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Surveillance Project with AI Integration

This Python backend will run 24/7 on a server and request images from a ESP32-CAM module plugged into a wall or battery. The ESP32-CAM sends 800x600 resolution images back.

YoloV7 is used to run the images and check for humans. An SQLite database is used to track the images that have people in them and those that do not. The images that don't have people in them are compressed to reduce space consumption.

TODO

The images with humans are flagged and kept forever. The images without humans are deleted in 28 days. These images are available to see on the web application powered by React. The link to that repo is here: TODO

Note

There are no security meaures. Please secure your WiFi network instead.

Below is old stuff

Note:

Create a copy of the config_example.json file and rename it to config.json. Change the passcode in the new config file to whatever you wish. The code from the ESP32-CAM's side should check that passcode as a basic precaution to make sure no one else on your network is seeing the images.

That being said, you must use the ESP32-CAM on a secured network as it is not hard to use a simple brute force attack to find the correct if one already has access to the same network the ESP32-CAM is running on.

The link to the code for the ESP32-CAM is here: https://github.com/Ali-Sab/esp32-cam-udp-server

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