Skip to content

draw_bounding_box

main(random_selection=False, headless=False, short_exec=False)

Shows how to obtain the bounding box of an articulated object. Draws the bounding box around the loaded object, a cabinet, and writes the visualized image to disk at the current directory named 'bbox_2d_[loose / tight]_img.png'.

NOTE: In the GUI, bounding boxes can be natively viewed by clicking on the sensor ((*)) icon at the top, and then selecting the appropriate bounding box modalities, and clicking "Show". See:

https://docs.omniverse.nvidia.com/prod_extensions/prod_extensions/ext_replicator/visualization.html#the-visualizer

Source code in omnigibson/examples/objects/draw_bounding_box.py 
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
def main(random_selection=False, headless=False, short_exec=False):
    """
    Shows how to obtain the bounding box of an articulated object.
    Draws the bounding box around the loaded object, a cabinet, and writes the visualized image to disk at the
    current directory named 'bbox_2d_[loose / tight]_img.png'.

    NOTE: In the GUI, bounding boxes can be natively viewed by clicking on the sensor ((*)) icon at the top,
    and then selecting the appropriate bounding box modalities, and clicking "Show". See:

    https://docs.omniverse.nvidia.com/prod_extensions/prod_extensions/ext_replicator/visualization.html#the-visualizer
    """
    og.log.info(f"Demo {__file__}\n    " + "*" * 80 + "\n    Description:\n" + main.__doc__ + "*" * 80)

    # Specify objects to load
    banana_cfg = dict(
        type="DatasetObject",
        name="banana",
        category="banana",
        model="vvyyyv",
        scale=[3.0, 5.0, 2.0],
        position=[-0.906661, -0.545106,  0.136824],
        orientation=[0, 0, 0.76040583, -0.6494482 ],
    )

    door_cfg = dict(
        type="DatasetObject",
        name="door",
        category="door",
        model="ohagsq",
        position=[-2.0, 0, 0.70000001],
        orientation=[0, 0, -0.38268343,  0.92387953],
    )

    # Create the scene config to load -- empty scene with a few objects
    cfg = {
        "scene": {
            "type": "Scene",
        },
        "objects": [banana_cfg, door_cfg],
    }

    # Create the environment
    env = og.Environment(configs=cfg, action_timestep=1/60., physics_timestep=1/60.)

    # Set camera to appropriate viewing pose
    cam = og.sim.viewer_camera
    cam.set_position_orientation(
        position=np.array([-4.62785 , -0.418575,  0.933943]),
        orientation=np.array([ 0.52196595, -0.4231939 , -0.46640436,  0.5752612 ]),
    )

    # Add bounding boxes to camera sensor
    bbox_modalities = ["bbox_3d", "bbox_2d_loose", "bbox_2d_tight"]
    for bbox_modality in bbox_modalities:
        cam.add_modality(bbox_modality)

    # Take a few steps to let objects settle
    for i in range(100):
        env.step(np.array([]))

    # Grab observations from viewer camera and write them to disk
    obs = cam.get_obs()

    for bbox_modality in bbox_modalities:
        # Print out each of the modalities
        og.log.info(f"Observation modality {bbox_modality}:\n{obs[bbox_modality]}")

        # Also write the 2d loose bounding box to disk
        if "3d" not in bbox_modality:
            colorized_img = colorize_bboxes(bboxes_2d_data=obs[bbox_modality], bboxes_2d_rgb=obs["rgb"], num_channels=4)
            fpath = f"{bbox_modality}_img.png"
            plt.imsave(fpath, colorized_img)
            og.log.info(f"Saving modality [{bbox_modality}] image to: {fpath}")

    # Always close environment down at end
    env.close()