Keras实例教程（五）- 使用 GTSRB 用于交通标志识别

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数据集

GTSRB dataset :
http://benchmark.ini.rub.de/?section=gtsrb&subsection=dataset#Downloads

该数据集包含43类交通标志，提供的资料中包含标注信息。

【0】数据准备

根据标注裁剪图像
在每类的文件夹中，包含若干.ppm格式的图片及一个.csv文件，csv中包含每个ppm图像的标注信息，根据标注信息进行图片裁剪.
将43类放到同一文件夹Final_Training下，裁剪示例代码：

import os
import sys
from PIL import Image
path = 'C:/Users/Documents/Dataset/GTSRB/Final_Training'csv_files = []
for dirpath, dirnames, filenames in os.walk(path, topdown=False):for filename in filenames:if filename.endswith('.csv'):csv_files.append(os.path.join(dirpath, filename))class TrafficSign:trafficSign_name = ''left_top_x = 0,left_top_y = 0,right_bottom_x = 0,right_bottom_y = 0,width = 0,height = 0,label = ''def tostring(self):print([self.trafficSign_name,self.width, self.height,self.left_top_x, self.left_top_y,self.right_bottom_x, self.right_bottom_y,self.label])for csv in csv_files:base_path = os.path.dirname(csv)# read csv datatrafficSigns = []with open(csv) as file:for line in file:if line.find('.ppm') == -1:continueraw_data = line.split(';')trafficSign = TrafficSign()trafficSign.trafficSign_name = raw_data[0]trafficSign.width = int(raw_data[1])trafficSign.height = int(raw_data[2])trafficSign.left_top_x = int(raw_data[3])trafficSign.left_top_y = int(raw_data[4])trafficSign.right_bottom_x = int(raw_data[5])trafficSign.right_bottom_y = int(raw_data[6])# trafficSign.label = raw_data[7]trafficSigns.append(trafficSign)# crop each image according to the csv in this folderfor dirpath, dirnames, filenames in os.walk(base_path, topdown=False):for filename in filenames:if not filename.endswith('.ppm'):continuefullPath = os.path.join(dirpath, filename)for sign in trafficSigns:if filename == sign.trafficSign_name:image = Image.open(fullPath)# start cropping according to this signregion = (sign.left_top_x, sign.left_top_y, sign.right_bottom_x, sign.right_bottom_y)image_crop = image.crop(region)# update the new image pathnewFullPath = fullPath.replace('GTSRB', 'GTSRB_img_Crop')newFullPath = newFullPath.replace('.ppm', '.bmp')if not os.path.exists(os.path.dirname(newFullPath)):os.makedirs(os.path.dirname(newFullPath))# save the imagesimage_crop.save(newFullPath)break

裁剪后的图片如下所示：
[外链图片转存失败(img-3vH99RSt-1563292422090)(https://note.youdao.com/yws/api/personal/file/907B91B4FD5A4474BE70166DF441E487?method=download&shareKey=86bfa606c65fbdbe99c36ba08d039d69)]

划分训练集和测试集
观察可以发现，交通标志应该是由远至近的序列中标注裁剪出来的，所以会呈现由小到大的规律，所以在准备训练集和测试集时，随机选择一定比例的方式(我选择80%训练，20%测试)，示例代码：

import os
import random
import shutil
path = 'C:/Users/Documents/Dataset/GTSRB_img_Crop/Final_Training'
dirs = []
split_percentage = 0.2
for dirpath, dirnames, filenames in os.walk(path, topdown=False):for dirname in dirnames:fullpath = os.path.join(dirpath, dirname)fileCount = len([name for name in os.listdir(fullpath) if os.path.isfile(os.path.join(fullpath, name))])files = os.listdir(fullpath)for index in range((int)(split_percentage * fileCount)):newIndex = random.randint(0, fileCount - 1)fullFilePath = os.path.join(fullpath, files[newIndex])newFullFilePath = fullFilePath.replace('Final_Training', 'Final_Validation')base_new_path = os.path.dirname(newFullFilePath)if not os.path.exists(base_new_path):os.makedirs(base_new_path)# move the filetry:shutil.move(fullFilePath, newFullFilePath)except IOError as error:print('skip moving from %s => %s' % (fullFilePath, newFullFilePath))

【1】训练和验证

结构十分简单，四个卷积层加上全连接层输出即可。其中的个别的超参数选择，我是参照了GTSRB比赛中成绩最好的那篇文章中提到的一些配置：

CNN with 3 Spatial Transformers, DeepKnowledge Seville, Álvaro Arcos-García and Juan A. Álvarez-García and Luis M. Soria-Morillo, Neural Networks
link

在这篇文章中，提到使用48*48的归一化尺寸以及一些其他的建议，可以详细参阅。如下示例代码简单跑一下（）：

import shutil
import os
import matplotlib.pyplot as plttrain_set_base_dir = 'C:/Users/Documents/Dataset/GTSRB_img_Crop/Final_Training'
validation_set_base_dir = 'C:/Users/Documents/Dataset/GTSRB_img_Crop/Final_Validation'# start image preprocess
from keras.preprocessing.image import ImageDataGeneratortrain_datagen = ImageDataGenerator(rescale=1. / 255
)
train_data_generator = train_datagen.flow_from_directory(directory=train_set_base_dir,target_size=(48, 48),batch_size=32,class_mode='categorical')validation_datagen = ImageDataGenerator(rescale=1. /255
)validation_data_generator = validation_datagen.flow_from_directory(directory=validation_set_base_dir,target_size=(48, 48),batch_size=32,class_mode='categorical'
)# define a simple CNN network
from keras.models import Sequential
from keras.layers import Conv2D, MaxPool2D, Flatten, Dense, Dropoutmodel = Sequential()# add Con2D layers
model.add(Conv2D(filters=32, kernel_size=(3, 3), activation='relu', input_shape=(48, 48, 3)))
model.add(MaxPool2D(pool_size=(2, 2), padding='valid'))model.add(Conv2D(filters=64, kernel_size=(3, 3), activation='relu'))
model.add(MaxPool2D(pool_size=(2, 2), padding='valid'))model.add(Conv2D(filters=128, kernel_size=(3, 3), activation='relu'))
model.add(MaxPool2D(pool_size=(2, 2), padding='valid'))model.add(Conv2D(filters=128, kernel_size=(3, 3), activation='relu'))
model.add(MaxPool2D(pool_size=(2, 2), padding='valid'))# flatten
model.add(Flatten())# dropOut layer
model.add(Dropout(0.2))# add one simple layer for classification
model.add(Dense(units=512, activation='relu'))# add output layer
model.add(Dense(units=43, activation='softmax'))# compile model
model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['acc'])# print model info
model.summary()
json_str = model.to_json()
print(json_str)
# fit_generator to fill in the dataset
history = model.fit_generator(generator=train_data_generator,steps_per_epoch=100,epochs=30,validation_data=validation_data_generator,validation_steps=50)# train done, save the models
model.save('C:/test/WorkingLogs/20181214/traffic_signs.h5')# plot the roc curve
acc = history.history['acc']
val_acc = history.history['val_acc']
loss = history.history['loss']
val_loss = history.history['val_loss']epochs = range(1, len(acc) + 1)
plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.legend()plt.figure()plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()plt.show()

简易的网络结构及参与训练测试的样本信息如下：

Found 32117 images belonging to 43 classes.
Found 7092 images belonging to 43 classes.
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_1 (Conv2D)            (None, 46, 46, 32)        896       
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 23, 23, 32)        0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 21, 21, 64)        18496     
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 10, 10, 64)        0         
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 8, 8, 128)         73856     
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 4, 4, 128)         0         
_________________________________________________________________
conv2d_4 (Conv2D)            (None, 2, 2, 128)         147584    
_________________________________________________________________
max_pooling2d_4 (MaxPooling2 (None, 1, 1, 128)         0         
_________________________________________________________________
flatten_1 (Flatten)          (None, 128)               0         
_________________________________________________________________
dropout_1 (Dropout)          (None, 128)               0         
_________________________________________________________________
dense_1 (Dense)              (None, 512)               66048     
_________________________________________________________________
dense_2 (Dense)              (None, 43)                22059     
=================================================================
Total params: 328,939
Trainable params: 328,939
Non-trainable params: 0
_________________________________________________________________

30 epochs的结果是：

100/100 [==============================] - 164s 2s/step - loss: 0.2009 - acc: 0.9556 - val_loss: 0.1103 - val_acc: 0.9755

[外链图片转存失败(img-ZboIncn5-1563292422092)(https://note.youdao.com/yws/api/personal/file/F3EDD169650C4688A3E488C78C1DBBE0?method=download&shareKey=3de2a6edc55e615c5cb07debdde37e0b)]
[外链图片转存失败(img-70alovNI-1563292422092)(https://note.youdao.com/yws/api/personal/file/29793553083A4AC2B59FC0F6E5A4B434?method=download&shareKey=fa5fd03cb56f6db8441b7795732be4be)]

【3】结论

从结果可以看出，即使是简单的网络结构，在精确标注的大量数据下可以获得很好的效果。同时还可以通过pre-trained模型如VGG-16等提取特征再加入某些层进行fine-tuned等。
在上面推荐的那片论文中，还提出使用spatial-transformer层进行优化，也很值得尝试。

这篇关于Keras实例教程（五）- 使用 GTSRB 用于交通标志识别的文章就介绍到这儿，希望我们推荐的文章对编程师们有所帮助！

Keras实例教程（五）- 使用 GTSRB 用于交通标志识别

数据集

【0】数据准备

【1】训练和验证

【3】结论

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Keras实例教程（五）- 使用 GTSRB 用于交通标志识别

数据集

【0】 数据准备

【1】训练和验证

【3】结论

相关文章

【0】数据准备