Open Source Computer Vision
Features
- Real Time Object Tracking
- Face Detection
# OpenCV program to detect face in real time # import libraries of python OpenCV # where its functionality resides import cv2 # load the required trained XML classifiers # https://github.com/Itseez/opencv/blob/master/data/haarcascades/haarcascade_frontalface_default.xml # Trained XML classifiers describes some features of some # object we want to detect a cascade function is trained # from a lot of positive(faces) and negative(non-faces) # images. face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml') # https://github.com/Itseez/opencv/blob/master/data/haarcascades/haarcascade_eye.xml # Trained XML file for detecting eyes eye_cascade = cv2.CascadeClassifier('haarcascade_eye.xml') # capture frames from a camera cap = cv2.VideoCapture(0) # loop runs if capturing has been initialized. while 1: # reads frames from a camera ret, img = cap.read() # convert to gray scale of each frames gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # Detects faces of different sizes in the input image faces = face_cascade.detectMultiScale(gray, 1.3, 5) for (x,y,w,h) in faces: # To draw a rectangle in a face cv2.rectangle(img,(x,y),(x+w,y+h),(255,255,0),2) roi_gray = gray[y:y+h, x:x+w] roi_color = img[y:y+h, x:x+w] # Detects eyes of different sizes in the input image eyes = eye_cascade.detectMultiScale(roi_gray) #To draw a rectangle in eyes for (ex,ey,ew,eh) in eyes: cv2.rectangle(roi_color,(ex,ey),(ex+ew,ey+eh),(0,127,255),2) # Display an image in a window cv2.imshow('img',img) # Wait for Esc key to stop k = cv2.waitKey(30) & 0xff if k == 27: break # Close the window cap.release() # De-allocate any associated memory usage cv2.destroyAllWindows()