MaliosDark/PetFinderPi

🐾 Welcome to PetFinderPi! 🐾

PetFinderPi is an innovative project designed to assist in the timely location and rescue of lost pets, with a primary focus on dogs and cats. This cutting-edge solution integrates a modular design, harnessing the capabilities of Python and the Raspberry Pi ecosystem to create an efficient and versatile system.

Key Features:

📷 Automated Image Capture

PetFinderPi utilizes the Raspberry Pi Camera to automatically capture images upon detecting motion. This feature aids in visually identifying potential lost pets and enhances the search process.

🕵️ Animal Classification using MobileNetV2

The project incorporates the powerful MobileNetV2 deep learning model to classify and identify animals within captured images. This ensures accurate recognition of lost pets, distinguishing them from other wildlife.

🌡️ Temperature Monitoring with DHT22 Sensor

PetFinderPi includes a DHT22 temperature sensor to monitor environmental conditions. This valuable data helps understand how temperature variations may impact the behavior of lost animals, optimizing search efforts.

🔄 Reinforcement Learning for Location Prediction

The system employs a reinforcement learning model to predict potential locations of lost pets. Factors such as confidence from animal classification, temperature, and time of day are considered. This predictive capability enhances the system's effectiveness in locating and rescuing missing pets.

PetFinderPi is not just a project; it's a compassionate solution aimed at reuniting families with their beloved pets. Explore the features, contribute to the code, and join us in making a positive impact on pet welfare! 🐾🏠

Prerequisites

Before you start, make sure you have:

🍓 Raspberry Pi Configured with Raspbian OS: Ensure your Raspberry Pi is set up with the Raspbian operating system.
📷 Raspberry Pi Camera Connected: Attach the Raspberry Pi Camera to capture high-quality images.
🌐 Internet Connection for Data Transmission: Establish an internet connection for transmitting captured data to a server.
⚙️ RFID Scanner Connected to USB Port: Connect an RFID scanner to a USB port for additional identification capabilities.
🌡️ DHT22 Temperature Sensor Wired Up: Set up the DHT22 sensor to measure temperature. ( i`m going to be Updating it to a DHT11 that can check humidity and temperature )

Installation

Clone the repository:

git clone https://github.com/MaliosDark/PetFinderPi.git

Navigate to the project folder:
```
cd PetFinderPi
```
Install dependencies: ( when available. Pips for installation are inside start.py )
```
pip install -r requirements.txt
```

Usage

Run the main script:
```
python start.py
```

The script will detect motion, capture images, classify animals, and predict locations using reinforcement learning.

Monitor the logs:

Check the logs/event_history.txt file for a detailed history of events.

Contributing

👩‍💻👨‍💻 Contributions are welcome! Feel free to open issues and pull requests.

License

This project is licensed under the GNU General Public License v3.0 - see the LICENSE file for details.

Running on Raspberry pi 3 x32 i encounter that i need it to install bazel for use tensorflow

Instalar dependencias necesarias:

sudo apt-get install -y pkg-config zip g++ zlib1g-dev unzip python3

Descargar las fuentes de Bazel:

wget https://github.com/bazelbuild/bazel/releases/download/3.7.2/bazel-3.7.2-dist.zip

Descomprimir el archivo:

mkdir bazel
cd bazel
unzip ../bazel-3.7.2-dist.zip

Configurar e instalar Bazel:
```
./compile.sh
```
Mover el ejecutable a un directorio en tu PATH:
```
sudo cp output/bazel /usr/local/bin/
```

Después de estos pasos, intenta verificar la versión de Bazel:

bazel version

Puedes intentar instalar TensorFlow directamente desde las fuentes utilizando el código fuente. Aquí tienes los pasos:

Instalar las dependencias necesarias:

sudo apt-get update
sudo apt-get install -y libhdf5-dev libc-ares-dev libeigen3-dev

Instalar el compilador de Cython:
```
pip3 install cython
```
Clonar el repositorio de TensorFlow para Raspberry Pi:
```
git clone https://github.com/tensorflow/tensorflow.git -b v2.6.0 --depth=1
```
Asegúrate de ajustar la etiqueta de versión (-b) según la versión deseada.
Configurar TensorFlow:
```
cd tensorflow
./configure
```
Durante la configuración, asegúrate de seleccionar las opciones adecuadas para tu sistema.

Compilar TensorFlow:

bazel build --local_ram_resources=2048 --local_cpu_resources=2 --verbose_failures -c opt --copt=-mfpu=neon-vfpv4 --copt=-funsafe-math-optimizations --copt=-ftree-vectorize --copt=-fomit-frame-pointer --copt=-funsafe-math-optimizations --copt=-march=native --copt=-Wno-sign-compare --cxxopt="-D_GLIBCXX_USE_CXX11_ABI=0" //tensorflow/tools/pip_package:build_pip_package

Esto tomará un tiempo y es posible que necesites ajustar las opciones según tu configuración.

Generar el paquete de TensorFlow:

./bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg

Instalar TensorFlow desde el paquete generado:
```
pip3 install /tmp/tensorflow_pkg/*.whl
```

Estos pasos deberían compilar e instalar TensorFlow en tu Raspberry Pi. Ten en cuenta que la compilación puede llevar bastante tiempo y es posible que necesites espacio libre en disco.

MaliosDark / PetFinderPi