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boundingboxvisualizer

boundingboxvisualizer

Module for BoundingBoxVisualizer

Classes

BoundingBoxVisualizer 

BoundingBoxVisualizer(
    hide_gt=False,
    hide_gt_over_threshold=True,
    iou_threshold=0.5,
    **kwargs
)

Bases: ImageVisualizer

Visualizer for images with ObjDetInstanceLabels (prediction and ground truth)

Source code in niceml/dashboard/visualizers/boundingboxvisualizer.py 
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def __init__(
    self,
    hide_gt: bool = False,
    hide_gt_over_threshold: bool = True,
    iou_threshold: float = 0.5,
    **kwargs,
):
    super().__init__(**kwargs)
    self.iou_threshold = iou_threshold
    self.hide_gt = hide_gt
    self.hide_gt_over_threshold = hide_gt_over_threshold
Functions
get_images_with_labels 
get_images_with_labels(image_data_container)

Returns images of 'image_data_container' with drawn prediction and ground truth labels

Parameters:

  • image_data_container (ImageContainer) –

    Container with an images label information

Returns:

  • List[ndarray] –

    List of images with prediction and ground truth labels drawn on them

Source code in niceml/dashboard/visualizers/boundingboxvisualizer.py 
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def get_images_with_labels(
    self, image_data_container: ImageContainer
) -> List[np.ndarray]:
    """
    Returns images of 'image_data_container' with drawn prediction and ground truth labels

    Args:
        image_data_container: Container with an images label information

    Returns:
        List of images with prediction and ground truth labels drawn on them
    """
    images = []
    for image_path in image_data_container.get_image_paths():
        try:
            image = self.image_loader(image_path).astype(np.uint8)
            images.append(image)
        except FileNotFoundError:
            logging.getLogger(__name__).warning("FileNotFoundError: %s", image_path)
    images = [
        Image.fromarray(image)
        .convert("RGB")
        .resize(size=image_data_container.image_visu_size.to_pil_size())
        for image in images
    ]

    draw_images = []

    if not check_instance_label_type(
        label_list=image_data_container.predictions, target_type=ObjDetInstanceLabel
    ) and not check_instance_label_type(
        label_list=image_data_container.ground_truth,
        target_type=ObjDetInstanceLabel,
    ):
        raise ValueError(
            "Type of predictions and ground truth labels is not ObjDetInstanceLabel"
        )
    scale_factor = image_data_container.image_visu_size.get_division_factor(
        image_data_container.model_output_size
    )
    image_data_container = image_data_container.scale_instance_labels(
        scale_factor=scale_factor
    )

    for image in images:
        draw_image = draw_labels_on_image(
            image=image,
            pred_bbox_label_list=image_data_container.predictions,
            gt_bbox_label_list=image_data_container.ground_truth,
            hide_gt=self.hide_gt,
            hide_gt_over_thresh=self.hide_gt_over_threshold,
            iou_threshold=self.iou_threshold,
        )
        draw_images.append(draw_image)

    return [np.array(draw_image) for draw_image in draw_images]

Functions