Configure a detector_3d_segmenter

Changed in RDK v0.2.36 and API v0.1.118

The detector_3d_segmenter vision service model takes 2D bounding boxes from an object detector, and, using the intrinsic parameters of the chosen camera, projects the pixels in the bounding box to points in 3D space. If the chosen camera is not equipped to do projections from 2D to 3D, then this vision model will fail. The label and the pixels associated with the 2D detections become the label and point cloud associated with the 3D segmenter.

Navigate to your machine’s Config tab on the Viam app. Click the Services subtab and click Create service in the lower-left corner. Select the Vision type, then select the Detector to 3D Segmenter model. Enter a name for your service and click Create.

In your vision service’s panel, fill in the Attributes field.

{
    "detector_name": "<detector_name>",
    "confidence_threshold_pct": <number>,
    "mean_k": <integer>,
    "sigma": <number>
}

Add the vision service object to the services array in your raw JSON configuration:

"services": [
    {
        "name": "<segmenter_name>",
        "type": "vision",
        "namespace": "rdk",
        "model": "detector_3d_segmenter",
        "attributes": {
            "detector_name": "my_detector",
            "confidence_threshold_pct": 0.5,
            "mean_k": 50,
            "sigma": 2.0
        }
    },
    ... // Other services
]
"services": [
    {
        "name": "my_segmenter",
        "type": "vision",
        "namespace": "rdk",
        "model": "detector_3d_segmenter",
        "attributes": {
            "detector_name": "my_detector",
            "confidence_threshold_pct": 0.5,
            "mean_k": 50,
            "sigma": 2.0
        }
    }
]

The following parameters are available for a detector_3d_segmenter.

ParameterInclusionDescription
detector_nameRequiredThe name of a registered detector vision service. The segmenter vision service uses the detections from "detector_name" to create the 3D segments.
confidence_threshold_pctOptionalA number between 0 and 1 which represents a filter on object confidence scores. Detections that score below the threshold will be filtered out in the segmenter. The default is 0.5.
mean_kRequiredAn integer parameter used in a subroutine to eliminate the noise in the point clouds. It should be set to be 5-10% of the minimum segment size. Start with 5% and go up if objects are still too noisy. If you don’t want to use the filtering, set the number to 0 or less.
sigmaRequiredA floating point parameter used in a subroutine to eliminate the noise in the point clouds. It should usually be set between 1.0 and 2.0. 1.25 is usually a good default. If you want the object result to be less noisy (at the risk of losing some data around its edges) set sigma to be lower.

Click Save config and proceed to test your segmenter.

Test your segmenter

The following code uses the GetObjectPointClouds method to run a segmenter vision model on an image from the machine’s camera "cam1":

from viam.services.vision import VisionClient

robot = await connect()

# Grab Viam's vision service for the segmenter
my_segmenter = VisionClient.from_robot(robot, "my_segmenter")

objects = await my_segmenter.get_object_point_clouds("cam1")

await robot.close()

To learn more about how to use segmentation, see the Python SDK docs.

import (
"go.viam.com/rdk/config"
"go.viam.com/rdk/services/vision"
"go.viam.com/rdk/components/camera"
)

cameraName := "cam1" // Use the same component name that you have in your machine configuration

// Get the vision service you configured with name "my_segmenter" from the machine
mySegmenter, err := vision.from_robot(robot, "my_segmenter")
if err != nil {
    logger.Fatalf("Cannot get vision service: %v", err)
}

// Get segments
segments, err := mySegmenter.ObjectPointClouds(context.Background(), cameraName, nil)
if err != nil {
    logger.Fatalf("Could not get segments: %v", err)
}
if len(segments) > 0 {
    logger.Info(segments[0])
}

To learn more about how to use segmentation, see the Go SDK docs.



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