Machine-learning-in-practice/face_point.py at main · Byeongsoo-Min/Machine-learning-in-practice · GitHub

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import cv2
import numpy as np
import os.path as path
import dlib
import os

dlib_detector = dlib.get_frontal_face_detector()
dlib_predictor = dlib.shape_predictor(path.join('C:\deep\Machine-learning-in-practice\data', 'shape_predictor_68_face_landmarks.dat'))

def boundary_points(points, width_percent=0.1, height_percent=0.1):
  """ Produce additional boundary points
  :param points: *m* x 2 array of x,y points
  :param width_percent: [-1, 1] percentage of width to taper inwards. Negative for opposite direction
  :param height_percent: [-1, 1] percentage of height to taper downwards. Negative for opposite direction
  :returns: 2 additional points at the top corners
  """
  x, y, w, h = cv2.boundingRect(np.array([points], np.int32))
  spacerw = int(w * width_percent)
  spacerh = int(h * height_percent)
  return [[x+spacerw, y+spacerh],
          [x+w-spacerw, y+spacerh]]


def face_points(img, add_boundary_points=True):
  return face_points_dlib(img, add_boundary_points)


def face_points_dlib(img, add_boundary_points=True):
  """ Locates 68 face points using dlib (http://dlib.net)
    Requires shape_predictor_68_face_landmarks.dat to be in face_morpher/data
    Download at: http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2
  :param img: an image array
  :param add_boundary_points: bool to add additional boundary points
  :returns: Array of x,y face points. Empty array if no face found
  """
  try:
    points = []
    rgbimg = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    rects = dlib_detector(rgbimg, 1)

    if rects and len(rects) > 0:
      # We only take the first found face
      shapes = dlib_predictor(rgbimg, rects[0])
      points = np.array([(shapes.part(i).x, shapes.part(i).y) for i in range(68)], np.int32)

      if add_boundary_points:
        # Add more points inwards and upwards as dlib only detects up to eyebrows
        points = np.vstack([
          points,
          boundary_points(points, 0.1, -0.03),
          boundary_points(points, 0.13, -0.05),
          boundary_points(points, 0.15, -0.08),
          boundary_points(points, 0.33, -0.12)])

    return points
  except Exception as e:
    print(e)
    return []