https://github.com/konynour/facedectection
This project demonstrates real-time face detection using OpenCV and a pre-trained SSD (Single Shot MultiBox Detector) model. The system captures video from a camera, processes the frames, detects faces, and displays the results with bounding boxes and confidence scores. This application showcases the use of deep learning models for object detection
https://github.com/konynour/facedectection
cv2 jupyter-notebook lambda-functions matplotlib numpy opencv python3
Last synced: 3 months ago
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This project demonstrates real-time face detection using OpenCV and a pre-trained SSD (Single Shot MultiBox Detector) model. The system captures video from a camera, processes the frames, detects faces, and displays the results with bounding boxes and confidence scores. This application showcases the use of deep learning models for object detection
- Host: GitHub
- URL: https://github.com/konynour/facedectection
- Owner: konynour
- Created: 2025-02-14T15:44:27.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2025-02-18T06:06:44.000Z (8 months ago)
- Last Synced: 2025-05-18T04:12:40.776Z (5 months ago)
- Topics: cv2, jupyter-notebook, lambda-functions, matplotlib, numpy, opencv, python3
- Language: Python
- Homepage:
- Size: 10.4 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Real-Time Face Detection using OpenCV and SSD
## 📌 Overview
This project demonstrates real-time face detection using OpenCV and a pre-trained SSD (Single Shot MultiBox Detector) model. It captures video from a camera, detects faces, and displays the results with bounding boxes and confidence scores.
## ⚠️ Disclaimer
**This project is intended for educational purposes only. The author is not responsible for any misuse.**
## 🚀 Features
- Real-time face detection using a pre-trained SSD model.
- Displays bounding boxes and confidence scores for detected faces.
- Automatic download and extraction of required assets (if not already present).
## 📂 Requirements
- Python 3.x
- OpenCV (`cv2`)
- `requests` library for downloading assets
## 🔧 Installation
### 1. Clone the Repository
```sh
git clone https://github.com/konynour/faceDectection.git
```
### 2. Navigate to the Project Folder
```sh
cd face-detection
```
### 3. Install Dependencies
```sh
pip install opencv-python requests
```
## ⚙️ Usage
### Downloading Assets
The script will automatically download and extract the required assets if they are not found in the current directory. These assets include a pre-trained SSD model and its configuration file.
### Running the Script
To run the face detection script, execute the following command:
```sh
python 1.py
```
This will open a window displaying the camera feed with detected faces highlighted by bounding boxes and confidence scores.
- Press **ESC** to exit the application.
### Command-Line Arguments
You can specify the camera source as a command-line argument. For example:
```sh
python 1.py 1
```
This will use the second camera (index 1) instead of the default camera (index 0).