基于MobileNetV2-SSD缺陷检测
（1） SSD论文连接
（2） MobileNetV2连接
实验部分
一 环境搭配
win10
pycharm
anaconda
cuda + cudnn
python 3.x
tensorflow-gpu
下载代码库
下载protoc作用是将Tensorflow object detection API模型文件中的.pro
文件编译成python文件。直接输入：protoc ./object_detection/protos/*.proto --python_out=. 就可以快速编译所有文件
添加两个环境变量：
\models\research
\models\research\slim
安装research & slim
cd slim
python setup.py install
测试是否安装成功（research目录）
python object_detection/builders/model_builder_test.py
安装成功会显示ok
二制作数据集
下载labelimg-master
打开cmd, 进入labelImg目录：
运行：
pyrcc5 -o resources.py resources.qrc命令
python labelImg.py
就可以打开labelImg了
通过画矩形框，打上标签，生成xml文件新建data文件夹，并生成train，test两个子文件夹
xlm转csv
"""
Created on 2020 7 11
@author: Huang hanlin
"""
import os
import glob
import pandas as pd
import xml.etree.ElementTree as ET
os.chdir('E:\\model-master\\research\\object_detection\\data\\imagess\\test')
path = 'E:\\model-master\\research\\object_detection\\data\\imagess\\test'
def xml_to_csv(path):
xml_list = []
for xml_file in glob.glob(path + '/*.xml'):
tree = ET.parse(xml_file)
root = tree.getroot()
for member in root.findall('object'):
value = (root.find('filename').text,
int(root.find('size')[0].text),
int(root.find('size')[1].text),
member[0].text,
int(member[4][0].text),
int(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():
image_path = path
xml_df = xml_to_csv(image_path)
xml_df.to_csv('maosi_test.csv', index=None)
print('Successfully converted xml to csv.')
main()
csv生成tf文件
from __future__ import division
from __future__ import print_function
from __future__ import absolute_import
#python generate_tfrecord.py --csv_input=maosi_train.csv --image_dir=train --output_path=train.record
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('image_dir', '', 'Path to the image directory')
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 == 'stain':
return 1
else:
return 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(), FLAGS.image_dir)
examples = pd.read_csv(FLAGS.csv_input)
grouped = split(examples, 'filename')
for group in grouped:
tf_example = create_tf_example(group, path)
writer.write(tf_example.SerializeToString())
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()
生成.pbtxt文件，内容为缺陷类别
三训练模型
1 下载fine-tune模型
2 修改参数
打开ssd_mobilenet_v2_coco.config，修改类别数目
3 fine-tune模型地址
4 修改数据集传输入口
4 cmd界面下执行python model_main.py --pipeline_config_path=training/ssdlite_mobilenet_v2_coco.config --model_dir=training --alsologtostder命令就可以开始训练了
四实验结果
生成checkpoint文件通过tensorboard --logdir=training查看
五表演真正技术时候到了
六 欢迎加我的github交流
本文地址：https://blog.csdn.net/weixin_42679015/article/details/107288438
