Object detection python tutorial: Automate your lights

Hardware requirements

To start building, you’ll need:

• A computer with a webcam: While this tutorial uses a MacBook Pro, any computer running macOS or 64-bit Linux will work. To explore other cameras used for object detection, head to our guide on object detection sensors. 
• Either a smart bulb or smart plug (we used the Kasa Smart Wi-Fi Plug Mini)
• Mobile phone (to download the Kasa Smart app)
• Table Lamp Base or similar

Software requirements

You’ll use the following software in this tutorial:

• Python 3.8 or newer
• Viam-server
• Viam Python SDK: This software development kit (SDK) lets you control your Viam-powered robot by writing custom scripts in the Python programming language. Install the Viam Python SDK by following these instructions.
• Project repo on GitHub

With the hardware and software in place, you’re now ready to start the project.

Add the component to your machine

Configure your webcam so that your machine can get the video stream from your camera:

1. On the Viam app, navigate to your machine’s page. Check that the part status dropdown in the upper left of the page, next to your machine’s name, reads “Live”; this indicates that your machine is turned on and that its instance of viam-server is in contact with the Viam app.
2. Click the + (Create) button next to your main part in the left-hand menu and select Component. Start typing “webcam” and select camera / webcam. Give your camera a name. This tutorial uses the name cam in all example code. Click Create.
3. Click the video path dropdown and select the webcam you’d like to use for this project from the list of suggestions.
4. Click Save in the top right corner of the screen to save your changes.

Test your physical camera

To test your camera, go to the CONTROL tab and click to expand your camera’s panel.

Toggle View cam to the "on" position. The video feed should display. If it doesn’t, double-check that your config is saved correctly, and check the LOGS tab for errors.

‍

Set up the object detection model

1. Configure the ML model service

Navigate to your machine’s “Configure” tab.

Click the + (Create) button next to your main part in the left-hand menu and select Service. Start typing ML model and select ML model / TFLite CPU from the builtin options.

Enter people as the name, then click Create.

In the new ML Model service panel, configure your service.

Select Deploy model on machine for the Deployment field. Then select the viam-labs:EfficientDet-COCO model from the Models dropdown.

2. Configure an mlmodel detector vision service

Click the + (Create) button next to your main part in the left-hand menu and select Service. Start typing ML model and select vision / ML model from the builtin options.

Enter myPeopleDetector as the name, then click Create.

In the new vision service panel, configure your service.

Select people from the ML Model dropdown.

Check that the object detection is working

To be able to test that the vision service is working, add a transform camera which will add bounding boxes and labels around the objects the service detects.

Click the + (Create) button next to your main part in the left-hand menu and select Component. Start typing “webcam” and select camera / transform. Give your transform camera the name detectionCam and click Create.

In the new transform camera panel, click on {} to go to advanced mode and replace the attributes JSON object with the following object which specifies the camera source that the transform camera will use, and defines a pipeline that adds the defined myPeopleDetector:

Copy{
  "source": "cam",
  "pipeline": [
    {
      "type": "detections",
      "attributes": {
        "detector_name": "myPeopleDetector",
        "confidence_threshold": 0.5
      }
    }
  ]
}

Click Save at the top right corner of the screen.

Create the main script file

On your computer, navigate to the directory where you want to put the code for this project.

1. Create a file there called lightupbot.py. This will be the main script for the machine.
2. Copy the entirety of this file and paste it into your lightupbot.py file.
3. Save lightupbot.py.

Connect the Python code to the robot

You need to tell the code how to access your specific robot (which in this case represents your computer and its webcam).

1. Navigate to the “Connect”CONNECT tab on the Viam app. Make sure Python is selected in the Language selector.
2. Get the robot address and API key from the code sample and set them as environment variables or add them at the top of lightupbot.py. API KEY AND API KEY ID: By default, the sample code does not include your machine API key and API key ID. We strongly recommend that you add your API key and API key ID as an environment variable and import this variable into your development environment as needed. To show your machine’s API key and API key ID in the sample code, toggle Include secret on the CONNECT tab’s Code sample page.

• CAUTION: Do not share your API key or machine address publicly. Sharing this information could compromise your system security by allowing unauthorized access to your machine, or to the computer running your machine.

3. You also need to tell the code how to access your smart plug. Add the host address (for example, 10.1.11.221) of your smart plug that you found in the kasa discover step to line 55 of lightupbot.py.

Run the Python code

Now you're ready to test your robot!

From a command line on your computer, navigate to the project directory and run the code with this command:

Copypython3 lightupbot.py

If the camera detects a person, it will print to the terminal “This is a person!” and turn on the smart plug. If it does not find a person, it will write “There’s nobody here” and will turn off the plug.

Try moving in and out of your webcam’s field of view. You will see your light turn on and off as the robot detects you!

Your terminal output should look like this as your project runs:

Copypython3 lightupbot.py
This is a person!
turning on
There's nobody here
turning off

You can actually detect any object that is listed in the labels.txt (such as a dog or a chair) but for this tutorial, we are detecting a person.

To detect something else with the camera, just change the string “person” on line 46 of lightupbot.py to a different item in the label.txt file.

Copyif d.class_name.lower() == "person":
    print("This is a person!")
    found = True