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face_recognize.py
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import cv2
import os
import numpy as np
from net.mtcnn import mtcnn
import utils.utils as utils
from keras.models import load_model
from keras.applications.mobilenet import MobileNet
from keras.layers import Dense,GlobalAveragePooling2D,Dropout,LeakyReLU
from keras import regularizers
from keras.models import Model
base_model = MobileNet(alpha=0.5, depth_multiplier=1, dropout=1e-3, include_top=False)
x = base_model.output
x = GlobalAveragePooling2D()(x)
x = Dropout(0.3)(x)
x = Dense(128,activation=LeakyReLU(alpha=0.01))(x)
predictions = Dense(7, activation='softmax',activity_regularizer=regularizers.l1(0.01))(x)
model = Model(inputs=base_model.input, outputs=predictions)
model.load_weights("./logs/ep026-loss1.031-val_loss1.057-acc0.616-val_acc0.582.h5")
class face_rec():
def __init__(self):
# 创建mtcnn对象
# 检测图片中的人脸
self.mtcnn_model = mtcnn()
# 门限函数
self.threshold = [0.5,0.8,0.9]
def recognize(self,draw):
#-----------------------------------------------#
# 人脸识别
# 先定位，再进行数据库匹配
#-----------------------------------------------#
height,width,_ = np.shape(draw)
draw_rgb = cv2.cvtColor(draw,cv2.COLOR_BGR2RGB)
# 检测人脸
rectangles = self.mtcnn_model.detectFace(draw_rgb, self.threshold)
if len(rectangles)==0:
return
# 转化成正方形
rectangles = utils.rect2square(np.array(rectangles,dtype=np.int32))
rectangles[:,0] = np.clip(rectangles[:,0],0,width)
rectangles[:,1] = np.clip(rectangles[:,1],0,height)
rectangles[:,2] = np.clip(rectangles[:,2],0,width)
rectangles[:,3] = np.clip(rectangles[:,3],0,height)
#-----------------------------------------------#
# 对检测到的人脸进行编码
#-----------------------------------------------#
face_encodings = []
for rectangle in rectangles:
landmark = (np.reshape(rectangle[5:15],(5,2)) - np.array([int(rectangle[0]),int(rectangle[1])]))/(rectangle[3]-rectangle[1])*160
crop_img = draw_rgb[int(rectangle[1]):int(rectangle[3]), int(rectangle[0]):int(rectangle[2])]
crop_img = cv2.resize(crop_img,(48,48))
new_img,_ = utils.Alignment_1(crop_img,landmark)
new_img = cv2.cvtColor(new_img, cv2.COLOR_RGB2GRAY)
new_img = np.expand_dims(new_img,2)
new_img = np.concatenate((new_img, new_img,new_img), axis=2)
# print(new_img.shape)
new_img = (new_img.reshape(1, 48, 48, 3))
new_img = np.array(new_img) / 255.0
pred = model.predict(new_img)
face_encoding = np.argmax(pred, axis=1)
face_encodings.append(face_encoding)
rectangles = rectangles[:,0:4]
glasses_list = ('Angry', 'Disgust', 'Fear', 'Happy', 'Sad', 'Surprise', 'Neutral')
face_names = []
for i in face_encodings:
glasses_list1 = glasses_list[int(face_encoding)]
face_names.append(glasses_list1)
#-----------------------------------------------#
# 画框~!~
#-----------------------------------------------#
for (left, top, right, bottom), name in zip(rectangles,face_names):
cv2.rectangle(draw, (left, top), (right, bottom), (0, 0, 255), 2)
font = cv2.FONT_HERSHEY_SIMPLEX
cv2.putText(draw, name, (left , bottom - 15), font, 0.55, (255, 255, 255), 2)
return draw
if __name__ == "__main__":
dududu = face_rec()
#识别图片
# img = cv2.imread('./test/timg.jpg')
# dududu.recognize(img)
# # img = cv2.resize(img,(1024,512))
# cv2.imshow('Video', img)
# cv2.waitKey(0)
#识别视频
video_capture = cv2.VideoCapture(0)
while True:
ret, draw = video_capture.read()
dududu.recognize(draw)
cv2.imshow('Video', draw)
if cv2.waitKey(20) & 0xFF == ord('q'):
break
video_capture.release()
cv2.destroyAllWindows()