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~~

Facecognition人脸识别原理大体可分为：

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。

1 开发环境

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

2 环境搭建

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

3 打造自己的人脸库

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

4、模型训练与保存

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")

5、人脸识别及跟踪

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)

6、结语

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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