face-rec.py

# -*- coding:utf8 -*-

import sys,os,dlib,glob,numpy

from skimage import io


if len(sys.argv) != 5:

    print r"请检查参数是否正确"

    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)
	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 = ['1','2','3','4','5','6','7','8','9','10','11']


# 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])


if (cd_sorted):
	print cd_sorted
	print "\n The person is: ",cd_sorted[0][0]
else:
	print "\n Nobady In here!"
dlib.hit_enter_to_continue()