This project uses the OpenCV library together with a pre-trained Deep Neural Network (DNN) model to detect faces in images. Face detection is a fundamental task in many applications such as security, photography and social networking. This software enables accurate and efficient face detection.
- Accurate face detection: Uses a pre-trained DNN model to achieve high accuracy in face detection.
- Support for local and online images**: You can process locally stored images or download and process images from URLs.
- Simple interface**: The code is easy to understand and modify, allowing it to be integrated into other projects.
| Advantages | Benefits |
|---|---|
| High accuracy | Reliable face detection in various conditions |
| Uses pre-trained models | No additional training required, ready to use |
| Support for multiple sources | Processes images locally and online |
| Fácil de usar | Clear and well-documented code for easy integration |
Before you start, make sure you have the following components installed on your system:
- Python 3: If you don't have Python installed, you can download it from python.org.
Install the necessary libraries using pip:
pip3 install opencv-python requests numpy
Download the following files and place them in the same directory as your Python script:
| File | Download link |
|---|---|
| deploy.prototxt | deploy.prototxt |
| res10_300x300_ssd_iter_140000.caffemodel | res10_300x300_ssd_iter_140000.caffemodel |
Make sure that the files "deploy.prototxt" y "res10_300x300_ssd_iter_140000.caffemodel" are in the correct directory.
ls -l
Save the code in a file called "face_detector.py" and run the script with the following command:
python3 face_detector.py
This face detection project is an excellent basis for various applications that require face identification. Thanks to the use of pre-trained DNN models and the flexibility of OpenCV, accurate and efficient results can be obtained quickly and easily.