Raspberry-Pi-TF-Lite-Object-Detection
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Cleanup old outdated packages.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!
GitHub
https://github.com/yanovsk/Raspberry-Pi-TF-Lite-Object-Detection
