Datasets

Warning

Dataset API is still fluid and may change. If you use Dataset API in your project until further notice, freeze the supervision version in your requirements.txt or setup.py.

DetectionDataset

Bases: BaseDataset

Contains information about a detection dataset. Handles lazy image loading and annotation retrieval, dataset splitting, conversions into multiple formats.

Attributes:

Name	Type	Description
classes	List[str]	List containing dataset class names.
images	Union[List[str], Dict[str, ndarray]]	Accepts a list of image paths, or dictionaries of loaded cv2 images with paths as keys. If you pass a list of paths, the dataset will lazily load images on demand, which is much more memory-efficient.
annotations	Dict[str, Detections]	Dictionary mapping image path to annotations. The dictionary keys match match the keys in images or entries in the list of image paths.

Source code in supervision/dataset/core.py

class DetectionDataset(BaseDataset):
    """
    Contains information about a detection dataset. Handles lazy image loading
    and annotation retrieval, dataset splitting, conversions into multiple
    formats.

    Attributes:
        classes (List[str]): List containing dataset class names.
        images (Union[List[str], Dict[str, np.ndarray]]):
            Accepts a list of image paths, or dictionaries of loaded cv2 images
            with paths as keys. If you pass a list of paths, the dataset will
            lazily load images on demand, which is much more memory-efficient.
        annotations (Dict[str, Detections]): Dictionary mapping
            image path to annotations. The dictionary keys match
            match the keys in `images` or entries in the list of
            image paths.
    """

    def __init__(
        self,
        classes: List[str],
        images: Union[List[str], Dict[str, np.ndarray]],
        annotations: Dict[str, Detections],
    ) -> None:
        self.classes = classes

        if set(images) != set(annotations):
            raise ValueError(
                "The keys of the images and annotations dictionaries must match."
            )
        self.annotations = annotations

        # Eliminate duplicates while preserving order
        self.image_paths = list(dict.fromkeys(images))

        self._images_in_memory: Dict[str, np.ndarray] = {}
        if isinstance(images, dict):
            self._images_in_memory = images
            warn_deprecated(
                "Passing a `Dict[str, np.ndarray]` into `DetectionDataset` is "
                "deprecated and will be removed in `supervision-0.26.0`. Use "
                "a list of paths `List[str]` instead."
            )
            # TODO: when supervision-0.26.0 is released, and Dict[str, np.ndarray]
            #       for images is no longer supported, also simplify the rest of
            #       the code. E.g. list(images) is no longer needed, and merge can
            #       be simplified.

    @property
    @deprecated(
        "`DetectionDataset.images` property is deprecated and will be removed in "
        "`supervision-0.26.0`. Iterate with `for path, image, annotation in dataset:` "
        "instead."
    )
    def images(self) -> Dict[str, np.ndarray]:
        """
        Load all images to memory and return them as a dictionary.

        !!! warning

            Only use this when you need all images at once.
            It is much more memory-efficient to initialize dataset with
            image paths and use `for path, image, annotation in dataset:`.
        """
        if self._images_in_memory:
            return self._images_in_memory

        images = {image_path: cv2.imread(image_path) for image_path in self.image_paths}
        return images

    def _get_image(self, image_path: str) -> np.ndarray:
        """Assumes that image is in dataset"""
        if self._images_in_memory:
            return self._images_in_memory[image_path]
        return cv2.imread(image_path)

    def __len__(self) -> int:
        return len(self._images_in_memory) or len(self.image_paths)

    def __getitem__(self, i: int) -> Tuple[str, np.ndarray, Detections]:
        """
        Returns:
            Tuple[str, np.ndarray, Detections]: The image path, image data,
                and its corresponding annotation at index i.
        """
        image_path = self.image_paths[i]
        image = self._get_image(image_path)
        annotation = self.annotations[image_path]
        return image_path, image, annotation

    def __iter__(self) -> Iterator[Tuple[str, np.ndarray, Detections]]:
        """
        Iterate over the images and annotations in the dataset.

        Yields:
            Iterator[Tuple[str, np.ndarray, Detections]]:
                An iterator that yields tuples containing the image path,
                the image data, and its corresponding annotation.
        """
        for i in range(len(self)):
            image_path, image, annotation = self[i]
            yield image_path, image, annotation

    def __eq__(self, other) -> bool:
        if not isinstance(other, DetectionDataset):
            return False

        if set(self.classes) != set(other.classes):
            return False

        if self.image_paths != other.image_paths:
            return False

        if self._images_in_memory or other._images_in_memory:
            if not np.array_equal(
                list(self._images_in_memory.values()),
                list(other._images_in_memory.values()),
            ):
                return False

        if self.annotations != other.annotations:
            return False

        return True

    def split(
        self,
        split_ratio: float = 0.8,
        random_state: Optional[int] = None,
        shuffle: bool = True,
    ) -> Tuple[DetectionDataset, DetectionDataset]:
        """
        Splits the dataset into two parts (training and testing)
            using the provided split_ratio.

        Args:
            split_ratio (float): The ratio of the training
                set to the entire dataset.
            random_state (Optional[int]): The seed for the random number generator.
                This is used for reproducibility.
            shuffle (bool): Whether to shuffle the data before splitting.

        Returns:
            Tuple[DetectionDataset, DetectionDataset]: A tuple containing
                the training and testing datasets.

        Examples:
            ```python
            import supervision as sv

            ds = sv.DetectionDataset(...)
            train_ds, test_ds = ds.split(split_ratio=0.7, random_state=42, shuffle=True)
            len(train_ds), len(test_ds)
            # (700, 300)
            ```
        """

        train_paths, test_paths = train_test_split(
            data=self.image_paths,
            train_ratio=split_ratio,
            random_state=random_state,
            shuffle=shuffle,
        )

        train_input: Union[List[str], Dict[str, np.ndarray]]
        test_input: Union[List[str], Dict[str, np.ndarray]]
        if self._images_in_memory:
            train_input = {path: self._images_in_memory[path] for path in train_paths}
            test_input = {path: self._images_in_memory[path] for path in test_paths}
        else:
            train_input = train_paths
            test_input = test_paths
        train_annotations = {path: self.annotations[path] for path in train_paths}
        test_annotations = {path: self.annotations[path] for path in test_paths}

        train_dataset = DetectionDataset(
            classes=self.classes,
            images=train_input,
            annotations=train_annotations,
        )
        test_dataset = DetectionDataset(
            classes=self.classes,
            images=test_input,
            annotations=test_annotations,
        )
        return train_dataset, test_dataset

    @classmethod
    def merge(cls, dataset_list: List[DetectionDataset]) -> DetectionDataset:
        """
        Merge a list of `DetectionDataset` objects into a single
            `DetectionDataset` object.

        This method takes a list of `DetectionDataset` objects and combines
        their respective fields (`classes`, `images`,
        `annotations`) into a single `DetectionDataset` object.

        Args:
            dataset_list (List[DetectionDataset]): A list of `DetectionDataset`
                objects to merge.

        Returns:
            (DetectionDataset): A single `DetectionDataset` object containing
            the merged data from the input list.

        Examples:
            ```python
            import supervision as sv

            ds_1 = sv.DetectionDataset(...)
            len(ds_1)
            # 100
            ds_1.classes
            # ['dog', 'person']

            ds_2 = sv.DetectionDataset(...)
            len(ds_2)
            # 200
            ds_2.classes
            # ['cat']

            ds_merged = sv.DetectionDataset.merge([ds_1, ds_2])
            len(ds_merged)
            # 300
            ds_merged.classes
            # ['cat', 'dog', 'person']
            ```
        """

        def is_in_memory(dataset: DetectionDataset) -> bool:
            return len(dataset._images_in_memory) > 0 or len(dataset.image_paths) == 0

        def is_lazy(dataset: DetectionDataset) -> bool:
            return len(dataset._images_in_memory) == 0

        all_in_memory = all([is_in_memory(dataset) for dataset in dataset_list])
        all_lazy = all([is_lazy(dataset) for dataset in dataset_list])
        if not all_in_memory and not all_lazy:
            raise ValueError(
                "Merging lazy and in-memory DetectionDatasets is not supported."
            )

        images_in_memory = {}
        for dataset in dataset_list:
            images_in_memory.update(dataset._images_in_memory)

        image_paths = list(
            chain.from_iterable(dataset.image_paths for dataset in dataset_list)
        )
        image_paths_unique = list(dict.fromkeys(image_paths))
        if len(image_paths) != len(image_paths_unique):
            duplicates = find_duplicates(image_paths)
            raise ValueError(
                f"Image paths {duplicates} are not unique across datasets."
            )
        image_paths = image_paths_unique

        classes = merge_class_lists(
            class_lists=[dataset.classes for dataset in dataset_list]
        )

        annotations = {}
        for dataset in dataset_list:
            annotations.update(dataset.annotations)
        for dataset in dataset_list:
            class_index_mapping = build_class_index_mapping(
                source_classes=dataset.classes, target_classes=classes
            )
            for image_path in dataset.image_paths:
                annotations[image_path] = map_detections_class_id(
                    source_to_target_mapping=class_index_mapping,
                    detections=annotations[image_path],
                )

        return cls(
            classes=classes,
            images=images_in_memory or image_paths,
            annotations=annotations,
        )

    def as_pascal_voc(
        self,
        images_directory_path: Optional[str] = None,
        annotations_directory_path: Optional[str] = None,
        min_image_area_percentage: float = 0.0,
        max_image_area_percentage: float = 1.0,
        approximation_percentage: float = 0.0,
    ) -> None:
        """
        Exports the dataset to PASCAL VOC format. This method saves the images
        and their corresponding annotations in PASCAL VOC format.

        Args:
            images_directory_path (Optional[str]): The path to the directory
                where the images should be saved.
                If not provided, images will not be saved.
            annotations_directory_path (Optional[str]): The path to
                the directory where the annotations in PASCAL VOC format should be
                saved. If not provided, annotations will not be saved.
            min_image_area_percentage (float): The minimum percentage of
                detection area relative to
                the image area for a detection to be included.
                Argument is used only for segmentation datasets.
            max_image_area_percentage (float): The maximum percentage
                of detection area relative to
                the image area for a detection to be included.
                Argument is used only for segmentation datasets.
            approximation_percentage (float): The percentage of
                polygon points to be removed from the input polygon,
                in the range [0, 1). Argument is used only for segmentation datasets.
        """
        if images_directory_path:
            save_dataset_images(
                dataset=self,
                images_directory_path=images_directory_path,
            )
        if annotations_directory_path:
            Path(annotations_directory_path).mkdir(parents=True, exist_ok=True)
            for image_path, image, annotations in self:
                annotation_name = Path(image_path).stem
                annotations_path = os.path.join(
                    annotations_directory_path, f"{annotation_name}.xml"
                )
                image_name = Path(image_path).name
                pascal_voc_xml = detections_to_pascal_voc(
                    detections=annotations,
                    classes=self.classes,
                    filename=image_name,
                    image_shape=image.shape,  # type: ignore
                    min_image_area_percentage=min_image_area_percentage,
                    max_image_area_percentage=max_image_area_percentage,
                    approximation_percentage=approximation_percentage,
                )

                with open(annotations_path, "w") as f:
                    f.write(pascal_voc_xml)

    @classmethod
    def from_pascal_voc(
        cls,
        images_directory_path: str,
        annotations_directory_path: str,
        force_masks: bool = False,
    ) -> DetectionDataset:
        """
        Creates a Dataset instance from PASCAL VOC formatted data.

        Args:
            images_directory_path (str): Path to the directory containing the images.
            annotations_directory_path (str): Path to the directory
                containing the PASCAL VOC XML annotations.
            force_masks (bool): If True, forces masks to
                be loaded for all annotations, regardless of whether they are present.

        Returns:
            DetectionDataset: A DetectionDataset instance containing
                the loaded images and annotations.

        Examples:
            ```python
            import roboflow
            from roboflow import Roboflow
            import supervision as sv

            roboflow.login()

            rf = Roboflow()

            project = rf.workspace(WORKSPACE_ID).project(PROJECT_ID)
            dataset = project.version(PROJECT_VERSION).download("voc")

            ds = sv.DetectionDataset.from_pascal_voc(
                images_directory_path=f"{dataset.location}/train/images",
                annotations_directory_path=f"{dataset.location}/train/labels"
            )

            ds.classes
            # ['dog', 'person']
            ```
        """

        classes, image_paths, annotations = load_pascal_voc_annotations(
            images_directory_path=images_directory_path,
            annotations_directory_path=annotations_directory_path,
            force_masks=force_masks,
        )

        return DetectionDataset(
            classes=classes, images=image_paths, annotations=annotations
        )

    @classmethod
    def from_yolo(
        cls,
        images_directory_path: str,
        annotations_directory_path: str,
        data_yaml_path: str,
        force_masks: bool = False,
        is_obb: bool = False,
    ) -> DetectionDataset:
        """
        Creates a Dataset instance from YOLO formatted data.

        Args:
            images_directory_path (str): The path to the
                directory containing the images.
            annotations_directory_path (str): The path to the directory
                containing the YOLO annotation files.
            data_yaml_path (str): The path to the data
                YAML file containing class information.
            force_masks (bool): If True, forces
                masks to be loaded for all annotations,
                regardless of whether they are present.
            is_obb (bool): If True, loads the annotations in OBB format.
                OBB annotations are defined as `[class_id, x, y, x, y, x, y, x, y]`,
                where pairs of [x, y] are box corners.

        Returns:
            DetectionDataset: A DetectionDataset instance
                containing the loaded images and annotations.

        Examples:
            ```python
            import roboflow
            from roboflow import Roboflow
            import supervision as sv

            roboflow.login()
            rf = Roboflow()

            project = rf.workspace(WORKSPACE_ID).project(PROJECT_ID)
            dataset = project.version(PROJECT_VERSION).download("yolov5")

            ds = sv.DetectionDataset.from_yolo(
                images_directory_path=f"{dataset.location}/train/images",
                annotations_directory_path=f"{dataset.location}/train/labels",
                data_yaml_path=f"{dataset.location}/data.yaml"
            )

            ds.classes
            # ['dog', 'person']
            ```
        """
        classes, image_paths, annotations = load_yolo_annotations(
            images_directory_path=images_directory_path,
            annotations_directory_path=annotations_directory_path,
            data_yaml_path=data_yaml_path,
            force_masks=force_masks,
            is_obb=is_obb,
        )
        return DetectionDataset(
            classes=classes, images=image_paths, annotations=annotations
        )

    def as_yolo(
        self,
        images_directory_path: Optional[str] = None,
        annotations_directory_path: Optional[str] = None,
        data_yaml_path: Optional[str] = None,
        min_image_area_percentage: float = 0.0,
        max_image_area_percentage: float = 1.0,
        approximation_percentage: float = 0.0,
    ) -> None:
        """
        Exports the dataset to YOLO format. This method saves the
        images and their corresponding annotations in YOLO format.

        Args:
            images_directory_path (Optional[str]): The path to the
                directory where the images should be saved.
                If not provided, images will not be saved.
            annotations_directory_path (Optional[str]): The path to the
                directory where the annotations in
                YOLO format should be saved. If not provided,
                annotations will not be saved.
            data_yaml_path (Optional[str]): The path where the data.yaml
                file should be saved.
                If not provided, the file will not be saved.
            min_image_area_percentage (float): The minimum percentage of
                detection area relative to
                the image area for a detection to be included.
                Argument is used only for segmentation datasets.
            max_image_area_percentage (float): The maximum percentage
                of detection area relative to
                the image area for a detection to be included.
                Argument is used only for segmentation datasets.
            approximation_percentage (float): The percentage of polygon points to
                be removed from the input polygon, in the range [0, 1).
                This is useful for simplifying the annotations.
                Argument is used only for segmentation datasets.
        """
        if images_directory_path is not None:
            save_dataset_images(
                dataset=self, images_directory_path=images_directory_path
            )
        if annotations_directory_path is not None:
            save_yolo_annotations(
                dataset=self,
                annotations_directory_path=annotations_directory_path,
                min_image_area_percentage=min_image_area_percentage,
                max_image_area_percentage=max_image_area_percentage,
                approximation_percentage=approximation_percentage,
            )
        if data_yaml_path is not None:
            save_data_yaml(data_yaml_path=data_yaml_path, classes=self.classes)

    @classmethod
    def from_coco(
        cls,
        images_directory_path: str,
        annotations_path: str,
        force_masks: bool = False,
    ) -> DetectionDataset:
        """
        Creates a Dataset instance from COCO formatted data.

        Args:
            images_directory_path (str): The path to the
                directory containing the images.
            annotations_path (str): The path to the json annotation files.
            force_masks (bool): If True,
                forces masks to be loaded for all annotations,
                regardless of whether they are present.

        Returns:
            DetectionDataset: A DetectionDataset instance containing
                the loaded images and annotations.

        Examples:
            ```python
            import roboflow
            from roboflow import Roboflow
            import supervision as sv

            roboflow.login()
            rf = Roboflow()

            project = rf.workspace(WORKSPACE_ID).project(PROJECT_ID)
            dataset = project.version(PROJECT_VERSION).download("coco")

            ds = sv.DetectionDataset.from_coco(
                images_directory_path=f"{dataset.location}/train",
                annotations_path=f"{dataset.location}/train/_annotations.coco.json",
            )

            ds.classes
            # ['dog', 'person']
            ```
        """
        classes, images, annotations = load_coco_annotations(
            images_directory_path=images_directory_path,
            annotations_path=annotations_path,
            force_masks=force_masks,
        )
        return DetectionDataset(classes=classes, images=images, annotations=annotations)

    def as_coco(
        self,
        images_directory_path: Optional[str] = None,
        annotations_path: Optional[str] = None,
        min_image_area_percentage: float = 0.0,
        max_image_area_percentage: float = 1.0,
        approximation_percentage: float = 0.0,
    ) -> None:
        """
        Exports the dataset to COCO format. This method saves the
        images and their corresponding annotations in COCO format.

        !!! tip

            The format of the mask is determined automatically based on its structure:

            - If a mask contains multiple disconnected components or holes, it will be
            saved using the Run-Length Encoding (RLE) format for efficient storage and
            processing.
            - If a mask consists of a single, contiguous region without any holes, it
            will be encoded as a polygon, preserving the outline of the object.

            This automatic selection ensures that the masks are stored in the most
            appropriate and space-efficient format, complying with COCO dataset
            standards.

        Args:
            images_directory_path (Optional[str]): The path to the directory
                where the images should be saved.
                If not provided, images will not be saved.
            annotations_path (Optional[str]): The path to COCO annotation file.
            min_image_area_percentage (float): The minimum percentage of
                detection area relative to
                the image area for a detection to be included.
                Argument is used only for segmentation datasets.
            max_image_area_percentage (float): The maximum percentage of
                detection area relative to
                the image area for a detection to be included.
                Argument is used only for segmentation datasets.
            approximation_percentage (float): The percentage of polygon points
                to be removed from the input polygon,
                in the range [0, 1). This is useful for simplifying the annotations.
                Argument is used only for segmentation datasets.
        """
        if images_directory_path is not None:
            save_dataset_images(
                dataset=self, images_directory_path=images_directory_path
            )
        if annotations_path is not None:
            save_coco_annotations(
                dataset=self,
                annotation_path=annotations_path,
                min_image_area_percentage=min_image_area_percentage,
                max_image_area_percentage=max_image_area_percentage,
                approximation_percentage=approximation_percentage,
            )

Attributes

images: Dict[str, np.ndarray] property

Load all images to memory and return them as a dictionary.

Warning

Only use this when you need all images at once. It is much more memory-efficient to initialize dataset with image paths and use for path, image, annotation in dataset:.

Functions

__getitem__(i)

Returns:

Type	Description
Tuple[str, ndarray, Detections]	Tuple[str, np.ndarray, Detections]: The image path, image data, and its corresponding annotation at index i.

Source code in supervision/dataset/core.py

def __getitem__(self, i: int) -> Tuple[str, np.ndarray, Detections]:
    """
    Returns:
        Tuple[str, np.ndarray, Detections]: The image path, image data,
            and its corresponding annotation at index i.
    """
    image_path = self.image_paths[i]
    image = self._get_image(image_path)
    annotation = self.annotations[image_path]
    return image_path, image, annotation

__iter__()

Iterate over the images and annotations in the dataset.

Yields:

Type	Description
Tuple[str, ndarray, Detections]	Iterator[Tuple[str, np.ndarray, Detections]]: An iterator that yields tuples containing the image path, the image data, and its corresponding annotation.

Source code in supervision/dataset/core.py

def __iter__(self) -> Iterator[Tuple[str, np.ndarray, Detections]]:
    """
    Iterate over the images and annotations in the dataset.

    Yields:
        Iterator[Tuple[str, np.ndarray, Detections]]:
            An iterator that yields tuples containing the image path,
            the image data, and its corresponding annotation.
    """
    for i in range(len(self)):
        image_path, image, annotation = self[i]
        yield image_path, image, annotation

as_coco(images_directory_path=None, annotations_path=None, min_image_area_percentage=0.0, max_image_area_percentage=1.0, approximation_percentage=0.0)

Exports the dataset to COCO format. This method saves the images and their corresponding annotations in COCO format.

Tip

The format of the mask is determined automatically based on its structure:

• If a mask contains multiple disconnected components or holes, it will be saved using the Run-Length Encoding (RLE) format for efficient storage and processing.
• If a mask consists of a single, contiguous region without any holes, it will be encoded as a polygon, preserving the outline of the object.

This automatic selection ensures that the masks are stored in the most appropriate and space-efficient format, complying with COCO dataset standards.

Parameters:

Name	Type	Description	Default
images_directory_path	Optional[str]	The path to the directory where the images should be saved. If not provided, images will not be saved.	None
annotations_path	Optional[str]	The path to COCO annotation file.	None
min_image_area_percentage	float	The minimum percentage of detection area relative to the image area for a detection to be included. Argument is used only for segmentation datasets.	0.0
max_image_area_percentage	float	The maximum percentage of detection area relative to the image area for a detection to be included. Argument is used only for segmentation datasets.	1.0
approximation_percentage	float	The percentage of polygon points to be removed from the input polygon, in the range [0, 1). This is useful for simplifying the annotations. Argument is used only for segmentation datasets.	0.0

Source code in supervision/dataset/core.py

def as_coco(
    self,
    images_directory_path: Optional[str] = None,
    annotations_path: Optional[str] = None,
    min_image_area_percentage: float = 0.0,
    max_image_area_percentage: float = 1.0,
    approximation_percentage: float = 0.0,
) -> None:
    """
    Exports the dataset to COCO format. This method saves the
    images and their corresponding annotations in COCO format.

    !!! tip

        The format of the mask is determined automatically based on its structure:

        - If a mask contains multiple disconnected components or holes, it will be
        saved using the Run-Length Encoding (RLE) format for efficient storage and
        processing.
        - If a mask consists of a single, contiguous region without any holes, it
        will be encoded as a polygon, preserving the outline of the object.

        This automatic selection ensures that the masks are stored in the most
        appropriate and space-efficient format, complying with COCO dataset
        standards.

    Args:
        images_directory_path (Optional[str]): The path to the directory
            where the images should be saved.
            If not provided, images will not be saved.
        annotations_path (Optional[str]): The path to COCO annotation file.
        min_image_area_percentage (float): The minimum percentage of
            detection area relative to
            the image area for a detection to be included.
            Argument is used only for segmentation datasets.
        max_image_area_percentage (float): The maximum percentage of
            detection area relative to
            the image area for a detection to be included.
            Argument is used only for segmentation datasets.
        approximation_percentage (float): The percentage of polygon points
            to be removed from the input polygon,
            in the range [0, 1). This is useful for simplifying the annotations.
            Argument is used only for segmentation datasets.
    """
    if images_directory_path is not None:
        save_dataset_images(
            dataset=self, images_directory_path=images_directory_path
        )
    if annotations_path is not None:
        save_coco_annotations(
            dataset=self,
            annotation_path=annotations_path,
            min_image_area_percentage=min_image_area_percentage,
            max_image_area_percentage=max_image_area_percentage,
            approximation_percentage=approximation_percentage,
        )

as_pascal_voc(images_directory_path=None, annotations_directory_path=None, min_image_area_percentage=0.0, max_image_area_percentage=1.0, approximation_percentage=0.0)

Exports the dataset to PASCAL VOC format. This method saves the images and their corresponding annotations in PASCAL VOC format.

Parameters:

Name	Type	Description	Default
images_directory_path	Optional[str]	The path to the directory where the images should be saved. If not provided, images will not be saved.	None
annotations_directory_path	Optional[str]	The path to the directory where the annotations in PASCAL VOC format should be saved. If not provided, annotations will not be saved.	None
min_image_area_percentage	float	The minimum percentage of detection area relative to the image area for a detection to be included. Argument is used only for segmentation datasets.	0.0
max_image_area_percentage	float	The maximum percentage of detection area relative to the image area for a detection to be included. Argument is used only for segmentation datasets.	1.0
approximation_percentage	float	The percentage of polygon points to be removed from the input polygon, in the range [0, 1). Argument is used only for segmentation datasets.	0.0

Source code in supervision/dataset/core.py

def as_pascal_voc(
    self,
    images_directory_path: Optional[str] = None,
    annotations_directory_path: Optional[str] = None,
    min_image_area_percentage: float = 0.0,
    max_image_area_percentage: float = 1.0,
    approximation_percentage: float = 0.0,
) -> None:
    """
    Exports the dataset to PASCAL VOC format. This method saves the images
    and their corresponding annotations in PASCAL VOC format.

    Args:
        images_directory_path (Optional[str]): The path to the directory
            where the images should be saved.
            If not provided, images will not be saved.
        annotations_directory_path (Optional[str]): The path to
            the directory where the annotations in PASCAL VOC format should be
            saved. If not provided, annotations will not be saved.
        min_image_area_percentage (float): The minimum percentage of
            detection area relative to
            the image area for a detection to be included.
            Argument is used only for segmentation datasets.
        max_image_area_percentage (float): The maximum percentage
            of detection area relative to
            the image area for a detection to be included.
            Argument is used only for segmentation datasets.
        approximation_percentage (float): The percentage of
            polygon points to be removed from the input polygon,
            in the range [0, 1). Argument is used only for segmentation datasets.
    """
    if images_directory_path:
        save_dataset_images(
            dataset=self,
            images_directory_path=images_directory_path,
        )
    if annotations_directory_path:
        Path(annotations_directory_path).mkdir(parents=True, exist_ok=True)
        for image_path, image, annotations in self:
            annotation_name = Path(image_path).stem
            annotations_path = os.path.join(
                annotations_directory_path, f"{annotation_name}.xml"
            )
            image_name = Path(image_path).name
            pascal_voc_xml = detections_to_pascal_voc(
                detections=annotations,
                classes=self.classes,
                filename=image_name,
                image_shape=image.shape,  # type: ignore
                min_image_area_percentage=min_image_area_percentage,
                max_image_area_percentage=max_image_area_percentage,
                approximation_percentage=approximation_percentage,
            )

            with open(annotations_path, "w") as f:
                f.write(pascal_voc_xml)

as_yolo(images_directory_path=None, annotations_directory_path=None, data_yaml_path=None, min_image_area_percentage=0.0, max_image_area_percentage=1.0, approximation_percentage=0.0)

Exports the dataset to YOLO format. This method saves the images and their corresponding annotations in YOLO format.

Parameters:

Name	Type	Description	Default
images_directory_path	Optional[str]	The path to the directory where the images should be saved. If not provided, images will not be saved.	None
annotations_directory_path	Optional[str]	The path to the directory where the annotations in YOLO format should be saved. If not provided, annotations will not be saved.	None
data_yaml_path	Optional[str]	The path where the data.yaml file should be saved. If not provided, the file will not be saved.	None
min_image_area_percentage	float	The minimum percentage of detection area relative to the image area for a detection to be included. Argument is used only for segmentation datasets.	0.0
max_image_area_percentage	float	The maximum percentage of detection area relative to the image area for a detection to be included. Argument is used only for segmentation datasets.	1.0
approximation_percentage	float	The percentage of polygon points to be removed from the input polygon, in the range [0, 1). This is useful for simplifying the annotations. Argument is used only for segmentation datasets.	0.0

Source code in supervision/dataset/core.py

def as_yolo(
    self,
    images_directory_path: Optional[str] = None,
    annotations_directory_path: Optional[str] = None,
    data_yaml_path: Optional[str] = None,
    min_image_area_percentage: float = 0.0,
    max_image_area_percentage: float = 1.0,
    approximation_percentage: float = 0.0,
) -> None:
    """
    Exports the dataset to YOLO format. This method saves the
    images and their corresponding annotations in YOLO format.

    Args:
        images_directory_path (Optional[str]): The path to the
            directory where the images should be saved.
            If not provided, images will not be saved.
        annotations_directory_path (Optional[str]): The path to the
            directory where the annotations in
            YOLO format should be saved. If not provided,
            annotations will not be saved.
        data_yaml_path (Optional[str]): The path where the data.yaml
            file should be saved.
            If not provided, the file will not be saved.
        min_image_area_percentage (float): The minimum percentage of
            detection area relative to
            the image area for a detection to be included.
            Argument is used only for segmentation datasets.
        max_image_area_percentage (float): The maximum percentage
            of detection area relative to
            the image area for a detection to be included.
            Argument is used only for segmentation datasets.
        approximation_percentage (float): The percentage of polygon points to
            be removed from the input polygon, in the range [0, 1).
            This is useful for simplifying the annotations.
            Argument is used only for segmentation datasets.
    """
    if images_directory_path is not None:
        save_dataset_images(
            dataset=self, images_directory_path=images_directory_path
        )
    if annotations_directory_path is not None:
        save_yolo_annotations(
            dataset=self,
            annotations_directory_path=annotations_directory_path,
            min_image_area_percentage=min_image_area_percentage,
            max_image_area_percentage=max_image_area_percentage,
            approximation_percentage=approximation_percentage,
        )
    if data_yaml_path is not None:
        save_data_yaml(data_yaml_path=data_yaml_path, classes=self.classes)

from_coco(images_directory_path, annotations_path, force_masks=False) classmethod

Creates a Dataset instance from COCO formatted data.

Parameters:

Name	Type	Description	Default
images_directory_path	str	The path to the directory containing the images.	required
annotations_path	str	The path to the json annotation files.	required
force_masks	bool	If True, forces masks to be loaded for all annotations, regardless of whether they are present.	False

Returns:

Name	Type	Description
DetectionDataset	DetectionDataset	A DetectionDataset instance containing the loaded images and annotations.

Examples:

import roboflow
from roboflow import Roboflow
import supervision as sv

roboflow.login()
rf = Roboflow()

project = rf.workspace(WORKSPACE_ID).project(PROJECT_ID)
dataset = project.version(PROJECT_VERSION).download("coco")

ds = sv.DetectionDataset.from_coco(
    images_directory_path=f"{dataset.location}/train",
    annotations_path=f"{dataset.location}/train/_annotations.coco.json",
)

ds.classes
# ['dog', 'person']

Source code in supervision/dataset/core.py

@classmethod
def from_coco(
    cls,
    images_directory_path: str,
    annotations_path: str,
    force_masks: bool = False,
) -> DetectionDataset:
    """
    Creates a Dataset instance from COCO formatted data.

    Args:
        images_directory_path (str): The path to the
            directory containing the images.
        annotations_path (str): The path to the json annotation files.
        force_masks (bool): If True,
            forces masks to be loaded for all annotations,
            regardless of whether they are present.

    Returns:
        DetectionDataset: A DetectionDataset instance containing
            the loaded images and annotations.

    Examples:
        ```python
        import roboflow
        from roboflow import Roboflow
        import supervision as sv

        roboflow.login()
        rf = Roboflow()

        project = rf.workspace(WORKSPACE_ID).project(PROJECT_ID)
        dataset = project.version(PROJECT_VERSION).download("coco")

        ds = sv.DetectionDataset.from_coco(
            images_directory_path=f"{dataset.location}/train",
            annotations_path=f"{dataset.location}/train/_annotations.coco.json",
        )

        ds.classes
        # ['dog', 'person']
        ```
    """
    classes, images, annotations = load_coco_annotations(
        images_directory_path=images_directory_path,
        annotations_path=annotations_path,
        force_masks=force_masks,
    )
    return DetectionDataset(classes=classes, images=images, annotations=annotations)

from_pascal_voc(images_directory_path, annotations_directory_path, force_masks=False) classmethod

Creates a Dataset instance from PASCAL VOC formatted data.

Parameters:

Name	Type	Description	Default
images_directory_path	str	Path to the directory containing the images.	required
annotations_directory_path	str	Path to the directory containing the PASCAL VOC XML annotations.	required
force_masks	bool	If True, forces masks to be loaded for all annotations, regardless of whether they are present.	False

Returns:

Name	Type	Description
DetectionDataset	DetectionDataset	A DetectionDataset instance containing the loaded images and annotations.

Examples:

import roboflow
from roboflow import Roboflow
import supervision as sv

roboflow.login()

rf = Roboflow()

project = rf.workspace(WORKSPACE_ID).project(PROJECT_ID)
dataset = project.version(PROJECT_VERSION).download("voc")

ds = sv.DetectionDataset.from_pascal_voc(
    images_directory_path=f"{dataset.location}/train/images",
    annotations_directory_path=f"{dataset.location}/train/labels"
)

ds.classes
# ['dog', 'person']

Source code in supervision/dataset/core.py

@classmethod
def from_pascal_voc(
    cls,
    images_directory_path: str,
    annotations_directory_path: str,
    force_masks: bool = False,
) -> DetectionDataset:
    """
    Creates a Dataset instance from PASCAL VOC formatted data.

    Args:
        images_directory_path (str): Path to the directory containing the images.
        annotations_directory_path (str): Path to the directory
            containing the PASCAL VOC XML annotations.
        force_masks (bool): If True, forces masks to
            be loaded for all annotations, regardless of whether they are present.

    Returns:
        DetectionDataset: A DetectionDataset instance containing
            the loaded images and annotations.

    Examples:
        ```python
        import roboflow
        from roboflow import Roboflow
        import supervision as sv

        roboflow.login()

        rf = Roboflow()

        project = rf.workspace(WORKSPACE_ID).project(PROJECT_ID)
        dataset = project.version(PROJECT_VERSION).download("voc")

        ds = sv.DetectionDataset.from_pascal_voc(
            images_directory_path=f"{dataset.location}/train/images",
            annotations_directory_path=f"{dataset.location}/train/labels"
        )

        ds.classes
        # ['dog', 'person']
        ```
    """

    classes, image_paths, annotations = load_pascal_voc_annotations(
        images_directory_path=images_directory_path,
        annotations_directory_path=annotations_directory_path,
        force_masks=force_masks,
    )

    return DetectionDataset(
        classes=classes, images=image_paths, annotations=annotations
    )

from_yolo(images_directory_path, annotations_directory_path, data_yaml_path, force_masks=False, is_obb=False) classmethod

Creates a Dataset instance from YOLO formatted data.

Parameters:

Name	Type	Description	Default
images_directory_path	str	The path to the directory containing the images.	required
annotations_directory_path	str	The path to the directory containing the YOLO annotation files.	required
data_yaml_path	str	The path to the data YAML file containing class information.	required
force_masks	bool	If True, forces masks to be loaded for all annotations, regardless of whether they are present.	False
is_obb	bool	If True, loads the annotations in OBB format. OBB annotations are defined as [class_id, x, y, x, y, x, y, x, y], where pairs of [x, y] are box corners.	False

Returns:

Name	Type	Description
DetectionDataset	DetectionDataset	A DetectionDataset instance containing the loaded images and annotations.

Examples:

import roboflow
from roboflow import Roboflow
import supervision as sv

roboflow.login()
rf = Roboflow()

project = rf.workspace(WORKSPACE_ID).project(PROJECT_ID)
dataset = project.version(PROJECT_VERSION).download("yolov5")

ds = sv.DetectionDataset.from_yolo(
    images_directory_path=f"{dataset.location}/train/images",
    annotations_directory_path=f"{dataset.location}/train/labels",
    data_yaml_path=f"{dataset.location}/data.yaml"
)

ds.classes
# ['dog', 'person']

Source code in supervision/dataset/core.py

@classmethod
def from_yolo(
    cls,
    images_directory_path: str,
    annotations_directory_path: str,
    data_yaml_path: str,
    force_masks: bool = False,
    is_obb: bool = False,
) -> DetectionDataset:
    """
    Creates a Dataset instance from YOLO formatted data.

    Args:
        images_directory_path (str): The path to the
            directory containing the images.
        annotations_directory_path (str): The path to the directory
            containing the YOLO annotation files.
        data_yaml_path (str): The path to the data
            YAML file containing class information.
        force_masks (bool): If True, forces
            masks to be loaded for all annotations,
            regardless of whether they are present.
        is_obb (bool): If True, loads the annotations in OBB format.
            OBB annotations are defined as `[class_id, x, y, x, y, x, y, x, y]`,
            where pairs of [x, y] are box corners.

    Returns:
        DetectionDataset: A DetectionDataset instance
            containing the loaded images and annotations.

    Examples:
        ```python
        import roboflow
        from roboflow import Roboflow
        import supervision as sv

        roboflow.login()
        rf = Roboflow()

        project = rf.workspace(WORKSPACE_ID).project(PROJECT_ID)
        dataset = project.version(PROJECT_VERSION).download("yolov5")

        ds = sv.DetectionDataset.from_yolo(
            images_directory_path=f"{dataset.location}/train/images",
            annotations_directory_path=f"{dataset.location}/train/labels",
            data_yaml_path=f"{dataset.location}/data.yaml"
        )

        ds.classes
        # ['dog', 'person']
        ```
    """
    classes, image_paths, annotations = load_yolo_annotations(
        images_directory_path=images_directory_path,
        annotations_directory_path=annotations_directory_path,
        data_yaml_path=data_yaml_path,
        force_masks=force_masks,
        is_obb=is_obb,
    )
    return DetectionDataset(
        classes=classes, images=image_paths, annotations=annotations
    )

merge(dataset_list) classmethod

Merge a list of DetectionDataset objects into a single DetectionDataset object.

This method takes a list of DetectionDataset objects and combines their respective fields (classes, images, annotations) into a single DetectionDataset object.

Parameters:

Name	Type	Description	Default
dataset_list	List[DetectionDataset]	A list of DetectionDataset objects to merge.	required

Returns:

Type	Description
DetectionDataset	A single DetectionDataset object containing
DetectionDataset	the merged data from the input list.

Examples:

import supervision as sv

ds_1 = sv.DetectionDataset(...)
len(ds_1)
# 100
ds_1.classes
# ['dog', 'person']

ds_2 = sv.DetectionDataset(...)
len(ds_2)
# 200
ds_2.classes
# ['cat']

ds_merged = sv.DetectionDataset.merge([ds_1, ds_2])
len(ds_merged)
# 300
ds_merged.classes
# ['cat', 'dog', 'person']

Source code in supervision/dataset/core.py

@classmethod
def merge(cls, dataset_list: List[DetectionDataset]) -> DetectionDataset:
    """
    Merge a list of `DetectionDataset` objects into a single
        `DetectionDataset` object.

    This method takes a list of `DetectionDataset` objects and combines
    their respective fields (`classes`, `images`,
    `annotations`) into a single `DetectionDataset` object.

    Args:
        dataset_list (List[DetectionDataset]): A list of `DetectionDataset`
            objects to merge.

    Returns:
        (DetectionDataset): A single `DetectionDataset` object containing
        the merged data from the input list.

    Examples:
        ```python
        import supervision as sv

        ds_1 = sv.DetectionDataset(...)
        len(ds_1)
        # 100
        ds_1.classes
        # ['dog', 'person']

        ds_2 = sv.DetectionDataset(...)
        len(ds_2)
        # 200
        ds_2.classes
        # ['cat']

        ds_merged = sv.DetectionDataset.merge([ds_1, ds_2])
        len(ds_merged)
        # 300
        ds_merged.classes
        # ['cat', 'dog', 'person']
        ```
    """

    def is_in_memory(dataset: DetectionDataset) -> bool:
        return len(dataset._images_in_memory) > 0 or len(dataset.image_paths) == 0

    def is_lazy(dataset: DetectionDataset) -> bool:
        return len(dataset._images_in_memory) == 0

    all_in_memory = all([is_in_memory(dataset) for dataset in dataset_list])
    all_lazy = all([is_lazy(dataset) for dataset in dataset_list])
    if not all_in_memory and not all_lazy:
        raise ValueError(
            "Merging lazy and in-memory DetectionDatasets is not supported."
        )

    images_in_memory = {}
    for dataset in dataset_list:
        images_in_memory.update(dataset._images_in_memory)

    image_paths = list(
        chain.from_iterable(dataset.image_paths for dataset in dataset_list)
    )
    image_paths_unique = list(dict.fromkeys(image_paths))
    if len(image_paths) != len(image_paths_unique):
        duplicates = find_duplicates(image_paths)
        raise ValueError(
            f"Image paths {duplicates} are not unique across datasets."
        )
    image_paths = image_paths_unique

    classes = merge_class_lists(
        class_lists=[dataset.classes for dataset in dataset_list]
    )

    annotations = {}
    for dataset in dataset_list:
        annotations.update(dataset.annotations)
    for dataset in dataset_list:
        class_index_mapping = build_class_index_mapping(
            source_classes=dataset.classes, target_classes=classes
        )
        for image_path in dataset.image_paths:
            annotations[image_path] = map_detections_class_id(
                source_to_target_mapping=class_index_mapping,
                detections=annotations[image_path],
            )

    return cls(
        classes=classes,
        images=images_in_memory or image_paths,
        annotations=annotations,
    )

split(split_ratio=0.8, random_state=None, shuffle=True)

Splits the dataset into two parts (training and testing) using the provided split_ratio.

Parameters:

Name	Type	Description	Default
split_ratio	float	The ratio of the training set to the entire dataset.	0.8
random_state	Optional[int]	The seed for the random number generator. This is used for reproducibility.	None
shuffle	bool	Whether to shuffle the data before splitting.	True

Returns:

Type	Description
Tuple[DetectionDataset, DetectionDataset]	Tuple[DetectionDataset, DetectionDataset]: A tuple containing the training and testing datasets.

Examples:

import supervision as sv

ds = sv.DetectionDataset(...)
train_ds, test_ds = ds.split(split_ratio=0.7, random_state=42, shuffle=True)
len(train_ds), len(test_ds)
# (700, 300)

Source code in supervision/dataset/core.py

def split(
    self,
    split_ratio: float = 0.8,
    random_state: Optional[int] = None,
    shuffle: bool = True,
) -> Tuple[DetectionDataset, DetectionDataset]:
    """
    Splits the dataset into two parts (training and testing)
        using the provided split_ratio.

    Args:
        split_ratio (float): The ratio of the training
            set to the entire dataset.
        random_state (Optional[int]): The seed for the random number generator.
            This is used for reproducibility.
        shuffle (bool): Whether to shuffle the data before splitting.

    Returns:
        Tuple[DetectionDataset, DetectionDataset]: A tuple containing
            the training and testing datasets.

    Examples:
        ```python
        import supervision as sv

        ds = sv.DetectionDataset(...)
        train_ds, test_ds = ds.split(split_ratio=0.7, random_state=42, shuffle=True)
        len(train_ds), len(test_ds)
        # (700, 300)
        ```
    """

    train_paths, test_paths = train_test_split(
        data=self.image_paths,
        train_ratio=split_ratio,
        random_state=random_state,
        shuffle=shuffle,
    )

    train_input: Union[List[str], Dict[str, np.ndarray]]
    test_input: Union[List[str], Dict[str, np.ndarray]]
    if self._images_in_memory:
        train_input = {path: self._images_in_memory[path] for path in train_paths}
        test_input = {path: self._images_in_memory[path] for path in test_paths}
    else:
        train_input = train_paths
        test_input = test_paths
    train_annotations = {path: self.annotations[path] for path in train_paths}
    test_annotations = {path: self.annotations[path] for path in test_paths}

    train_dataset = DetectionDataset(
        classes=self.classes,
        images=train_input,
        annotations=train_annotations,
    )
    test_dataset = DetectionDataset(
        classes=self.classes,
        images=test_input,
        annotations=test_annotations,
    )
    return train_dataset, test_dataset

ClassificationDataset

Bases: BaseDataset

Contains information about a classification dataset, handles lazy image loading, dataset splitting.

Attributes:

Name	Type	Description
classes	List[str]	List containing dataset class names.
images	Union[List[str], Dict[str, ndarray]]	List of image paths or dictionary mapping image name to image data.
annotations	Dict[str, Classifications]	Dictionary mapping image name to annotations.

Source code in supervision/dataset/core.py

@dataclass
class ClassificationDataset(BaseDataset):
    """
    Contains information about a classification dataset, handles lazy image
    loading, dataset splitting.

    Attributes:
        classes (List[str]): List containing dataset class names.
        images (Union[List[str], Dict[str, np.ndarray]]):
            List of image paths or dictionary mapping image name to image data.
        annotations (Dict[str, Classifications]): Dictionary mapping
            image name to annotations.
    """

    def __init__(
        self,
        classes: List[str],
        images: Union[List[str], Dict[str, np.ndarray]],
        annotations: Dict[str, Classifications],
    ) -> None:
        self.classes = classes

        if set(images) != set(annotations):
            raise ValueError(
                "The keys of the images and annotations dictionaries must match."
            )
        self.annotations = annotations

        # Eliminate duplicates while preserving order
        self.image_paths = list(dict.fromkeys(images))

        self._images_in_memory: Dict[str, np.ndarray] = {}
        if isinstance(images, dict):
            self._images_in_memory = images
            warn_deprecated(
                "Passing a `Dict[str, np.ndarray]` into `ClassificationDataset` is "
                "deprecated and will be removed in a future release. Use "
                "a list of paths `List[str]` instead."
            )

    @property
    @deprecated(
        "`DetectionDataset.images` property is deprecated and will be removed in "
        "`supervision-0.26.0`. Iterate with `for path, image, annotation in dataset:` "
        "instead."
    )
    def images(self) -> Dict[str, np.ndarray]:
        """
        Load all images to memory and return them as a dictionary.

        !!! warning

            Only use this when you need all images at once.
            It is much more memory-efficient to initialize dataset with
            image paths and use `for path, image, annotation in dataset:`.
        """
        if self._images_in_memory:
            return self._images_in_memory

        images = {image_path: cv2.imread(image_path) for image_path in self.image_paths}
        return images

    def _get_image(self, image_path: str) -> np.ndarray:
        """Assumes that image is in dataset"""
        if self._images_in_memory:
            return self._images_in_memory[image_path]
        return cv2.imread(image_path)

    def __len__(self) -> int:
        return len(self._images_in_memory) or len(self.image_paths)

    def __getitem__(self, i: int) -> Tuple[str, np.ndarray, Classifications]:
        """
        Returns:
            Tuple[str, np.ndarray, Classifications]: The image path, image data,
                and its corresponding annotation at index i.
        """
        image_path = self.image_paths[i]
        image = self._get_image(image_path)
        annotation = self.annotations[image_path]
        return image_path, image, annotation

    def __iter__(self) -> Iterator[Tuple[str, np.ndarray, Classifications]]:
        """
        Iterate over the images and annotations in the dataset.

        Yields:
            Iterator[Tuple[str, np.ndarray, Detections]]:
                An iterator that yields tuples containing the image path,
                the image data, and its corresponding annotation.
        """
        for i in range(len(self)):
            image_path, image, annotation = self[i]
            yield image_path, image, annotation

    def __eq__(self, other) -> bool:
        if not isinstance(other, ClassificationDataset):
            return False

        if set(self.classes) != set(other.classes):
            return False

        if self.image_paths != other.image_paths:
            return False

        if self._images_in_memory or other._images_in_memory:
            if not np.array_equal(
                list(self._images_in_memory.values()),
                list(other._images_in_memory.values()),
            ):
                return False

        if self.annotations != other.annotations:
            return False

        return True

    def split(
        self,
        split_ratio: float = 0.8,
        random_state: Optional[int] = None,
        shuffle: bool = True,
    ) -> Tuple[ClassificationDataset, ClassificationDataset]:
        """
        Splits the dataset into two parts (training and testing)
            using the provided split_ratio.

        Args:
            split_ratio (float): The ratio of the training
                set to the entire dataset.
            random_state (Optional[int]): The seed for the
                random number generator. This is used for reproducibility.
            shuffle (bool): Whether to shuffle the data before splitting.

        Returns:
            Tuple[ClassificationDataset, ClassificationDataset]: A tuple containing
            the training and testing datasets.

        Examples:
            ```python
            import supervision as sv

            cd = sv.ClassificationDataset(...)
            train_cd,test_cd = cd.split(split_ratio=0.7, random_state=42,shuffle=True)
            len(train_cd), len(test_cd)
            # (700, 300)
            ```
        """
        train_paths, test_paths = train_test_split(
            data=self.image_paths,
            train_ratio=split_ratio,
            random_state=random_state,
            shuffle=shuffle,
        )

        train_input: Union[List[str], Dict[str, np.ndarray]]
        test_input: Union[List[str], Dict[str, np.ndarray]]
        if self._images_in_memory:
            train_input = {path: self._images_in_memory[path] for path in train_paths}
            test_input = {path: self._images_in_memory[path] for path in test_paths}
        else:
            train_input = train_paths
            test_input = test_paths
        train_annotations = {path: self.annotations[path] for path in train_paths}
        test_annotations = {path: self.annotations[path] for path in test_paths}

        train_dataset = ClassificationDataset(
            classes=self.classes,
            images=train_input,
            annotations=train_annotations,
        )
        test_dataset = ClassificationDataset(
            classes=self.classes,
            images=test_input,
            annotations=test_annotations,
        )

        return train_dataset, test_dataset

    def as_folder_structure(self, root_directory_path: str) -> None:
        """
        Saves the dataset as a multi-class folder structure.

        Args:
            root_directory_path (str): The path to the directory
                where the dataset will be saved.
        """
        os.makedirs(root_directory_path, exist_ok=True)

        for class_name in self.classes:
            os.makedirs(os.path.join(root_directory_path, class_name), exist_ok=True)

        for image_save_path, image, annotation in self:
            image_name = Path(image_save_path).name
            class_id = (
                annotation.class_id[0]
                if annotation.confidence is None
                else annotation.get_top_k(1)[0][0]
            )
            class_name = self.classes[class_id]
            image_save_path = os.path.join(root_directory_path, class_name, image_name)
            cv2.imwrite(image_save_path, image)

    @classmethod
    def from_folder_structure(cls, root_directory_path: str) -> ClassificationDataset:
        """
        Load data from a multiclass folder structure into a ClassificationDataset.

        Args:
            root_directory_path (str): The path to the dataset directory.

        Returns:
            ClassificationDataset: The dataset.

        Examples:
            ```python
            import roboflow
            from roboflow import Roboflow
            import supervision as sv

            roboflow.login()
            rf = Roboflow()

            project = rf.workspace(WORKSPACE_ID).project(PROJECT_ID)
            dataset = project.version(PROJECT_VERSION).download("folder")

            cd = sv.ClassificationDataset.from_folder_structure(
                root_directory_path=f"{dataset.location}/train"
            )
            ```
        """
        classes = os.listdir(root_directory_path)
        classes = sorted(set(classes))

        image_paths = []
        annotations = {}

        for class_name in classes:
            class_id = classes.index(class_name)

            for image in os.listdir(os.path.join(root_directory_path, class_name)):
                image_path = str(os.path.join(root_directory_path, class_name, image))
                image_paths.append(image_path)
                annotations[image_path] = Classifications(
                    class_id=np.array([class_id]),
                )

        return cls(
            classes=classes,
            images=image_paths,
            annotations=annotations,
        )

Attributes

images: Dict[str, np.ndarray] property

Load all images to memory and return them as a dictionary.

Warning

Only use this when you need all images at once. It is much more memory-efficient to initialize dataset with image paths and use for path, image, annotation in dataset:.

Functions

__getitem__(i)

Returns:

Type	Description
Tuple[str, ndarray, Classifications]	Tuple[str, np.ndarray, Classifications]: The image path, image data, and its corresponding annotation at index i.

Source code in supervision/dataset/core.py

def __getitem__(self, i: int) -> Tuple[str, np.ndarray, Classifications]:
    """
    Returns:
        Tuple[str, np.ndarray, Classifications]: The image path, image data,
            and its corresponding annotation at index i.
    """
    image_path = self.image_paths[i]
    image = self._get_image(image_path)
    annotation = self.annotations[image_path]
    return image_path, image, annotation

__iter__()

Iterate over the images and annotations in the dataset.

Yields:

Type	Description
Tuple[str, ndarray, Classifications]	Iterator[Tuple[str, np.ndarray, Detections]]: An iterator that yields tuples containing the image path, the image data, and its corresponding annotation.

Source code in supervision/dataset/core.py

def __iter__(self) -> Iterator[Tuple[str, np.ndarray, Classifications]]:
    """
    Iterate over the images and annotations in the dataset.

    Yields:
        Iterator[Tuple[str, np.ndarray, Detections]]:
            An iterator that yields tuples containing the image path,
            the image data, and its corresponding annotation.
    """
    for i in range(len(self)):
        image_path, image, annotation = self[i]
        yield image_path, image, annotation

as_folder_structure(root_directory_path)

Saves the dataset as a multi-class folder structure.

Parameters:

Name	Type	Description	Default
root_directory_path	str	The path to the directory where the dataset will be saved.	required

Source code in supervision/dataset/core.py

def as_folder_structure(self, root_directory_path: str) -> None:
    """
    Saves the dataset as a multi-class folder structure.

    Args:
        root_directory_path (str): The path to the directory
            where the dataset will be saved.
    """
    os.makedirs(root_directory_path, exist_ok=True)

    for class_name in self.classes:
        os.makedirs(os.path.join(root_directory_path, class_name), exist_ok=True)

    for image_save_path, image, annotation in self:
        image_name = Path(image_save_path).name
        class_id = (
            annotation.class_id[0]
            if annotation.confidence is None
            else annotation.get_top_k(1)[0][0]
        )
        class_name = self.classes[class_id]
        image_save_path = os.path.join(root_directory_path, class_name, image_name)
        cv2.imwrite(image_save_path, image)

from_folder_structure(root_directory_path) classmethod

Load data from a multiclass folder structure into a ClassificationDataset.

Parameters:

Name	Type	Description	Default
root_directory_path	str	The path to the dataset directory.	required

Returns:

Name	Type	Description
ClassificationDataset	ClassificationDataset	The dataset.

Examples:

import roboflow
from roboflow import Roboflow
import supervision as sv

roboflow.login()
rf = Roboflow()

project = rf.workspace(WORKSPACE_ID).project(PROJECT_ID)
dataset = project.version(PROJECT_VERSION).download("folder")

cd = sv.ClassificationDataset.from_folder_structure(
    root_directory_path=f"{dataset.location}/train"
)

Source code in supervision/dataset/core.py

@classmethod
def from_folder_structure(cls, root_directory_path: str) -> ClassificationDataset:
    """
    Load data from a multiclass folder structure into a ClassificationDataset.

    Args:
        root_directory_path (str): The path to the dataset directory.

    Returns:
        ClassificationDataset: The dataset.

    Examples:
        ```python
        import roboflow
        from roboflow import Roboflow
        import supervision as sv

        roboflow.login()
        rf = Roboflow()

        project = rf.workspace(WORKSPACE_ID).project(PROJECT_ID)
        dataset = project.version(PROJECT_VERSION).download("folder")

        cd = sv.ClassificationDataset.from_folder_structure(
            root_directory_path=f"{dataset.location}/train"
        )
        ```
    """
    classes = os.listdir(root_directory_path)
    classes = sorted(set(classes))

    image_paths = []
    annotations = {}

    for class_name in classes:
        class_id = classes.index(class_name)

        for image in os.listdir(os.path.join(root_directory_path, class_name)):
            image_path = str(os.path.join(root_directory_path, class_name, image))
            image_paths.append(image_path)
            annotations[image_path] = Classifications(
                class_id=np.array([class_id]),
            )

    return cls(
        classes=classes,
        images=image_paths,
        annotations=annotations,
    )

split(split_ratio=0.8, random_state=None, shuffle=True)

Splits the dataset into two parts (training and testing) using the provided split_ratio.

Parameters:

Name	Type	Description	Default
split_ratio	float	The ratio of the training set to the entire dataset.	0.8
random_state	Optional[int]	The seed for the random number generator. This is used for reproducibility.	None
shuffle	bool	Whether to shuffle the data before splitting.	True

Returns:

Type	Description
ClassificationDataset	Tuple[ClassificationDataset, ClassificationDataset]: A tuple containing
ClassificationDataset	the training and testing datasets.

Examples:

import supervision as sv

cd = sv.ClassificationDataset(...)
train_cd,test_cd = cd.split(split_ratio=0.7, random_state=42,shuffle=True)
len(train_cd), len(test_cd)
# (700, 300)

Source code in supervision/dataset/core.py

def split(
    self,
    split_ratio: float = 0.8,
    random_state: Optional[int] = None,
    shuffle: bool = True,
) -> Tuple[ClassificationDataset, ClassificationDataset]:
    """
    Splits the dataset into two parts (training and testing)
        using the provided split_ratio.

    Args:
        split_ratio (float): The ratio of the training
            set to the entire dataset.
        random_state (Optional[int]): The seed for the
            random number generator. This is used for reproducibility.
        shuffle (bool): Whether to shuffle the data before splitting.

    Returns:
        Tuple[ClassificationDataset, ClassificationDataset]: A tuple containing
        the training and testing datasets.

    Examples:
        ```python
        import supervision as sv

        cd = sv.ClassificationDataset(...)
        train_cd,test_cd = cd.split(split_ratio=0.7, random_state=42,shuffle=True)
        len(train_cd), len(test_cd)
        # (700, 300)
        ```
    """
    train_paths, test_paths = train_test_split(
        data=self.image_paths,
        train_ratio=split_ratio,
        random_state=random_state,
        shuffle=shuffle,
    )

    train_input: Union[List[str], Dict[str, np.ndarray]]
    test_input: Union[List[str], Dict[str, np.ndarray]]
    if self._images_in_memory:
        train_input = {path: self._images_in_memory[path] for path in train_paths}
        test_input = {path: self._images_in_memory[path] for path in test_paths}
    else:
        train_input = train_paths
        test_input = test_paths
    train_annotations = {path: self.annotations[path] for path in train_paths}
    test_annotations = {path: self.annotations[path] for path in test_paths}

    train_dataset = ClassificationDataset(
        classes=self.classes,
        images=train_input,
        annotations=train_annotations,
    )
    test_dataset = ClassificationDataset(
        classes=self.classes,
        images=test_input,
        annotations=test_annotations,
    )

    return train_dataset, test_dataset

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