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https://github.com/jenslys/opencv-sec-camera
OpenCV (Computer vision) Security Camera System
https://github.com/jenslys/opencv-sec-camera
computer-vision opencv python security-camera
Last synced: 3 days ago
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OpenCV (Computer vision) Security Camera System
- Host: GitHub
- URL: https://github.com/jenslys/opencv-sec-camera
- Owner: jenslys
- Created: 2024-06-02T12:58:31.000Z (7 months ago)
- Default Branch: master
- Last Pushed: 2024-06-04T11:12:56.000Z (7 months ago)
- Last Synced: 2024-06-26T16:26:36.095Z (7 months ago)
- Topics: computer-vision, opencv, python, security-camera
- Language: Python
- Homepage:
- Size: 5.86 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Security Camera System
This project sets up a security camera system using a Raspberry Pi, AWS S3, and OpenCV for body detection. The system captures video when a body is detected, stores the video locally or uploads it to an S3 bucket, sends a notification via Pushover, and deletes the local video file after uploading.
*This was made as part of the course "SSS3000R-1" at USN.*
## Features
- Real-time body detection using OpenCV's pre-trained Haar cascade model.
- Video recording with H264 encoding.
- Option to store videos locally or upload them to AWS S3.
- Automatic deletion of local files after successful upload to S3.
- Sending notifications with video links using Pushover.
- Logging with Loguru for debugging and monitoring.
## Prerequisites
- Raspberry Pi with a camera module.
- Python 3.6 or later.
- AWS S3 account and relevant credentials.
- Haar cascade model file for body detection.
- Pushover account and API token.
## Demo
Link to demo: [Security Camera System Demo](https://youtu.be/YV6XeBje7cA)
## Setup
1. **Install Dependencies**
```bash
pip install -r requirements.txt
```
2. **Environment Variables**
Create a `.env` file in the project directory and add the following environment variables:
```ini
R2_ENDPOINT_URL=
R2_ACCESS_KEY=
R2_SECRET_ACCESS_KEY=
R2_PUBLIC_URL=
PUSHOVER_APP_TOKEN=
PUSHOVER_USER_KEY=
```
3. **Configure AWS S3**
Ensure you have an S3 bucket named `detection` or modify the code to suit your bucket name.
4. **Run the script**
```bash
python main.py [--local]
```
Use the `--local` flag to store videos locally without uploading to S3.
## Usage
- The system initializes the camera and waits for body detection.
- When a body is detected, video recording starts and continues until no bodies are detected. Additionally, it has a 7-second buffer before stopping the recording in case the body moves out of the frame.
- If the `--local` flag is not set, recorded videos are uploaded to the specified S3 bucket and deleted locally after a successful upload.
- After uploading the video to S3, a notification is sent using the Pushover API with a link to the video.
## File Description
- **main.py**: The main script that sets up the camera, detects bodies, and handles video recording, uploading, and notifications.
- **.env**: Environment file storing AWS credentials, Pushover credentials, and endpoint URLs.
## Acknowledgements
- OpenCV for providing the Haar cascade model for body detection.
- Loguru for advanced logging.
- AWS for providing scalable storage solutions.
- Pushover for providing notification services.