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使用opencv实现人脸识别

最近搞了两个星期的opencv,人脸识别方向,感觉没有什么前途,看不到论文在哪里啊。

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# !/usr/bin/env python
# -*- coding:utf-8-*-
import os
import sys
import cv2
import numpy as np

z = {} # 存储关于每张图片对应的lable
for_pre = [] # 存储用来进行测试的图片,规则是每个人10张图,5张用来训练,5张用来测试
def normalize(X, low, high, dtype=None):
"""对数据进行正常化处理,让其处于最高和最低值之间."""
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 read_images(path, sz=None):
"""从文件夹中读取图像,并且将其大小限制在一定范围之内

参数:
path: 图片的路径
sz: 设定图像的大小以元组的形式,例如(92,112)

返回值:
返回一个list的数据[X,y]

X: 一个numpy的数组,里面存储的是所有的图片的矩阵.
y:一个list存储的,都是与X中图片对应的lable
"""
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:
im = cv2.imread(os.path.join(subject_path, filename), cv2.IMREAD_GRAYSCALE)
# resize to given size (if given)
if (sz is not None):
im = cv2.resize(im, sz)
if y.count(c) > 4:
for_pre.append({'no':c,'src':np.asarray(im, dtype=np.uint8)})
else:
X.append(np.asarray(im, dtype=np.uint8))
y.append(c)
global z
z[os.path.join(subject_path, filename)] = c
except IOError, (errno, strerror):
print "I/O error({0}): {1}".format(errno, strerror)
except:
print "Unexpected error:", sys.exc_info()[0]
raise
c = c+1
return [X,y]

def prediction(model):
"""图像预测

参数:
model: 就是图片训练的那个model

数据集中每个人存储了10张图片,我把其中的5张存储到for_pre,作为训练数据。用已知的lable和预测的lable作比较,得出图片识别正确的概率
"""
tn = 0 # 识别正确的图片数
for item in for_pre:
[p_label, p_confidence] = model.predict(cv2.resize(item['src'],(92,112)))
if p_label == item['no']:
tn = tn+1
else:
print 'the answer is %d,' % item['no'],
print "Predicted label = %d (confidence=%.2f)" % (p_label, p_confidence)

print "总共有%d次预测,其中正确次数为%d" %(len(for_pre),tn)

if __name__ == "__main__":
# This is where we write the images, if an output_dir is given
# in command line:
out_dir = None
# You'll need at least a path to your image data, please see
# the tutorial coming with this source code on how to prepare
# your image data:
if len(sys.argv) < 2:
print "USAGE: face_rec.py </path/to/images> [</path/to/store/images/at>]"
sys.exit()
# Now read in the image data. This must be a valid path!
[X,y] = read_images(sys.argv[1], (92, 112))

# Convert labels to 32bit integers. This is a workaround for 64bit machines,
# because the labels will truncated else. This will be fixed in code as
# soon as possible, so Python users don't need to know about this.
# Thanks to Leo Dirac for reporting:
y = np.asarray(y, dtype=np.int32)
# If a out_dir is given, set it:
if len(sys.argv) == 3:
out_dir = sys.argv[2]
# Create the Eigenfaces model. We are going to use the default
# parameters for this simple example, please read the documentation
# for thresholding:
model = cv2.createEigenFaceRecognizer()
# Read
# Learn the model. Remember our function returns Python lists,
# so we use np.asarray to turn them into NumPy lists to make
# the OpenCV wrapper happy:
model.train(np.asarray(X), np.asarray(y))
prediction(model) # 图片预测
#
#
# You can see the available parameters with getParams():
print model.getParams()
# Now let's get some data:
mean = model.getMat("mean")
print out_dir + '/out.xml'
f = open(out_dir + '/out.xml','w')
model.save(out_dir + '/out.xml')
eigenvectors = model.getMat("eigenvectors")
# We'll save the mean, by first normalizing it:
mean_norm = normalize(mean, 0, 255, dtype=np.uint8)
mean_resized = mean_norm.reshape(X[0].shape)
if out_dir is None:
cv2.imshow("mean", mean_resized)
else:
cv2.imwrite("%s/mean.png" % (out_dir), mean_resized)
# Turn the first (at most) 16 eigenvectors into grayscale
# images. You could also use cv::normalize here, but sticking
# to NumPy is much easier for now.
# Note: eigenvectors are stored by column:
# for i in xrange(min(len(X), 16)):
# eigenvector_i = eigenvectors[:,i].reshape(X[0].shape)
# eigenvector_i_norm = normalize(eigenvector_i, 0, 255, dtype=np.uint8)
# # Show or save the images:
# if out_dir is None:
# cv2.imshow("%s/eigenface_%d" % (out_dir,i), eigenvector_i_norm)
# else:
# cv2.imwrite("%s/eigenface_%d.png" % (out_dir,i), eigenvector_i_norm)
# Show the images:
print z
if out_dir is None:
cv2.waitKey(0)

代码执行效果如下
人脸识别执行效果
我在这里输出的是那些预测错误的。总共有200次预测,其中正确次数为186。这预测率有点低啊,我用的数据都是来自于 AT&T Facedatabase。一共40个人,每个人10张图,图片宽高是92*112像素,全部是灰度图像。
至于如何提高图片识别的效率,我也不知道。

参考文献

1 http://docs.opencv.org/modules/contrib/doc/facerec/facerec_tutorial.html
2 python调用opencv实现人脸识别

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