cam = cv2.VideoCapture(0)

faceCascade = cv2.CascadeClassifier('Cascades/haarcascade_frontalface_default.xml')

eyeCascade = cv2.CascadeClassifier('Cascades/haarcascade_eye.xml')

smileCascade = cv2.CascadeClassifier('Cascades/haarcascade_smile.xml')

minW = 0.1*cam.get(3)

minH = 0.1*cam.get(4)

while True:

rect, pic = cam.read()

gray = cv2.cvtColor(pic, cv2.COLOR_BGR2GRAY)

faces = faceCascade.detectMultiScale(

gray, #input grayscale image

scaleFactor = 1.3, # how much the image is reduced in size by

minNeighbors = 5, # higher number means lower false +ves

minSize = (30, 30) # minimum rectangle size for a face

)

img_g = None

img_c = None

for (x, y, w, h) in faces: # (x,y) -> top left corner coordinate, w -> width, h -> height

cv2.rectangle(pic, (x, y), (x+w, y+h), (255, 0, 0), 2)

img_g = gray[y:y+h, x:x+w]

img_c = pic[y:y+h, x:x+w]

eyes = eyeCascade.detectMultiScale(

img_g,

scaleFactor = 1.5,

minNeighbors = 5,

minSize = (5, 5),

)

for (x_e, y_e, w_e, h_e) in eyes:

cv2.rectangle(img_c, (x_e, y_e), (x_e + w_e, y_e + h_e), (0, 255, 0), 2)

smile = smileCascade.detectMultiScale(

img_g,

scaleFactor = 1.5,

minNeighbors = 15,

minSize = (25, 25),

)

for (x_s, y_s, w_s, h_s) in smile:

cv2.rectangle(img_c, (x_s, y_s), (x_s + w_s, y_s + h_s), (0, 255, 0), 2)

pic = cv2.flip(pic, 1)

ret, buffer = cv2.imencode('.jpg', pic)

pic = buffer.tobytes()

yield (b'--frame\r\n'

b'Content-Type: image/jpeg\r\n\r\n' + pic + b'\r\n') # concat frame one by one and show result

k = cv2.waitKey(20)

if k == 27: # 'ESC' to quit

break

cam.release()

cv2.destroyAllWindows()