Chicks4FreeID.py

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],
                        }