raspberry_pi_tflite
This repository is a slightly modified version of TensorFlow Lite Python object detection example with Pi Camera.
An example to use TensorFlow Lite on a Raspberry Pi to perform real-time object detection using images streamed from the Pi Camera. It draws a bounding box around each detected object in the camera preview (when the object score is above a given threshold).
Requirements
- Raspberry Pi (tested with Raspberry Pi 4 model 4GB)
- UbuntuOS (tested with Ubuntu Desktop 21.04)
- Raspberry Pi Camera (test with V2 camera module)
May work with other versions.
Setup your hardware
Install UbuntuOS on your Raspberry Pi.
You also need to connect and configure the Pi Camera..
Since the image stream from the camera is processed by OpenCV VideoCapture(),
USB cameras can also be used.
Install 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 tflite_runtime package:
pip3 install --extra-index-url https://google-coral.github.io/py-repo/ tflite_runtime
If you use Rasberry Pi OS (OS other than Ubuntu), follow Python quickstart.
You also have to install additional Python packages:
pip3 install -r requirements.txt
Download the example files
Use TensorFlow Lite Python object detection example with Pi Camera script to download the MobileNet model and labels file:
# The script takes an argument specifying where you want to save the model files
bash download.sh ./models
Run the example
python3 detect.py \
--model ./models/detect.tflite \
--labels ./models/coco_labels.txt \
--threshold 0.5
You should see the camera feed appear on the monitor attached to your Raspberry Pi. Put some objects in front of the camera, like a coffee mug or keyboard, and you'll see boxes drawn around those that the model recognizes, including the label and score for each. It also prints the amount of time it took to perform each inference in milliseconds at the top-left corner of the screen.
For more information about executing inferences with TensorFlow Lite, read TensorFlow Lite inference.