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Triggering events based on the presence of people has been the dream of many geeks and DIY automation junkies for a while. Having your house to turn the lights on or off when you enter or exit your living room is an interesting application, for instance. Most of the solutions out there to solve these kinds of problems, even more high-end solutions like the Philips Hue sensors, detect motion, not actual people presence — which means that the lights will switch off once you lay on your couch like a sloth. The ability to turn off music and/or tv when you exit the room and head to your bedroom, without the hassle of switching all the buttons off, is also an interesting corollary. Detecting the presence of people in your room while you’re not at home is another interesting application.
Thermal cameras coupled with deep neural networks are a much more robust strategy to actually detect the presence of people. Unlike motion sensors, they will detect the presence of people even when they aren’t moving. And unlike optical cameras, they detect bodies by measuring the heat that they emit in the form of infrared radiation, and are therefore much more robust — their sensitivity doesn’t depend on lighting conditions, on the position of the target, or the colour. Before exploring the thermal camera solution, I tried for a while to build a model that instead relied on optical images from a traditional webcam. The differences are staggering: I trained the optical model on more than ten thousands 640x480 images taken all through a week in different lighting conditions, while I trained the thermal camera model on a dataset of 900 24x32 images taken during a single day. Even with more complex network architectures, the optical model wouldn’t score above a 91% accuracy in detecting the presence of people, while the thermal model would achieve around 99% accuracy within a single training phase of a simpler neural network. Despite the high potential, there’s not much out there in the market — there’s been some research work on the topic (if you google “people detection thermal camera” you’ll mostly find research papers) and a few high-end and expensive products for professional surveillance. In lack of ready-to-go solutions for my house, I decided to take on my duty and build my own solution — making sure that it can easily be replicated by anyone.
Prepare the hardware
For this example we'll use the following hardware:
-
A RaspberryPi (cost: around $35). In theory any model should work, but it’s probably not a good idea to use a single-core RaspberryPi Zero for machine learning tasks — the task itself is not very expensive (we’ll only use the Raspberry for doing predictions on a trained model, not to train the model), but it may still suffer some latency on a Zero. Plus, it may be really painful to install some of the required libraries (like Tensorflow or OpenCV) on the
arm6architecture used by the RaspberryPi Zero. Any better performing model (from RPi3 onwards) should definitely do the job. -
A thermal camera. For this project, I’ve used the MLX90640 Pimoroni breakout camera (cost: $55), as it’s relatively cheap, easy to install, and it provides good results. This camera comes in standard (55°) and wide-angle (110°) versions. I’ve used the wide-angle model as the camera monitors a large living room, but take into account that both have the same resolution (32x24 pixels), so the wider angle comes with the cost of a lower spatial resolution. If you want to use a different thermal camera there’s not much you’ll need to change, as long as it comes with a software interface for RaspberryPi and it’s compatible with Platypush.
Setting up the MLX90640 on your RaspberryPi if you have a Breakout Garden it’s easy as a pie. Fit the Breakout Garden on top of your RaspberryPi. Fit the camera breakout into an I2C slot. Boot the RaspberryPi. Done. Otherwise, you can also connect the device directly to the RaspberryPi I2C interface, either using the right hardware PINs or the software emulation layer.
Prepare the software
I tested my code on Raspbian, but with a few minor modifications it should be easily adaptable to any distribution installed on the RaspberryPi.
The software support for the thermal camera requires a bit of work. The MLX90640 doesn’t come (yet) with a Python ready-to-use interface, but a C++ open-source driver is provided - and that's the driver that is wrapped by the Platypush integration. Instructions to install it:
# Install the dependencies
[sudo] apt-get install libi2c-dev
# Enable the I2C interface
echo dtparam=i2c_arm=on | sudo tee -a /boot/config.txt
# It's advised to configure the SPI bus baud rate to
# 400kHz to support the higher throughput of the sensor
echo dtparam=i2c1_baudrate=400000 | sudo tee -a /boot/config.txt
# A reboot is required here if you didn't have the
# options above enabled in your /boot/config.txt
[sudo] reboot
# Clone the driver's codebase
git clone https://github.com/pimoroni/mlx90640-library
cd mlx90640-library
# Compile the rawrgb example
make clean
make bcm2835
make I2C_MODE=LINUX examples/rawrgb
If it all went well you should see an executable named rawrgb under the examples directory. If you run it you should
see a bunch of binary data — that’s the raw binary representation of the frames captured by the camera. Remember where
it is located or move it to a custom bin folder, as it’s the executable that platypush will use to interact with the
camera module.
This post assumes that you have already installed and configured Platypush on your system. If not, head to my post on getting started with Platypush, the readthedocs page, the Gitlab page or the wiki.
Install also the Python dependencies for the HTTP server, the MLX90640 plugin and Tensorflow:
[sudo] pip install 'platypush[http,tensorflow,mlx90640]'
Heading to your computer (we'll be using it for building the model that will be used on the RaspberryPi), install OpenCV, Tensorflow and Jupyter and my utilities for handling images:
# For image manipulation
[sudo] pip install opencv
# Install Jupyter notebook to run the training code
[sudo] pip install jupyterlab
# Then follow the instructions at https://jupyter.org/install
# Tensorflow framework for machine learning and utilities
[sudo] pip install tensorflow numpy matplotlib
# Clone my repository with the image and training utilities
# and the Jupyter notebooks that we'll use for training.
git clone https://github.com/BlackLight/imgdetect-utils
Capturing phase
Now that you’ve got all the hardware and software in place, it’s time to start capturing frames with your camera and use
them to train your model. First, configure
the MLX90640 plugin in your
Platypush configuration file (by default, ~/.config/platypush/config.yaml):
# Enable the webserver
backend.http:
enabled: True
camera.ir.mlx90640:
fps: 16 # Frames per second
rotate: 270 # Can be 0, 90, 180, 270
rawrgb_path: /path/to/your/rawrgb
Restart the service, and if you haven't already create a user from the web interface at http://your-rpi:8008. You
should now be able to take pictures through the API:
curl -XPOST -H 'Content-Type: application/json' -d '
{
"type":"request",
"action":"camera.ir.mlx90640.capture",
"args": {
"output_file":"~/snap.png",
"scale_factor":20
}
}' -a 'username:password' http://localhost:8008/execute
If everything went well, the thermal picture should be stored under ~/snap.png. In my case it looks like this while
I’m in standing front of the sensor:
Notice the glow at the bottom-right corner — that’s actually the heat from my RaspberryPi 4 CPU. It’s there in all the images I take, and you may probably see similar results if you mounted your camera on top of the Raspberry itself, but it shouldn’t be an issue for your model training purposes.
If you open the web panel (http://your-host:8008) you’ll also notice a new tab, represented by the sun icon, that you
can use to monitor your camera from a web interface.