Create CholecSeg8k.py for custom data loading

CholecSeg8k.py

@@ -0,0 +1,100 @@
+import os
+import datasets
+
+_CITATION = ""
+_DESCRIPTION = "CholecSeg8K dataset for semantic segmentation in laparoscopic cholecystectomy surgery."
+_HOMEPAGE_URL = "https://www.kaggle.com/datasets/newslab/cholecseg8k"
+_DATA_URL = "data/CholecSeg8k.zip"
+_LICENSE= "cc-by-nc-sa-4.0"
+
+
+class CholecSeg8KConfig(datasets.BuilderConfig):
+    """CholecSeg8K dataset for semantic segmentation in laparoscopic cholecystectomy surgery."""
+ 
+    def __init__(self, name, description, homepage, data_url):
+        """BuilderConfig for CholecSeg8k.
+        Args:
+          data_url: `string`, url to download the zip file from.
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(CholecSeg8KConfig, self).__init__(
+            name=self.name,
+            version=datasets.Version("1.0.0"),
+            description=self.description,
+            )
+        self.name = name
+        self.description = description
+        self.homepage = homepage
+        self.data_url = data_url
+
+
+def _build_config(name):
+    return CholecSeg8KConfig(
+        name=name,
+        description=_DESCRIPTION,
+        homepage=_HOMEPAGE_URL,
+        data_url=_DATA_URL,
+    )
+
+
+class CholecSeg8K(datasets.GeneratorBasedBuilder):
+
+    BUILDER_CONFIGS = [_build_config("all")]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "image": datasets.Image(),
+                    "color_mask": datasets.Image(),
+                    "watershed_mask": datasets.Image(),
+                    "annotation_mask": datasets.Image(),
+                }
+            ),
+            supervised_keys=None,
+            homepage=_HOMEPAGE_URL,
+            citation=_CITATION,
+            license=_LICENSE,
+        )
+
+    def _split_generators(self, dl_manager):
+
+        datapath = dl_manager.download_and_extract(_DATA_URL)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"datapath": datapath},
+            ),
+        ]
+
+
+    def _generate_examples(self, datapath):
+        """Yields examples."""
+        key=0
+        datapath = os.path.join(datapath, "CholecSeg8k")
+        
+        for video_folder in os.listdir(datapath):
+            video_folder_path = os.path.join(datapath, video_folder)
+
+            for clip_folder in os.listdir(video_folder_path):
+                clip_folder_path = os.path.join(video_folder_path, clip_folder)
+
+                for file in os.listdir(clip_folder_path):
+                    if file.endswith("_endo.png"):  # Check for endoscopic images
+                        image_path = os.path.join(clip_folder_path, file)
+
+                        # Construct paths for each mask type
+                        base_filename = file.replace("_endo.png", "")
+                        color_mask_path = os.path.join(clip_folder_path, f"{base_filename}_endo_color_mask.png")
+                        watershed_mask_path = os.path.join(clip_folder_path, f"{base_filename}_endo_watershed_mask.png")
+                        annotation_mask_path = os.path.join(clip_folder_path, f"{base_filename}_endo_mask.png")
+
+                        yield key, {
+                            "image": image_path,
+                            "color_mask": color_mask_path,
+                            "watershed_mask": watershed_mask_path,
+                            "annotation_mask": annotation_mask_path,
+                        }
+                        key+=1