当前位置 博文首页 > 详解基于Facecognition+Opencv快速搭建人脸识别及跟踪应用
人脸识别技术已经相当成熟，面对满大街的人脸识别应用，像单位门禁、刷脸打卡、App解锁、刷脸支付、口罩检测........
作为一个图像处理的爱好者，怎能放过人脸识别这一环呢！调研开搞，发现了超实用的Facecognition！现在和大家分享下~~
1、通过hog算子定位人脸，也可以用cnn模型，但本文没试过；
2、Dlib有专门的函数和模型，实现人脸68个特征点的定位。通过图像的几何变换（仿射、旋转、缩放），使各个特征点对齐（将眼睛、嘴等部位移到相同位置）；
3、训练一个神经网络，将输入的脸部图像生成为128维的预测值。训练的大致过程为：将同一人的两张不同照片和另一人的照片一起喂入神经网络，不断迭代训练，使同一人的两张照片编码后的预测值接近，不同人的照片预测值拉远；
4、将陌生人脸预测为128维的向量，与人脸库中的数据进行比对，找出阈值范围内欧氏距离最小的人脸，完成识别。
PyCharm： PyCharm Community Edition 2020.3.2 x64
Python：Python 3.8.7
Opencv：opencv-python 4.5.1.48
Facecognition：1.3.0
Dlb：dlb 0.5.0
本文不做PyCharm和Python安装，这个自己搞不定，就别玩了~
pip install opencv-python pip install face-recognition pip install face-recognition-models pip install dlb
通过opencv、facecogniton定位人脸并保存人脸头像，生成人脸数据集，代码如下：
import face_recognition
import cv2
import os
def builddataset():
Video_face = cv2.VideoCapture(0)
num=0
while True:
flag, frame = Video_face.read();
if flag:
cv2.imshow('frame', frame)
cv2.waitKey(2)
else:
break
face_locations = face_recognition.face_locations(frame)
if face_locations:
x_face = frame[face_locations[0][0]-50:face_locations[0][2]+50, face_locations[0][3]-50:face_locations[0][1]+50];
#x_face = cv2.resize(x_face, dsize=(200, 200));
bo_photo = cv2.imwrite("%s\%d.jpg" % ("traindataset/ylb", num), x_face);
print("保存成功:%d" % num)
num=num+1
else:
print("****未检查到头像****")
Video_face.release()
if __name__ == '__main__':
builddataset();
pass
通过数据集进行训练，得到人脸识别码，以numpy数据形式保存（人脸识别码）模型
def __init__(self, trainpath,labelname,modelpath, predictpath):
self.trainpath = trainpath
self.labelname = labelname
self.modelpath = modelpath
self.predictpath = predictpath
# no doc
def train(self, trainpath, modelpath):
encodings = []
dirs = os.listdir(trainpath)
for k,dir in enumerate(dirs):
filelist = os.listdir(trainpath+'/'+dir)
for i in range(0, len(filelist)):
imgname = trainpath + '/'+dir+'/%d.jpg' % (i)
picture_of_me = face_recognition.load_image_file(imgname)
face_locations = face_recognition.face_locations(picture_of_me)
if face_locations:
print(face_locations)
my_face_encoding = face_recognition.face_encodings(picture_of_me,
face_locations)[0]
encodings.append(my_face_encoding)
if encodings:
numpy.save(modelpath, encodings)
print(len(encodings))
print("model train is sucess")
else:
print("model train is failed")
通过opencv启动摄像头并获取视频，加载训练好模型完成识别及跟踪，为避免视频卡顿设置了隔帧处理。
def predicvideo(self,names,model):
Video_face = cv2.VideoCapture(0)
num=0
recongnition=[]
unknown_face_locations=[]
while True:
flag, frame = Video_face.read();
frame = cv2.flip(frame, 1) # 镜像操作
num=num+1
if flag:
self.predictpeople(num, recongnition,unknown_face_locations,frame, names, encodings)
else:
break
Video_face.release()
def predictpeople(self, condition,recongnition,unknown_face_locations,unknown_picture,labels,encodings):
if condition%5==0:
face_locations = face_recognition.face_locations(unknown_picture)
unknown_face_encoding = face_recognition.face_encodings(unknown_picture,face_locations)
unknown_face_locations.clear()
recongnition.clear()
for index, value in enumerate(unknown_face_encoding):
unknown_face_locations.append(face_locations[index])
results = face_recognition.compare_faces(encodings, value, 0.4)
splitresult = numpy.array_split(results, len(labels))
trueNum=[]
a1 = ''
for item in splitresult:
number = numpy.sum(item)
trueNum.append(number)
if numpy.max(trueNum) > 0:
id = numpy.argsort(trueNum)[-1]
a1 = labels[id]
cv2.rectangle(unknown_picture,
pt1=(unknown_face_locations[index][1], unknown_face_locations[index][0]),
pt2=(unknown_face_locations[index][3], unknown_face_locations[index][2]),
color=[0, 0, 255],
thickness=2);
cv2.putText(unknown_picture, a1,
(unknown_face_locations[index][1], unknown_face_locations[index][0]),
cv2.FONT_ITALIC, 1, [0, 0, 255], 2);
else:
a1 = "unkown"
cv2.rectangle(unknown_picture,
pt1=(unknown_face_locations[index][1], unknown_face_locations[index][0]),
pt2=(unknown_face_locations[index][3], unknown_face_locations[index][2]),
color=[0, 0, 255],
thickness=2);
cv2.putText(unknown_picture, a1,
(unknown_face_locations[index][1], unknown_face_locations[index][0]),
cv2.FONT_ITALIC, 1, [0, 0, 255], 2);
recongnition.append(a1)
else:
self.drawRect(unknown_picture,recongnition,unknown_face_locations)
cv2.imshow('face', unknown_picture)
cv2.waitKey(1)
通过opencv启动摄像头并获取实时视频，为避免过度卡顿采取隔帧处理；利用Facecognition实现模型的训练、保存、识别，二者结合实现了实时视频人脸的多人识别及跟踪，希望对大家有所帮助~！
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