new file: tumorsbrain.py

README.md

@@ -1,7 +1,36 @@
 ---
-# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/datasetcard.md?plain=1
-# Doc / guide: https://huggingface.co/docs/hub/datasets-cards
-{}
+dataset_info:
+  features:
+  - name: image
+    dtype: image
+  - name: image_id
+    dtype: int64
+  - name: objects
+    sequence:
+    - name: id
+      dtype: int64
+    - name: area
+      dtype: float64
+    - name: bbox
+      sequence: float32
+      length: 4
+    - name: label
+      dtype:
+        class_label:
+          names:
+            '0': negative
+            '1': positive
+    - name: iscrowd
+      dtype: bool
+  splits:
+  - name: train
+    num_bytes: 11482275
+    num_examples: 893
+  - name: test
+    num_bytes: 2794404
+    num_examples: 223
+  download_size: 12628405
+  dataset_size: 14276679
 ---
 
 # Dataset Card for Dataset Name

tumorsbrain.py

@@ -0,0 +1,214 @@
+import os
+import json
+from pathlib import Path
+from typing import Dict, Any, List, Union, Iterator, Tuple
+
+import datasets
+from datasets.download.download_manager import DownloadManager, ArchiveIterable
+
+# Typing
+_TYPING_BOX = Tuple[float, float, float, float]
+
+_DESCRIPTION = """\
+Training image sets and labels/bounding box coordinates for detecting brain
+tumors in MR images.
+- The datasets JPGs exported at their native size and are separated by plan
+(Axial, Coronal and Sagittal).
+- Tumors were hand labeled using https://makesense.ai
+- Bounding box coordinates and MGMT positive labels were marked on ~400 images
+for each plane in the T1wCE series from the RSNA-MICCAI competition data.
+"""
+
+_URLS = {
+    "train": "https://huggingface.co/datasets/chanelcolgate/tumorsbrain/resolve/main/data/train.zip",
+    "test": "https://huggingface.co/datasets/chanelcolgate/tumorsbrain/resolve/main/data/test.zip",
+    "annotations": "https://huggingface.co/datasets/chanelcolgate/tumorsbrain/resolve/main/data/annotations.zip",
+}
+
+_PATHS = {
+    "annotations": {
+        "train": Path("_annotations.coco.train.json"),
+        "test": Path("_annotations.coco.test.json"),
+    },
+    "images": {"train": Path("train"), "test": Path("test")},
+}
+
+_CLASSES = ["negative", "positive"]
+
+_SPLITS = ["train", "test"]
+
+
+def round_box_values(box, decimals=2):
+    return [round(val, decimals) for val in box]
+
+
+class COCOHelper:
+    """Helper class to load COCO annotations"""
+
+    def __init__(self, annotation_path: Path, images_dir: Path) -> None:
+        with open(annotation_path, "r") as file:
+            data = json.load(file)
+        self.data = data
+
+        dict_id2annot: Dict[int, Any] = {}
+        for annot in self.annotations:
+            dict_id2annot.setdefault(annot["image_id"], []).append(annot)
+
+        # Sort by id
+        dict_id2annot = {
+            k: list(sorted(v, key=lambda a: a["id"]))
+            for k, v in dict_id2annot.items()
+        }
+
+        self.dict_path2annot: Dict[str, Any] = {}
+        self.dict_path2id: Dict[str, Any] = {}
+        for img in self.images:
+            path_img = images_dir / str(img["file_name"])
+            path_img_str = str(path_img)
+            idx = int(img["id"])
+            annot = dict_id2annot.get(idx, [])
+            self.dict_path2annot[path_img_str] = annot
+            self.dict_path2id[path_img_str] = img["id"]
+
+    def __len__(self) -> int:
+        return len(self.data["images"])
+
+    @property
+    def images(self) -> List[Dict[str, Union[str, int]]]:
+        return self.data["images"]
+
+    @property
+    def annotations(self) -> List[Any]:
+        return self.data["annotations"]
+
+    @property
+    def categories(self) -> List[Dict[str, Union[str, int]]]:
+        return self.data["categories"]
+
+    def get_annotations(self, image_path: str) -> List[Any]:
+        return self.dict_path2annot.get(image_path, [])
+
+    def get_image_id(self, image_path: str) -> int:
+        return self.dict_path2id.get(image_path, -1)
+
+
+class COCOThienviet(datasets.GeneratorBasedBuilder):
+    """COCO Thienviet dataset."""
+
+    VERSION = datasets.Version("1.0.1")
+
+    def _info(self) -> datasets.DatasetInfo:
+        """
+        Return the dataset metadata and features.
+
+        Returns:
+            DatasetInfo: Metadata and features of the dataset.
+        """
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "image": datasets.Image(),
+                    "image_id": datasets.Value("int64"),
+                    "objects": datasets.Sequence(
+                        {
+                            "id": datasets.Value("int64"),
+                            "area": datasets.Value("float64"),
+                            "bbox": datasets.Sequence(
+                                datasets.Value("float32"), length=4
+                            ),
+                            "label": datasets.ClassLabel(names=_CLASSES),
+                            "iscrowd": datasets.Value("bool"),
+                        }
+                    ),
+                }
+            ),
+        )
+
+    def _split_generators(
+        self, dl_manager: DownloadManager
+    ) -> List[datasets.SplitGenerator]:
+        """
+        Provides the split information and downloads the data.
+
+        Args:
+            dl_manager (DownloadManager): The DownloadManager to use for
+                downloading and extracting data.
+
+        Returns:
+            List[SplitGenerator]: List of SplitGenerator objects representing
+                the data splits.
+        """
+        archive_annots = dl_manager.download_and_extract(_URLS["annotations"])
+
+        splits = []
+        for split in _SPLITS:
+            archive_split = dl_manager.download(_URLS[split])
+            annotation_path = (
+                Path(archive_annots) / _PATHS["annotations"][split]
+            )
+            images = dl_manager.iter_archive(archive_split)
+            if split == "train":
+                splits.append(
+                    datasets.SplitGenerator(
+                        name=datasets.Split.TRAIN,
+                        gen_kwargs={
+                            "annotation_path": annotation_path,
+                            "images_dir": _PATHS["images"][split],
+                            "images": images,
+                        },
+                    )
+                )
+            else:
+                splits.append(
+                    datasets.SplitGenerator(
+                        name=datasets.Split.TEST,
+                        gen_kwargs={
+                            "annotation_path": annotation_path,
+                            "images_dir": _PATHS["images"][split],
+                            "images": images,
+                        },
+                    )
+                )
+        return splits
+
+    def _generate_examples(
+        self, annotation_path: Path, images_dir: Path, images: ArchiveIterable
+    ) -> Iterator:
+        """
+        Generates examples for the dataset.
+
+        Args:
+            annotation_path (Path): The path to the annotation file.
+            images_dir (Path): The path to the directory containing the images.
+            images: (ArchiveIterable): An iterable containing the images.
+
+        Yields:
+            Dict[str, Union[str, Image]]: A dictionary containing the
+                generated examples.
+        """
+        coco_annotation = COCOHelper(annotation_path, images_dir)
+
+        for image_path, f in images:
+            annotations = coco_annotation.get_annotations(
+                os.path.normpath(image_path)
+            )
+            ret = {
+                "image": {"path": image_path, "bytes": f.read()},
+                "image_id": coco_annotation.get_image_id(
+                    os.path.normpath(image_path)
+                ),
+                "objects": [
+                    {
+                        "id": annot["id"],
+                        "area": annot["area"],
+                        "bbox": round_box_values(
+                            annot["bbox"], 2
+                        ),  # [x, y, w, h]
+                        "label": annot["category_id"],
+                        "iscrowd": bool(annot["iscrowd"]),
+                    }
+                    for annot in annotations
+                ],
+            }
+            yield image_path, ret