added mvtec_capsule.py

mvtec_capsule.py

@@ -0,0 +1,127 @@
+import os
+from enum import Enum
+from pathlib import Path
+
+import datasets
+from pandas import DataFrame
+
+_HOMEPAGE = "https://www.mvtec.com/company/research/datasets/mvtec-ad"
+_LICENSE = "cc-by-nc-sa-4.0"
+_CITATION = """\
+@misc{
+    the-mvtec-anomaly-detection-dataset,
+    title = { The MVTec Anomaly Detection Dataset: A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection },
+    type = { Open Source Dataset },
+    author = { Paul Bergmann, Kilian Batzner, Michael Fauser, David Sattlegger, Carsten Steger },
+    howpublished = { \\url{ https://link.springer.com/article/10.1007%2Fs11263-020-01400-4 } },
+    url = { https://link.springer.com/article/10.1007%2Fs11263-020-01400-4 },
+}
+"""
+
+
+class LabelName(int, Enum):
+    NORMAL = 0
+    ABNORMAL = 1
+
+
+class MVTECCapsule(datasets.GeneratorBasedBuilder):
+    """satellite-building-segmentation instance segmentation dataset"""
+
+    VERSION = datasets.Version("1.0.0")
+    _URL = "https://huggingface.co/datasets/alexsu52/mvtec_capsule/resolve/main/capsule.tar.xz"
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "image": datasets.Image(),
+                "mask": datasets.Image(),
+                "label": datasets.ClassLabel(names=["normal", "abnormal"]),
+            }
+        )
+        return datasets.DatasetInfo(
+            features=features,
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+            license=_LICENSE,
+        )
+
+    def _split_generators(self, dl_manager):
+        folder_dir = dl_manager.download_and_extract(self._URL)
+        category_dir = os.path.join(folder_dir, "capsule")
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "category_dir": category_dir,
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "category_dir": category_dir,
+                    "split": "test",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, category_dir, split):
+        extensions = (".png", ".PNG")
+
+        root = Path(category_dir)
+        samples_list = [(str(root),) + f.parts[-3:] for f in root.glob(r"**/*") if f.suffix in extensions]
+        if not samples_list:
+            raise RuntimeError(f"Found 0 images in {root}")
+
+        samples = DataFrame(samples_list, columns=["path", "split", "label", "image_path"])
+
+        # Modify image_path column by converting to absolute path
+        samples["image_path"] = samples.path + "/" + samples.split + "/" + samples.label + "/" + samples.image_path
+
+        # Create label index for normal (0) and anomalous (1) images.
+        samples.loc[(samples.label == "good"), "label_index"] = LabelName.NORMAL
+        samples.loc[(samples.label != "good"), "label_index"] = LabelName.ABNORMAL
+        samples.label_index = samples.label_index.astype(int)
+
+        # separate masks from samples
+        mask_samples = samples.loc[samples.split == "ground_truth"].sort_values(by="image_path", ignore_index=True)
+        samples = samples[samples.split != "ground_truth"].sort_values(by="image_path", ignore_index=True)
+
+        # assign mask paths to anomalous test images
+        samples["mask_path"] = ""
+        samples.loc[
+            (samples.split == "test") & (samples.label_index == LabelName.ABNORMAL), "mask_path"
+        ] = mask_samples.image_path.values
+
+        # assert that the right mask files are associated with the right test images
+        if len(samples.loc[samples.label_index == LabelName.ABNORMAL]):
+            assert (
+                samples.loc[samples.label_index == LabelName.ABNORMAL]
+                .apply(lambda x: Path(x.image_path).stem in Path(x.mask_path).stem, axis=1)
+                .all()
+            ), "Mismatch between anomalous images and ground truth masks. Make sure the mask files in 'ground_truth' \
+                    folder follow the same naming convention as the anomalous images in the dataset (e.g. image: \
+                    '000.png', mask: '000.png' or '000_mask.png')."
+
+        if split:
+            samples = samples[samples.split == split].reset_index(drop=True)
+
+        for idx in range(len(samples)):
+            image_path = samples.iloc[idx].image_path
+            mask_path = samples.iloc[idx].mask_path
+            label_index = samples.iloc[idx].label_index
+
+            with open(image_path, "rb") as f:
+                image_bytes = f.read()
+
+            if mask_path:
+                with open(mask_path, "rb") as f:
+                    mask_bytes = f.read()
+            else:
+                mask_bytes = bytes(len(image_bytes))
+
+            yield idx, {
+                "image": {"path": image_path, "bytes": image_bytes},
+                "mask": {"path": mask_path, "bytes": mask_bytes},
+                "label": label_index,
+            }