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Model Module

ImageBoundingBoxes

Class that represents the detected objects bounding boxes from an object detection model on an image.

Source code in devices\raspberry_pi_5\src\model\__init__.py 
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class ImageBoundingBoxes:
    """
    Class that represents the detected objects bounding boxes from an object detection model on an image.
    """

    def __init__(
        self,
        xywhn: Optional[List] = None,
        xyxy: Optional[List] = None,
        xywh: Optional[List] = None,
        xyxyn: Optional[List] = None,
        cls: Optional[List] = None,
        conf: Optional[List] = None,
        n: Optional[int] = None
    ):
        """
        Initialize the ImageBoundingBoxes instance with bounding box coordinates, classes, and confidences.

        Args:
            xywh (Optional[List]): Bounding box coordinates in the format (x_center, y_center, width, height).
            xyxy (Optional[List]): Bounding box coordinates in the format (x1, y1, x2, y2).
            xywhn (Optional[List]): Normalized bounding box coordinates in the format (x_center, y_center, width, height).
            xyxyn (Optional[List]): Normalized bounding box coordinates in the format (x1, y1, x2, y2).
            cls (Optional[List]): Class indices for each detected object.
            conf (Optional[List]): Confidence scores for each detected object.
            n (Optional[int]): Number of detected objects.
        """
        self.__xywh = xywh
        self.__xywhn = xywhn
        self.__xyxy = xyxy
        self.__xyxyn = xyxyn
        self.__cls = cls
        self.__conf = conf
        self.__n = n

    def __str__(self) -> str:
        """
        String representation of the objects detected in the image.

        Returns:
            str: A formatted string containing the bounding boxes, classes, and confidences.
        """
        bounding_boxes = []
        for i in range(self.__n):
            bounding_box_attributes = [
                f"Class: {int(self.__cls[i])}",
                f"Confidence: {self.__conf[i]}",
                f"(X0, Y0): ({self.__xyxyn[i][0]}, {self.__xyxyn[i][1]})",
                f"(X1, Y1): ({self.__xyxyn[i][2]}, {self.__xyxyn[i][3]})",
            ]
            bounding_boxes.append(
                f"Box {i + 1}:\n\t" + "\n\t".join(bounding_box_attributes)
            )
        return "\n".join(bounding_boxes)

    @staticmethod
    def from_pt_cpu_boxes(boxes) -> 'ImageBoundingBoxes':
        """
        Initialize a new ImageBoundingBoxes instances from a PyTorch CPU tensor.

        Args:
            boxes (torch.Tensor): The bounding boxes' tensor.

        Returns:
            ImageBoundingBoxes: An instance containing the bounding boxes, classes, and confidences.
        """
        return ImageBoundingBoxes(
            xywh=boxes.xywh.cpu().numpy(),
            xywhn=boxes.xywhn.cpu().numpy(),
            xyxy=boxes.xyxy.cpu().numpy(),
            xyxyn=boxes.xyxyn.cpu().numpy(),
            cls=boxes.cls.cpu().numpy(),
            conf=boxes.conf.cpu().numpy(),
            n=len(boxes.cls)
        )

    @staticmethod
    def from_pt_cpu(input_data: List) -> 'ImageBoundingBoxes':
        """
        Extract detections from the input data.

        Args:
            input_data (List): Raw detections from the model.

        Returns:
            ImageBoundingBoxes: An instance containing the bounding boxes, classes, and confidences.
        """
        return ImageBoundingBoxes.from_pt_cpu_boxes(input_data[0].boxes)

    @staticmethod
    def from_hailo(
        input_data: List,
        threshold: float = 0.5
    ) -> 'ImageBoundingBoxes':
        """
        Extract detections from the input data.

        Args:
            input_data (List): Raw detections from the model.
            threshold (float): Score threshold for filtering detections. Defaults to 0.5.

        Returns:
            ImageBoundingBoxes: An instance containing the bounding boxes, classes, and confidences.
        """
        boxes, scores, classes = [], [], []
        num_detections = 0

        for i, detection in enumerate(input_data):
            if len(detection) == 0:
                continue

            for det in detection:
                bbox, score = det[:4], det[4]

                if score >= threshold:
                    boxes.append(bbox)
                    scores.append(score)
                    classes.append(i)
                    num_detections += 1

        return ImageBoundingBoxes(
            n=num_detections, xyxy=boxes, cls=classes,
            conf=scores
        )

    def get_number_of_objects(self) -> int:
        """
        Get the number of detected objects.

        Returns:
            int: The number of detected objects.
        """
        return self.__n

    def get_xyxy(self) -> List:
        """
        Get the bounding box coordinates in the format (x1, y1, x2, y2).

        Returns:
            List: A list of bounding box coordinates in the format (x1, y1, x2, y2).
        """
        return self.__xyxy

    def get_xywh(self) -> List:
        """
        Get the bounding box coordinates in the format (x_center, y_center, width, height).

        Returns:
            List: A list of bounding box coordinates in the format (x_center, y_center, width, height).
        """
        return self.__xywh

    def get_xywhn(self) -> List:
        """
        Get the bounding box coordinates in the format (x_center, y_center, width, height) normalized.

        Returns:
            List: A list of bounding box coordinates in the format (x_center, y_center, width, height) normalized.
        """
        return self.__xywhn

    def get_xyxyn(self) -> List:
        """
        Get the bounding box coordinates in the format (x1, y1, x2, y2) normalized.

        Returns:
            List: A list of bounding box coordinates in the format (x1, y1, x2, y2) normalized.
        """
        return self.__xyxyn

    def get_classes(self) -> List:
        """
        Get the classes of the detected objects.

        Returns:
            List: A list of class indices for each detected object.
        """
        return self.__cls

    def get_confidences(self) -> List:
        """
        Get the confidence of the detected objects.

        Returns:
            List: A list of confidence scores for each detected object.
        """
        return self.__conf

    def get_boxes(self) -> tuple:
        """
        Get the bounding box coordinates and class of the detected objects.

        Returns:
            tuple: A tuple containing the class, confidence, and bounding box coordinates in xyxy format.
        """
        return self.__cls, self.__conf, self.__xyxy

__init__(xywhn=None, xyxy=None, xywh=None, xyxyn=None, cls=None, conf=None, n=None)

Initialize the ImageBoundingBoxes instance with bounding box coordinates, classes, and confidences.

Parameters:

Name Type Description Default
xywh Optional[List]

Bounding box coordinates in the format (x_center, y_center, width, height).

 None
xyxy Optional[List]

Bounding box coordinates in the format (x1, y1, x2, y2).

 None
xywhn Optional[List]

Normalized bounding box coordinates in the format (x_center, y_center, width, height).

 None
xyxyn Optional[List]

Normalized bounding box coordinates in the format (x1, y1, x2, y2).

 None
cls Optional[List]

Class indices for each detected object.

 None
conf Optional[List]

Confidence scores for each detected object.

 None
n Optional[int]

Number of detected objects.

 None
Source code in devices\raspberry_pi_5\src\model\__init__.py 
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def __init__(
    self,
    xywhn: Optional[List] = None,
    xyxy: Optional[List] = None,
    xywh: Optional[List] = None,
    xyxyn: Optional[List] = None,
    cls: Optional[List] = None,
    conf: Optional[List] = None,
    n: Optional[int] = None
):
    """
    Initialize the ImageBoundingBoxes instance with bounding box coordinates, classes, and confidences.

    Args:
        xywh (Optional[List]): Bounding box coordinates in the format (x_center, y_center, width, height).
        xyxy (Optional[List]): Bounding box coordinates in the format (x1, y1, x2, y2).
        xywhn (Optional[List]): Normalized bounding box coordinates in the format (x_center, y_center, width, height).
        xyxyn (Optional[List]): Normalized bounding box coordinates in the format (x1, y1, x2, y2).
        cls (Optional[List]): Class indices for each detected object.
        conf (Optional[List]): Confidence scores for each detected object.
        n (Optional[int]): Number of detected objects.
    """
    self.__xywh = xywh
    self.__xywhn = xywhn
    self.__xyxy = xyxy
    self.__xyxyn = xyxyn
    self.__cls = cls
    self.__conf = conf
    self.__n = n

__str__()

String representation of the objects detected in the image.

Returns:

Name Type Description
str str

A formatted string containing the bounding boxes, classes, and confidences.
Source code in devices\raspberry_pi_5\src\model\__init__.py 
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def __str__(self) -> str:
    """
    String representation of the objects detected in the image.

    Returns:
        str: A formatted string containing the bounding boxes, classes, and confidences.
    """
    bounding_boxes = []
    for i in range(self.__n):
        bounding_box_attributes = [
            f"Class: {int(self.__cls[i])}",
            f"Confidence: {self.__conf[i]}",
            f"(X0, Y0): ({self.__xyxyn[i][0]}, {self.__xyxyn[i][1]})",
            f"(X1, Y1): ({self.__xyxyn[i][2]}, {self.__xyxyn[i][3]})",
        ]
        bounding_boxes.append(
            f"Box {i + 1}:\n\t" + "\n\t".join(bounding_box_attributes)
        )
    return "\n".join(bounding_boxes)

from_hailo(input_data, threshold=0.5) staticmethod

Extract detections from the input data.

Parameters:

Name Type Description Default
input_data List

Raw detections from the model.

 required
threshold float

Score threshold for filtering detections. Defaults to 0.5.

 0.5

Returns:

Name Type Description
ImageBoundingBoxes ImageBoundingBoxes

An instance containing the bounding boxes, classes, and confidences.
Source code in devices\raspberry_pi_5\src\model\__init__.py 
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@staticmethod
def from_hailo(
    input_data: List,
    threshold: float = 0.5
) -> 'ImageBoundingBoxes':
    """
    Extract detections from the input data.

    Args:
        input_data (List): Raw detections from the model.
        threshold (float): Score threshold for filtering detections. Defaults to 0.5.

    Returns:
        ImageBoundingBoxes: An instance containing the bounding boxes, classes, and confidences.
    """
    boxes, scores, classes = [], [], []
    num_detections = 0

    for i, detection in enumerate(input_data):
        if len(detection) == 0:
            continue

        for det in detection:
            bbox, score = det[:4], det[4]

            if score >= threshold:
                boxes.append(bbox)
                scores.append(score)
                classes.append(i)
                num_detections += 1

    return ImageBoundingBoxes(
        n=num_detections, xyxy=boxes, cls=classes,
        conf=scores
    )

from_pt_cpu(input_data) staticmethod

Extract detections from the input data.

Parameters:

Name Type Description Default
input_data List

Raw detections from the model.

 required

Returns:

Name Type Description
ImageBoundingBoxes ImageBoundingBoxes

An instance containing the bounding boxes, classes, and confidences.
Source code in devices\raspberry_pi_5\src\model\__init__.py 
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@staticmethod
def from_pt_cpu(input_data: List) -> 'ImageBoundingBoxes':
    """
    Extract detections from the input data.

    Args:
        input_data (List): Raw detections from the model.

    Returns:
        ImageBoundingBoxes: An instance containing the bounding boxes, classes, and confidences.
    """
    return ImageBoundingBoxes.from_pt_cpu_boxes(input_data[0].boxes)

from_pt_cpu_boxes(boxes) staticmethod

Initialize a new ImageBoundingBoxes instances from a PyTorch CPU tensor.

Parameters:

Name Type Description Default
boxes Tensor

The bounding boxes' tensor.

 required

Returns:

Name Type Description
ImageBoundingBoxes ImageBoundingBoxes

An instance containing the bounding boxes, classes, and confidences.
Source code in devices\raspberry_pi_5\src\model\__init__.py 
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@staticmethod
def from_pt_cpu_boxes(boxes) -> 'ImageBoundingBoxes':
    """
    Initialize a new ImageBoundingBoxes instances from a PyTorch CPU tensor.

    Args:
        boxes (torch.Tensor): The bounding boxes' tensor.

    Returns:
        ImageBoundingBoxes: An instance containing the bounding boxes, classes, and confidences.
    """
    return ImageBoundingBoxes(
        xywh=boxes.xywh.cpu().numpy(),
        xywhn=boxes.xywhn.cpu().numpy(),
        xyxy=boxes.xyxy.cpu().numpy(),
        xyxyn=boxes.xyxyn.cpu().numpy(),
        cls=boxes.cls.cpu().numpy(),
        conf=boxes.conf.cpu().numpy(),
        n=len(boxes.cls)
    )

get_boxes()

Get the bounding box coordinates and class of the detected objects.

Returns:

Name Type Description
tuple tuple

A tuple containing the class, confidence, and bounding box coordinates in xyxy format.
Source code in devices\raspberry_pi_5\src\model\__init__.py 
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def get_boxes(self) -> tuple:
    """
    Get the bounding box coordinates and class of the detected objects.

    Returns:
        tuple: A tuple containing the class, confidence, and bounding box coordinates in xyxy format.
    """
    return self.__cls, self.__conf, self.__xyxy

get_classes()

Get the classes of the detected objects.

Returns:

Name Type Description
List List

A list of class indices for each detected object.
Source code in devices\raspberry_pi_5\src\model\__init__.py 
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def get_classes(self) -> List:
    """
    Get the classes of the detected objects.

    Returns:
        List: A list of class indices for each detected object.
    """
    return self.__cls

get_confidences()

Get the confidence of the detected objects.

Returns:

Name Type Description
List List

A list of confidence scores for each detected object.
Source code in devices\raspberry_pi_5\src\model\__init__.py 
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def get_confidences(self) -> List:
    """
    Get the confidence of the detected objects.

    Returns:
        List: A list of confidence scores for each detected object.
    """
    return self.__conf

get_number_of_objects()

Get the number of detected objects.

Returns:

Name Type Description
int int

The number of detected objects.
Source code in devices\raspberry_pi_5\src\model\__init__.py 
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def get_number_of_objects(self) -> int:
    """
    Get the number of detected objects.

    Returns:
        int: The number of detected objects.
    """
    return self.__n

get_xywh()

Get the bounding box coordinates in the format (x_center, y_center, width, height).

Returns:

Name Type Description
List List

A list of bounding box coordinates in the format (x_center, y_center, width, height).
Source code in devices\raspberry_pi_5\src\model\__init__.py 
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def get_xywh(self) -> List:
    """
    Get the bounding box coordinates in the format (x_center, y_center, width, height).

    Returns:
        List: A list of bounding box coordinates in the format (x_center, y_center, width, height).
    """
    return self.__xywh

get_xywhn()

Get the bounding box coordinates in the format (x_center, y_center, width, height) normalized.

Returns:

Name Type Description
List List

A list of bounding box coordinates in the format (x_center, y_center, width, height) normalized.
Source code in devices\raspberry_pi_5\src\model\__init__.py 
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def get_xywhn(self) -> List:
    """
    Get the bounding box coordinates in the format (x_center, y_center, width, height) normalized.

    Returns:
        List: A list of bounding box coordinates in the format (x_center, y_center, width, height) normalized.
    """
    return self.__xywhn

get_xyxy()

Get the bounding box coordinates in the format (x1, y1, x2, y2).

Returns:

Name Type Description
List List

A list of bounding box coordinates in the format (x1, y1, x2, y2).
Source code in devices\raspberry_pi_5\src\model\__init__.py 
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def get_xyxy(self) -> List:
    """
    Get the bounding box coordinates in the format (x1, y1, x2, y2).

    Returns:
        List: A list of bounding box coordinates in the format (x1, y1, x2, y2).
    """
    return self.__xyxy

get_xyxyn()

Get the bounding box coordinates in the format (x1, y1, x2, y2) normalized.

Returns:

Name Type Description
List List

A list of bounding box coordinates in the format (x1, y1, x2, y2) normalized.
Source code in devices\raspberry_pi_5\src\model\__init__.py 
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def get_xyxyn(self) -> List:
    """
    Get the bounding box coordinates in the format (x1, y1, x2, y2) normalized.

    Returns:
        List: A list of bounding box coordinates in the format (x1, y1, x2, y2) normalized.
    """
    return self.__xyxyn