前一篇博文已经安装好了tensorflow的object detection 模块。本博文主要记录如何准备自己的数据集。
参考博文：
TensorFlow Object Detection API教程——制作自己的数据集
深度学习入门篇--手把手教你用 TensorFlow 训练模型
https://github.com/datitran/raccoon_dataset
一、标记数据
1、使用精灵标注助手进行标记数据。
二、将数据集转换为TFRecord文件格式。本部分参考博文
1、将标记的xml文件分为train test validation三部分。
其中src_xml目录下存放着标签xml文件，有多少张标记的图片，就有多少个xml文件。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。代码如下 ：
import os
import random
import time
import shutil
xmlfilepath = r'src_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.makedirs(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.makedirs(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.makedirs(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))
2、把xml转换成csv文件。
运行完成后，在annotations文件夹所在目录下，会生成一个data文件夹，里面存放着生成的csv文件，代码如下：
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():
save_dir = os.path.join(os.getcwd(), 'data')
if (not os.path.exists(save_dir)):
os.mkdir(save_dir)
for directory in ['train', 'test', 'validation']:
xml_path = os.path.join(os.getcwd(), 'annotations/{}'.format(directory))
xml_df = xml_to_csv(xml_path)
xml_df.to_csv('data/whsyxt_{}_labels.csv'.format(directory), index=None)
print('Successfully converted xml to csv.')
main()
3、转换成tfrecords文件
运行一下代码，将csv文件，转换成tfrecords文件。
"""
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', 'data/whsyxt_train_labels.csv', 'Path to the CSV input')
# flags.DEFINE_string('output_path', 'data/whsyxt_train.tfrecord', 'Path to output TFRecord')
# flags.DEFINE_string('csv_input', 'data/whsyxt_test_labels.csv', 'Path to the CSV input')
# flags.DEFINE_string('output_path', 'data/whsyxt_test.tfrecord', 'Path to output TFRecord')
flags.DEFINE_string('csv_input', 'data/whsyxt_validation_labels.csv', 'Path to the CSV input')
flags.DEFINE_string('output_path', 'data/whsyxt_validation.tfrecord', 'Path to output TFRecord')
FLAGS = flags.FLAGS
# TO-DO replace this with label map
def class_text_to_int(row_label):
if row_label == 'person':
return 1
elif row_label == 'car':
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()