暑假留校学习笔记（1）——基于树莓派3B+的人脸识别方案

 

引语：本片文章介绍的是在树莓派上实现人脸识别的方案，具体涉及到的原理我在这篇文章中就不做过多解释了。

需要准备的材料（主要）：树莓派3B+、罗技270i 电脑摄像头。

人脸识别流程 ：

人脸检测，图像采集：

                                        

                                                                    图1.1

 

   模型训练：

                                         

                                                                    图1.2

人脸识别：

                                         

                                                                  图1.3

在写代码之前需要建立文件夹：

编写代码:需要将程序放在相应的文件夹下

take_photo.py
import cv2
import numpy as np
import os

#def generate_img("/home/pi/Public/face_recognize/date"):
face_cascade =cv2.CascadeClassifier('/home/pi/Public/face_recognize/haarcascade_frontalface_default.xml')
if (not os.path.isdir("/home/pi/Public/face_recognize/data/XiaoMing")):
    os.makedirs("/home/pi/Public/face_recognize/data/Xiaoming")
cap = cv2.VideoCapture(0)
count = 0
while True:
    ret,frame = cap.read()
    x,y = frame.shape[0:2]
    small_frame = cv2.resize(frame, (int(y/2), int(x/2)))
    result = small_frame.copy()
    gray = cv2.cvtColor(small_frame, cv2.COLOR_BGR2GRAY)
    faces = face_cascade.detectMultiScale(gray, 1.3, 5)
    for (x, y, w, h) in faces:
        result = cv2.rectangle(result, (x, y), (x+w, y+h), (255, 0, 0), 2)
        f = cv2.resize(gray[y:y+h, x:x+w], (200, 200))
        if count<40:
            cv2.imwrite("/home/pi/Public/face_recognize/data/XiaoMing/Xiaoming"+ '%s.pgm' % str(count), f)
            print(count)
            count+=1
            cv2.imshow('face', result)
            if cv2.waitKey(30) & 0xFF == ord('q'):
              break
cap.release()
cv2.destroyAllWindows()
#if__name__=='__take_photo__':
    #generate_img("/home/pi/Desktop/face_recognize/deta/Xiaoming")

face_recognize.py
import os
import sys
import cv2
import numpy as np

def read_images(path,sz=None):
    c = 0
    X, y = [], []
    names = []
    for dirname, dirnames, filenames in os.walk(path):
        for subdirname in dirnames:
            subject_path = os.path.join(dirname, subdirname)
            for filename in os.listdir(subject_path):
                try:
                    if (filename == ".directory"):
                        continue
                    filepath = os.path.join(subject_path, filename)
                    im = cv2.imread(os.path.join(subject_path, filename),cv2.IMREAD_GRAYSCALE)
                    if (im is None):
                        print("image" + filepath + "is None")
                    if (sz is not None):
                        im = cv2.resize(im, sz)
                    X.append(np.asarray(im, dtype=np.uint8))
                    y.append(c)
                except:
                    print("unexpected error")
                    raise
            c = c+1
            names.append(subdirname)
    return [names, X, y]

read_dir = "/home/pi/Public/face_recognize/data"
[names, X, y] = read_images(read_dir)
y = np.asarray(y,dtype=np.int32)
model = cv2.face_EigenFaceRecognizer.create()
model.train(np.asarray(X), np.asarray(y))
face_cascade =cv2.CascadeClassifier('/home/pi/Public/face_recognize/haarcascade_frontalface_default.xml')
cap = cv2.VideoCapture(0)
while True:
    ret, frame = cap.read()
    x, y = frame.shape[0:2]
    small_frame = cv2.resize(frame, (int(y/2), int(x/2)))
    result = small_frame.copy()
    gray = cv2.cvtColor(small_frame, cv2.COLOR_BGR2GRAY)
    faces = face_cascade.detectMultiScale(gray, 1.3, 5)
    for (x, y, w, h) in faces:
        result = cv2.rectangle(result, (x,y), (x+w,y+h), (255, 0, 0), 2)
        roi = gray[x:x+w,y:y+h]
        try:
            roi = cv2.resize(roi, (200,200), interpolation=cv2.INTER_LINEAR)
            [p_label, p_confidence] = model.predict(roi)
            #print(names[p_label])
            cv2.putText(result, names[p_label], (x, y-20), cv2.FONT_HERSHEY_SIMPLEX,1, 255, 2)
        except:        
            continue
    cv2.imshow("recognize_face",result)
    if cv2.waitKey(30)&0xFF == ord('q'):
        break
cap.release()
cv2.destroyAllWindows()

注意：这里用到了一个xml文件，需要的同学可以去搜索下载

 然后运行我们的程序

将目录切换到程序所在目录，运行take_photo.py程序：

然后运行face_recognize.py程序