TensorFlow Lite Python image segmentation example with Raspberry Pi.

This example uses TensorFlow Lite with Python on a Raspberry Pi to perform real-time image segmentation using images streamed from the camera.

At the end of this page, there are extra steps to accelerate the example using the Coral USB Accelerator to increase inference speed.

Set up your hardware

Before you begin, you need to set up your Raspberry Pi with Raspberry Pi OS (preferably updated to Buster).

You also need to connect and configure the Pi Camera if you use the Pi Camera. This code also works with USB camera connect to the Raspberry Pi.

And to see the results from the camera, you need a monitor connected to the Raspberry Pi. It's okay if you're using SSH to access the Pi shell (you don't need to use a keyboard connected to the Pi)—you only need a monitor attached to the Pi to see the camera stream.

Install the TensorFlow Lite runtime

In this project, all you need from the TensorFlow Lite API is the Interpreter class. So instead of installing the large tensorflow package, we're using the much smaller tflite_runtime package.

To install this on your Raspberry Pi, follow the instructions in the Python quickstart.

You can install the TFLite runtime using this script.

sh setup.sh

Download the example files

First, clone this Git repo onto your Raspberry Pi like this:

git clone https://github.com/tensorflow/examples --depth 1

Then use our script to install a couple Python packages, and download the Deeplabv3 model:

cd examples/lite/examples/image_segmentation/raspberry_pi

# The script install the required dependencies and download the TFLite models.
sh setup.sh

Run the example

python3 segment.py

• You can optionally specify the model parameter to set the TensorFlow Lite model to be used:
  • The default value is deeplabv3.tflite
  • Image segmentation models from TensorFlow Hub with metadata are supported.
• You can optionally specify the displayMode parameter to change how the segmentation result is displayed:
  • Use values: overlay, side-by-side.
  • The default value is overlay.
• Example usage:

python3 main.py
  --model somemodel.tflite
  --displayMode side-by-side

Overlay mode

Side-by-side mode

For more information about executing inferences with TensorFlow Lite, read TensorFlow Lite inference.

Speed up model inference (optional)

If you want to significantly speed up the inference time, you can attach an Coral USB Accelerator—a USB accessory that adds the Edge TPU ML accelerator to any Linux-based system.

If you have a Coral USB Accelerator, you can run the sample with it enabled:

1. First, be sure you have completed the USB Accelerator setup instructions.

2. Run the image segmentation script using the EdgeTPU TFLite model and enable the EdgeTPU option.

python3 main.py \
  --enableEdgeTPU
  --model deeplabv3_edgetpu.tflite

You should see significantly faster inference speeds.

For more information about creating and running TensorFlow Lite models with Coral devices, read TensorFlow models on the Edge TPU.