感想
- 前一段时间，利用tensorflow object detection跑了一些demo，然后成功的训练了自己的模型，这里我把我的方法分享出来，希望能够帮助大家。
- tensorflow object detection api的github 开源地址为，https://github.com/tensorflow/models，这个模块比较新，有很多都在不断更新。我这里就object detection 来分享一下
1 数据集制作
我这里利用了voc格式的数据，事先要把数据集准备好，我把xml放在了 merged_xml文件夹下，把图片放在了images文件夹
我的xml文件示例为：
<?xml version="1.0" ?>
<annotation>
<folder>VOC2007</folder>
<filename>000009.jpg</filename>
<size>
<depth>3</depth>
<width>500</width>
<height>375</height>
</size>
<object>
<name>person</name>
<pose>Right</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>150</xmin>
<ymin>141</ymin>
<xmax>229</xmax>
<ymax>284</ymax>
</bndbox>
</object>
<object>
<name>person</name>
<pose>Right</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>285</xmin>
<ymin>201</ymin>
<xmax>327</xmax>
<ymax>331</ymax>
</bndbox>
</object>
<object>
<name>person</name>
<pose>Left</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>258</xmin>
<ymin>198</ymin>
<xmax>297</xmax>
<ymax>329</ymax>
</bndbox>
</object>
</annotation>
解析也是按照这个解析的
然后利用下面的 train_test_split.py把xml数据集分为了train test validation三部分，代码如下：
import os
import random
import time
import shutil
xmlfilepath=r'merged_xml'
saveBasePath=r"./annotations"
trainval_percent=0.9
train_percent=0.85
total_xml = os.listdir(xmlfilepath)
num=len(total_xml)
list=range(num)
tv=int(num*trainval_percent)
tr=int(tv*train_percent)
trainval= random.sample(list,tv)
train=random.sample(trainval,tr)
print("train and val size",tv)
print("train size",tr)
# print(total_xml[1])
start = time.time()
# print(trainval)
# print(train)
test_num=0
val_num=0
train_num=0
# for directory in ['train','test',"val"]:
# xml_path = os.path.join(os.getcwd(), 'annotations/{}'.format(directory))
# if(not os.path.exists(xml_path)):
# os.mkdir(xml_path)
# # shutil.copyfile(filePath, newfile)
# print(xml_path)
for i in list:
name=total_xml[i]
# print(i)
if i in trainval: #train and val set
# ftrainval.write(name)
if i in train:
# ftrain.write(name)
# print("train")
# print(name)
# print("train: "+name+" "+str(train_num))
directory="train"
train_num+=1
xml_path = os.path.join(os.getcwd(), 'annotations/{}'.format(directory))
if(not os.path.exists(xml_path)):
os.mkdir(xml_path)
filePath=os.path.join(xmlfilepath,name)
newfile=os.path.join(saveBasePath,os.path.join(directory,name))
shutil.copyfile(filePath, newfile)
else:
# fval.write(name)
# print("val")
# print("val: "+name+" "+str(val_num))
directory="validation"
xml_path = os.path.join(os.getcwd(), 'annotations/{}'.format(directory))
if(not os.path.exists(xml_path)):
os.mkdir(xml_path)
val_num+=1
filePath=os.path.join(xmlfilepath,name)
newfile=os.path.join(saveBasePath,os.path.join(directory,name))
shutil.copyfile(filePath, newfile)
# print(name)
else: #test set
# ftest.write(name)
# print("test")
# print("test: "+name+" "+str(test_num))
directory="test"
xml_path = os.path.join(os.getcwd(), 'annotations/{}'.format(directory))
if(not os.path.exists(xml_path)):
os.mkdir(xml_path)
test_num+=1
filePath=os.path.join(xmlfilepath,name)
newfile=os.path.join(saveBasePath,os.path.join(directory,name))
shutil.copyfile(filePath, newfile)
# print(name)
# End time
end = time.time()
seconds=end-start
print("train total : "+str(train_num))
print("validation total : "+str(val_num))
print("test total : "+str(test_num))
total_num=train_num+val_num+test_num
print("total number : "+str(total_num))
print( "Time taken : {0} seconds".format(seconds))
运行完以后，annotations文件夹下就放好了分类的xml，annotations有三个目录，分别是train，test，validation。
然后把xml转换成csv文件，我的代码文件名为xml_to_csv.py,，运行代码前，需要建一个data目录，用来放生成的csv文件，然后我的代码为：
import os
import glob
import pandas as pd
import xml.etree.ElementTree as ET
def xml_to_csv(path):
xml_list = []
for xml_file in glob.glob(path + '/*.xml'):
tree = ET.parse(xml_file)
root = tree.getroot()
# print(root)
print(root.find('filename').text)
for member in root.findall('object'):
value = (root.find('filename').text,
int(root.find('size')[1].text), #width
int(root.find('size')[2].text), #height
member[0].text,
int(member[4][0].text),
int(float(member[4][1].text)),
int(member[4][2].text),
int(member[4][3].text)
)
xml_list.append(value)
column_name = ['filename', 'width', 'height', 'class', 'xmin', 'ymin', 'xmax', 'ymax']
xml_df = pd.DataFrame(xml_list, columns=column_name)
return xml_df
def main():
for directory in ['train','test','validation']:
xml_path = os.path.join(os.getcwd(), 'annotations/{}'.format(directory))
# image_path = os.path.join(os.getcwd(), 'merged_xml')
xml_df = xml_to_csv(xml_path)
# xml_df.to_csv('whsyxt.csv', index=None)
xml_df.to_csv('data/whsyxt_{}_labels.csv'.format(directory), index=None)
print('Successfully converted xml to csv.')
main()
做完这一步以后，我们就来生成tfrecords文件，我的python文件名为generate_tfrecord.py,代码为：
"""
Usage:
# From tensorflow/models/
# Create train data:
python generate_tfrecord.py --csv_input=data/train_labels.csv --output_path=train.record
# Create test data:
python generate_tfrecord.py --csv_input=data/test_labels.csv --output_path=test.record
"""
from __future__ import division
from __future__ import print_function
from __future__ import absolute_import
import os
import io
import pandas as pd
import tensorflow as tf
from PIL import Image
from object_detection.utils import dataset_util
from collections import namedtuple, OrderedDict
flags = tf.app.flags
flags.DEFINE_string('csv_input', '', 'Path to the CSV input')
flags.DEFINE_string('output_path', '', 'Path to output TFRecord')
FLAGS = flags.FLAGS
# TO-DO replace this with label map
def class_text_to_int(row_label):
if row_label == 'car':
return 1
elif row_label == 'person':
return 2
else:
None
def split(df, group):
data = namedtuple('data', ['filename', 'object'])
gb = df.groupby(group)
return [data(filename, gb.get_group(x)) for filename, x in zip(gb.groups.keys(), gb.groups)]
def create_tf_example(group, path):
with tf.gfile.GFile(os.path.join(path, '{}'.format(group.filename)), 'rb') as fid:
encoded_jpg = fid.read()
encoded_jpg_io = io.BytesIO(encoded_jpg)
image = Image.open(encoded_jpg_io)
width, height = image.size
filename = group.filename.encode('utf8')
image_format = b'jpg'
xmins = []
xmaxs = []
ymins = []
ymaxs = []
classes_text = []
classes = []
for index, row in group.object.iterrows():
xmins.append(row['xmin'] / width)
xmaxs.append(row['xmax'] / width)
ymins.append(row['ymin'] / height)
ymaxs.append(row['ymax'] / height)
classes_text.append(row['class'].encode('utf8'))
classes.append(class_text_to_int(row['class']))
tf_example = tf.train.Example(features=tf.train.Features(feature={
'image/height': dataset_util.int64_feature(height),
'image/width': dataset_util.int64_feature(width),
'image/filename': dataset_util.bytes_feature(filename),
'image/source_id': dataset_util.bytes_feature(filename),
'image/encoded': dataset_util.bytes_feature(encoded_jpg),
'image/format': dataset_util.bytes_feature(image_format),
'image/object/bbox/xmin': dataset_util.float_list_feature(xmins),
'image/object/bbox/xmax': dataset_util.float_list_feature(xmaxs),
'image/object/bbox/ymin': dataset_util.float_list_feature(ymins),
'image/object/bbox/ymax': dataset_util.float_list_feature(ymaxs),
'image/object/class/text': dataset_util.bytes_list_feature(classes_text),
'image/object/class/label': dataset_util.int64_list_feature(classes),
}))
return tf_example
def main(_):
writer = tf.python_io.TFRecordWriter(FLAGS.output_path)
path = os.path.join(os.getcwd(), 'images')
examples = pd.read_csv(FLAGS.csv_input)
grouped = split(examples, 'filename')
num=0
for group in grouped:
num+=1
tf_example = create_tf_example(group, path)
writer.write(tf_example.SerializeToString())
if(num%100==0): #每完成100个转换，打印一次
print(num)
writer.close()
output_path = os.path.join(os.getcwd(), FLAGS.output_path)
print('Successfully created the TFRecords: {}'.format(output_path))
if __name__ == '__main__':
tf.app.run()
我运行的命令为：
python3 generate_tfrecord.py --csv_input=data/whsyxt_train_labels.csv --output_path=data/whsyxt_train.tfrecord
python3 generate_tfrecord.py --csv_input=data/whsyxt_test_labels.csv --output_path=data/whsyxt_test.tfrecord
python3 generate_tfrecord.py --csv_input=data/whsyxt_validation_labels.csv --output_path=data/whsyxt_validation.tfrecord
然后就获得了这三个训练需要的文件啦，训练方法请见我的下一篇博客
参考文献
[1].Introduction and Use - Tensorflow Object Detection API Tutorial.https://pythonprogramming.net/introduction-use-tensorflow-object-detection-api-tutorial/
