Learn about Visualization

Local Visualization

This section will present how to visualize the detection/tracking results with local visualizer.

If you want to draw prediction results, you can turn this feature on by setting draw=True in TrackVisualizationHook as follows.

default_hooks = dict(visualization=dict(type='TrackVisualizationHook', draw=True))

Specifically, the TrackVisualizationHook has the following arguments:

• draw: whether to draw prediction results. If it is False, it means that no drawing will be done. Defaults to False.

• interval: The interval of visualization. Defaults to 30.

• score_thr: The threshold to visualize the bboxes and masks. Defaults to 0.3.

• show: Whether to display the drawn image. Default to False.

• wait_time: The interval of show (s). Defaults to 0.

• test_out_dir: directory where painted images will be saved in testing process.

• file_client_args: Arguments to instantiate a FileClient. Defaults to dict(backend='disk').

In the TrackVisualizationHook, a visualizer will be called to implement visualization, i.e., DetLocalVisualizer for VID task and TrackLocalVisualizer for MOT, VIS, SOT, VOS tasks. We will present the details below. You can refer to MMEngine for more details about Visualization and Hook.

Detection Visualization

We realize the detection visualization with class DetLocalVisualizer. You can call it as follows.

visualizer = dict(type='DetLocalVisualizer')

It has the following arguments:

• name: Name of the instance. Defaults to ‘visualizer’.

• image: The origin image to draw. The format should be RGB. Defaults to None.

• vis_backends: Visual backend config list. Defaults to None.

• save_dir: Save file dir for all storage backends. If it is None, the backend storage will not save any data.

• bbox_color: Color of bbox lines. The tuple of color should be in BGR order. Defaults to None.

• text_color: Color of texts. The tuple of color should be in BGR order. Defaults to (200, 200, 200).

• line_width: The linewidth of lines. Defaults to 3.

• alpha: The transparency of bboxes or mask. Defaults to 0.8.

Here is a visualization example of DFF:

test_img_29

Tracking Visualization

We realize the tracking visualization with class TrackLocalVisualizer. You can call it as follows.

visualizer = dict(type='TrackLocalVisualizer')

It has the following arguments, which has the same meaning of that in DetLocalVisualizer.

name, image, vis_backends, save_dir, line_width, alpha.

Here is a visualization example of DeepSORT:

test_img_89