face_recognition.py

import dlib
from imageio import imread
import glob
import numpy as np

detector = dlib.get_frontal_face_detector()
predictor_path = 'shape_predictor_68_face_landmarks.dat'
predictor = dlib.shape_predictor(predictor_path)
face_rec_model_path = 'dlib_face_recognition_resnet_model_v1.dat'
facerec = dlib.face_recognition_model_v1(face_rec_model_path)


def get_feature(path):
	img = imread(path)
	dets = detector(img)
	print('检测到了 %d 个人脸' % len(dets))
	# 这里假设每张图只有一个人脸
	shape = predictor(img, dets[0])
	face_vector = facerec.compute_face_descriptor(img, shape)
	return(face_vector)

def distance(a,b):
	a,b = np.array(a), np.array(b)
	sub = np.sum((a-b)**2)
	add = (np.sum(a**2)+np.sum(b**2))/2.
	return sub/add

path_lists1 = ["f1.jpg","f2.jpg"]
path_lists2 = ["赵丽颖照片.jpg","赵丽颖测试.jpg"]

feature_lists1 = [get_feature(path) for path in path_lists1]
feature_lists2 = [get_feature(path) for path in path_lists2]

print("feature 1 shape",feature_lists1[0].shape)

out1 = distance(feature_lists1[0],feature_lists1[1])
out2 = distance(feature_lists2[0],feature_lists2[1])

print("diff distance is",out1)
print("same distance is",out2)

def classifier(a,b,t = 0.09):
	if(distance(a,b)<=t):
		ret = True
	else :
		ret = False
	return(ret)

print("f1 is 赵丽颖",classifier(feature_lists1[0],feature_lists2[1]))
print("f2 is 赵丽颖",classifier(feature_lists1[1],feature_lists2[1]))
print("赵丽颖照片.jpg is 赵丽颖测试.jpg",classifier(feature_lists2[0],feature_lists2[1]))