python 基于dlib库的人脸检测的实现

import dlibimport cv2import 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_detectordetector = 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)