LBP人脸识别的python实现

    这几天看了看LBP及其人脸识别的流程，并在网络上搜相应的python代码，有，但代码质量不好，于是自己就重新写了下，对于att_faces数据集的识别率能达到95.0%~99.0%（40种类型，每种随机选5张训练，5张识别），全部代码如下，不到80行哦。

#coding:utf-8
import numpy as np
import cv2, os, math, os.path, glob, random

g_mapping=[
0, 1, 2, 3, 4, 58, 5, 6, 7, 58, 58, 58, 8, 58, 9, 10,
11, 58, 58, 58, 58, 58, 58, 58, 12, 58, 58, 58, 13, 58, 14, 15,
16, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58,
17, 58, 58, 58, 58, 58, 58, 58, 18, 58, 58, 58, 19, 58, 20, 21,
22, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58,
58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58,
23, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58,
24, 58, 58, 58, 58, 58, 58, 58, 25, 58, 58, 58, 26, 58, 27, 28,
29, 30, 58, 31, 58, 58, 58, 32, 58, 58, 58, 58, 58, 58, 58, 33,
58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 34,
58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58,
58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 35,
36, 37, 58, 38, 58, 58, 58, 39, 58, 58, 58, 58, 58, 58, 58, 40,
58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 41,
42, 43, 58, 44, 58, 58, 58, 45, 58, 58, 58, 58, 58, 58, 58, 46,
47, 48, 58, 49, 58, 58, 58, 50, 51, 52, 58, 53, 54, 55, 56, 57]

def loadImageSet(folder, sampleCount=5):
trainData = []; testData = []; yTrain=[]; yTest = [];
for k in range(1,41):
folder2 = os.path.join(folder, 's%d' %k)
data = [cv2.imread(d.encode('gbk'),0) for d in glob.glob(os.path.join(folder2, '*.pgm'))]
sample = random.sample(range(10), sampleCount)
trainData.extend([data[i] for i in range(10) if i in sample])
testData.extend([data[i] for i in range(10) if i not in sample])
yTest.extend([k]* (10-sampleCount))
yTrain.extend([k]* sampleCount)
return trainData, testData, np.array(yTrain), np.array(yTest)

def LBP(I, radius=2, count=8):       #得到图像的LBP特征
dh = np.round([radius*math.sin(i*2*math.pi/count) for i in range(count)])
dw = np.round([radius*math.cos(i*2*math.pi/count) for i in range(count)])

height ,width = I.shape
lbp = np.zeros(I.shape, dtype = np.int)
I1 = np.pad(I, radius, 'edge')
for k in range(count):
h,w = radius+dh[k], radius+dw[k]
lbp += ((I>I1[h:h+height, w:w+width])<<k)
return lbp

def calLbpHistogram(lbp, hCount=7, wCount=5, maxLbpValue=255): #分块计算lbp直方图
height,width = lbp.shape
res = np.zeros((hCount*wCount, max(g_mapping)+1), dtype=np.float)
assert(maxLbpValue+1 == len(g_mapping))

for h  in range(hCount):
for w in range(wCount):
blk = lbp[height*h/hCount:height*(h+1)/hCount, width*w/wCount:width*(w+1)/wCount]
hist1 = np.bincount(blk.ravel(), minlength=maxLbpValue)

hist = res[h*wCount+w,:]
for v,k in zip(hist1, g_mapping):
hist[k] += v
hist /= hist.sum()
return res

def main(folder=u'E:/迅雷下载/faceProcess/att_faces'):
trainImg, testImg, yTrain, yTest = loadImageSet(folder)

xTrain = np.array([calLbpHistogram(LBP(d)).ravel() for d in trainImg])
xTest  = np.array([calLbpHistogram(LBP(d)).ravel() for d in testImg])

lsvc = cv2.SVM()                              #支持向量机方法
svm_params = dict( kernel_type = cv2.SVM_LINEAR, svm_type = cv2.SVM_C_SVC, C=2.67, gamma=5.383 )
lsvc.train(np.float32(xTrain), np.float32(yTrain), params = svm_params)
lsvc_y_predict = np.array( [lsvc.predict(d) for d in np.float32(xTest)])
print u'支持向量机识别率', (lsvc_y_predict == np.array(yTest)).mean()

if __name__ == '__main__':
main()

　　下面是对mnist手写数字数据集的识别，修改了数据集的载入，并加了图像的倾斜校正，识别率达到96%（如果使用sklearn的svm，效率会更高一些。）

import cPickle
import gzip,math
import numpy as np
import os, glob, random, cv2

SZ = 28
def deskew(img):
m = cv2.moments(img)
if abs(m['mu02']) < 1e-2:
return img.copy()
skew = m['mu11']/m['mu02']
M = np.float32([[1, skew, -0.5*SZ*skew], [0, 1, 0]])
img = cv2.warpAffine(img,M,(SZ, SZ),flags=cv2.WARP_INVERSE_MAP|cv2.INTER_LINEAR)
return img

g_mapping=[
0, 1, 2, 3, 4, 58, 5, 6, 7, 58, 58, 58, 8, 58, 9, 10,
11, 58, 58, 58, 58, 58, 58, 58, 12, 58, 58, 58, 13, 58, 14, 15,
16, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58,
17, 58, 58, 58, 58, 58, 58, 58, 18, 58, 58, 58, 19, 58, 20, 21,
22, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58,
58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58,
23, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58,
24, 58, 58, 58, 58, 58, 58, 58, 25, 58, 58, 58, 26, 58, 27, 28,
29, 30, 58, 31, 58, 58, 58, 32, 58, 58, 58, 58, 58, 58, 58, 33,
58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 34,
58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58,
58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 35,
36, 37, 58, 38, 58, 58, 58, 39, 58, 58, 58, 58, 58, 58, 58, 40,
58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 41,
42, 43, 58, 44, 58, 58, 58, 45, 58, 58, 58, 58, 58, 58, 58, 46,
47, 48, 58, 49, 58, 58, 58, 50, 51, 52, 58, 53, 54, 55, 56, 57]

def loadImageSet():
with gzip.open('./mnist.pkl.gz') as fp:
train_set, valid_set, test_set = cPickle.load(fp)

xTrain = train_set[0]; s1 = xTrain.shape; xTrain = xTrain.reshape((s1[0],28,28))
xTest = test_set[0];   s2 = xTest.shape;  xTest = xTest.reshape((s2[0],28,28))
xTrain = np.array([deskew(d) for d in xTrain])
xTest  = np.array([deskew(d) for d in xTest])
return xTrain, xTest, train_set[1],  test_set[1]

def LBP(I, radius=2, count=8):       #得到图像的LBP特征
dh = np.round([radius*math.sin(i*2*math.pi/count) for i in range(count)])
dw = np.round([radius*math.cos(i*2*math.pi/count) for i in range(count)])

height ,width = I.shape
lbp = np.zeros(I.shape, dtype = np.int)
I1 = np.pad(I, radius, 'edge')
for k in range(count):
h,w = radius+dh[k], radius+dw[k]
lbp += ((I>I1[h:h+height, w:w+width])<<k)
return lbp

def calLbpHistogram(lbp, hCount=2, wCount=2, maxLbpValue=255): #分块计算lbp直方图
height,width = lbp.shape
res = np.zeros((hCount*wCount, max(g_mapping)+1), dtype=np.float)
assert(maxLbpValue+1 == len(g_mapping))

for h  in range(hCount):
for w in range(wCount):
blk = lbp[height*h/hCount:height*(h+1)/hCount, width*w/wCount:width*(w+1)/wCount]
hist1 = np.bincount(blk.ravel(), minlength=maxLbpValue)

hist = res[h*wCount+w,:]
for v,k in zip(hist1, g_mapping):
hist[k] += v
hist /= hist.sum()
return res

def main():
trainImg, testImg, yTrain, yTest = loadImageSet()

xTrain = np.array([calLbpHistogram(LBP(d)).ravel() for d in trainImg])
xTest  = np.array([calLbpHistogram(LBP(d)).ravel() for d in testImg])

lsvc = cv2.SVM()                              #支持向量机方法
svm_params = dict( kernel_type = cv2.SVM_LINEAR, svm_type = cv2.SVM_C_SVC, C=2.67, gamma=5.383 )
lsvc.train(np.float32(xTrain), np.float32(yTrain), params = svm_params)
lsvc_y_predict = np.array( [lsvc.predict(d) for d in np.float32(xTest)])
print u'支持向量机', (lsvc_y_predict == np.array(yTest)).mean()

if __name__ == '__main__':
main()