filtered_camera

README

Viam filtered camera module

If your smart machine captures a lot of data, you can filter captured data to selectively store only the data that meets certain criteria. This module allows you to filter image data by:

• Specified classification labels and required confidence scores
• Detected objects and their associated required confidence scores

This allows you to:

• Classify images and only sync images that have the required label
• Look for objects in an image and sync the images that have a certain object

This module also allows you to specifcy a time window for syncing the data captured in the N seconds before the capture criteria were met.

Configure your filtered camera

[!NOTE]
Before configuring your camera, you must create a machine.

Navigate to the CONFIGURE tab of your machine’s page in the Viam app. Add camera / filtered-camera to your machine.

On the new component panel, copy and paste the following attribute template into your camera’s Attributes box.

{
    "camera": "<your_camera_name>",
    "vision": "<your_vision_service_name>",
    "window_seconds": <time_window_for_capture>,
    "classifications": {
        "<label>": <confidence_score>,
        "<label>": <confidence_score>
    },
    "objects": {
        "<label>": <confidence_score>,
        "<label>": <confidence_score>
    }
}

Remove the "classifications" or "objects" section depending on if your ML model is a classifier or detector.

[!NOTE]
For more information, see Configure a Machine.

Attributes

The following attributes are available for erh:camera:filtered-camera bases:

Name	Type	Inclusion	Description
camera	string	Required	The name of the camera to filter images for.
vision	string	Required	The vision service used for image classifications or detections.
window_seconds	float64	Optional	The size of the time window (in seconds) during which images are buffered. When a condition is met, a confidence score for a detection/classification exceeds the required confidence score, the buffered images are stored, allowing us to see the photos taken in the N number of seconds preceding the condition being met.
classifications	float64	Optional	A map of classification labels and the confidence scores required for filtering. Use this if the ML model behind your vision service is a classifier. You can find these labels by testing your vision service.
objects	float64	Optional	A map of object detection labels and the confidence scores required for filtering. Use this if the ML model behind your vision service is a detector. You can find these labels by testing your vision service.

Example configurations:

{
    "camera": "my_camera",
    "vision": "my_red_car_detector",
    "windowSeconds": 5,
    "objects": {
        "red_car": 0.6
    }
}

Local Development

To use the filtered_camera module, clone this repository to your machine’s computer, navigate to the module directory, and run:

go build

On your robot’s page in the Viam app, enter the [module’s executable path](/registry/create/#prepare-the-module-for-execution, then click Add module. The name must use only lowercase characters. Then, click Save in the top right corner of the screen.

Next Steps

• To test your camera, go to the CONTROL tab.
• To test that your camera's images are filtering correctly after configuring data capture, go to the DATA tab.

License

Copyright 2021-2023 Viam Inc.
Apache 2.0

Documentation

Index

• Variables
• type Config
  • func (cfg *Config) Validate(path string) ([]string, error)

Variables

var Model = resource.ModelNamespace("erh").WithFamily("camera").WithModel("filtered-camera")

Types

type Config

type Config struct {
	Camera        string
	Vision        string
	WindowSeconds int `json:"window_seconds"`

	Classifications map[string]float64
	Objects         map[string]float64
}

func (*Config) Validate

func (cfg *Config) Validate(path string) ([]string, error)