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kvasir-points_datasets_script.py

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+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""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"
+            }
+
+#datasets-cli test  /global/D1/projects/HOST/Datasets/hyper-kvasir/sushant-experiments/kvasir-points_datasets_script.py --save_info --all_configs --trust_remote_code
+# huggingface-cli upload kvasir-points . . --repo-type dataset