利用TF_Object_detection_API 训练自己的模型

安装Tensorflow_Object_detection_API 依赖库

Protobuf 、Python-tk、Pillow 1.0、lxml、tf Slim、Jupyter notebook、Matplotlib、Tensorflow、Cython、cocoapi

具体请参考：https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/installation.md

安装依赖库：(具体可参考官方文档)

下载源码：

git clone https://github.com/tensorflow/models sudo apt-get install protobuf-compiler python-pil python-lxml python-tk
sudo pip3 install Cython
sudo pip3 install jupyter
sudo pip3 install matplotlib

#或者使用pip安装：
sudo pip install Cython
sudo pip install pillow
sudo pip install lxml
sudo pip install jupyter
sudo pip install matplotlib

如果使用COCO作为评价指标的话，需要接入coco的pythonApi，

git clone https://github.com/cocodataset/cocoapi.git cd cocoapi/PythonAPI
make
cp -r pycocotools <path_to_tensorflow>/models/research/

编译项目

From tensorflow/models/research/首先protoc编译项目，然后添加环境变量 Mac端： ~./bash_profile

protoc object_detection/protos/*.proto --python_out=.

export PYTHONPATH=$PYTHONPATH:`pwd`:`pwd`/slim

如果protoc版本过低，请对应环境下载 https://link.zhihu.com/?target=https%3A//github.com/google/protobuf/releasessudo cp bin/protoc /usr/bin/protoc 再次尝试编译、添加环境

测试安装Ok：

python3 object_detection/builders/model_builder_test.py

# 如果返回Ok 则安装成功，运行setup

python3 setup.py install

制作自己的数据集 并使用API传输训练

利用labelImag标注数据，生成xml信息，利用Xml-to-csv.py转换成voc的格式，xml-to-csv脚本：注意按照自己的文件结构对应修改，我的结构：

-train_data/
--...
-images/
--test/
---testingimages.jpg
---image.xml
--train/
---testingimages.jpg
---image.xml
--..yourimages.jpg
-xml_to_csv.py

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()
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():
for directory in ['train','test']:
image_path = os.path.join(os.getcwd(), 'images/{}'.format(directory))
xml_df = xml_to_csv(image_path)
xml_df.to_csv('train_data/{}_labels.csv'.format(directory), index=None)
print('Successfully converted xml to csv.')

main()

将Csv格式的图片信息转换为tf_record格式，提供API训练

首先将上述的images、data移到model/research/object_detedtion文件夹下：利用generate_tfrecord.py转换格式需要修改 返回的类别和名称 以及文件路径名https://github.com/junqiangwu/My_Tensorflow/blob/master/object-detection/generate_tfrecord.py##From model/research/object_detection/

python3 generate_tfrecord.py --csv_input=train_data/train_labels.csv  --output_path=train.record

python3 generate_tfrecord.py --csv_input=train_data/test_labels.csv  --output_path=test.record

会在object_detection目录下生成两个.record文件，将它移到train_data目录下，train_data目录下包含：两个csv 和 两个 .record

#在object_detection目录下:
-images/
--test/
---testingimages.jpg
--train/
---testingimages.jpg
--..yourimages.jpg
-train_data
--train_labels.csv
--test_labels.csv
--train.record
--test.record

下载预训练模型，配置网络结构信息：

wget http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_coco_11_06_2017.tar.gz

mkdir training
在training文件夹下编写训练数据标签：object_detection.pbtxt
item {
id: 1
name: 'macncheese' #物品类别
}

从object_detection/samples/config/ssd_mobilenet_v1_pets.config移到training文件下：并作出修改： num_class: 1 batch_size: 24 fine_tune_checkpoint: "ssd_mobilenet_v1_coco_11_06_2017/model.ckpt"

train_input_reader: {
tf_record_input_reader {
input_path: "train_data/train.record"
}
label_map_path: "training/object-detection.pbtxt"
}

最后在object_detection文件夹下：运行命令：python3 train.py --logtostderr --train_dir=training/ --pipeline_config_path=training/ssd_mobilenet_v1_pets.config#train_dir: 训练输出文件的路径 #pipeline_config: 网络配置文件的路径#测试输出模型的准确性  利用.py 转换 .pb #From model/research/object_detection/ python3 export_inference_graph.py 
--input_type image_tensor 
--pipeline_config_path training/ssd_mobilenet_v1_pets.config 
--trained_checkpoint_prefix training/model.ckpt-388 
--output_directory mac_n_cheese_inference_graph

#input_type : 保持一致
#pipeline: 网络结构配置图
#train_checkpoint: ckpt模型保存路径 既上面训练路径的设置位置
#out: 输出文件

#最后利用jupyter notebook加载pb模型进行测试

#修改object_detection_tutorial.ipynb

# What model to download.
MODEL_NAME = 'mac_n_cheese_inference_graph'

#Path to frozen detection graph. This is the actual model that is used for the object detection.
PATH_TO_CKPT = MODEL_NAME + '/frozen_inference_graph.pb'

# List of the strings that is used to add correct label for each box.
PATH_TO_LABELS = os.path.join('training', 'object-detection.pbtxt')

NUM_CLASSES = 1

#删除downloand程序，修改加载测试图片的路径，运行即可

所有的配置文件在：https://github.com/junqiangwu/My_Tensorflow/tree/master/object-detection