Python dlib 模块，hit_enter_to_continue() 实例源码

def encode(detector, shape_predictor, model, image, win=None):
  """Encodes faces from a single image into a 128 dim descriptor.

  Args:
    detector: dlib face detector object
    shape_predictor: dlib shape predictor object
    model: dlib convnet model
    image: image as numpy array
    win: dlib window object for vizualization if VIZ flag == 1

  Returns:
    list of descriptors (np array) for each face detected in image
  """
  # dlib comments:
  # Ask the detector to find the bounding boxes of each face. The 1 in the
  # second argument indicates that we should upsample the image 1 time. This
  # will make everything bigger and allow us to detect more faces.
  dets = detector(img, 1)
  print("Number of faces detected: {}".format(len(dets)))

  descriptors = []
  for k, d in enumerate(dets):
    print("Detection {}: Left: {} Top: {} Right: {} Bottom: {}".format(
        k, d.left(), d.top(), d.right(), d.bottom()))
    # Get the landmarks/parts for the face in box d.
    shape = sp(img, d)
    # Draw the face landmarks on the screen so we can see what face is currently being processed.

    if win is not None:
      win.clear_overlay()
      win.set_image(img)
      win.add_overlay(d)
      win.add_overlay(shape)
      dlib.hit_enter_to_continue()

    # Compute the 128D vector that describes the face in img identified by shape
    face_descriptor = facerec.compute_face_descriptor(img, shape)
    descriptors.append(np.asarray(list(face_descriptor)))

  return descriptors