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/releases
sudo 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/
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 == 'macncheese':
return 1
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()
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