This Flask application serves as the backend for processing MQTT messages related to vehicle streams and feeding them to a machine learning (ML) model. It then stores the footages to the Google Cloud Storage bucket and the metric data to the MySQL database.
The application subscribes to the following MQTT topics:
open_stream: Triggers stream initiation. Expects the payload to be the vehicle's UUID.stream/#: Receives image frame buffers from the IoT device. The UUID is extracted from the subtopic.close_stream: Triggers stream termination. Expects the payload to be the vehicle's UUID.
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open_stream:- Verifies if the received UUID exists in the database.
- If found, creates in-memory lists to store footage and metrics.
- If not found, inserts the vehicle into the database and then creates the lists.
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stream/#:- Identifies the vehicle UUID from the subtopic.
- Converts the buffer images to base64 strings for live streaming (published to
base64/{UUID}topic). - Feeds the images to the ML model for processing.
- If distractions or objects are detected, efficiently appends the frames and metrics to the corresponding in-memory lists.
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close_stream:- Checks if the lists are empty. If so, simply deletes them.
- If not empty:
- Saves footages to Google Cloud Storage.
- Saves metrics and the corresponding footage URLs to the MySQL database.
- Deletes the in-memory lists.
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If you do not use devcontainer, ensure you have Python 3.12 installed:
python --version
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Create virtual environment and activate it:
test -d venv || (python -m venv .venv && . .venv/bin/activate)
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Install the required Python packages:
pip install -r requirements-pytorch.txt && pip install -r requirements.txt -
Create a copy of the
.env.examplefile and rename it to.env:cp .env.example .env
Update the configuration values as needed.
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Run the application:
python app.py
This project is licensed under the MIT License, providing an open and permissive licensing approach for further development and distribution.