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@pengshp
Created May 1, 2017 19:03
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Dlib库人脸识别
#!/usr/bin/env python
# -*- coding: UTF-8 -*-
import sys
import os
import dlib
import glob
import numpy
from skimage import io
import time
if len(sys.argv) != 5:
print "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)
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)
time.sleep(8)
# 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()
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