import datasets |
|
|
| import os |
| import json |
| import pandas as pd |
| import hashlib |
|
|
|
|
| cal_mid = lambda bx: [[[float(box['xmin'] + box['xmax']) / 2, float(box['ymin'] + box['ymax']) / 2] for box in bx]] |
|
|
|
|
| def cal_sha256(file_path): return hashlib.sha256( |
| open(file_path, 'rb').read()).hexdigest() |
|
|
|
|
| hyper_label_img_path = '/global/D1/projects/HOST/Datasets/hyper-kvasir/labeled-images/image-labels.csv' |
|
|
| hyper_df = pd.read_csv(hyper_label_img_path) |
|
|
| hyper_seg_img_path = '/global/D1/projects/HOST/Datasets/hyper-kvasir/segmented-images/bounding-boxes.json' |
| hyper_seg_img_base_path = "/global/D1/projects/HOST/Datasets/hyper-kvasir/segmented-images/images" |
|
|
| instr_seg_img_path = '/global/D1/projects/HOST/Datasets/kvasir-instrument/bboxes.json' |
| instr_seg_img_base_path = '/global/D1/projects/HOST/Datasets/kvasir-instrument/images/' |
|
|
| hyper_seg_imgs = json.load(open(hyper_seg_img_path)) |
| instr_seg_imgs = json.load(open(instr_seg_img_path)) |
|
|
| _CITATION = """\ |
| @article{kvasir, |
| title={Kvasir-instrument and Hyper-Kvasir datasets for bounding box annotations}, |
| author={Sushant Gautam and collaborators}, |
| year={2024} |
| } |
| """ |
|
|
| _DESCRIPTION = """ |
| Filtered Kvasir-instrument and Hyper-Kvasir datasets with bounding boxes for medical imaging tasks. |
| Each entry contains images, bounding box coordinates, and additional metadata. |
| """ |
|
|
| _HOMEPAGE = "https://example.com/kvasir-hyper-bbox" |
|
|
| _LICENSE = "CC BY-NC 4.0" |
|
|
| _URLS = { |
| "filtered_data": "https://example.com/kvasir-hyper-bbox-dataset.zip" |
| } |
|
|
|
|
| class KvasirHyperBBox(datasets.GeneratorBasedBuilder): |
| """Dataset for Kvasir-instrument and Hyper-Kvasir with bounding boxes.""" |
|
|
| VERSION = datasets.Version("1.0.0") |
|
|
| BUILDER_CONFIGS = [ |
| datasets.BuilderConfig( |
| name="bbox_dataset", |
| version=VERSION, |
| description="Dataset with bounding box annotations." |
| ) |
| ] |
|
|
| DEFAULT_CONFIG_NAME = "bbox_dataset" |
|
|
| def _info(self): |
| features = datasets.Features({ |
| "image_data": datasets.Image(), |
| "image_sha256": datasets.Value("string"), |
| "points": datasets.Sequence(datasets.Sequence(datasets.Sequence(datasets.Value("float32")))), |
| "count": datasets.Value("int64"), |
| "label": datasets.Value("string"), |
| "collection_method": datasets.Value("string"), |
| "classification": datasets.Value("string"), |
| "organ": datasets.Value("string") |
| }) |
|
|
| return datasets.DatasetInfo( |
| description=_DESCRIPTION, |
| homepage=_HOMEPAGE, |
| license=_LICENSE, |
| citation=_CITATION, |
| features=features |
| ) |
|
|
| def _split_generators(self, dl_manager): |
| return [ |
| datasets.SplitGenerator( |
| name=datasets.Split.TRAIN, |
| gen_kwargs={}, |
| ) |
| ] |
|
|
| def _generate_examples(self): |
| for key, entry in hyper_seg_imgs.items(): |
| img_path = os.path.join(hyper_seg_img_base_path, f"{key}.jpg") |
| hyper_entry = hyper_df.loc[hyper_df['Video file'] == key].iloc[0] |
| yield key, { |
| "image_data": open(img_path, 'rb').read(), |
| "image_sha256": cal_sha256(img_path), |
| "points": cal_mid(entry['bbox']), |
| "count": len(entry['bbox']), |
| "label": hyper_entry.Finding, |
| "collection_method": 'counting', |
| "classification": hyper_entry.Classification, |
| "organ": hyper_entry.Organ |
| } |
|
|
| for key, entry in instr_seg_imgs.items(): |
| img_path = os.path.join(instr_seg_img_base_path, f"{key}.jpg") |
| assert len(cal_mid(entry['bbox'])) > 0 |
| yield key, { |
| "image_data": open(img_path, 'rb').read(), |
| "image_sha256": cal_sha256(img_path), |
| "points": cal_mid(entry['bbox']), |
| "count": len(entry['bbox']), |
| "label": "instrument", |
| "collection_method": "counting", |
| "classification": "instrument", |
| "organ": "instrument" |
| } |
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