树莓派3B+ 人脸识别(OpenCV)

相信大家都看了前面的OpenCV安装和人脸检测教程，有了基础后，现在我们正式进入重头戏——人脸识别 的教程。
注意：该教程面向python2.7+OpenCV2.4.9(官方源)
其它版本需进行一些小的修改，文中会具体注明。

1.生成人脸识别数据

目录结构
./data 数据根目录
./data/generate 自动生成的人脸数据
./data/datamap.csv 人脸数据对应数据
./data/Mengcheng 人物一文件夹
./data/Kaixin 人物二文件夹
./data/Mengying 人物三文件夹
（这里三个人物可自行修改，文件夹为人物名，里面存放图片）

// python脚本，请保存为genrate.py
#!/usr/bin/env python
#coding=utf-8
import cv2
import os  
import sys

face_cascade=cv2.CascadeClassifier('/usr/share/opencv/haarcascades/haarcascade_frontalface_default.xml')

def makedir(path):
    path=path.strip().rstrip('/')
    if os.path.exists(path) is False:
        os.makedirs( path );

def generate(root_argv,dirname):
    subject_dir_path = os.path.join(root_argv, dirname)
    print 'seek:'+subject_dir_path
    count=0
    for filename in os.listdir(subject_dir_path): 
        if filename == ".directory":
            continue
        imgPath = os.path.join(subject_dir_path, filename)
        try:
            print 'read:'+imgPath
            img = cv2.imread(imgPath)
            gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
            outdir=os.path.join(root_argv,'generate',dirname)
            makedir(outdir)
            faces=face_cascade.detectMultiScale(gray,1.3,5)
            for x,y,w,h in faces:
                f=cv2.resize(gray[y:y+h,x:x+w],(200,200))
                outPath=os.path.join(root_argv,'generate',dirname,'%s.pgm' % str(count))
                print 'write:'+ outPath
                cv2.imwrite(outPath, f)
                count+=1

except:
            pass

if __name__ == '__main__':
    if len(sys.argv)==1:
        print ("USAGE: generate.py <人脸图片存放路径>")
        exit(0)

root_argv=sys.argv[1]
    for dirname in os.listdir(root_argv):
        file_path = os.path.join(root_argv, dirname)  
        if os.path.isdir(file_path): 
            if dirname == 'generate':
                continue
            generate(root_argv,dirname)

// python脚本，请保存为genrate.py

#!/usr/bin/env python

#coding=utf-8

import cv2

import os

import sys

face_cascade=cv2.CascadeClassifier('/usr/share/opencv/haarcascades/haarcascade_frontalface_default.xml')

def makedir(path):

path=path.strip().rstrip('/')

if os.path.exists(path) is False:

os.makedirs( path );

def generate(root_argv,dirname):

subject_dir_path = os.path.join(root_argv, dirname)

print 'seek:'+subject_dir_path

count=0

for filename in os.listdir(subject_dir_path):

if filename == ".directory":

continue

imgPath = os.path.join(subject_dir_path, filename)

try:

print 'read:'+imgPath

img = cv2.imread(imgPath)

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

outdir=os.path.join(root_argv,'generate',dirname)

makedir(outdir)

faces=face_cascade.detectMultiScale(gray,1.3,5)

for x,y,w,h in faces:

f=cv2.resize(gray[y:y+h,x:x+w],(200,200))

outPath=os.path.join(root_argv,'generate',dirname,'%s.pgm' % str(count))

print 'write:'+ outPath

cv2.imwrite(outPath, f)

count+=1

except:

pass

if __name__ == '__main__':

if len(sys.argv)==1:

print ("USAGE: generate.py <人脸图片存放路径>")

exit(0)

root_argv=sys.argv[1]

for dirname in os.listdir(root_argv):

file_path = os.path.join(root_argv, dirname)

if os.path.isdir(file_path):

if dirname == 'generate':

continue

generate(root_argv,dirname)

在控制台执行python generate.py ./data即可自动生成人脸识别数据
生成后，请自行打开./data/generate/下生成人脸数据是否正常，如果不正常的，请清理掉，防止后续有干扰。如图：

2.生成人脸数据对应表

人脸数据生成了，还得写一个映射表，让机器知道人脸数据对应那个人物。

// python脚本，请保存为create_csv.py

#!/usr/bin/env python

#coding=utf-8

import sys

import os.path

#import Image

if __name__ == '__main__':

if len(sys.argv) != 2:

print "usage: create_csv.py <生成的人脸数据路径>"

sys.exit(0)

BASE_PATH=sys.argv[1]

SEPARATOR=";"

label = 0

for dirpath, dirnames, filenames in os.walk(BASE_PATH):

for subdirname in dirnames:

subject_path = os.path.join(dirpath, subdirname)

#print subject_path

for filename in os.listdir(subject_path):

image_filename = subject_path + "/"+ filename

#print(image_filename)

#img=Image.open(image_filename)

## img=img.resize((92,112))

#img.show()

abs_path = "%s/%s" % (subject_path, filename)

print("%s%s%d"%(abs_path,SEPARATOR, label))

# print("%s"%(abs_path))

label = label + 1

然后执行python create_csv.py ./data/generate/ > ./data/datamap.csv
打开生成的datamap.csv文件，查看对应关系（如图所示）：

这样，人脸数据就准备好了。

3.人脸识别

// python脚本，请保存为facerec.py
#!/usr/bin/env python
#coding=utf-8
### Imports ###################################################################

import multiprocessing as mp
import cv2
import os
import sys
import time
import numpy as np

### Setup #####################################################################

resX = 640
resY = 480

# The face cascade file to be used
face_cascade = cv2.CascadeClassifier('/usr/share/opencv/lbpcascades/lbpcascade_frontalface.xml')

#三种识别算法
#model = cv2.createEigenFaceRecognizer()
model = cv2.createFisherFaceRecognizer()
#model = cv2.createLBPHFaceRecognizer()

t_start = time.time()
fps = 0

### Helper Functions ##########################################################

def normalize(X, low, high, dtype=None):
    """Normalizes a given array in X to a value between low and high."""
    X = np.asarray(X)
    minX, maxX = np.min(X), np.max(X)
    # normalize to [0...1].
    X = X - float(minX)
    X = X / float((maxX - minX))
    # scale to [low...high].
    X = X * (high-low)
    X = X + low
    if dtype is None:
        return np.asarray(X)
    return np.asarray(X, dtype=dtype)

def load_images(path, sz=None):
    c = 0
    X,y = [], []
    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:
                    filepath = os.path.join(subject_path, filename)
                    if os.path.isdir(filepath):
                        continue
                    img = cv2.imread(os.path.join(subject_path, filename), cv2.IMREAD_GRAYSCALE)
                    if (img is None):
                        print ("image " + filepath + " is none")
                    else:
                        print (filepath)
                    # resize to given size (if given)
                    if (sz is not None):
                        img = cv2.resize(img, (200, 200))

X.append(np.asarray(img, dtype=np.uint8))
                    y.append(c)
                # except IOError, (errno, strerror):
                #     print ("I/O error({0}): {1}".format(errno, strerror))
                except:
                    print ("Unexpected error:", sys.exc_info()[0])
                    raise
            print (c)
            c = c+1

print (y)
    return [X,y]

def get_faces( img ):

gray = cv2.cvtColor( img, cv2.COLOR_BGR2GRAY )
    faces = face_cascade.detectMultiScale(gray, 1.3, 5)

return faces, img, gray

def draw_frame( faces, img, gray ):

global xdeg
    global ydeg
    global fps
    global time_t

# Draw a rectangle around every face
    for ( x, y, w, h ) in faces:

cv2.rectangle( img, ( x, y ),( x + w, y + h ), ( 200, 255, 0 ), 2 )
        #-----rec-face
        roi = gray[x:x+w, y:y+h]
        try:
            roi = cv2.resize(roi, (200, 200), interpolation=cv2.INTER_LINEAR)
            params = model.predict(roi)
            sign=("%s %.2f" % (names[params[0]], params[1]))
            cv2.putText(img, sign, (x, y-2), cv2.FONT_HERSHEY_SIMPLEX, 0.5, ( 0, 0, 255 ), 2 )
            if (params[0] == 0):
                cv2.imwrite('face_rec.jpg', img)
        except:
            continue

# Calculate and show the FPS
    fps = fps + 1
    sfps = fps / (time.time() - t_start)
    cv2.putText(img, "FPS : " + str( int( sfps ) ), ( 10, 15 ), cv2.FONT_HERSHEY_SIMPLEX, 0.5, ( 0, 0, 255 ), 2 )

cv2.imshow( "recognize-face", img )

### Main ######################################################################

if __name__ == '__main__':

camera = cv2.VideoCapture(0)
    camera.set(cv2.cv.CV_CAP_PROP_FRAME_WIDTH,resX)  
    camera.set(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT,resY)

pool = mp.Pool( processes=4 )

# -----------init-rec----------
    # 人名要与datamap.csv里面的对应，不要弄错了顺序
    names = ['Mengying', 'Kaixin', 'Mengcheng']
    if len(sys.argv) < 2:
        print ("USAGE: facerec.py <人脸数据存放路径> [<数据对应表>]")
        sys.exit()

[X,y] = load_images(sys.argv[1])
    y = np.asarray(y, dtype=np.int32)

if len(sys.argv) == 3:
        out_dir = sys.argv[2]

model.train(np.asarray(X), np.asarray(y))
    # ------init finish---------

read, img = camera.read()
    pr1 = pool.apply_async( get_faces, [ img ] )   
    read, img = camera.read()
    pr2 = pool.apply_async( get_faces, [ img ] )  
    read, img = camera.read() 
    pr3 = pool.apply_async( get_faces, [ img ] )   
    read, img = camera.read()
    pr4 = pool.apply_async( get_faces, [ img ] )

fcount = 1
    while (True):
        read, img = camera.read()

if   fcount == 1:
            pr1 = pool.apply_async( get_faces, [ img ] )
            faces, img, gray=pr2.get()
            draw_frame( faces, img, gray )

elif fcount == 2:
            pr2 = pool.apply_async( get_faces, [ img ] )
            faces, img, gray=pr3.get()
            draw_frame( faces, img, gray )

elif fcount == 3:
            pr3 = pool.apply_async( get_faces, [ img ] )
            faces, img, gray=pr4.get()
            draw_frame( faces, img, gray )

elif fcount == 4:
            pr4 = pool.apply_async( get_faces, [ img ] )
            faces, img, gray=pr1.get()
            draw_frame( faces, img, gray )
            fcount = 0

fcount += 1

if cv2.waitKey(1000 // 12) & 0xff == ord("q"):
            break

camera.release()
    cv2.destroyAllWindows()

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// python脚本，请保存为facerec.py

#!/usr/bin/env python

#coding=utf-8

### Imports ###################################################################

import multiprocessing as mp

import cv2

import os

import sys

import time

import numpy as np

### Setup #####################################################################

resX = 640

resY = 480

# The face cascade file to be used

face_cascade = cv2.CascadeClassifier('/usr/share/opencv/lbpcascades/lbpcascade_frontalface.xml')

#三种识别算法

#model = cv2.createEigenFaceRecognizer()

model = cv2.createFisherFaceRecognizer()

#model = cv2.createLBPHFaceRecognizer()

t_start = time.time()

fps = 0

### Helper Functions ##########################################################

def normalize(X, low, high, dtype=None):

"""Normalizes a given array in X to a value between low and high."""

X = np.asarray(X)

minX, maxX = np.min(X), np.max(X)

# normalize to [0...1].

X = X - float(minX)

X = X / float((maxX - minX))

# scale to [low...high].

X = X * (high-low)

X = X + low

if dtype is None:

return np.asarray(X)

return np.asarray(X, dtype=dtype)

def load_images(path, sz=None):

c = 0

X,y = [], []

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:

filepath = os.path.join(subject_path, filename)

if os.path.isdir(filepath):

continue

img = cv2.imread(os.path.join(subject_path, filename), cv2.IMREAD_GRAYSCALE)

if (img is None):

print ("image " + filepath + " is none")

else:

print (filepath)

# resize to given size (if given)

if (sz is not None):

img = cv2.resize(img, (200, 200))

X.append(np.asarray(img, dtype=np.uint8))

y.append(c)

# except IOError, (errno, strerror):

# print ("I/O error({0}): {1}".format(errno, strerror))

except:

print ("Unexpected error:", sys.exc_info()[0])

raise

print (c)

c = c+1

print (y)

return [X,y]

def get_faces( img ):

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

faces = face_cascade.detectMultiScale(gray, 1.3, 5)

return faces, img, gray

def draw_frame( faces, img, gray ):

global xdeg

global ydeg

global fps

global time_t

# Draw a rectangle around every face

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

cv2.rectangle( img, ( x, y ),( x + w, y + h ), ( 200, 255, 0 ), 2 )

#-----rec-face

roi = gray[x:x+w, y:y+h]

try:

roi = cv2.resize(roi, (200, 200), interpolation=cv2.INTER_LINEAR)

params = model.predict(roi)

sign=("%s %.2f" % (names[params[0]], params[1]))

cv2.putText(img, sign, (x, y-2), cv2.FONT_HERSHEY_SIMPLEX, 0.5, ( 0, 0, 255 ), 2 )

if (params[0] == 0):

cv2.imwrite('face_rec.jpg', img)

except:

continue

# Calculate and show the FPS

fps = fps + 1

sfps = fps / (time.time() - t_start)

cv2.putText(img, "FPS : " + str( int( sfps ) ), ( 10, 15 ), cv2.FONT_HERSHEY_SIMPLEX, 0.5, ( 0, 0, 255 ), 2 )

cv2.imshow( "recognize-face", img )

### Main ######################################################################

if __name__ == '__main__':

camera = cv2.VideoCapture(0)

camera.set(cv2.cv.CV_CAP_PROP_FRAME_WIDTH,resX)

camera.set(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT,resY)

pool = mp.Pool( processes=4 )

# -----------init-rec----------

# 人名要与datamap.csv里面的对应，不要弄错了顺序

names = ['Mengying', 'Kaixin', 'Mengcheng']

if len(sys.argv) < 2:

print ("USAGE: facerec.py <人脸数据存放路径> [<数据对应表>]")

sys.exit()

[X,y] = load_images(sys.argv[1])

y = np.asarray(y, dtype=np.int32)

if len(sys.argv) == 3:

out_dir = sys.argv[2]

model.train(np.asarray(X), np.asarray(y))

# ------init finish---------

read, img = camera.read()

pr1 = pool.apply_async( get_faces, [ img ] )

read, img = camera.read()

pr2 = pool.apply_async( get_faces, [ img ] )

read, img = camera.read()

pr3 = pool.apply_async( get_faces, [ img ] )

read, img = camera.read()

pr4 = pool.apply_async( get_faces, [ img ] )

fcount = 1

while (True):

read, img = camera.read()

if fcount == 1:

pr1 = pool.apply_async( get_faces, [ img ] )

faces, img, gray=pr2.get()

draw_frame( faces, img, gray )

elif fcount == 2:

pr2 = pool.apply_async( get_faces, [ img ] )

faces, img, gray=pr3.get()

draw_frame( faces, img, gray )

elif fcount == 3:

pr3 = pool.apply_async( get_faces, [ img ] )

faces, img, gray=pr4.get()

draw_frame( faces, img, gray )

elif fcount == 4:

pr4 = pool.apply_async( get_faces, [ img ] )

faces, img, gray=pr1.get()

draw_frame( faces, img, gray )

fcount = 0

fcount += 1

if cv2.waitKey(1000 // 12) & 0xff == ord("q"):

break

camera.release()

cv2.destroyAllWindows()

注意：这里对于OpenCV3版本需要修改如下内容：
cv2.createEigenFaceRecognizer()
—>cv2.face.createEigenFaceRecognizer()
cv2.rectangle()
—>img=cv2.rectangle()

然后执行python facerec.py ./data/generate ./data/datamap.csv，此时，摄像头会自动打开，在显示屏上可以看到画面，如图：

如果想测试得更理想，应选择脸型差别比较大的来测试，其次是素材高清一些，摄像头成像清晰一些。

4.扩展资料

对于OpenCV有三种人脸识别方法，它们分别基于三种不同的算法：Eigenfaces、Fisherfaces和Local Binary Pattern Histogram（LBPH）。

首先，所有的方法都有类似的过积，即都使用了分好类的训练数据集（人脸数据库，每个人都有很多样本）来进行“训练”，对图像或视频中检测到的人脸进行分析，并从两方面来确定：是否识别到目标，目标真正被识别到的置信度的度量，这也称为置信度评分。

Eigenfaces是通过PCA来处理的。PCA是计算机视觉中提到最多的数学概。PCA的本质是识别某个训练集上（比如人脸数据库）的主成分，并计算出训练集（图像或帧中检测到的人脸）相对于数据库的发散程度，并输出一个值。该值越小，表明人脸数据库和检测到的人脸之间差别就越小；0值表示完全匹配。
Fisherfaces是从PCA衍生并发展起来的概念，它采用更复杂的逻辑。尽管计算更加密集，但比Eigenfaces更容易得到准确的效果。
LBPH粗略地（在非常高的层次上）将检测到的人脸分成小单元，并将其与模型中的对应单元进行比较，对每个区域的匹配值产生一个直方图。由于这种方法的灵活性．LBPH是唯一允许模型样本人脸和检测到的人脸在形状、大小上可以不同的人脸识别算法。个人认为这是最准确的算法，但是每个算法都有其长处和缺点。

5.扩展书籍

看较多网友对此文章有较多疑问，但我也没有较深入去研究具体原理。故无法对你们的提问作出准确的解答，在此推荐一本书给大家，相信能化解你们的疑问![本文章代码也有部分来自此书]
OpenCV 3计算机视觉 Python语言实现(第二版)

转载请注明：徐自远的乱七八糟小站 » 树莓派3B+ 人脸识别(OpenCV)

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树莓派3B+ 人脸识别(OpenCV)