563 lines
22 KiB
Markdown
563 lines
22 KiB
Markdown
[//]: # (title: Detect people with a RaspberryPi, a thermal camera, Platypush and Tensorflow)
|
||
[//]: # (description: Use cheap components and open-source software to build a robust presence detector.)
|
||
[//]: # (image: /img/people-detect-1.png)
|
||
[//]: # (author: Fabio Manganiello <fabio@platypush.tech>)
|
||
[//]: # (published: 2019-09-27)
|
||
|
||
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](https://www2.meethue.com/en-us/p/hue-motion-sensor/046677473389), 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 `arm6` architecture 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](https://shop.pimoroni.com/products/mlx90640-thermal-camera-breakout) 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](https://docs.platypush.tech/en/latest/platypush/plugins/camera.ir.mlx90640.html).
|
||
|
||
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](https://radiostud.io/howto-i2c-communication-rpi/),
|
||
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](https://github.com/pimoroni/mlx90640-library) -
|
||
and that's the driver that is wrapped by the Platypush integration. Instructions to install it:
|
||
|
||
```shell
|
||
# 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](https://blog.platypush.tech/article/Ultimate-self-hosted-automation-with-Platypush),
|
||
the [readthedocs page](https://docs.platypush.tech/en/latest/), the
|
||
[Gitlab page](https://git.platypush.tech/platypush/platypush) or
|
||
[the wiki](https://git.platypush.tech/platypush/platypush/-/wikis/home).
|
||
|
||
Install also the Python dependencies for the HTTP server, the MLX90640 plugin and Tensorflow:
|
||
|
||
```shell
|
||
[sudo] pip install 'platypush[http,tensorflow,mlx90640]'
|
||
```
|
||
|
||
Tensorflow may also require some additional dependencies installable via `apt-get`:
|
||
|
||
```shell
|
||
[sudo] apt-get install python3-numpy \
|
||
libatlas-base-dev \
|
||
libblas-dev \
|
||
liblapack-dev \
|
||
python3-dev \
|
||
gfortran \
|
||
python3-setuptools \
|
||
python3-scipy \
|
||
python3-h5py
|
||
```
|
||
|
||
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:
|
||
|
||
```shell
|
||
# 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 ~/projects/imgdetect-utils
|
||
```
|
||
|
||
## Capture 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](https://docs.platypush.tech/en/latest/platypush/plugins/camera.ir.mlx90640.html) in your
|
||
Platypush configuration file (by default, `~/.config/platypush/config.yaml`):
|
||
|
||
```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:
|
||
|
||
```shell
|
||
curl -XPOST \
|
||
-H "Authorization: Bearer $PP_TOKEN" \
|
||
-H 'Content-Type: application/json' -d '
|
||
{
|
||
"type":"request",
|
||
"action":"camera.ir.mlx90640.capture",
|
||
"args": {
|
||
"output_file":"~/snap.png",
|
||
"scale_factor":20
|
||
}
|
||
}' http://localhost:8008/execute
|
||
```
|
||
|
||
If everything went well, the thermal picture should be stored under `~/snap.png`. In my case it looks like this while
|
||
I’m in standing front of the sensor:
|
||
|
||
|
||
|
||
Notice the glow at the bottom-right corner — that’s actually the heat from my RaspberryPi 4 CPU. It’s there in all the
|
||
images I take, and you may probably see similar results if you mounted your camera on top of the Raspberry itself, but
|
||
it shouldn’t be an issue for your model training purposes.
|
||
|
||
If you open the web panel (`http://your-host:8008`) you’ll also notice a new tab, represented by the sun icon, that you
|
||
can use to monitor your camera from a web interface.
|
||
|
||
|
||
|
||
You can also monitor the camera directly outside of the webpanel by pointing your browser to
|
||
`http://your-host:8008/camera/ir/mlx90640/stream?rotate=270&scale_factor=20`.
|
||
|
||
Now add a cronjob to your `config.yaml` to take snapshots every minute:
|
||
|
||
```yaml
|
||
cron.ThermalCameraSnapshotCron:
|
||
cron_expression: '* * * * *'
|
||
actions:
|
||
- action: camera.ir.mlx90640.capture
|
||
args:
|
||
output_file: "${__import__(’datetime’).datetime.now().strftime(’/home/pi/datasets/people_detect/images/%Y-%m-%d_%H-%M-%S.jpg’)}"
|
||
grayscale: true
|
||
```
|
||
|
||
Or directly as a Python script under e.g. `~/.config/platypush/thermal.py` (make sure that `~/.config/platypush/__init__.py` also exists so the folder is recognized as a Python module):
|
||
|
||
```python
|
||
from datetime import datetime
|
||
|
||
from platypush.cron import cron
|
||
from platypush.utils import run
|
||
|
||
|
||
@cron('* * * * *')
|
||
def take_thermal_picture(**context):
|
||
run('camera.ir.mlx90640.capture', grayscale=True,
|
||
output_file=datetime.now().strftime('/home/pi/datasets/people_detect/images/%Y-%m-%d_%H-%m-%S.jpg'))
|
||
```
|
||
|
||
The images will be stored under `/home/pi/datasets/people_detect/images` (make sure that the directory exists before starting
|
||
the service) in the format `YYYY-mm-dd_HH-MM-SS.jpg`. No scale factor is applied — even if the images will be tiny we’ll
|
||
only need them to train our model. Also, we’ll convert the images to grayscale — the neural network will be lighter and
|
||
actually more accurate, as it will only have to rely on one variable per pixel without being tricked by RGB
|
||
combinations.
|
||
|
||
Restart Platypush and verify that every minute a new picture is created under
|
||
your images directory. Let it run for a few hours or days until you’re happy
|
||
with the number of samples. Try to balance the numbers of pictures with no
|
||
people in the room and those with people in the room, trying to cover as many
|
||
cases as possible — e.g. sitting, standing in different points of the room etc.
|
||
As I mentioned earlier, in my case I only needed less than 1000 pictures with
|
||
enough variety to achieve accuracy levels above 99%.
|
||
|
||
## Labelling phase
|
||
|
||
Once you’re happy with the number of samples you’ve taken, copy the images over
|
||
to the machine you’ll be using to train your model (they should be all small
|
||
JPEG files weighing under 500 bytes each). Copy them to your local machine:
|
||
|
||
```shell
|
||
BASEDIR=~/datasets/people_detect
|
||
mkdir -p "$BASEDIR"
|
||
|
||
# Copy the images
|
||
scp -r pi@raspberry:/home/pi/datasets/people_detect ~
|
||
IMGDIR="$BASEDIR/images"
|
||
|
||
# This directory will contain the raw numpy training
|
||
# data parsed from the images (useful if you want to
|
||
# re-train the model without having to reprocess all
|
||
# the images)
|
||
DATADIR="$BASEDIR/data"
|
||
|
||
mkdir -p "$IMGDIR"
|
||
mkdir -p "$DATADIR"
|
||
|
||
# Create the labels for the images. Each label is a
|
||
# directory under $IMGDIR
|
||
mkdir "$IMGDIR/negative"
|
||
mkdir "$IMGDIR/positive"
|
||
```
|
||
|
||
Once the images have been copied, and the directories for the labels created,
|
||
run the `label.py` script provided in the repository to interactively label the
|
||
images:
|
||
|
||
```shell
|
||
UTILS_DIR=~/projects/imgdetect-utils
|
||
cd "$UTILS_DIR"
|
||
python utils/label.py -d "$IMGDIR" --scale-factor 10
|
||
```
|
||
|
||
Each image will open in a new window and you can label it by typing either 1
|
||
(negative) or 2 (positive) - the label names are gathered from the names of the
|
||
directories you created at the previous step:
|
||
|
||
|
||
|
||
At the end of the procedure the `negative` and `positive` directories under the
|
||
images directory should have been populated.
|
||
|
||
## Training phase
|
||
|
||
Once we’ve got all the labelled images it’s time to train our model. A
|
||
[`train.ipynb`](https://github.com/BlackLight/imgdetect-utils/blob/master/notebooks/ir/train.ipynb)
|
||
Jupyter notebook is provided under `notebooks/ir` and it should be
|
||
relatively self-explanatory:
|
||
|
||
```python
|
||
import os
|
||
|
||
import numpy as np
|
||
import matplotlib.pyplot as plt
|
||
|
||
import tensorflow as tf
|
||
from tensorflow import keras
|
||
from tensorflow.keras.preprocessing.image import ImageDataGenerator
|
||
|
||
# Define the dataset directory - replace it with the path on your local
|
||
# machine where you have stored the previously labelled dataset.
|
||
dataset_dir = os.path.join(os.path.expanduser('~'), 'datasets', 'people_detect')
|
||
|
||
# Define the size of the input images. In the case of an
|
||
# MLX90640 it will be (24, 32) for horizontal images and
|
||
# (32, 24) for vertical images
|
||
image_size = (32, 24)
|
||
|
||
# Image generator batch size
|
||
batch_size = 64
|
||
|
||
# Number of training epochs
|
||
epochs = 5
|
||
|
||
# Instantiate a generator that puts 30% of the images into the validation set
|
||
# and normalizes their pixel values between 0 and 1
|
||
generator = ImageDataGenerator(rescale=1./255, validation_split=0.3)
|
||
|
||
train_data = generator.flow_from_directory(dataset_dir,
|
||
target_size=image_size,
|
||
batch_size=batch_size,
|
||
subset='training',
|
||
class_mode='categorical',
|
||
color_mode='grayscale')
|
||
|
||
test_data = generator.flow_from_directory(dataset_dir,
|
||
target_size=image_size,
|
||
batch_size=batch_size,
|
||
subset='validation',
|
||
class_mode='categorical',
|
||
color_mode='grayscale')
|
||
```
|
||
|
||
After initializing the generators, let's take a look at a sample of 25 images from the training set together with their
|
||
labels:
|
||
|
||
```python
|
||
index_to_label = {
|
||
index: label
|
||
for label, index in train_data.class_indices.items()
|
||
}
|
||
|
||
plt.figure(figsize=(10, 10))
|
||
batch = train_data.next()
|
||
|
||
for i in range(min(25, len(batch[0]))):
|
||
img = batch[0][i]
|
||
|
||
label = index_to_label[np.argmax(batch[1][i])]
|
||
plt.subplot(5, 5, i+1)
|
||
plt.xticks([])
|
||
plt.yticks([])
|
||
plt.grid(False)
|
||
|
||
# Note the np.squeeze call - matplotlib can't
|
||
# process grayscale images unless the extra
|
||
# 1-sized dimension is removed.
|
||
plt.imshow(np.squeeze(img))
|
||
plt.xlabel(label)
|
||
|
||
plt.show()
|
||
```
|
||
|
||
You should see an image like this:
|
||
|
||
|
||
|
||
Let's now declare a model and train it on the given training set:
|
||
|
||
```python
|
||
model = keras.Sequential([
|
||
# Layer 1: flatten the input images
|
||
keras.layers.Flatten(input_shape=image_size),
|
||
# Layer 2: fully-connected layer with 80% the neurons as the input images
|
||
# and RELU activation function
|
||
keras.layers.Dense(round(0.8 * image_size[0] * image_size[1]),
|
||
activation=tf.nn.relu),
|
||
# Layer 2: fully-connected layer with 30% the neurons as the input images
|
||
# and RELU activation function
|
||
keras.layers.Dense(round(0.3 * image_size[0] * image_size[1]),
|
||
activation=tf.nn.relu),
|
||
# Layer 3: fully-connected layer with as many units as the output labels
|
||
# and Softmax activation function
|
||
keras.layers.Dense(len(train_data.class_indices),
|
||
activation=tf.nn.softmax)
|
||
])
|
||
|
||
# Compile the model for classification, use the Adam optimizer and pick
|
||
# accuracy as optimization metric
|
||
model.compile(loss='categorical_crossentropy',
|
||
optimizer='adam',
|
||
metrics=['accuracy'])
|
||
|
||
# Train the model in batches
|
||
history = model.fit(
|
||
train_data,
|
||
steps_per_epoch=train_data.samples/batch_size,
|
||
validation_data=test_data,
|
||
validation_steps=test_data.samples/batch_size,
|
||
epochs=epochs
|
||
)
|
||
|
||
# Example output:
|
||
# Epoch 1/5 loss: 0.2529 - accuracy: 0.9196 - val_loss: 0.0543 - val_accuracy: 0.9834
|
||
# Epoch 2/5 loss: 0.0572 - accuracy: 0.9801 - val_loss: 0.0213 - val_accuracy: 0.9967
|
||
# Epoch 3/5 loss: 0.0254 - accuracy: 0.9915 - val_loss: 0.0080 - val_accuracy: 1.0000
|
||
# Epoch 4/5 loss: 0.0117 - accuracy: 0.9979 - val_loss: 0.0053 - val_accuracy: 0.9967
|
||
# Epoch 5/5 loss: 0.0058 - accuracy: 1.0000 - val_loss: 0.0046 - val_accuracy: 0.9983
|
||
```
|
||
|
||
We can now see how the accuracy of the model progressed over the iteration:
|
||
|
||
```python
|
||
epochs = history.epoch
|
||
accuracy = history.history['accuracy']
|
||
fig = plt.figure()
|
||
plot = fig.add_subplot()
|
||
plot.set_xlabel('epoch')
|
||
plot.set_ylabel('accuracy')
|
||
plot.plot(epochs, accuracy)
|
||
```
|
||
|
||
The output should look like this:
|
||
|
||
|
||
|
||
By constraining the problem properly (i.e. translating "detect people in an image" to "infer the presence of people by
|
||
telling if there are more white halos than usual in a small grayscale image") we have indeed managed to achieve high
|
||
levels of accuracy both on the training and validation set despite using a relatively small dataset.
|
||
|
||
## Deploying the model
|
||
|
||
Once you are happy with the model, it's time to save it so it can be deployed to your RaspberryPi for real-time
|
||
predictions:
|
||
|
||
```python
|
||
def model_save(model, target, labels=None, overwrite=True):
|
||
import json
|
||
import pathlib
|
||
|
||
# Check if we should save it like a .h5/.pb file or as a directory
|
||
model_dir = pathlib.Path(target)
|
||
if str(target).endswith('.h5') or \
|
||
str(target).endswith('.pb'):
|
||
model_dir = model_dir.parent
|
||
|
||
# Create the model directory if it doesn't exist
|
||
pathlib.Path(model_dir).mkdir(parents=True, exist_ok=True)
|
||
|
||
# Save the Tensorflow model using the .save method
|
||
model.save(target, overwrite=overwrite)
|
||
|
||
# Save the label names of your model in a separate JSON file
|
||
if labels:
|
||
labels_file = os.path.join(model_dir, 'labels.json')
|
||
with open(labels_file, 'w') as f:
|
||
f.write(json.dumps(list(labels)))
|
||
|
||
model_dir = os.path.expanduser('~/models/people_detect')
|
||
model_save(model, model_dir,
|
||
labels=train_data.class_indices.keys(), overwrite=True)
|
||
```
|
||
|
||
If you managed to execute the whole notebook then you’ll have your model saved under `~/models/people_detect`.
|
||
You can now copy it over to the RaspberryPi and use it to do predictions (first create `~/models` on the RaspberryPi
|
||
if it's not available already):
|
||
|
||
```shell
|
||
scp -r ~/models/people_detect pi@raspberry:/home/pi/models
|
||
```
|
||
|
||
## Detect people in the room
|
||
|
||
Once the Tensorflow model has been deployed to the RaspberryPi you can quickly test how it performs against some
|
||
pictures taken on the device using
|
||
the [`tensorflow.predict`](https://docs.platypush.tech/en/latest/platypush/plugins/tensorflow.html#platypush.plugins.tensorflow.TensorflowPlugin.predict)
|
||
method:
|
||
|
||
```shell
|
||
curl -XPOST \
|
||
-H "Authorization: Bearer $PP_TOKEN" \
|
||
-H 'Content-Type: application/json' -d '
|
||
{
|
||
"type":"request",
|
||
"action":"tensorflow.predict",
|
||
"args": {
|
||
"inputs": "~/datasets/people_detect/positive/some_image.jpg",
|
||
"model": "~/models/people_detect"
|
||
}
|
||
}' http://your-raspberry-pi:8008/execute
|
||
```
|
||
|
||
Expected output:
|
||
|
||
```json
|
||
{
|
||
"id": "<response-id>",
|
||
"type": "response",
|
||
"target": "http",
|
||
"origin": "raspberrypi",
|
||
"response": {
|
||
"output": {
|
||
"model": "~/models/people_detect",
|
||
"outputs": [
|
||
{
|
||
"negative": 0,
|
||
"positive": 1
|
||
}
|
||
],
|
||
"predictions": [
|
||
"positive"
|
||
]
|
||
},
|
||
"errors": []
|
||
}
|
||
}
|
||
```
|
||
|
||
Once the structure of the response is clear, we can replace the previous cronjob that stores pictures at regular
|
||
intervals with a new one that captures pictures and feeds them to the previously trained model to make predictions (I'll
|
||
use a Python script stored under `~/.config/platypush/scripts` in this case, but it will also work with a cron defined
|
||
in YAML in `config.yaml`) and, for example, turns on the lights when presence is detected and turns them off when
|
||
presence is no longer detected (I'll use
|
||
the [`light.hue`](https://docs.platypush.tech/en/latest/platypush/plugins/light.hue.html) plugin in this example):
|
||
|
||
```python
|
||
import os
|
||
from platypush.context import get_plugin
|
||
from platypush.cron import cron
|
||
|
||
|
||
@cron('* * * * * */30')
|
||
def check_presence(**context):
|
||
# Get plugins by name
|
||
camera = get_plugin('camera.ir.mlx90640')
|
||
tensorflow = get_plugin('tensorflow')
|
||
lights = get_plugin('light.hue')
|
||
|
||
image_file = '/tmp/frame.jpg'
|
||
model_file = os.path.expanduser('~/models/people_detect/saved_model.h5')
|
||
camera.capture_image(
|
||
image_file=image_file, grayscale=True)
|
||
|
||
prediction = tensorflow.predict(
|
||
inputs=image_file, model=model_file)['predictions'][0]
|
||
|
||
if prediction == 'positive':
|
||
lights.on()
|
||
else:
|
||
lights.off()
|
||
```
|
||
|
||
Restart the service and let it run. Every 30 seconds the cron will run, take a picture, check if people are detected in
|
||
that picture and turn the lights on/off accordingly.
|
||
|
||
## What's next?
|
||
|
||
That’s your call! Feel free to experiment with more elaborate rules, for example to change the status of the music/video
|
||
playing in the room when someone enters, using Platypush media plugins. Or say a custom good morning text when you first
|
||
enter the room in the morning. Or build your own surveillance system to track the presence of people when you’re not at
|
||
home. Or enhance the model to detect also the number of people in the room, not only the presence. Or you can combine it
|
||
with an optical flow sensor, distance sensor, laser range sensor or optical camera (platypush provides plugins for many
|
||
of them) to build an even more robust system that also detects and tracks movements or proximity to the sensor, and so
|
||
on.
|