This project aims to utilize the OpenCV library and a cascade classifier to detect and track keypoints such as the corners of the eyes and the edges of the face in images or videos. The cascade classifier is trained on a dataset of faces and eyes and then used to identify the keypoints. The library also includes functionality for detecting edges in images, which can aid in tasks like image segmentation and object recognition. The utilization of OpenCV provides a range of tools for image and video processing, including a cascade classifier, which ensures the library to detect keypoints and edges in real-time with high accuracy and reliability. Additionally, the OpenCV provides various image processing capabilities such as filtering, thresholding, and feature extraction that were utilized in this project.

* git clone git://github.com/ak811/ira.git

• Install OpenCV:

pip install opencv-python

• Install Matplotlib:

pip install matplotlib

• Install NumPy:

pip install numpy

* The documentation will be provided soon.

def live_detection_by_camera():
   cap = cv2.VideoCapture(0)

   while True:

       ret, frame = cap.read(0)

       frame = detect_face(frame)

       cv2.imshow('Video Face Detection', frame)

       c = cv2.waitKey(1)
       # Esc key
       if c == 27:
           break

   cap.release()
   cv2.destroyAllWindows()