import dlib
import cv2
import os
 
def resize(img, width=None, height=None, inter=cv2.INTER_AREA):
  """
  initialize the dimensions of the input image and obtain
  the image size
  """
 
  dim = None
  (h, w) = img.shape[:2]
 
  if width is None and height is None:
    return img
  if width is None:
    r = height / float(h)
    dim = (int(w * r), height)
  else:
    r = width / float(w)
    dim = (width, int(h * r))
  # resize the image
  resized = cv2.resize(img, dim, interpolation=inter)
  # return the resized image
  return resized
 
# 使用 Dlib 的正面人脸检测器 frontal_face_detector
detector = dlib.get_frontal_face_detector()
 
# 图片所在路径
imgs_path = 'test/'
filelist = os.listdir(imgs_path)
# 使用 detector 检测器来检测图像中的人脸
for img_path in filelist:
  img = cv2.imread(imgs_path + img_path)
  img = resize(img, width=512)
  faces = detector(img, 1)
  print("人脸数 / Faces in all: ", len(faces))
  for i, d in enumerate(faces):
    w = d.right() - d.left()
    h = d.bottom() - d.top()
    d_left = int(d.left() - w * 0.25)
    d_right = int(d.right() + w * 0.25)
    d_top = int(d.top() - w * 0.70)
    d_bottom = int(d.bottom() + w * 0.2)
    print("第", i + 1, "个人脸的矩形框坐标：",
       "left:", d_left, "right:", d_right, "top:", d_top, "bottom:", d_bottom)
    cv2.rectangle(img, tuple([d_left, d_top]), tuple([d_right, d_bottom]), (0, 255, 255), 2)
  cv2.imshow("img", img)
  cv2.waitKey(0)
  cv2.imwrite('./result.jpg',img)