TinyScrubCam

TinyScrubCam is an innovative edge-AI device powered by Google Coral Micro, designed for wildlife detection. Leveraging advanced AI technology, it operates efficiently with a lower power and a smaller size, making it an ideal solution for monitoring and studying wildlife in various environments.

Hardware wiring diagram

Note: The wiring for the MP3 control is not following standard practice. You might want to try out the wiring in the CircuitDiagram-No-BJT.drawio.png first.

Description

TinyScrubCam utilizes Google Coral Micro platform to bring AI capabilities to wildlife detection. This approach allows for real-time processing without the need for constant internet connectivity, making wildlife monitoring more accessible and effective.

Installation

Follow these steps to set up and run your TinyScrubCam device:

Clone the Repository

Begin by cloning the TinyScrubCam repository to your local machine:

git clone https://github.com/conservationtechlab/tinyscrubcam.git

Prepare the Build Environment

1. Clean the Build Environment:

   • Navigate to the project directory and remove the CMakeCache file inside the out directory to ensure a fresh start.

2. Generate Build Files:

   • Run CMake to configure the project and generate build files:

     cmake -B out -S .

3. Build the Application:

   • For Windows users, using below command to build the application

     make -C out -j4

   • For Linux users, utilize the number of cores in your system for a faster build process:

     make -C out -j$(nproc)

   • For macOS users, similarly leverage your system's CPU cores:

     make -C out -j$(sysctl -n hw.ncpu)

Flash the Application

Once the build is complete, you can flash the application onto the board with the following command:

python3 coralmicro/scripts/flashtool.py --build_dir out --elf_path out/detect_objects_http_usb

Setting Up a Python Virtual Environment

To avoid any conflicts with system-wide packages and ensure a consistent development environment, it's recommended to use a Python virtual environment. Here's how to set one up:

1. Install Python 3 Virtual Environment:

   • If not already installed, you can install the virtualenv tool using pip:

     pip3 install virtualenv

2. Create a Virtual Environment:

   • In the project directory, create a new virtual environment:

     python3 -m venv tinyscrubcam-env

3. Activate the Virtual Environment:

   • On macOS and Linux:

     source tinyscrubcam-env/bin/activate

   • On Windows:

     tinyscrubcam-env\Scripts\activate

4. Install Dependencies:

   • With the virtual environment activated, install the required Python packages:

     pip3 install -r requirements.txt

View the images stored on the TinyScrubCam

1. Plug the coral micro to a linux system computer
2. On Chrome, open the url

   10.10.10.1

• Note that only linux computers can view the images through this web streaming.
• Chrome is a preferred browser for viewing the images, other browsers could have JSON issues.
• The website provides functionalities such as deleting all the images from the server and saving all the images as a zip file. Those should provide an easy way to use the device and manage all the data.

Conclusion

By following these instructions, you should have TinyScrubCam set up and ready for wildlife detection. The use of a virtual environment for Python ensures that all dependencies are managed effectively, providing a smooth development and deployment process.