现在有这样的一个场景：给一张行人的小矩形框图片， 根据该行人的特征识别出性别。

分析：

（1），行人的姿态各异，变化多端。很难提取图像的特定特征

（2），正常人肉眼判别行人的根据是身材比例，头发长度等。（如果是冬天的情况下，行人穿着厚实，性别识别更加难）

solution：

针对难以提取特定特征的图像，可以采用卷积神经网络CNN去自动提取并训练。

数据准备： 

采用 PETA数据集，Pedestrain Attribute Recognition At Far Distance。 该数据集一共包含了19000张标记了行人穿着及性别信息的图片。

Peta dataset source url:   http://mmlab.ie.cuhk.edu.hk/projects/PETA.html

数据处理：

针对下载解压之后的数据集，采用的流程是：

（1）对每一张图片进行resize， resize到特定的大小（实验中定为50*150），

（2）对正类负类样本的不均衡情况，进行rebalance处理，实验中对少数类样本进行随机选择n张进行data augmentation之后重新加入到dataset中。

（3）划分training set和testing set， 根据train/test ratio将整个数据样本随机分为两部分。

（4）对training set 进行data augmentation 处理。 扩大训练数据量。 （操作包括： 翻转，滤波等）

#!/usr/bin/env python

#-*- encoding: utf-8 -*- 

#########

## The python code to preprocess the images and resize them into (50, 150)

## Date: 2016-09-19

#########

import os, sys, cv2

import numpy as np

import random 

image_cnt = 0

MIN_HEIGHT = 120

MIN_WIDTH = 40

targetLabel = [] 

positive_cnt = 0

negative_cnt = 0 

def readImage( filePath , targetDir ):

	global image_cnt, positive_cnt, negative_cnt

	global targetLabel

	if not os.path.isdir( filePath ):

		print('{} is not a dir'.format(filePath))

		return None

	listFile = os.listdir( filePath )

	labelDict = {}

	with open( filePath + 'Label.txt', 'r') as reader:

		for line in reader:

			lines = line.split()

			for i in range(1, len(lines)):

				if lines[i] == 'personalMale':

					label = 1

				elif lines[i] == 'personalFemale':

					label = 0

				else:

					continue

				labelDict[lines[0]] = label

				break 

	for i in range(len(listFile)):

		if len(listFile[i]) > 4 and (listFile[i][-4:] == '.bmp' or listFile[i][-4:] == '.jpg' or \

			listFile[i][-4:] == '.png' or listFile[i][-5:] == '.jpeg'):

			imageName = filePath + listFile[i]

			img = cv2.imread( imageName )

			if not img.data:

				continue

			height, width = img.shape[:2]

			if height < MIN_HEIGHT or width < MIN_WIDTH:

				continue

			fileName = str( image_cnt ) + '.jpeg'

			identity = listFile[i].find('_')

			if  identity == -1:

				identity = len(listFile[i])

			idd = listFile[i][:identity]

			if labelDict.has_key( idd ) :

				targetLabel.append([ fileName, labelDict[idd]])

				if labelDict[idd] == 0:

					negative_cnt += 1

				else:

					positive_cnt += 1

				img = cv2.resize(img, (50, 150), interpolation=cv2.INTER_CUBIC)

				cv2.imwrite(targetDir + fileName, img)

				image_cnt += 1

			else:

				print('file {} do not have label'.format(listFile[i]) )

####### pyramid operator

def MinAndEnlarge(img, Minus_pixel = 3):

	img = img[(3*Minus_pixel):(150 - 3*Minus_pixel), Minus_pixel:(50 - Minus_pixel), :]

	img = cv2.resize(img, (50, 150), interpolation = cv2.INTER_CUBIC )

	return img 

####### rotate operator

def Flip(img, operator = 1):

	if operator == 1:

		img = cv2.flip(img, 1)

	else:

		img = cv2.flip(img, 0)

	return img 

####### median blurring the image

def Blur(img, kernel_size=5):

	img = cv2.medianBlur(img, kernel_size)

	return img 

def EnlargeData( filePath , targetDir ):

	global image_cnt, targetLabel

	total_sample = len(targetLabel)

	for i in range(total_sample):

		img = cv2.imread( filePath + targetLabel[i][0] )

		fileLabel = targetLabel[i][1]

		if not img.data:

			print('no exits image file {}'.format( filePath + targetLabel[i][0]) )

		#

		img1 = MinAndEnlarge(img, 3)

		fileName = str(image_cnt) + '.jpeg'

		cv2.imwrite( targetDir + fileName, img1 )

		image_cnt += 1

		targetLabel.append( [fileName, fileLabel] )

		#

		img2 = Flip(img1)

		fileName = str(image_cnt) + '.jpeg'

		cv2.imwrite( targetDir + fileName, img2 )

		image_cnt += 1

		targetLabel.append( [fileName, fileLabel] )

		#

		img3 = Blur(img, 5)

		fileName = str(image_cnt) + '.jpeg'

		cv2.imwrite( targetDir + fileName, img3 )

		image_cnt += 1

		targetLabel.append( [fileName, fileLabel] )

		#

		img4 = Blur(img1, 5)

		fileName = str(image_cnt) + '.jpeg'

		cv2.imwrite( targetDir + fileName, img4 )

		image_cnt += 1

		targetLabel.append([fileName, fileLabel])

		#

		img5 = Blur(img2, 5)

		fileName = str(image_cnt) + '.jpeg'

		cv2.imwrite( targetDir + fileName, img5 )

		image_cnt += 1

		targetLabel.append([fileName, fileLabel])

	print('The total number of images is {}'.format(image_cnt)) 

def saveLabel( targetDir ):

	global targetLabel

	with open(targetDir + 'label.txt', 'w') as writer:

		for i in range(len(targetLabel)):

			writer.write( str( targetLabel[i][0] ) + ' ' + str(targetLabel[i][1]) + '\n' ) 

##### ReBalance operator

#######  num (the number of minority class should added)

#######  n_or_p (the label of minority class)

#######  op_chose( 1--symmetrical flip; 0--rotate image; )

def ReBalance( targetDir, num, n_or_p, op_chose = 0):

	global targetLabel, image_cnt

	total_sample = len(targetLabel)

	Contain = {}

	while 1:

		if num <= 0:

			break

		key_id = random.randint(0, total_sample-1)

		if Contain.has_key( key_id ) or targetLabel[key_id][1] != n_or_p:

			continue

		img = cv2.imread( targetDir + targetLabel[key_id][0] )

		if op_chose == 0:

			img = cv2.flip(img, 1)

		elif op_chose == 1:

			img = cv2.flip(img, 0)

		fileName = str(image_cnt) + '.jpeg'

		cv2.imwrite(targetDir + fileName, img)

		image_cnt += 1

		targetLabel.append([fileName, n_or_p])

		num -= 1

	print('Finish add {} images'.format(image_cnt - total_sample))

	print('Now the class is balanced and total num is {}'.format(image_cnt))

	print('image_cnt is {} and len(_targetLabel_) is {} '.format(image_cnt, len(targetLabel))) 

def divide( targetDir, trainDir, testDir, test_ratio = 0.20):

	global targetLabel

	total_sample = len(targetLabel)

	assert( test_ratio < 1)

	test_num = int(total_sample * test_ratio )

	test_half_num = test_num // 2; ml_cnt = 0; fm_cnt = 0

	testLabel = [] ; trainLabel = []

	for i in range(total_sample):

		if  ml_cnt < test_half_num and targetLabel[i][1] == 1:

			ml_cnt += 1

			img = cv2.imread( targetDir + targetLabel[i][0] )

			cv2.imwrite( testDir +  targetLabel[i][0], img )

			testLabel.append(targetLabel[i])

		elif fm_cnt < test_half_num and targetLabel[i][1] == 0:

			fm_cnt += 1

			img = cv2.imread( targetDir + targetLabel[i][0] )

			cv2.imwrite( testDir +  targetLabel[i][0], img )

			testLabel.append(targetLabel[i])

		else:

			img = cv2.imread( targetDir + targetLabel[i][0] )

			cv2.imwrite( trainDir + targetLabel[i][0], img )

			trainLabel.append(targetLabel[i])

	# train

	with open( trainDir + 'label.txt', 'w') as writer:

		for i in range(len(trainLabel)):

			writer.write( str( trainLabel[i][0] ) + ' ' + str(trainLabel[i][1]) + '\n' )

	with open( testDir + 'label.txt', 'w') as writer:

		for i in range(len(testLabel)):

			writer.write( str(testLabel[i][0]) + ' ' + str(testLabel[i][1]) + '\n')

	print('has divide into train with {} samples and test with {} samples'.format(len(trainLabel), len(testLabel)) )

	return trainLabel, testLabel 

def DivideSet( targetDir, trainDir, testDir, test_ratio = 0.20):

	global targetLabel

	total_sample = len(targetLabel)

	assert( test_ratio < 1 )

	test_num = int(test_ratio * total_sample)

	test_half_num = test_num //2 ; ml_cnt = test_half_num; fm_cnt = test_half_num

	testLabel = [] ; trainLabel = [] ; testDict = {}

	while ml_cnt > 0 or fm_cnt > 0:

		idd = random.randint(0, total_sample-1)

		if testDict.has_key( targetLabel[idd][0] ):

			continue

		if targetLabel[idd][1] == 1 and ml_cnt > 0:

			img = cv2.imread( targetDir + targetLabel[idd][0] )

			cv2.imwrite( testDir + targetLabel[idd][0], img )

			testLabel.append( targetLabel[idd] )

			testDict[targetLabel[idd][0]] = idd

			ml_cnt -= 1

		if targetLabel[idd][1] == 0 and fm_cnt > 0:

			img = cv2.imread( targetDir + targetLabel[idd][0] )

			cv2.imwrite( testDir + targetLabel[idd][0], img )

			testLabel.append( targetLabel[idd] )

			testDict[targetLabel[idd][0]] = idd

			fm_cnt -= 1

	for i in range(total_sample):

		if not testDict.has_key( targetLabel[i][0] ):

			trainLabel.append( targetLabel[i] )

			img = cv2.imread( targetDir + targetLabel[i][0] )

			cv2.imwrite( trainDir + targetLabel[i][0], img )

	## save the trainset and testset

	with open( trainDir + 'label.txt', 'w') as writer:

		for i in range(len(trainLabel)):

			writer.write( str( trainLabel[i][0] ) + ' ' + str(trainLabel[i][1]) + '\n' )

	with open( testDir + 'label.txt', 'w') as writer:

		for i in range(len(testLabel)):

			writer.write( str(testLabel[i][0]) + ' ' + str(testLabel[i][1]) + '\n')

	print('has divide into train with {} samples and test with {} samples'.format(len(trainLabel), len(testLabel)) )

	return trainLabel, testLabel 

def EnlargeTrain( fileDir, targetDir, trainLabel , start_cnt):

	total_sample = len(trainLabel)

	new_cnt = start_cnt

	for i in range(total_sample):

		img = cv2.imread( fileDir + trainLabel[i][0] )

		fileLabel = trainLabel[i][1]

		if not img.data:

			print('no exits image file {}'.format( fileDir + trainLabel[i][0]) )

			continue

		#

		img1 = MinAndEnlarge(img, 3)

		fileName = str(new_cnt) + '.jpeg'

		cv2.imwrite( targetDir + fileName, img1 )

		new_cnt += 1

		trainLabel.append( [fileName, fileLabel] )

		#

		img2 = Flip(img1)

		fileName = str(new_cnt) + '.jpeg'

		cv2.imwrite( targetDir + fileName, img2 )

		new_cnt += 1

		trainLabel.append( [fileName, fileLabel] )

		#

		img3 = Blur(img, 5)

		fileName = str(new_cnt) + '.jpeg'

		cv2.imwrite( targetDir + fileName, img3 )

		new_cnt += 1

		trainLabel.append( [fileName, fileLabel] )

		#

		img4 = Blur(img1, 5)

		fileName = str(new_cnt) + '.jpeg'

		cv2.imwrite( targetDir + fileName, img4 )

		new_cnt += 1

		trainLabel.append([fileName, fileLabel])

		#

		img5 = Blur(img2, 5)

		fileName = str(new_cnt) + '.jpeg'

		cv2.imwrite( targetDir + fileName, img5 )

		new_cnt += 1

		trainLabel.append([fileName, fileLabel])

	print('The total number of training images is {}'.format(new_cnt))

	with open( targetDir + 'label.txt', 'w') as writer:

		for i in range(len(trainLabel)):

			writer.write( str( trainLabel[i][0] ) + ' ' + str(trainLabel[i][1]) + '\n' )

	print('The trainLabel size is {}'.format(len(trainLabel)) ) 

if __name__ == '__main__':

	fileHead = '/home/zhangyd/source/PETA_dataset/'

	filePath = ['3DPeS', 'CAVIAR4REID', 'CUHK', 'GRID','MIT', 'PRID','SARC3D','TownCentre', 'VIPeR','i-LID']

	savePath = '/home/zhangyd/source/peta/'

	for i in range(len(filePath)):

		path = fileHead + filePath[i] + '/archive/'

		print ('runing dataset {}'.format(filePath[i]) )

		readImage( path, savePath )

		print ('The cnt is {}'.format( image_cnt ))

	#EnlargeData( savePath, savePath )

	saveLabel( savePath )

	print( 'we have {} positive labels and {} negative labels '.format( positive_cnt, negative_cnt ))

	if positive_cnt > negative_cnt:

		add_num = positive_cnt - negative_cnt

		ReBalance( savePath, add_num, 0, 0)

	else:

		add_num = negative_cnt - positive_cnt

		ReBalance( savePath, add_num, 1, 0)

	print('The total dataset is in {}'.format(savePath))

	TrainsavePath = '/home/zhangyd/source/peta_v1/petaTrain/'

	TestsavePath = '/home/zhangyd/source/peta_v1/petaTest/'

	trainLabel, testLabel =  DivideSet(savePath, TrainsavePath, TestsavePath, 0.2 )

	start_cnt = len(targetLabel)

	EnlargeTrain( TrainsavePath, TrainsavePath, trainLabel, start_cnt )

	print('the end')

　　

实验

使用caffe的create_lmdb.sh 转换图像数据 成 imbd数据集。

定义 prototxt 等信息。

CNN 的结构是：

训练的参数设置：

# The train/test net protocol buffer definition

net: "examples/peta/petanet_train_test.prototxt"

# test_iter specifies how many forward passes the test should carry out.

# In the case of MNIST, we have test batch size 100 and 100 test iterations,

# covering the full 10,000 testing images.

test_iter: 100

# Carry out testing every 500 training iterations.

test_interval: 500

# The base learning rate, momentum and the weight decay of the network.

base_lr: 0.01

momentum: 0.9

weight_decay: 0.0005

# The learning rate policy

lr_policy: "inv"

gamma: 0.0001

power: 0.75

# Display every 100 iterations

display: 100

# The maximum number of iterations

max_iter: 10000

# snapshot intermediate results

snapshot: 5000

snapshot_prefix: "examples/peta/petanet"

# solver mode: CPU or GPU

solver_mode: GPU

使用论文《Learned vs. Hand-Crafted Features for Pedestrian Gender Recognition》中的网络结构，取得了较好的训练结果：

I0922 00:07:32.204310 16398 solver.cpp:337] Iteration 10000, Testing net (#0)

I0922 00:07:34.001411 16398 solver.cpp:404]     Test net output #0: accuracy = 0.8616

I0922 00:07:34.001471 16398 solver.cpp:404]     Test net output #1: loss = 0.721973 (* 1 = 0.721973 loss)

I0922 00:07:34.001479 16398 solver.cpp:322] Optimization Done.

I0922 00:07:34.001485 16398 caffe.cpp:254] Optimization Done.

实验分析：

因为网络不大，网络也比较简单，在GPU下进行训练，消耗的显存大概是几百M，不到1G的显存。网络结构经典，也取得较好的训练结果。

我的拓展： 自己设计的CNN网络

吸取了GoogleNet的网络特征， 引入inception， 重新设计网络。

是两个 inception 组成， 后面加上一个FC层。

其中Snapshot的网络结构prototxt文件是：

name: "petaNet"

layer {

	name: "data"

	type: "Input"

	top: "data"

	input_param {

		shape: {

			dim: 1

			dim: 3

			dim: 50

			dim: 150

		}

	}

}

### ------------

layer {

	name: "conv1"

	type: "Convolution"

	bottom: "data"

	top: "conv1"

	param {

		lr_mult: 1

	}

	param {

		lr_mult: 2

	}

	convolution_param {

		num_output: 20

		kernel_size: 3

		stride: 1

		weight_filler {

			type: "xavier"

		}

		bias_filler {

			type: "constant"

		}

	}

}

layer {

	name: "relu1"

	type: "ReLU"

	bottom: "conv1"

	top: "conv1"

}

##-------

# Inception 3a

##-------

layer {

	name: "inc1_conv1"

	bottom: "conv1"

	top: "inc1_conv1"

	type: "Convolution"

	param {	lr_mult: 1 }

	param {	lr_mult: 2 }

	convolution_param {

		num_output: 20

		kernel_size: 7

		stride: 1

		weight_filler {	type: "xavier" }

		bias_filler	{ type: "constant" }

	}

}

layer {

	name: "inc1_conv1_relu"

	type: "ReLU"

	bottom: "inc1_conv1"

	top: "inc1_conv1"

}

layer {

	name: "inc1_conv2_1"

	type: "Convolution"

	bottom: "conv1"

	top: "inc1_conv2_1"

	param { lr_mult: 1 }

	param { lr_mult: 2 }

	convolution_param {

		num_output: 50

		kernel_size: 3

		stride: 1

		weight_filler { type: "xavier" }

		bias_filler { type: "constant" }

	}

}

layer {

	name: "inc1_conv2_1_relu"

	type: "ReLU"

	bottom: "inc1_conv2_1"

	top:	"inc1_conv2_1"

}

layer {

	name: "inc1_conv2_2"

	type: "Convolution"

	bottom: "inc1_conv2_1"

	top:	"inc1_conv2_2"

	param { lr_mult: 1 }

	param { lr_mult: 2 }

	convolution_param {

		num_output: 50

		kernel_size: 3

		stride: 1

		weight_filler { type: "xavier" }

		bias_filler	{ type: "constant" }

	}

}

layer {

	name: "inc1_conv2_2_relu"

	type: "ReLU"

	bottom: "inc1_conv2_2"

	top: "inc1_conv2_2"

}

layer {

	name: "inc1_conv2_3"

	type: "Convolution"

	bottom: "inc1_conv2_2"

	top:	"inc1_conv2_3"

	param { lr_mult: 1 }

	param { lr_mult: 2 }

	convolution_param {

		num_output: 50

		kernel_size: 3

		stride: 1

		weight_filler { type: "xavier" }

		bias_filler { type: "constant" }

	}

}

layer {

	name: "inc1_conv2_3_relu"

	type: "ReLU"

	bottom: "inc1_conv2_3"

	top:	"inc1_conv2_3"

}

layer {

	name: "inc1_conv3_1"

	type: "Convolution"

	bottom: "conv1"

	top:	"inc1_conv3_1"

	param { lr_mult: 1 }

	param { lr_mult: 2 }

	convolution_param {

		num_output: 20

		kernel_size: 5

		stride: 1

		weight_filler { type: "xavier" }

		bias_filler { type: "constant" }

	}

}

layer {

	name: "inc1_conv3_1_relu"

	type: "ReLU"

	bottom: "inc1_conv3_1"

	top: "inc1_conv3_1"

}

layer {

	name: "inc1_conv3_2"

	type: "Convolution"

	bottom: "inc1_conv3_1"

	top:	"inc1_conv3_2"

	param { lr_mult: 1 }

	param { lr_mult: 2 }

	convolution_param {

		num_output: 20

		kernel_size: 3

		stride: 1

		weight_filler { type: "xavier" }

		bias_filler { type: "constant" }

	}

}

layer {

	name: "inc1_conv3_2_relu"

	type: "ReLU"

	bottom: "inc1_conv3_2"

	top:	"inc1_conv3_2"

}

layer {

	name: "inc1_concat"

	type: "Concat"

	bottom: "inc1_conv1"

	bottom: "inc1_conv2_3"

	bottom: "inc1_conv3_2"

	top: 	"inc1_concat"

}

#-----end of Inception 3a 

layer {

	name: "pool1"

	type: "Pooling"

	bottom: "inc1_concat"

	top: "pool1"

	pooling_param {

		pool: MAX

		kernel_size: 2

		stride: 2

	}

}

##------

# Inception 2B

##------

layer {

	name: "inc2_conv1_1"

	type: "Convolution"

	bottom: "pool1"

	top: "inc2_conv1_1"

	param { lr_mult: 1 }

	param { lr_mult: 2 }

	convolution_param {

		num_output: 120

		kernel_size: 3

		stride: 1

		weight_filler { type: "xavier" }

		bias_filler { type: "constant" }

	}

}

layer {

	name: "inc2_conv1_1_relu"

	type: "ReLU"

	bottom: "inc2_conv1_1"

	top: "inc2_conv1_1"

}

layer {

	name: "inc2_conv1_2"

	type: "Convolution"

	bottom: "inc2_conv1_1"

	top:	"inc2_conv1_2"

	param { lr_mult: 1 }

	param { lr_mult: 2 }

	convolution_param {

		num_output: 120

		kernel_size: 3

		stride: 1

		weight_filler { type: "xavier" }

		bias_filler { type: "constant" }

	}

}

layer {

	name: "inc2_conv1_2_relu"

	type: "ReLU"

	bottom: "inc2_conv1_2"

	top: "inc2_conv1_2"

}

layer {

	name: "inc2_conv2"

	type: "Convolution"

	bottom: "pool1"

	top:	"inc2_conv2"

	param { lr_mult: 1 }

	param { lr_mult: 2 }

	convolution_param {

		num_output: 120

		kernel_size: 5

		stride: 1

		weight_filler { type: "xavier" }

		bias_filler { type: "constant" }

	}

}

layer {

	name: "inc2_conv2_relu"

	type: "ReLU"

	bottom: "inc2_conv2"

	top:	"inc2_conv2"

}

layer {

	name: "inc2_concat"

	type: "Concat"

	bottom: "inc2_conv1_2"

	bottom: "inc2_conv2"

	top: "inc2_concat"

}

##----end of Inception 2B 

layer {

	name: "pool2"

	type: "Pooling"

	bottom: "inc2_concat"

	top:	"pool2"

	pooling_param {

		pool: MAX

		kernel_size: 2

		stride: 2

	}

}

layer {

	name: "fc1"

	type: "InnerProduct"

	bottom: "pool2"

	top:	"fc1"

	param { lr_mult: 1 }

	param { lr_mult: 2 }

	inner_product_param {

		num_output: 2

		weight_filler { type: "xavier" }

		bias_filler { type: "constant" }

	}

}

#### ----------

layer {

	name: "prob"

	type: "Softmax"

	bottom: "fc1"

	top: "prob"

}

　　

取得的效果比论文中的网络结构差点， 训练结果是：

I0927 00:11:42.485725 20295 solver.cpp:317] Iteration 10000, loss = 0.0678897

I0927 00:11:42.485771 20295 solver.cpp:337] Iteration 10000, Testing net (#0)

I0927 00:12:06.291497 20295 solver.cpp:404]     Test net output #0: accuracy = 0.8448

I0927 00:12:06.291554 20295 solver.cpp:404]     Test net output #1: loss = 0.614111 (* 1 = 0.614111 loss)

I0927 00:12:06.291563 20295 solver.cpp:322] Optimization Done.

I0927 00:12:06.291568 20295 caffe.cpp:254] Optimization Done.

　　

实验分析：

因为该网络的组成较为复杂， inception包含着较大的子网络， 因为训练的时候，需要消耗GPU显存为3G多。训练时间也较长些。

reference:  

Learned vs. Hand-Crafted Features for Pedestrian Gender Recognition   Grigory Antipov,Sid-Ahmed Berrani

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