Project Info

PetPal

This project did not submit a demo video on Devpost.

Inspiration

We were inspired by the emotional impact of losing a pet and wanted to create a tech-powered solution to help reunite animals with their families. Combining edge AI with remote sensing felt like the perfect fit for this challenge.

What it does

Raspberry Pet detects cats and dogs in real-time using a Raspberry Pi and Coral TPU. When a pet is spotted, it captures images and a short video clip, uploads them to Supabase Storage, and logs the detection for review in a web app — all without human supervision.

How we built it

We retrained and tuned a TensorFlow Lite object classification model (MobileNetV2) on a dataset of labeled pet images. The model runs on the Coral Edge TPU for low-latency inference. A motion detection pipeline using OpenCV on the Pi triggers the camera, processes detections, and uploads media and metadata to Supabase. The frontend is built with Flask, and detections appear in a live dashboard.

What's next

for RaspberryPet

🐾 Pet Finder – Pi Camera + Coral TPU + Flask Stream

Real-time object detection using a Raspberry Pi with a Pi Camera Module V2 and Coral USB Accelerator. Streams video over Flask. Training and model prep is done on a laptop, and inference runs on the Pi.

🔧 Raspberry Pi Setup (Bookworm 64-bit)

1. System packages

sudo apt update && sudo apt install -y \
python3-picamera2 v4l-utils libusb-1.0-0-dev libedgetpu1-std \
make build-essential libssl-dev zlib1g-dev libbz2-dev libreadline-dev \
libsqlite3-dev wget curl llvm libncurses5-dev libncursesw5-dev \
xz-utils tk-dev libffi-dev liblzma-dev git

2. Install pyenv

curl https://pyenv.run | bash

Then add to your ~/.bashrc:

export PATH="$HOME/.pyenv/bin:$PATH"
eval "$(pyenv init --path)"
eval "$(pyenv virtualenv-init -)"

Restart your shell:

exec "$SHELL"

3. Python 3.9 + virtualenv

pyenv install 3.9.18
python3.9 -m venv ~/coral-venv --system-site-packages
source ~/coral-venv/bin/activate

4. Install Python packages

pip install -r requirements.txt
pip install --extra-index-url https://google-coral.github.io/py-repo/ tflite-runtime
pip install https://github.com/google-coral/pycoral/releases/download/v2.0.0/pycoral-2.0.0-cp39-cp39-linux_aarch64.whl

5. Run the stream

python stream.py

View at:
http://<your-pi-ip>:5000/video_feed

💻 Laptop Setup (Training + Model Prep)

1. Python env for training

pyenv install 3.9.18
'''

#### Create a virtual environment using Python 3.9
```bash
python3.9 -m venv coral-train
source coral-train/bin/activate

Alternative (if you prefer pyenv-virtualenv)

pyenv virtualenv 3.9.18 coral-train
pyenv activate coral-train
'''

#### Install training dependencies
```bash
pip install tensorflow-macos==2.13.0 numpy pandas opencv-python

2. (After building/tuning your model): Convert + compile model

tflite_convert --output_file=model.tflite ...
edgetpu_compiler model.tflite

3. Copy model to Pi

scp model_edgetpu.tflite pi@<pi-ip>:~/models/

🧠 Notes

Uses picamera2 for fast streaming
libedgetpu1-std is installed (not max)
Tested on Raspberry Pi OS Bookworm 64-bit + Coral USB Accelerator

Analysis

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Technology

Found in codeClaimed only

FlaskIn code
HTMLIn code
JavaScriptIn code
PythonIn code
ReactIn code
TypeScriptIn code
SupabaseClaimed
TensorFlowClaimed

6 of 8 appear in the indexed code. 2 claimed on Devpost could not be matched to code, which may simply mean the tool leaves no trace in the repository.

AI coding agents

No AI coding agent signals were found in this repository.

Detected from committed agent config files and commit authorship. Absence of a signal is not proof an agent was unused.

Codebase size

Source size

68 KB

Source files

Counts recognized source files only; vendored directories, binaries and lockfiles are excluded, so this is smaller than the repository on disk.