tf.keras.visualization.plot_bounding_box_gallery

Plots a gallery of images with bounding boxes.

tf.keras.visualization.plot_bounding_box_gallery(
    images,
    bounding_box_format,
    y_true=None,
    y_pred=None,
    value_range=(0, 255),
    true_color=(0, 188, 212),
    pred_color=(255, 235, 59),
    line_thickness=2,
    font_scale=1.0,
    text_thickness=None,
    class_mapping=None,
    ground_truth_mapping=None,
    prediction_mapping=None,
    legend=False,
    legend_handles=None,
    rows=None,
    cols=None,
    data_format=None,
    **kwargs
)

This function can display both ground truth and predicted bounding boxes on a set of images. It supports various bounding box formats and can include class labels and a legend.

images A 4D tensor or NumPy array of images. Shape should be (batch_size, height, width, channels).
bounding_box_format The format of the bounding boxes. See keras.utils.bounding_boxes.convert_format for supported formats (e.g., "xyxy", "yxyx", "xywh", "center_xywh", "centeryxhw", and the "rel*" variants).
y_true A dictionary containing the ground truth bounding boxes and labels. Should have the same structure as the bounding_boxes argument in keras.visualization.draw_bounding_boxes. Defaults to None.
y_pred A dictionary containing the predicted bounding boxes and labels. Should have the same structure as y_true. Defaults to None.
value_range A tuple specifying the value range of the images (e.g., (0, 255) or (0, 1)). Defaults to (0, 255).
true_color A tuple of three integers representing the RGB color for the ground truth bounding boxes. Defaults to (0, 188, 212).
pred_color A tuple of three integers representing the RGB color for the predicted bounding boxes. Defaults to (255, 235, 59).
line_thickness The thickness of the bounding box lines. Defaults to 2.
font_scale The scale of the font used for labels. Defaults to 1.0.
text_thickness The thickness of the bounding box text. Defaults to line_thickness.
class_mapping A dictionary mapping class IDs to class names. Used for both ground truth and predicted boxes if ground_truth_mapping and prediction_mapping are not provided. Defaults to None.
ground_truth_mapping A dictionary mapping class IDs to class names specifically for ground truth boxes. Overrides class_mapping for ground truth. Defaults to None.
prediction_mapping A dictionary mapping class IDs to class names specifically for predicted boxes. Overrides class_mapping for predictions. Defaults to None.
legend A boolean indicating whether to show a legend. Defaults to False.
legend_handles A list of matplotlib Patch objects to use for the legend. If this is provided, the legend argument will be ignored. Defaults to None.
rows The number of rows in the image gallery. Required if the images are not batched. Defaults to None.
cols The number of columns in the image gallery. Required if the images are not batched. Defaults to None.
data_format The image data format "channels_last" or "channels_first". Defaults to the Keras backend data format.
kwargs Additional keyword arguments to be passed to keras.visualization.plot_image_gallery.

The output of keras.visualization.plot_image_gallery.

ValueError If images is not a 4D tensor/array or if both legend and legend_handles are specified.
ImportError if matplotlib is not installed