Run object detection model on the Raspberry Pi

Intro

Using TensorFlow Lite with Python is great for embedded devices based on Linux, such as Raspberry Pi.

This is the guide for installing TensorFlow Lite on the Raspberry Pi and running pre-trained object detection models on it.

Step 1. Setting up Rasperry Pi

Upgrade Raspbian Stretch to Buster

(If you on Buster, skip this step and simply run sudo apt-get update and sudo apt-get dist-upgrade)

$ sudo apt-get update && sudo apt-get upgrade -y

Verify nothing is wrong. Verify no errors are reported after each command. Fix as required (you’re on your own here!).

$ dpkg -C
$ apt-mark showhold

Prepare apt-get Sources

Update the sources to apt-get. This replaces “stretch” with “buster” in the repository locations giving apt-get access to the new version’s binaries.

$ sudo sed -i 's/stretch/buster/g' /etc/apt/sources.list    
$ sudo sed -i 's/stretch/buster/g' /etc/apt/sources.list.d/raspi.list

Verify this caught them all by running the following, expecting no output. If the command returns anything having previously run the sed commands above, it means more files may need tweaking. Run the sed command for each. The aim is to replace all instances of “stretch”.

$ grep -lnr stretch /etc/apt

Speed up subsequent steps by removing the list change package.

$ sudo apt-get remove apt-listchanges

Do the Upgrade

To update existing packages without updating kernel modules or removing packages, run the following.

$ sudo apt-get update && sudo apt-get upgrade -y

Alternatively, to include kernel modules and removing packages if required, run the following

$ sudo apt-get update && sudo apt-get full-upgrade -y

Cleanup old outdated packages.

$ sudo apt-get autoremove -y && sudo apt-get autoclean

Verify with

 cat /etc/os-release.

Update Firmware

$ sudo rpi-update

and

sudo apt-get install -y python3-pip

and

pip3 install --upgrade setuptools

2. Making sure camera interface is enabled in the Raspberry Pi Configuration menu

Click the Pi icon in the top left corner of the screen, select Preferences -> Raspberry Pi Configuration, and go to the Interfaces tab and verify Camera is set to Enabled. If it isn't, enable it now, and reboot the Raspberry Pi.

Converting Tensorflow to Tensorflow Lite

Using TensorFlow Lite converter. It takes TensorFlow model and generates a TensorFlow Lite model (an optimized FlatBuffer format identified by the .tflite file extension).

Step 2. Install TF Lite dependecies and set up virtual environment

clone this repo

git clone https://github.com/yanovsk/Raspberry-Pi-TF-Lite-Object-Detection

rename the folder to "tfliteod"

mv Raspberry-Pi-TF-Lite-Object-Detection tfliteod
cd tfliteod

run shell script to install get_pi_requirements

bash get_pi_req.sh

Note: shell script will auto install the lastest version of Tensorflow. To install specific version of TF, run pip3 install tensorflow==x.xx (where x.xx stands for the version you want to install)

Set up virtual environment

Install vitrtualenv

pip3 install virtualenv 

Then, create the "tfliteod-env" virtual environment by issuing:

python3 -m venv tfliteod-env

This will create a folder called tfliteod-env inside the tflite1 directory. The tfliteod-env folder will hold all the package libraries for this environment. Next, activate the environment by issuing:

source tfliteod-env/bin/activate

Step 3. Set up TensorFlow Lite detection model

Once, tensorflow is install we can proceed to seting up the object detection model.

We can use either pre-trained model or train it on our end. For the simplicity sake let's use pre-trained sample model by google

Download the sample model (also could be done thru direct link here)

wget https://storage.googleapis.com/download.tensorflow.org/models/tflite/coco_ssd_mobilenet_v1_1.0_quant_2018_06_29.zip

upzip it

unzip coco_ssd_mobilenet_v1_1.0_quant_2018_06_29.zip -d Sample_model

Step 4. Run the model

Note: the model should work on either Picamera module or any other webcam plugged in to the Raspberry Pi as a usb device.

From home/pi/tfliteod run the following command:

python3 TFL_object_detection.py --modeldir=Sample_model

After initializing webcam window should pop-up on your Raspebbery Pi and object detection should work.

Note: this model can recongnize only 80 common objects (check labels.txt for more info on metadata)

However, you can custom train the model using this guide.

Happy hacking!