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()

  

LBP人脸识别的python实现的更多相关文章

  1. gabor变换人脸识别的python实现,att_faces数据集平均识别率99%

    大家都说gabor做人脸识别是传统方法中效果最好的,这几天就折腾实现了下,网上的python实现实在太少,github上的某个版本还误导了我好几天,后来采用将C++代码封装成dll供python调用的 ...

  2. PCA人脸识别的python实现

    这几天看了看PCA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92.5%~98.0%(40种类型,每种随 ...

  3. iOS活体人脸识别的Demo和一些思路

    代码地址如下:http://www.demodashi.com/demo/12011.html 之前公司项目需要,研究了一下人脸识别和活体识别,并运用免费的讯飞人脸识别,在其基础上做了二次开发,添加了 ...

  4. 人脸检测? 对Python来说太简单, 调用dlib包就可以完成

    "Dlib 是一个现代化的 C ++ 工具包,包含用于创建复杂软件的机器学习算法和工具 " .它使您能够直接在 Python 中运行许多任务,其中一个例子就是人脸检测. 安装 dl ...

  5. 百度Aip人脸识别之python代码

    用python来做人脸识别代码量少 思路清晰, 在使用之前我们需要在我们的配置的编译器中通过pip install baidu-aip  即可 from aip import AipFace 就可以开 ...

  6. 人脸识别之Python DLib库进行人脸关键点识别

    一.首先安装DLib模块 这里只介绍linux安装的过程,windows安装过程请自行百度 1.首先,安装dlib.skimage前:先安装libboost sudo apt-get install ...

  7. 转《在浏览器中使用tensorflow.js进行人脸识别的JavaScript API》

    作者 | Vincent Mühle 编译 | 姗姗 出品 | 人工智能头条(公众号ID:AI_Thinker) [导读]随着深度学习方法的应用,浏览器调用人脸识别技术已经得到了更广泛的应用与提升.在 ...

  8. face_recognition 人脸识别报错

    [root@localhost examples]# python facerec_from_video_file.py RuntimeError: module compiled against A ...

  9. face-api.js:一个在浏览器中进行人脸识别的 JavaScript 接口

    Mark! 本文将为大家介绍一个建立在「tensorflow.js」内核上的 javascript API——「face-api.js」,它实现了三种卷积神经网络架构,用于完成人脸检测.识别和特征点检 ...

随机推荐

  1. 转:C# WinForm窗体及其控件的自适应

    一.说明 2012-11-30 曾经写过 <C# WinForm窗体及其控件自适应各种屏幕分辨率>  ,其中也讲解了控件自适应的原理.近期有网友说,装在panel里面的控件,没有效果? 这 ...

  2. 使用jqGrid过程中出现的问题

    在使用jqGrid过程中,需要后台查询数据添加到表格中,在js中循环调用addRowData方法时出现浏览器崩溃现象. 原因:jqGrid的addRowData方法中做了一系列的处理,在后台返回数据量 ...

  3. Collection集合 总结笔记

    2:Set集合(理解)     (1)Set集合的特点         无序,唯一     (2)HashSet集合(掌握)         A:底层数据结构是哈希表(是一个元素为链表的数组)     ...

  4. print in或者not in, 判断在不在里面

    print("不疼" in "麻花疼")        # 结果False print("不疼"in "真不疼") # ...

  5. python3 装饰器全解

    本章结构: 1.理解装饰器的前提准备 2.装饰器:无参/带参的被装饰函数,无参/带参的装饰函数 3.装饰器的缺点 4.python3的内置装饰器 5.本文参考 理解装饰器的前提:1.所有东西都是对象( ...

  6. php解决约瑟夫环的问题

    php里面解决约瑟夫环还是比较方面的,但是下面的方法太费空间 <?php class SelectKing{ private $m;//幅度 private $n;//总数 public fun ...

  7. P2619 [国家集训队2]Tree I

    Description 给你一个无向带权连通图,每条边是黑色或白色.让你求一棵最小权的恰好有need条白色边的生成树. 题目保证有解. Input 第一行V,E,need分别表示点数,边数和需要的白色 ...

  8. Linux - 搭建FastDFS分布式文件系统

    1. FastDFS简介 说明:FastDFS简介部分的理论知识全部来自于博主bojiangzhou的 <用FastDFS一步步搭建文件管理系统>,在此感谢博主的无私分享.当然最最要感谢的 ...

  9. Day15 集合(二)

    Set简介 定义 public interface Set<E> extends Collection<E> {} Set是一个继承于Collection的接口,即Set也是集 ...

  10. php后台+前端开发过程整理

    一.PHP后台从数据库中获取数据 1. 建立数据库连接: //在本项目中封装了数据库的各种操作 $dbConn = $this->_createMysqlConn(); 2. 执行sql语句 $ ...