[人工智能]树莓派人脸识别

安装OpenCV（具体步骤可参考上一篇文章）

常规更新一下

sudo apt-get update

sudo apt-get upgrade

接下来就是漫长的装库过程了

sudo apt-get install build-essential \
    cmake \
    gfortran \
    git \
    wget \  
    curl \
    graphicsmagick \
    libgraphicsmagick1-dev \
    libatlas-base-dev \
    libavcodec-dev \
    libavformat-dev \
    libboost-all-dev \
    libgtk2.0-dev \
    libjpeg-dev \
    liblapack-dev \
    libswscale-dev \
    libv4l-dev \
    pkg-config \
    python3-dev \
    python3-numpy \
    python3-pip \
    zip

接下来就是编译过程

首先新建一个文件夹dataset用于存放人脸图片
创建第一个Python文本，目的是对人脸进行检测和储存

import cv2
import os

cam = cv2.VideoCapture(0)
cam.set(3, 640) # width
cam.set(4, 480) # height

face_detector = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')

# For each person, enter one numeric face id
face_id = input('\n enter user id end press <return> ==>  ')

print("\n Initializing face capture  \n Look the camera and wait ...")

count = 0

while(True):

    ret, img = cam.read()
    #img = cv2.flip(img, -1)
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    faces = face_detector.detectMultiScale(gray, 1.3, 5)

    for (x,y,w,h) in faces:

        cv2.rectangle(img, (x,y), (x+w,y+h), (255,0,0), 2)     
        count += 1

        # Save the captured image into the dataset folder
        cv2.imwrite("dataset/User." + str(face_id) + '.' + str(count) + ".jpg", gray[y:y+h,x:x+w])

        cv2.imshow('camera', img)

    k = cv2.waitKey(100) & 0xff
    if k == 27:
        break
    elif count >= 50: # Take 50 face sample and stop video
         break

print("\n Exiting Program and cleanup stuff")
cam.release()
cv2.destroyAllWindows()

这里用到了一个haarcascade_frontalface_default.xml，我会放在最后的百度网盘里

再新建一个文件夹trainer用于存放数据
没有PIL的兄弟们可以用pip安装一下，下面会用到

pip3 install pillow

创建第二个Python文本，目的是将储存的人脸图片转换成数据库

import cv2
import numpy as np
from PIL import Image
import os

# Path for face image database
path = 'dataset'

recognizer = cv2.face.LBPHFaceRecognizer_create()
detector = cv2.CascadeClassifier("haarcascade_frontalface_default.xml");

def getImagesAndLabels(path):

    imagePaths = [os.path.join(path,f) for f in os.listdir(path)]     
    faceSamples=[]
    ids = []

    for imagePath in imagePaths:

        PIL_img = Image.open(imagePath).convert('L') # convert it to grayscale
        img_numpy = np.array(PIL_img,'uint8')

        id = int(os.path.split(imagePath)[-1].split(".")[1])
        faces = detector.detectMultiScale(img_numpy)

        for (x,y,w,h) in faces:
            faceSamples.append(img_numpy[y:y+h,x:x+w])
            ids.append(id)

    return faceSamples,ids

print ("\n Training faces  \n It will take a few seconds  \n Please wait...")
faces,ids = getImagesAndLabels(path)
recognizer.train(faces, np.array(ids))

# Save the model into 'trainer/trainer.yml'
recognizer.save('trainer/trainer.yml') # recognizer.save() worked on Mac, but not on Pi

# Print the numer of faces trained and end program
print("\n {0} faces trained  \n Exiting Program".format(len(np.unique(ids))))

创建第三个Python文本，作用是将人脸识别出来

import cv2
import numpy as np
import os 

recognizer = cv2.face.LBPHFaceRecognizer_create()
recognizer.read('trainer/trainer.yml')
cascadePath = "haarcascade_frontalface_default.xml"
faceCascade = cv2.CascadeClassifier(cascadePath)

font = cv2.FONT_HERSHEY_SIMPLEX

#iniciate id counter
id = 0

# names related to ids: example ==> Marcelo: id=1,  etc
names = ['zero', 'one', 'two', 'three', 'four', 'five'] 

cam = cv2.VideoCapture(0)
cam.set(3, 640) # widht
cam.set(4, 480) # height

# Define min window size to be recognized as a face
minW = 0.1*cam.get(3)
minH = 0.1*cam.get(4)

while True:

    ret, img =cam.read()
    #img = cv2.flip(img, -1)

    gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)

    faces = faceCascade.detectMultiScale( 
        gray,
        scaleFactor = 1.2,
        minNeighbors = 5,
        flags = 0,
        minSize = (int(minW), int(minH))
       )

    for(x,y,w,h) in faces:

        cv2.rectangle(img, (x,y), (x+w,y+h), (0,255,0), 2)

        id, confidence = recognizer.predict(gray[y:y+h,x:x+w])

        # Check if confidence is less them 100 ==> "0" is perfect match 
        if (confidence < 70):
            id = names[id]
            confidence = "  {0}%".format(round(100 - confidence))
        else:
            id = "unknown"
            confidence = "  {0}%".format(round(100 - confidence))
        
        cv2.putText(img, str(id), (x+5,y-5), font, 1, (255,255,255), 2)
        cv2.putText(img, str(confidence), (x+5,y+h-5), font, 1, (255,255,0), 1)  
    
    cv2.imshow('camera',img) 

    k = cv2.waitKey(10) & 0xff
    if k == 27:
        break

print("\n Exiting Program and cleanup stuff")
cam.release()

下面是我编译好的代码库，大家可以参考学习
链接：

https://pan.baidu.com/s/1HTfyYWPwifJDaVN5D6ykTQ 

提取码：

cv22