Seamless Face Recognition on the Go with Python
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AI-Powered Face Detection and Recognition Code Seamless, Accurate, and Ready-to-Deploy
Unlock the power of advanced AI-driven face detection and recognition with our Python-based solution. Designed for ease of implementation, this robust algorithm integrates seamlessly into your projects, whether for security systems, attendance tracking, or personalized user experiences.
You just need One Photo and the accuracy is off the Chart.
Key Features:
- Plug-and-Play Functionality: Minimal setup required for rapid deployment.
- High Accuracy and Speed: Powered by state-of-the-art computer vision libraries like OpenCV and deep learning models.
- Customizable and Scalable: Easily adaptable for real-time applications, including surveillance, access control, and analytics.
- Lightweight and Efficient: Optimized for both edge devices and cloud-based platforms.
- Flexible Integration: Works effortlessly across platforms with Python support.
Why Choose This Code?
- Save development time with a pre-built, thoroughly tested algorithm.
- Implement cutting-edge face recognition with ease, even on-the-fly.
- Benefit from clear documentation and straightforward integration examples.
Upgrade your application with cutting-edge face detection and recognition technology today! Ideal for businesses and developers aiming to deliver secure, efficient, and personalized solutions.
Plug-and-Play FunctionalityHigh Accuracy and Speed
Customizable and Scalable
Lightweight and Efficient
Flexible Integration
Installation Instructions
1. To install face_recognition
pip install face_recognition
Note: The `face_recognition` library depends on `dlib`, which requires CMake and development tools. Make sure to have these installed on your system. For example:
- On Ubuntu/Debian, execute the below commands:
sudo apt-get install cmake
sudo apt-get install build-essential
- On macOS, execute the below commands:
brew install cmake
If you encounter issues, consider installing `dlib` first:
pip install dlib
2. Install `opencv-python` (for `cv2`):
pip install opencv-python
Change and Adaptation Instructions
You can then, access the functions in the code file by importing and initialising an object as shown below:
from face_rec import Facerec
sfr = Facerec()
Store the images that you want to the model to recognise in a folder and use the below command to point to the directory:
sfr.load_encoding_images("your-folder-address")
Run the algorithm using the run_camera function that can be executed as,
sfr.run_camera()
The run_camera() function has a number of optional parameters that you can use to modify the output,
1. color: It accepts a tuple with three values in it. The default is (0,0,200), which gives you a red color frame.
2. thickness: An Integer value that determines the thickness of the Frame.
3. text_color: It accepts a tuple with three values in it. The default is (0,0,200), which gives you a red color text.
4. text_thickness: It accepts an Integer value to adjust the thickness of the text.
5. text_size: It accepts an Integer value to adjust the size of the text.
Price Information
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