#!/usr/bin/python
# -*- coding: utf-8 -*-
import time
import dlib
import numpy as np
class faceDiscernModel:
def __init__(self):
# 加载预训练人脸检测CNN模型
self.cnn_face_model = "./model/mmod_human_face_detector.dat"
self.cnn_face_detector = dlib.cnn_face_detection_model_v1(self.cnn_face_model)
# 加载人脸特征检测模型
self.predictor_path = "./model/shape_predictor_5_face_landmarks.dat"
self.predictor = dlib.shape_predictor(self.predictor_path)
# 加载人脸特征提取模型
self.featureModel = "./model/dlib_face_recognition_resnet_model_v1.dat"
self.feater = dlib.face_recognition_model_v1(self.featureModel)
def get_faces_feature(self,imgPath):
# 读取人脸图片
img = dlib.load_rgb_image(imgPath)
feat = []
# 检测每个人脸的边界框
dets = self.cnn_face_detector(img, 1)
# len(dets) 是检测到的人脸数量
for i, d in enumerate(dets):
# print("Detection {}: Left: {} Top: {} Right: {} Bottom: {} Confidence: {}".format(
# i, d.rect.left(), d.rect.top(), d.rect.right(), d.rect.bottom(), d.confidence))
# 检测 box i 内的人脸关键点
shape = self.predictor(img, d.rect)
# 计算特征向量
face_descriptor = self.feater.compute_face_descriptor(img, shape)
feat.append(face_descriptor)
return feat
def Euclidean_distance_test(self,feature_1,feature_2):
return np.sqrt(np.sum((np.array(feature_1)-np.array(feature_2))**2))
def face_compare(self,imgPath_1,imgPath_2,assess=0.6):
feature_1 = self.get_faces_feature(imgPath_1)
feature_2 = self.get_faces_feature(imgPath_2)
score = self.Euclidean_distance_test(feature_1=feature_1,feature_2=feature_2)
if score > assess:
return False
elif 0< score < assess:
return True
else:
return False
if __name__ == '__main__':
imgPath_1 = "./faceImage/test.jpg"
imgPath_2 = "./faceImage/test1.jpg"
faceDiscern = faceDiscernModel()
st = time.time()
result = faceDiscern.face_compare(imgPath_1,imgPath_2)
print(time.time()-st)
print(result)