Tech Stack: PyCharm Community (IDE), Python, OpenCV, NumPy, CMake, ✓ d-lib for ML and computer vision, used for face and landmark detection, and facial feature extraction. Python GUI libraries: Tkinter, PyQT, and wxPython. Features & Techniques: HOG (Histogram of Oriented Gradients), SVM (Support Vector Machines), CNN
Introduction The project is a comprehensive application leveraging cutting-edge technologies in the realm of computer vision and machine learning. Developed using the PyCharm Community IDE and powered by the versatile Python programming language, the project integrates prominent libraries such as OpenCV, NumPy, CMake, and dlib to achieve sophisticated functionality in face and landmark detection, as well as facial feature extraction.
Tech Stack PyCharm Community (IDE) PyCharm Community serves as the development environment, providing a powerful and user-friendly platform for coding, testing, and debugging.
Python: The project is written in Python, a high-level programming language known for its readability and versatility.
OpenCV: OpenCV (Open Source Computer Vision Library) is a crucial component, enabling a wide range of computer vision tasks with its extensive set of functions.
NumPy: NumPy facilitates efficient numerical operations and array manipulations, enhancing the project's computational capabilities.
CMake: CMake is employed for cross-platform build automation, streamlining the compilation process.
dlib: dlib emerges as a powerhouse for machine learning and computer vision, playing a pivotal role in face and landmark detection, as well as facial feature extraction.
Python GUI Libraries The project incorporates three prominent Python GUI libraries:
Tkinter PyQt wxPython Features & Techniques
The project employs a variety of techniques, including:
HOG (Histogram of Oriented Gradients): HOG is utilized for object detection, providing a robust method for extracting features in images.
SVM (Support Vector Machines): Support Vector Machines are employed for classification tasks, enhancing the project's capabilities in pattern recognition.
CNN (Convolutional Neural Network): The integration of Convolutional Neural Networks empowers the project with deep learning capabilities, contributing to advanced image analysis.