A project for ENEL 400, based on image detection using Tensor flow on Raspberry Pi which uses a camera to detect the number of people entering and exiting a store.
The 3 stages of the project:
- https://www.youtube.com/watch?v=xjZYEzk6qSo&t=3s
- https://www.youtube.com/watch?v=cgwiGRqhjds&t=63s
- https://www.youtube.com/watch?v=8usop44kELs&t=5s
- Setting up the Raspberry Pi and Getting Updates
- Organizing our Workspace and Virtual Environment
- Installing the Prerequisites
- Running Object Detection on Image, Video, or Pi Camera
Before we can get started, we must have access to the Raspberry Pi's Desktop Interface. This can be done with VNC Viewer or the standard Monitor and HDMI.
Once you have access to the Desktop Interface, either remote or physical, open up a terminal. Retrieve updates for the Raspberry Pi with
sudo apt-get update
sudo apt-get dist-upgrade
Depending on how recently you setup or updated your Pi, this can be instantaneous or lengthy. After your Raspberry Pi is up-to-date, we should make sure our Camera is enabled. First to open up the System Interface, use
sudo raspi-config
Then navigate to Interfacing Options -> Camera and make sure it is enabled. Then hit Finish and reboot if necessary.
Then, your going to want to clone this repository with
git clone https://github.com/armaanpriyadarshan/TensorFlow-2-Lite-Object-Detection-on-the-Raspberry-Pi.git
This name is a bit long so let's trim it down with
mv TensorFlow-2-Lite-Object-Detection-on-the-Raspberry-Pi tensorflow
We are now going to create a Virtual Environment to avoid version conflicts with previously installed packages on the Raspberry Pi. First, let's install virtual env with
sudo pip3 install virtualenv
Now, we can create our tensorflow virtual environment with
python3 -m venv tensorflow
There should now be a bin folder inside of our tensorflow directory. So let's change directories with
cd tensorflow
We can then activate our Virtual Envvironment with
source bin/activate
Note: Now that we have a virtual environment, everytime you start a new terminal, you will no longer be in the virtual environment. You can reactivate it manually or issue echo "source tensorflow/bin/activate" >> ~/.bashrc. This basically activates our Virtual Environment as soon as we open a new terminal. You can tell if the Virtual Environment is active by the name showing up in parenthesis next to the working directory.
When you issue ls, your tensorflow directory should now look something like this
(https://github.com/tensorflow/tensorflow/releases/tag/v2.2.0)
This step should be relatively simple. I have compressed all the commands into one shellscript which you can run with
bash install-prerequisites.sh
This might take a few minutes, but after everything has been installed you should get this message
Prerequisites Installed Successfully
Now, it's best to test our installation of the tflite_runtime module. To do this first type
python
From the Python terminal, enter these lines
Python 3.7.3 (default, Jul 25 2020, 13:03:44)
[GCC 8.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import tflite_runtime as tf
>>> print(tf.__version__)
If everything went according to plan, you should get
2.5.0
Now we're finally ready to run object detection! In this repository, I've included a sample model that I converted as well as 3 object detection scripts utilizing OpenCV. If you want to convert a custom model or a pre-trained model, you can look at the TensorFlow Lite Conversion Guide that I wrote. A majority of the code is from Edje Electronics' tutorial with a few small adjustments.
TFLite-PiCamera-od.py: This program uses the Pi Camera to perform object detection. It also counts and displays the number of objects detected in the frame. The usage is
usage: TFLite-PiCamera-od.py [-h] [--model MODEL] [--labels LABELS]
[--threshold THRESHOLD] [--resolution RESOLUTION]
optional arguments:
-h, --help show this help message and exit
--model MODEL Provide the path to the TFLite file, default is
models/model.tflite
--labels LABELS Provide the path to the Labels, default is
models/labels.txt
--threshold THRESHOLD
Minimum confidence threshold for displaying detected
objects
--resolution RESOLUTION
Desired webcam resolution in WxH. If the webcam does
not support the resolution entered, errors may occur.
TFLite-Image-od.py: This program takes a single image as input. I haven't managed to get it to run inference on a directory yet. If you do, feel free to share it as it might help others. This script also counts the number of detections. The usage is
usage: TFLite-Image-od.py [-h] [--model MODEL] [--labels LABELS]
[--image IMAGE] [--threshold THRESHOLD]
[--resolution RESOLUTION]
optional arguments:
-h, --help show this help message and exit
--model MODEL Provide the path to the TFLite file, default is
models/model.tflite
--labels LABELS Provide the path to the Labels, default is
models/labels.txt
--image IMAGE Name of the single image to perform detection on
--threshold THRESHOLD
Minimum confidence threshold for displaying detected
objects
--resolution RESOLUTION
Desired webcam resolution in WxH. If the webcam does
not support the resolution entered, errors may occur.
TFLite-Video-od.py: This program is similar to the last two however it takes a video as input. The usage is
usage: TFLite-Video-od.py [-h] [--model MODEL] [--labels LABELS]
[--video VIDEO] [--threshold THRESHOLD]
[--resolution RESOLUTION]
optional arguments:
-h, --help show this help message and exit
--model MODEL Provide the path to the TFLite file, default is
models/model.tflite
--labels LABELS Provide the path to the Labels, default is
models/labels.txt
--video VIDEO Name of the video to perform detection on
--threshold THRESHOLD
Minimum confidence threshold for displaying detected
objects
--resolution RESOLUTION
Desired webcam resolution in WxH. If the webcam does
not support the resolution entered, errors may occur.
Now to finally test it out, run
python TFLite-PiCamera-od.py