anchorencoding

anchorencoding

Module for anchor encoding

Classes

AnchorEncoder

Bases: ABC

Abstract AnchorEncoder

Functions

decode_anchors abstractmethod

decode_anchors(anchor_list, encodings)

"Decodes an anchor list with corresponding labels to a numpy array

Source code in niceml/mlcomponents/objdet/anchorencoding.py

@abstractmethod
def decode_anchors(self, anchor_list: List[BoundingBox], encodings: np.ndarray):
    """ "Decodes an anchor list with corresponding labels to a numpy array"""

encode_anchors abstractmethod

encode_anchors(
    anchor_list, gt_labels, num_classes, box_variance
)

Encodes an anchor list with corresponding labels to a numpy array

Source code in niceml/mlcomponents/objdet/anchorencoding.py

@abstractmethod
def encode_anchors(
    self,
    anchor_list: List[BoundingBox],
    gt_labels: List[ObjDetInstanceLabel],
    num_classes: int,
    box_variance: List[float],
) -> np.ndarray:
    """Encodes an anchor list with corresponding labels to a numpy array"""

OptimizedAnchorEncoder dataclass

Bases: AnchorEncoder

Class to encode anchors before model optimization

Functions

decode_anchors

decode_anchors(anchor_list, encodings)

Decodes encoded bounding boxes in an optimized way Args: anchor_list: List of bounding boxes representing the anchors encodings: 2D array with at least the four coordinates of the bounding boxes in xywh format. This is optionally followed by a mask flag (POSITIVE,NEGATIVE,IGNORE) and a one-hot encoded class vector

Returns:

• –

  Same as encodings but with decoded bounding box coordinates

Source code in niceml/mlcomponents/objdet/anchorencoding.py

def decode_anchors(self, anchor_list: List[BoundingBox], encodings: np.ndarray):
    """
    Decodes encoded bounding boxes in an optimized way
    Args:
        anchor_list: List of bounding boxes representing the anchors
        encodings: 2D array with at least the four coordinates
        of the bounding boxes in xywh format.
        This is optionally followed by a mask flag (POSITIVE,NEGATIVE,IGNORE)
        and a one-hot encoded class vector

    Returns:
        Same as encodings but with decoded bounding box coordinates

    """

    if (
        self.anchor_array_stored_xywh is None
        or len(anchor_list) != self.anchor_array_stored_xywh.shape[0]
    ):
        self.anchor_array_stored_xywh = np.array(
            [box.get_absolute_ullr() for box in anchor_list]
        )

    box_variance = self.box_variance  # pylint: disable=no-member
    decoded_boxes = decode_boxes(
        anchor_boxes_xywh=self.anchor_array_stored_xywh,
        encoded_array_xywh=encodings[:, :4],
        box_variances=np.array(box_variance),
    )

    encodings[:, :4] = decoded_boxes

    return encodings

encode_anchors

encode_anchors(
    anchor_list, gt_labels, num_classes, box_variance
)

Encodes an anchor list to a numpy array

Source code in niceml/mlcomponents/objdet/anchorencoding.py

def encode_anchors(
    self,
    anchor_list: List[BoundingBox],
    gt_labels: List[ObjDetInstanceLabel],
    num_classes: int,
    box_variance: List[float],
) -> np.ndarray:
    """Encodes an anchor list to a numpy array"""
    box: BoundingBox
    label: ObjDetInstanceLabel
    if (
        self.anchor_array_stored_ullr is None
        or len(anchor_list) != self.anchor_array_stored_ullr.shape[0]
    ):
        self.anchor_array_stored_ullr = np.array(
            [box.get_absolute_ullr() for box in anchor_list]
        )

    if len(gt_labels) == 0:
        anchor_shape = self.anchor_array_stored_ullr.shape
        target_array = np.zeros(
            (anchor_shape[0], anchor_shape[1] + 1 + num_classes)
        )
        target_array[:, anchor_shape[1]] = NEGATIVE_MASK_VALUE
        return target_array

    gt_box_array = np.array(
        [label.bounding_box.get_absolute_ullr() for label in gt_labels]
    )
    iou_matrix = compute_iou_matrix(self.anchor_array_stored_ullr, gt_box_array)
    class_index_array = np.array([label.class_index for label in gt_labels])
    target_array = compute_target_gt_array(
        self.anchor_array_stored_ullr,
        gt_box_array,
        iou_matrix=iou_matrix,
        box_variances=np.array(box_variance),
        class_index_array=class_index_array,
        match_iou=self.match_iou,
        ignore_iou=self.ignore_iou,
        num_classes=num_classes,
    )
    return target_array

SimpleAnchorEncoder dataclass

Bases: AnchorEncoder

Class to encode anchors before model optimization

Functions

decode_anchors

decode_anchors(anchor_list, encodings)

Decodes encoded bounding boxes Args: anchor_list: List of bounding boxes representing the anchors encodings: 2D array with at least the four coordinates of the bounding boxes in xywh format. This is optionally followed by a mask flag (POSITIVE,NEGATIVE,IGNORE) and a one-hot encoded class vector

Returns:

• ndarray –

  Same as encodings but with decoded bounding box coordinates

Source code in niceml/mlcomponents/objdet/anchorencoding.py

def decode_anchors(
    self, anchor_list: List[BoundingBox], encodings: np.ndarray
) -> np.ndarray:
    """
    Decodes encoded bounding boxes
    Args:
        anchor_list: List of bounding boxes representing the anchors
        encodings: 2D array with at least the four coordinates
        of the bounding boxes in xywh format.
        This is optionally followed by a mask flag (POSITIVE,NEGATIVE,IGNORE)
        and a one-hot encoded class vector

    Returns:
        Same as encodings but with decoded bounding box coordinates

    """
    decoded_box_predictions: List[np.ndarray] = []

    for anchor, prediction in zip(anchor_list, encodings):
        box_variance = self.box_variance  # pylint: disable=no-member
        decoded_box = anchor.decode(
            predicted_values=list(prediction[:4]), box_variance=box_variance
        )

        decoded_box_predictions.append(
            np.array(list(decoded_box.get_absolute_ullr()))
        )
    decoded_box_prediction_array = np.array(decoded_box_predictions)
    encodings[:, :4] = decoded_box_prediction_array

    return encodings

encode_anchors

encode_anchors(
    anchor_list, gt_labels, num_classes, box_variance
)

Encodes an anchor list to a numpy array

Source code in niceml/mlcomponents/objdet/anchorencoding.py

def encode_anchors(  # pylint: disable=too-many-locals
    self,
    anchor_list: List[BoundingBox],
    gt_labels: List[ObjDetInstanceLabel],
    num_classes: int,
    box_variance: List[float],
) -> np.ndarray:
    """Encodes an anchor list to a numpy array"""
    encoded_feature_list: List[List[float]] = []

    for anchor in anchor_list:
        if len(gt_labels) == 0:
            target_bbox = anchor
            target_label = None
            prediction_flag = NEGATIVE_MASK_VALUE
        else:
            max_iou = 0
            target_bbox = gt_labels[0].bounding_box
            target_label = None
            prediction_flag = NEGATIVE_MASK_VALUE

            for label_instance in gt_labels:
                gt_bbox = label_instance.bounding_box
                iou = anchor.calc_iou(gt_bbox)

                if iou > max_iou:
                    max_iou = iou
                    target_bbox = gt_bbox
                    target_label = label_instance.class_index
                    prediction_flag = POSITIVE_MASK_VALUE

            if self.match_iou > max_iou > self.ignore_iou:
                target_label = None
                prediction_flag = IGNORE_MASK_VALUE

            elif max_iou < self.ignore_iou:
                target_label = None
                prediction_flag = NEGATIVE_MASK_VALUE

        cur_encoding = anchor.encode(target_bbox, box_variance)
        cur_encoding.append(prediction_flag)
        target_label_vector = [0] * num_classes
        if target_label is not None:
            target_label_vector[target_label] = 1
        cur_encoding += target_label_vector
        encoded_feature_list.append(cur_encoding)

    target_bbox_array = np.array(encoded_feature_list)
    return target_bbox_array

Functions