Update Chicks4FreeID.py

Chicks4FreeID.py

@@ -1,486 +1,486 @@
-from pathlib import Path
-from typing import Set
-
-from datasets import DatasetBuilder, GeneratorBasedBuilder, DatasetInfo, Features, Image, ClassLabel, Array3D, DownloadManager, SplitGenerator, BuilderConfig, Version
-import numpy as np
-import datasets
-
-VERSION = "v1_240507_SMALL"
-HF_VERSION = "1.0.0"
-
-# Available Dataset View Names
-full_dataset_name =                 "full-dataset"
-semantic_segmentation_name =        "semantic-segmentation"
-instance_segmentation_name =        "instance-segmentation"
-animal_category_anomoalies_name =   "animal-category-anomalies"
-re_id_best_name =                   "chicken-re-id-best-visibility"
-#re_id_good_name =                   "chicken-re-id-good-visibility"
-#re_id_bad_name =                    "chicken-re-id-bad-visibility"
-re_id_full_name =                   "chicken-re-id-all-visibility"
-
-
-# Example usage
-# from datasets import load_dataset
-# dataset = datasets.load_dataset(
-#    "dariakern/Chicks4FreeID", 
-#    "chicken-re-id-best-visibility", 
-#    as_supervised=True, 
-#    trust_remote_code=True
-# )
-
-##### ONTOLOTGY ######
-
-
-ontologies = {
-    "v1_240507": 
-    {'tools': [{'classifications': [{'instructions': 'coop',
-                                 'options': [{'label': '10'},
-                                             {'label': '1'},
-                                             {'label': '2'},
-                                             {'label': '3'},
-                                             {'label': '4'},
-                                             {'label': '5'},
-                                             {'label': '6'},
-                                             {'label': '7'},
-                                             {'label': '8'},
-                                             {'label': '9'},
-                                             {'label': '11'}],
-                                 'required': True,
-                                 'type': 'radio'},
-                                {'instructions': 'identity',
-                                 'options': [{'label': 'Beate'},
-                                             {'label': 'Borghild'},
-                                             {'label': 'Eleonore'},
-                                             {'label': 'Mona'},
-                                             {'label': 'Henriette'},
-                                             {'label': 'Margit'},
-                                             {'label': 'Millie'},
-                                             {'label': 'Sigrun'},
-                                             {'label': 'Kristina'},
-                                             {'label': 'Unknown'},
-                                             {'label': 'Tina'},
-                                             {'label': 'Gretel'},
-                                             {'label': 'Lena'},
-                                             {'label': 'Yolkoono'},
-                                             {'label': 'Skimmy'},
-                                             {'label': 'Mavi'},
-                                             {'label': 'Mirmir'},
-                                             {'label': 'Nugget'},
-                                             {'label': 'Fernanda'},
-                                             {'label': 'Isolde'},
-                                             {'label': 'Mechthild'},
-                                             {'label': 'Brunhilde'},
-                                             {'label': 'Spiderman'},
-                                             {'label': 'Brownie'},
-                                             {'label': 'Camy'},
-                                             {'label': 'Samy'},
-                                             {'label': 'Yin'},
-                                             {'label': 'Yuriko'},
-                                             {'label': 'Renate'},
-                                             {'label': 'Regina'},
-                                             {'label': 'Monika'},
-                                             {'label': 'Heidi'},
-                                             {'label': 'Erna'},
-                                             {'label': 'Marina'},
-                                             {'label': 'Kathrin'},
-                                             {'label': 'Isabella'},
-                                             {'label': 'Amalia'},
-                                             {'label': 'Edeltraut'},
-                                             {'label': 'Erdmute'},
-                                             {'label': 'Oktavia'},
-                                             {'label': 'Siglinde'},
-                                             {'label': 'Ulrike'},
-                                             {'label': 'Hermine'},
-                                             {'label': 'Matilda'},
-                                             {'label': 'Chantal'},
-                                             {'label': 'Chayenne'},
-                                             {'label': 'Jaqueline'},
-                                             {'label': 'Mandy'},
-                                             {'label': 'Henny'},
-                                             {'label': 'Shady'},
-                                             {'label': 'Shorty'}],
-                                 'required': True,
-                                 'type': 'radio'},
-                                {'instructions': 'visibility',
-                                 'options': [{'label': 'best'},
-                                             {'label': 'good'},
-                                             {'label': 'bad'}],
-                                 'required': True,
-                                 'type': 'radio'}],
-            'color': '#1e1cff',
-            'name': 'chicken',
-            'required': False,
-            'tool': 'superpixel'},
-           {'color': '#FF34FF',
-            'name': 'background',
-            'required': False,
-            'tool': 'superpixel'},
-           {'classifications': [{'instructions': 'coop',
-                                 'options': [{'label': '1'},
-                                             {'label': '2'},
-                                             {'label': '3'},
-                                             {'label': '4'},
-                                             {'label': '5'},
-                                             {'label': '6'},
-                                             {'label': '7'},
-                                             {'label': '8'},
-                                             {'label': '9'},
-                                             {'label': '10'},
-                                             {'label': '11'}],
-                                 'required': True,
-                                 'type': 'radio'},
-                                {'instructions': 'identity',
-                                 'options': [{'label': 'Evelyn'},
-                                             {'label': 'Marley'}],
-                                 'required': True,
-                                 'type': 'radio'},
-                                {'instructions': 'visibility',
-                                 'options': [{'label': 'best'},
-                                             {'label': 'good'},
-                                             {'label': 'bad'}],
-                                 'required': True,
-                                 'type': 'radio'}],
-            'color': '#FF4A46',
-            'name': 'duck',
-            'required': False,
-            'tool': 'superpixel'},
-           {'classifications': [{'instructions': 'coop',
-                                 'options': [{'label': '1'},
-                                             {'label': '2'},
-                                             {'label': '3'},
-                                             {'label': '4'},
-                                             {'label': '5'},
-                                             {'label': '6'},
-                                             {'label': '7'},
-                                             {'label': '8'},
-                                             {'label': '9'},
-                                             {'label': '10'},
-                                             {'label': '11'}],
-                                 'required': True,
-                                 'type': 'radio'},
-                                {'instructions': 'identity',
-                                 'options': [{'label': 'Elvis'},
-                                             {'label': 'Jackson'}],
-                                 'required': True,
-                                 'type': 'radio'},
-                                {'instructions': 'visibility',
-                                 'options': [{'label': 'best'},
-                                             {'label': 'good'},
-                                             {'label': 'bad'}],
-                                 'required': True,
-                                 'type': 'radio'}],
-            'color': '#ff0000',
-            'name': 'rooster',
-            'required': False,
-            'tool': 'superpixel'}]}
-}
-
-
-ontologies["v1_240507_SMALL"] = ontologies["v1_240507"]
-
-
-class Ontology:
-    ontology: dict = None
-    def __init__(self, version_name: str):
-        self.ontology: dict = ontologies[version_name]
-
-
-    def names(self, class_name, tool_name=None, drop_unkown=False):
-        """
-        Returns a list of all possible names for a given category (accross all tools)
-        """
-        if class_name == "animal_category":
-            return list({tool["name"] for tool in self.ontology["tools"]} - {"background"})
-
-        result = set()
-        for tool in self.ontology["tools"]:
-            if "classifications" in tool:
-                for classification in tool["classifications"]:
-                    if classification["instructions"] == class_name and (tool_name is None or tool_name == tool["name"]):
-                        result.update({option["label"] for option in classification["options"] if not (drop_unkown and option["label"] == "Unknown")})
-        return list(result)
-    
-    def get_color_map(self):
-        """
-        Returns a dictionary mapping class names to their respective colors
-        """
-        return {tool["name"]: tool["color"] for tool in self.ontology["tools"]}
-    
-
-
-
-
-
-
-ontology = Ontology(VERSION)
-
-# Feature Names
-IMAGE = "image"
-image_feature = {IMAGE: Image()}
-
-SEGMENTATION_MAKS = "segmentation_mask"
-segmentation_mask_feature = {SEGMENTATION_MAKS: Image()}
-
-INSTANCE_MASK = "instance_mask"
-instance_mask_feature = {INSTANCE_MASK: Image()}
-
-CROP = "crop"
-crop_feature = {CROP: Image()}
-
-ID = "identity"
-identity_feature = {ID: ClassLabel(names=ontology.names(ID))}
-chicken_only_identitiy_feature = {ID: ClassLabel(names=ontology.names(ID, "chicken", drop_unkown=True))}
-
-VISIBILITY = "visibility"
-visibility_feature = {VISIBILITY: ClassLabel(names=ontology.names(VISIBILITY))} 
-
-COOP = "coop"
-coop_feature = {COOP: ClassLabel(names=ontology.names(COOP))} 
-
-CATEGORY = "animal_category"
-animal_category_feature = {CATEGORY: ClassLabel(names=ontology.names(CATEGORY))} 
-
-INSTANCES = "instances"
-instance_features = {
-    **crop_feature,
-    **instance_mask_feature,
-    **identity_feature,
-    **visibility_feature,
-    **animal_category_feature,
-}
-
-all_features = {
-    **image_feature,
-    **segmentation_mask_feature,
-    **coop_feature,
-    INSTANCES: [instance_features],
-}
-
-
-
-
-
-def name_to_dict(filename: str):
-    """
-    Converts a filename to a dictionary object by splitting the filename by underscores and using the even indices as keys and the odd indices as values.
-    """
-    return {filename.split('_')[i]: filename.split('_')[i + 1] for i in range(0, len(filename.split('_')) - 1, 2)}
-
-
-class ChicksDataset(GeneratorBasedBuilder):
-    BUILDER_CONFIGS = [
-        BuilderConfig(name=full_dataset_name, version=Version(HF_VERSION), description="The complete dataset including all features and image types. Includes all coops, visibility ratings, identities, and animal categories, as well as segmentation masks and instance masks."),
-        BuilderConfig(name=semantic_segmentation_name, version=Version(HF_VERSION), description="Includes images and color-coded segmentation masks."),
-        BuilderConfig(name=instance_segmentation_name, version=Version(HF_VERSION), description="Includes images and a corresponding sequence of binary instance segmentation masks for each instance on the image."),
-        BuilderConfig(name=animal_category_anomoalies_name, version=Version(HF_VERSION), description="Includes images of mostly chicken, but also some roosters and ducks, which make up the anomalies in the dataset."),
-        BuilderConfig(name=re_id_best_name, version=Version(HF_VERSION), description="Includes crops of chickens which have the best visibility rating for re-identification."),
-        #BuilderConfig(name=re_id_good_name, version=Version(HF_VERSION), description="Includes crops of chickens which have neither the best nor the worst visibility rating for re-identification."),
-        #BuilderConfig(name=re_id_bad_name, version=Version(HF_VERSION), description="Includes crops of chickens which have the worst (bad) visibility rating for re-identification."),
-        BuilderConfig(name=re_id_full_name, version=Version(HF_VERSION), description="Includes crops of chickens with all visibilities for re-identification without any filtering on visibility rating."),
-    ]
-    
-
-    def _info(self, *args, **kwargs):
-        
-        if self.config.name == full_dataset_name:
-            return DatasetInfo(
-                features=Features(all_features),
-            )
-        
-        elif self.config.name in [
-            re_id_full_name, re_id_best_name, 
-            # re_id_good_name, re_id_bad_name
-        ]:
-            return DatasetInfo(
-                features=Features({
-                    **crop_feature,
-                    **chicken_only_identitiy_feature,
-                }),
-                supervised_keys=(
-                    CROP,
-                    ID,
-                ),
-            )
-
-        
-        elif self.config.name == semantic_segmentation_name:
-            return DatasetInfo(
-                features=Features({
-                    **image_feature, 
-                    **segmentation_mask_feature,
-                }),
-                supervised_keys=(
-                    IMAGE,
-                    SEGMENTATION_MAKS,
-                )
-            )
-        
-        elif self.config.name == instance_segmentation_name:
-            return DatasetInfo(
-                features=Features({
-                    **image_feature, 
-                    INSTANCES: [instance_mask_feature],
-                }),
-                supervised_keys=(
-                    IMAGE,
-                    INSTANCES, # TODO use nested reference to instance_mask_feature
-                )
-            )
-        
-        elif self.config.name == animal_category_anomoalies_name:
-            return DatasetInfo(
-                features=Features({
-                    **crop_feature,
-                    **animal_category_feature,
-                }),
-                supervised_keys=(
-                    CROP,
-                    CATEGORY
-                )
-            )
-
-    def _split_generators(self, dl_manager: DownloadManager):
-        URL = f"https://huggingface.co/datasets/dariakern/Chicks4FreeID/resolve/main/{VERSION}.zip?download=true"
-        base_path = Path(dl_manager.download_and_extract(URL))
-
-        # Only offer train test split for chicken-re-id task
-        if self.config.name in [
-            re_id_full_name, 
-            re_id_best_name
-        ]:
-            from sklearn.model_selection import train_test_split
-
-            # all crop files (only chicken, remove unknowns)
-            all_crops = sorted([
-                crop_file 
-                for crop_file 
-                in base_path.rglob(f"**/{VERSION}/reId/chicken/**/*crop_*.png") 
-                if "Unknown" not in crop_file.parts
-            ])
-            # all identity targets (labels)
-            identities = [name_to_dict(crop.stem)[ID] for crop in all_crops]
-
-            if VERSION == "v1_240507_SMALL":
-                train_crops, test_crops = all_crops, all_crops
-            else:
-                # Splitting the dataset into train and test using stratified train_test_split
-                train_crops, test_crops, _, _ = train_test_split(
-                    all_crops, identities, test_size=0.2, stratify=identities, shuffle=True, random_state=42
-                )
-    
-            return [
-                SplitGenerator(
-                    gen_kwargs={"base_path": base_path, "split": set(train_crops)},
-                    name=datasets.Split.TRAIN,
-                ),
-                SplitGenerator(
-                    gen_kwargs={"base_path": base_path, "split": set(test_crops)},
-                    name=datasets.Split.TEST,
-                )
-            ]
-        else:
-            return [
-                SplitGenerator(
-                    name=datasets.Split.TRAIN, 
-                    gen_kwargs={"base_path": base_path, "split": None})
-            ]
-
-    
-    def _generate_all(self, base_path: Path, split: Set[Path]=None):
-        """
-        Generates all examples for the dataset, including all features. 
-
-        Args:
-            base_path (Path): The base path to the dataset
-            split (Set[Path]): The paths to all instance crops to include in the current dataset
-        """
-        img_dir = base_path / f"{VERSION}/images"
-        mask_dir = base_path / f"{VERSION}/masks"
-        reid_dir = base_path / f"{VERSION}/reId"
-        
-        # Collecting images, segmentation masks, and instance masks
-        for img_file in img_dir.iterdir():
-            image_id = img_file.stem
-            image_path = img_file
-            segmentation_mask_path = mask_dir / f"{image_id}_segmentationMask.png"
-            instance_masks = list(mask_dir.rglob(f"{image_id}_instanceMask_*.png"))
-            instance_crops = list(reid_dir.rglob(f"**/{image_id}_crop_*.png"))
-            
-            # Check if all crops have a corresponding instance mask
-            assert len(instance_masks) == len(instance_crops) and len(instance_masks) > 0
-
-            # Remove any instance_crops that are not in crops_split
-            if split is not None:
-                instance_crops = [crop for crop in instance_crops if crop in split]
-
-            instance_data = []
-            infos = {}
-            for instance_mask_path, crop_path in zip(instance_masks, instance_crops):
-                infos = name_to_dict(crop_path.stem)
-                instance_data.append({
-                    INSTANCE_MASK: str(instance_mask_path),
-                    CROP: str(crop_path),
-                    VISIBILITY: infos[VISIBILITY],
-                    ID: infos[ID],
-                    CATEGORY: crop_path.relative_to(reid_dir).parts[0],
-                })
-            
-
-            if instance_data:
-                yield image_id, {
-                    IMAGE: str(image_path),
-                    SEGMENTATION_MAKS: str(segmentation_mask_path),
-                    COOP: infos[COOP],
-                    INSTANCES: instance_data,
-                }
-
-
-    def _generate_examples(self, **kwargs):
-        if self.config.name in [full_dataset_name]:
-            yield from self._generate_all(**kwargs)
-        
-        elif self.config.name == semantic_segmentation_name:
-            for image_id, example in self._generate_all(**kwargs):
-                yield image_id, {
-                    IMAGE: example[IMAGE],
-                    SEGMENTATION_MAKS: example[SEGMENTATION_MAKS],
-                }
-
-        elif self.config.name == instance_segmentation_name:
-            for image_id, example in self._generate_all(**kwargs):
-                yield image_id, {
-                    IMAGE: example[IMAGE],
-                    INSTANCES: [
-                        {
-                            INSTANCE_MASK: instance[INSTANCE_MASK]
-                        }
-                        for instance in example[INSTANCES]
-                    ]
-                }
-
-        elif self.config.name == animal_category_anomoalies_name:
-            for image_id, example in self._generate_all(**kwargs):
-                for instance in example[INSTANCES]:
-                    instance_id = Path(instance[CROP]).stem
-                    yield instance_id, {
-                        CROP: instance[CROP],
-                        CATEGORY: instance[CATEGORY],
-                    }
-
-        elif self.config.name in [
-            re_id_best_name, re_id_full_name, 
-            # re_id_good_name, re_id_bad_name
-        ]:
-            for image_id, example in self._generate_all(**kwargs):
-                for instance in example[INSTANCES]:
-                    
-                    # Conditions for filtering
-                    use_all = self.config.name == re_id_full_name
-                    selected_visibility = instance[VISIBILITY] == self.config.name.split("-")[-2]
-
-                    if use_all or selected_visibility:
-                        instance_id = Path(instance[CROP]).stem
-                        yield instance_id, {
-                            CROP: instance[CROP],
-                            ID: instance[ID],
-                        }
+from pathlib import Path
+from typing import Set
+
+from datasets import DatasetBuilder, GeneratorBasedBuilder, DatasetInfo, Features, Image, ClassLabel, Array3D, DownloadManager, SplitGenerator, BuilderConfig, Version
+import numpy as np
+import datasets
+
+VERSION = "v1_240507_SMALL"
+HF_VERSION = "1.0.0"
+
+# Available Dataset View Names
+full_dataset_name =                 "full-dataset"
+semantic_segmentation_name =        "semantic-segmentation"
+instance_segmentation_name =        "instance-segmentation"
+animal_category_anomoalies_name =   "animal-category-anomalies"
+re_id_best_name =                   "chicken-re-id-best-visibility"
+#re_id_good_name =                   "chicken-re-id-good-visibility"
+#re_id_bad_name =                    "chicken-re-id-bad-visibility"
+re_id_full_name =                   "chicken-re-id-all-visibility"
+
+
+# Example usage
+# from datasets import load_dataset
+# dataset = datasets.load_dataset(
+#    "dariakern/Chicks4FreeID", 
+#    "chicken-re-id-best-visibility", 
+#    as_supervised=True, 
+#    trust_remote_code=True
+# )
+
+##### ONTOLOTGY ######
+
+
+ontologies = {
+    "v1_240507": 
+    {'tools': [{'classifications': [{'instructions': 'coop',
+                                 'options': [{'label': '10'},
+                                             {'label': '1'},
+                                             {'label': '2'},
+                                             {'label': '3'},
+                                             {'label': '4'},
+                                             {'label': '5'},
+                                             {'label': '6'},
+                                             {'label': '7'},
+                                             {'label': '8'},
+                                             {'label': '9'},
+                                             {'label': '11'}],
+                                 'required': True,
+                                 'type': 'radio'},
+                                {'instructions': 'identity',
+                                 'options': [{'label': 'Beate'},
+                                             {'label': 'Borghild'},
+                                             {'label': 'Eleonore'},
+                                             {'label': 'Mona'},
+                                             {'label': 'Henriette'},
+                                             {'label': 'Margit'},
+                                             {'label': 'Millie'},
+                                             {'label': 'Sigrun'},
+                                             {'label': 'Kristina'},
+                                             {'label': 'Unknown'},
+                                             {'label': 'Tina'},
+                                             {'label': 'Gretel'},
+                                             {'label': 'Lena'},
+                                             {'label': 'Yolkoono'},
+                                             {'label': 'Skimmy'},
+                                             {'label': 'Mavi'},
+                                             {'label': 'Mirmir'},
+                                             {'label': 'Nugget'},
+                                             {'label': 'Fernanda'},
+                                             {'label': 'Isolde'},
+                                             {'label': 'Mechthild'},
+                                             {'label': 'Brunhilde'},
+                                             {'label': 'Spiderman'},
+                                             {'label': 'Brownie'},
+                                             {'label': 'Camy'},
+                                             {'label': 'Samy'},
+                                             {'label': 'Yin'},
+                                             {'label': 'Yuriko'},
+                                             {'label': 'Renate'},
+                                             {'label': 'Regina'},
+                                             {'label': 'Monika'},
+                                             {'label': 'Heidi'},
+                                             {'label': 'Erna'},
+                                             {'label': 'Marina'},
+                                             {'label': 'Kathrin'},
+                                             {'label': 'Isabella'},
+                                             {'label': 'Amalia'},
+                                             {'label': 'Edeltraut'},
+                                             {'label': 'Erdmute'},
+                                             {'label': 'Oktavia'},
+                                             {'label': 'Siglinde'},
+                                             {'label': 'Ulrike'},
+                                             {'label': 'Hermine'},
+                                             {'label': 'Matilda'},
+                                             {'label': 'Chantal'},
+                                             {'label': 'Chayenne'},
+                                             {'label': 'Jaqueline'},
+                                             {'label': 'Mandy'},
+                                             {'label': 'Henny'},
+                                             {'label': 'Shady'},
+                                             {'label': 'Shorty'}],
+                                 'required': True,
+                                 'type': 'radio'},
+                                {'instructions': 'visibility',
+                                 'options': [{'label': 'best'},
+                                             {'label': 'good'},
+                                             {'label': 'bad'}],
+                                 'required': True,
+                                 'type': 'radio'}],
+            'color': '#1e1cff',
+            'name': 'chicken',
+            'required': False,
+            'tool': 'superpixel'},
+           {'color': '#FF34FF',
+            'name': 'background',
+            'required': False,
+            'tool': 'superpixel'},
+           {'classifications': [{'instructions': 'coop',
+                                 'options': [{'label': '1'},
+                                             {'label': '2'},
+                                             {'label': '3'},
+                                             {'label': '4'},
+                                             {'label': '5'},
+                                             {'label': '6'},
+                                             {'label': '7'},
+                                             {'label': '8'},
+                                             {'label': '9'},
+                                             {'label': '10'},
+                                             {'label': '11'}],
+                                 'required': True,
+                                 'type': 'radio'},
+                                {'instructions': 'identity',
+                                 'options': [{'label': 'Evelyn'},
+                                             {'label': 'Marley'}],
+                                 'required': True,
+                                 'type': 'radio'},
+                                {'instructions': 'visibility',
+                                 'options': [{'label': 'best'},
+                                             {'label': 'good'},
+                                             {'label': 'bad'}],
+                                 'required': True,
+                                 'type': 'radio'}],
+            'color': '#FF4A46',
+            'name': 'duck',
+            'required': False,
+            'tool': 'superpixel'},
+           {'classifications': [{'instructions': 'coop',
+                                 'options': [{'label': '1'},
+                                             {'label': '2'},
+                                             {'label': '3'},
+                                             {'label': '4'},
+                                             {'label': '5'},
+                                             {'label': '6'},
+                                             {'label': '7'},
+                                             {'label': '8'},
+                                             {'label': '9'},
+                                             {'label': '10'},
+                                             {'label': '11'}],
+                                 'required': True,
+                                 'type': 'radio'},
+                                {'instructions': 'identity',
+                                 'options': [{'label': 'Elvis'},
+                                             {'label': 'Jackson'}],
+                                 'required': True,
+                                 'type': 'radio'},
+                                {'instructions': 'visibility',
+                                 'options': [{'label': 'best'},
+                                             {'label': 'good'},
+                                             {'label': 'bad'}],
+                                 'required': True,
+                                 'type': 'radio'}],
+            'color': '#ff0000',
+            'name': 'rooster',
+            'required': False,
+            'tool': 'superpixel'}]}
+}
+
+
+ontologies["v1_240507_SMALL"] = ontologies["v1_240507"]
+
+
+class Ontology:
+    ontology: dict = None
+    def __init__(self, version_name: str):
+        self.ontology: dict = ontologies[version_name]
+
+
+    def names(self, class_name, tool_name=None, drop_unkown=False):
+        """
+        Returns a list of all possible names for a given category (accross all tools)
+        """
+        if class_name == "animal_category":
+            return sorted(list({tool["name"] for tool in self.ontology["tools"]} - {"background"}))
+
+        result = []
+        for tool in self.ontology["tools"]:
+            if "classifications" in tool:
+                for classification in tool["classifications"]:
+                    if classification["instructions"] == class_name and (tool_name is None or tool_name == tool["name"]):
+                        result.extend([option["label"] for option in classification["options"] if not (drop_unkown and option["label"] == "Unknown")])
+        return list(result)
+    
+    def get_color_map(self):
+        """
+        Returns a dictionary mapping class names to their respective colors
+        """
+        return {tool["name"]: tool["color"] for tool in self.ontology["tools"]}
+    
+
+
+
+
+
+
+ontology = Ontology(VERSION)
+
+# Feature Names
+IMAGE = "image"
+image_feature = {IMAGE: Image()}
+
+SEGMENTATION_MAKS = "segmentation_mask"
+segmentation_mask_feature = {SEGMENTATION_MAKS: Image()}
+
+INSTANCE_MASK = "instance_mask"
+instance_mask_feature = {INSTANCE_MASK: Image()}
+
+CROP = "crop"
+crop_feature = {CROP: Image()}
+
+ID = "identity"
+identity_feature = {ID: ClassLabel(names=ontology.names(ID))}
+chicken_only_identitiy_feature = {ID: ClassLabel(names=ontology.names(ID, "chicken", drop_unkown=True))}
+
+VISIBILITY = "visibility"
+visibility_feature = {VISIBILITY: ClassLabel(names=ontology.names(VISIBILITY))} 
+
+COOP = "coop"
+coop_feature = {COOP: ClassLabel(names=ontology.names(COOP))} 
+
+CATEGORY = "animal_category"
+animal_category_feature = {CATEGORY: ClassLabel(names=ontology.names(CATEGORY))} 
+
+INSTANCES = "instances"
+instance_features = {
+    **crop_feature,
+    **instance_mask_feature,
+    **identity_feature,
+    **visibility_feature,
+    **animal_category_feature,
+}
+
+all_features = {
+    **image_feature,
+    **segmentation_mask_feature,
+    **coop_feature,
+    INSTANCES: [instance_features],
+}
+
+
+
+
+
+def name_to_dict(filename: str):
+    """
+    Converts a filename to a dictionary object by splitting the filename by underscores and using the even indices as keys and the odd indices as values.
+    """
+    return {filename.split('_')[i]: filename.split('_')[i + 1] for i in range(0, len(filename.split('_')) - 1, 2)}
+
+
+class ChicksDataset(GeneratorBasedBuilder):
+    BUILDER_CONFIGS = [
+        BuilderConfig(name=full_dataset_name, version=Version(HF_VERSION), description="The complete dataset including all features and image types. Includes all coops, visibility ratings, identities, and animal categories, as well as segmentation masks and instance masks."),
+        BuilderConfig(name=semantic_segmentation_name, version=Version(HF_VERSION), description="Includes images and color-coded segmentation masks."),
+        BuilderConfig(name=instance_segmentation_name, version=Version(HF_VERSION), description="Includes images and a corresponding sequence of binary instance segmentation masks for each instance on the image."),
+        BuilderConfig(name=animal_category_anomoalies_name, version=Version(HF_VERSION), description="Includes images of mostly chicken, but also some roosters and ducks, which make up the anomalies in the dataset."),
+        BuilderConfig(name=re_id_best_name, version=Version(HF_VERSION), description="Includes crops of chickens which have the best visibility rating for re-identification."),
+        #BuilderConfig(name=re_id_good_name, version=Version(HF_VERSION), description="Includes crops of chickens which have neither the best nor the worst visibility rating for re-identification."),
+        #BuilderConfig(name=re_id_bad_name, version=Version(HF_VERSION), description="Includes crops of chickens which have the worst (bad) visibility rating for re-identification."),
+        BuilderConfig(name=re_id_full_name, version=Version(HF_VERSION), description="Includes crops of chickens with all visibilities for re-identification without any filtering on visibility rating."),
+    ]
+    
+
+    def _info(self, *args, **kwargs):
+        
+        if self.config.name == full_dataset_name:
+            return DatasetInfo(
+                features=Features(all_features),
+            )
+        
+        elif self.config.name in [
+            re_id_full_name, re_id_best_name, 
+            # re_id_good_name, re_id_bad_name
+        ]:
+            return DatasetInfo(
+                features=Features({
+                    **crop_feature,
+                    **chicken_only_identitiy_feature,
+                }),
+                supervised_keys=(
+                    CROP,
+                    ID,
+                ),
+            )
+
+        
+        elif self.config.name == semantic_segmentation_name:
+            return DatasetInfo(
+                features=Features({
+                    **image_feature, 
+                    **segmentation_mask_feature,
+                }),
+                supervised_keys=(
+                    IMAGE,
+                    SEGMENTATION_MAKS,
+                )
+            )
+        
+        elif self.config.name == instance_segmentation_name:
+            return DatasetInfo(
+                features=Features({
+                    **image_feature, 
+                    INSTANCES: [instance_mask_feature],
+                }),
+                supervised_keys=(
+                    IMAGE,
+                    INSTANCES, # TODO use nested reference to instance_mask_feature
+                )
+            )
+        
+        elif self.config.name == animal_category_anomoalies_name:
+            return DatasetInfo(
+                features=Features({
+                    **crop_feature,
+                    **animal_category_feature,
+                }),
+                supervised_keys=(
+                    CROP,
+                    CATEGORY
+                )
+            )
+
+    def _split_generators(self, dl_manager: DownloadManager):
+        URL = f"https://huggingface.co/datasets/dariakern/Chicks4FreeID/resolve/main/{VERSION}.zip?download=true"
+        base_path = Path(dl_manager.download_and_extract(URL))
+
+        # Only offer train test split for chicken-re-id task
+        if self.config.name in [
+            re_id_full_name, 
+            re_id_best_name
+        ]:
+            from sklearn.model_selection import train_test_split
+
+            # all crop files (only chicken, remove unknowns)
+            all_crops = sorted([
+                crop_file 
+                for crop_file 
+                in base_path.rglob(f"**/{VERSION}/reId/chicken/**/*crop_*.png") 
+                if "Unknown" not in crop_file.parts
+            ])
+            # all identity targets (labels)
+            identities = [name_to_dict(crop.stem)[ID] for crop in all_crops]
+
+            if VERSION == "v1_240507_SMALL":
+                train_crops, test_crops = all_crops, all_crops
+            else:
+                # Splitting the dataset into train and test using stratified train_test_split
+                train_crops, test_crops, _, _ = train_test_split(
+                    all_crops, identities, test_size=0.2, stratify=identities, shuffle=True, random_state=42
+                )
+    
+            return [
+                SplitGenerator(
+                    gen_kwargs={"base_path": base_path, "split": set(train_crops)},
+                    name=datasets.Split.TRAIN,
+                ),
+                SplitGenerator(
+                    gen_kwargs={"base_path": base_path, "split": set(test_crops)},
+                    name=datasets.Split.TEST,
+                )
+            ]
+        else:
+            return [
+                SplitGenerator(
+                    name=datasets.Split.TRAIN, 
+                    gen_kwargs={"base_path": base_path, "split": None})
+            ]
+
+    
+    def _generate_all(self, base_path: Path, split: Set[Path]=None):
+        """
+        Generates all examples for the dataset, including all features. 
+
+        Args:
+            base_path (Path): The base path to the dataset
+            split (Set[Path]): The paths to all instance crops to include in the current dataset
+        """
+        img_dir = base_path / f"{VERSION}/images"
+        mask_dir = base_path / f"{VERSION}/masks"
+        reid_dir = base_path / f"{VERSION}/reId"
+        
+        # Collecting images, segmentation masks, and instance masks
+        for img_file in img_dir.iterdir():
+            image_id = img_file.stem
+            image_path = img_file
+            segmentation_mask_path = mask_dir / f"{image_id}_segmentationMask.png"
+            instance_masks = list(mask_dir.rglob(f"{image_id}_instanceMask_*.png"))
+            instance_crops = list(reid_dir.rglob(f"**/{image_id}_crop_*.png"))
+            
+            # Check if all crops have a corresponding instance mask
+            assert len(instance_masks) == len(instance_crops) and len(instance_masks) > 0
+
+            # Remove any instance_crops that are not in crops_split
+            if split is not None:
+                instance_crops = [crop for crop in instance_crops if crop in split]
+
+            instance_data = []
+            infos = {}
+            for instance_mask_path, crop_path in zip(instance_masks, instance_crops):
+                infos = name_to_dict(crop_path.stem)
+                instance_data.append({
+                    INSTANCE_MASK: str(instance_mask_path),
+                    CROP: str(crop_path),
+                    VISIBILITY: infos[VISIBILITY],
+                    ID: infos[ID],
+                    CATEGORY: crop_path.relative_to(reid_dir).parts[0],
+                })
+            
+
+            if instance_data:
+                yield image_id, {
+                    IMAGE: str(image_path),
+                    SEGMENTATION_MAKS: str(segmentation_mask_path),
+                    COOP: infos[COOP],
+                    INSTANCES: instance_data,
+                }
+
+
+    def _generate_examples(self, **kwargs):
+        if self.config.name in [full_dataset_name]:
+            yield from self._generate_all(**kwargs)
+        
+        elif self.config.name == semantic_segmentation_name:
+            for image_id, example in self._generate_all(**kwargs):
+                yield image_id, {
+                    IMAGE: example[IMAGE],
+                    SEGMENTATION_MAKS: example[SEGMENTATION_MAKS],
+                }
+
+        elif self.config.name == instance_segmentation_name:
+            for image_id, example in self._generate_all(**kwargs):
+                yield image_id, {
+                    IMAGE: example[IMAGE],
+                    INSTANCES: [
+                        {
+                            INSTANCE_MASK: instance[INSTANCE_MASK]
+                        }
+                        for instance in example[INSTANCES]
+                    ]
+                }
+
+        elif self.config.name == animal_category_anomoalies_name:
+            for image_id, example in self._generate_all(**kwargs):
+                for instance in example[INSTANCES]:
+                    instance_id = Path(instance[CROP]).stem
+                    yield instance_id, {
+                        CROP: instance[CROP],
+                        CATEGORY: instance[CATEGORY],
+                    }
+
+        elif self.config.name in [
+            re_id_best_name, re_id_full_name, 
+            # re_id_good_name, re_id_bad_name
+        ]:
+            for image_id, example in self._generate_all(**kwargs):
+                for instance in example[INSTANCES]:
+                    
+                    # Conditions for filtering
+                    use_all = self.config.name == re_id_full_name
+                    selected_visibility = instance[VISIBILITY] == self.config.name.split("-")[-2]
+
+                    if use_all or selected_visibility:
+                        instance_id = Path(instance[CROP]).stem
+                        yield instance_id, {
+                            CROP: instance[CROP],
+                            ID: instance[ID],
+                        }