def recognition(frame):
cascade = face_cascade()
frame = filter.grayscale(frame)
minSize = (cfg.get_options('size'), cfg.get_options('size'))
faces = cascade.detectMultiScale(
frame,
scaleFactor=cfg.get_options('scaleFactor'),
minNeighbors=cfg.get_options('minNeighbors'),
minSize=minSize,
flags=cv2.CASCADE_SCALE_IMAGE)
return frame, faces
def image(gray, width, height):
return cv2.resize(gray, (width, height),
interpolation=cfg.get_options('interpolation'))
def scale(frame):
alt = int(frame.shape[0] / frame.shape[1] * cfg.get_options('resize'))
return image(frame, cfg.get_options('resize'), alt)
def compare(gray, rect):
factor = cfg.get_options('size')
(x, y, w, h) = rect
gray = image(gray[y:y + h, x:x + w], factor, factor)
return gray
def text(frame, text, rect):
(x, y, w, h) = rect
cv2.rectangle(frame, (x - 1, y - 25), (x + 2 + w, y), cfg.get_options('color'), cv2.FILLED)
cv2.putText(frame, text, (x + 6, y - 6), cv2.FONT_HERSHEY_DUPLEX, 0.5, (0, 0, 0), cfg.get_options('filled'))
def rectangle(frame, rect):
color = cfg.get_options('color')
(x, y, w, h) = rect
cv2.rectangle(frame, (x, y), (x + w, y + h), color, cfg.get_options('filled'))
def face_cascade():
return cv2.CascadeClassifier(cfg.get_options('classificator'))