girl-face-rec.py

#!/usr/bin/env python
# coding: utf-8
from skimage import io
import sys,os,dlib,glob,numpy


if len(sys.argv) != 5:
  print("请检查参数是否正确")
  exit()
else:
  print(len(sys.argv))
  exit()
# 1.人脸关键点检测器
predictor_path = sys.argv[1]
# 2.人脸识别模型
face_rec_model_path = sys.argv[2]
# 3.候选人脸文件夹
faces_folder_path = sys.argv[3]
# 4.需识别的人脸
img_path = sys.argv[4]
# 1.加载正脸检测器
detector = dlib.get_frontal_face_detector()
# 2.加载人脸关键点检测器
sp = dlib.shape_predictor(predictor_path)
# 3. 加载人脸识别模型
facerec = dlib.face_recognition_model_v1(face_rec_model_path)
# win = dlib.image_window()
# 候选人脸描述子list
descriptors = []
# 对文件夹下的每一个人脸进行:
# 1.人脸检测
# 2.关键点检测
# 3.描述子提取
for f in glob.glob(os.path.join(faces_folder_path, "*.jpg")):
  print("Processing file: {}".format(f))
  img = io.imread(f)
  #win.clear_overlay()
  #win.set_image(img)
  # 1.人脸检测
  dets = detector(img, 1)
  print("Number of faces detected: {}".format(len(dets)))
  for k, d in enumerate(dets): 
    # 2.关键点检测
    shape = sp(img, d)
    # 画出人脸区域和和关键点
    # win.clear_overlay()
    # win.add_overlay(d)
    # win.add_overlay(shape)
    # 3.描述子提取，128D向量
    face_descriptor = facerec.compute_face_descriptor(img, shape)
    # 转换为numpy array
    v = numpy.array(face_descriptor) 
    descriptors.append(v)
# 对需识别人脸进行同样处理
# 提取描述子，不再注释
img = io.imread(img_path)
dets = detector(img, 1)
dist = []
for k, d in enumerate(dets):
  shape = sp(img, d)
  face_descriptor = facerec.compute_face_descriptor(img, shape)
  d_test = numpy.array(face_descriptor) 
  # 计算欧式距离
  for i in descriptors:
    dist_ = numpy.linalg.norm(i-d_test)
    dist.append(dist_)
# 候选人名单
candidate = ['Unknown1','Unknown2','Shishi','Unknown4','Bingbing','Feifei']
# 候选人和距离组成一个dict
c_d = dict(zip(candidate,dist))
cd_sorted = sorted(c_d.iteritems(), key=lambda d:d[1])
print("\n The person is: ",cd_sorted[0][0])
dlib.hit_enter_to_continue()