TensorFlow目标检测api训练mobilenet ssd教程
install anaconda
安装依赖
sudo apt-get install libgl1-mesa-glx libegl1-mesa libxrandr2 libxrandr2 libxss1 libxcursor1 libxcomposite1 libasound2 libxi6 libxtst6下载安装文件
安装
chmod +x Anaconda3-2019.10-Linux-x86_64.sh
./Anaconda3-2019.10-Linux-x86_64.sh开始是一堆霸王条款，一直回车，然后输入yes
Please answer 'yes' or 'no':'
>>> yes然后选择安装路径，默认就好，直接回车，等待安装即可。
新建环境
conda create --name obj_detection python=3.6激活环境
conda activate obj_detectiondownload tensorflow models
git clone https://github.com/tensorflow/models.gitinstall tf requirements
python
pip install Cython contextlib2 pillow lxml jupyter matplotlib absl-py tensorflow==1.14注：由于默认下载最新的tensorflow2，由于keras为tf2的御用接口框架，于是slim就找不到了，而且包的结构发生了翻天覆地的变化，找不到tf.contrib了，说白了tf2和tf1是两个不兼容的框架，而该项目是用tf1写的，所以我们指定一个tf1的版本。
当然如果你的机器有gpu想用gpu训练的话，请安装gpu版的tensorflow
pip install tensorflow-gpu==1.14protoc
解压后改名字为protoc，然后复制到刚刚下载的models项目的research目录下，然后在research目录下运行：
./protoc/bin/protoc object_detection/protos/*.proto --python_out=.
export PYTHONPATH=$PYTHONPATH:`pwd`:`pwd`/slim
python setup.py build
python setup.py install运行如下Python文件检验环境是否正确
python object_detection/builders/model_builder_test.py成功的话是这样的：
[此处省略部分打印信息...]
Ran 17 tests in 0.228s
OK (skipped=1)prepare dataset
-
数据集分为训练、验证、测试
import os import random import time import shutil # 所有标注的xml文件目录 xmlfilepath = r'/home/zheshi/h4tv/datasets/VOCdevkit/VOCBall2k/Annotations' # 分割数据集后存储的xml文件路径，会存放在这个路径下分成train validation test三个目录 saveBasePath = r"/home/zheshi/h4tv/datasets/VOCdevkit/VOCBall2k/VienDataset/Annotations" # 分割比例 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) start = time.time() test_num = 0 val_num = 0 train_num = 0 for i in list: name = total_xml[i] if i in trainval: # train and val set if i in train: directory = "train" train_num += 1 xml_path = os.path.join(saveBasePath, directory) print(xml_path) 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: directory = "validation" xml_path = os.path.join(saveBasePath, directory) print(xml_path) 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) else: # test set directory = "test" xml_path = os.path.join(saveBasePath, directory) print(xml_path) 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) # 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)) -
xml转csv
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'): print(xml_file) tree = ET.parse(xml_file) # print(root.find('filename').text) for member in root.findall('object'): value = (root.find('filename').text, int(root.find('size')[0].text), # width int(root.find('size')[1].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(): # 这里是存放转换后的三个csv的位置 csv_root = r"/home/zheshi/h4tv/datasets/VOCdevkit/VOCBall2k/VienDataset" # 这个是上一步存放分割后的三个xml文件夹的路径 annotation_root = r"/home/zheshi/h4tv/datasets/VOCdevkit/VOCBall2k/VienDataset/Annotations" for directory in ['train', 'test', 'validation']: xml_path = os.path.join(annotation_root, directory) xml_df = xml_to_csv(xml_path) xml_df.to_csv(csv_root + '/ball_{}_labels.csv'.format(directory), index=None) print('Successfully converted xml to csv.') main() -
image和label数据转为tfrecord格式:
generate_tfrecord.pyfrom __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 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 # 设置要检测的类型，如果再有就加上elif按照if的格式以此累加， # 例如elif row_label == 'shit': return 2 def class_text_to_int(row_label, filename): if row_label == 'ball': return 1 else: print("------------------nonetype:", filename) 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.io.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'png' 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'], group.filename)) 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.io.TFRecordWriter(FLAGS.output_path) # 训练用的图片的路径 path = '/home/zheshi/h4tv/datasets/VOCdevkit/VOCBall2k/JPEGImages' 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.compat.v1.app.run()
执行代码：
python generate_tfrecord.py --csv_input=data/ball_train_labels.csv --output_path=data/ball_train.tfrecord其中csv_input是之前转换的三个csv的路径，output_path是输出的tfrecord的路径，train、test、validation需要分别运行一次。
Config
在项目中创建一个存放配置文件的目录，比如命名为vien_data，然后在其目录下创建标签分类的配置文件label_map.pbtxt，如果需要检测多个，依次往下排，id依次+1
item {
id: 1
name: 'ball'
}从项目的models\research\object_detection\samples\configs\ssd_mobilenet_v1_pets.config复制一份配置的模板文件到vien_data中，我们就命名为ssd_mobilenet_v1_ball.config好了，然后修改配置文件。
如果没有预训练的model文件，配置文件中fine_tune_checkpoint要设置为空。
然后还需要修改训练集和验证集的路径，在文件末尾，修改input_path为你的训练和测试集的tfrecord路径，label_map_path为上面创建的label_map.pbtxt的路径
train_input_reader: {
tf_record_input_reader {
input_path: "/home/zheshi/h4tv/datasets/VOCdevkit/VOCBall2k/VienDataset/ball_train.tfrecord"
}
label_map_path: "/home/zheshi/tensorflow/models/research/object_detection/vien_data/ball_label_map.pbtxt"
}
eval_config: {
metrics_set: "coco_detection_metrics"
num_examples: 1100
}
eval_input_reader: {
tf_record_input_reader {
input_path: "/home/zheshi/h4tv/datasets/VOCdevkit/VOCBall2k/VienDataset/ball_validation.tfrecord"
}
label_map_path: "/home/zheshi/tensorflow/models/research/object_detection/vien_data/ball_label_map.pbtxt"
shuffle: false
num_readers: 1
}Train
在项目的research目录下执行（其中train_dir是训练出来的结果存放的路径，pipeline_config_path是上面复制修改的配置文件ssd_mobilenet_v1_ball.config路径）：
python legacy/train.py --logtostderr \
--train_dir=/home/zheshi/tensorflow/models/research/object_detection/vien_train_gpu_models \
--pipeline_config_path=/home/zheshi/tensorflow/models/research/object_detection/vien_data/ssd_mobilenet_v1_ball.config如果遇到ModuleNotFoundError: No module named 'object_detection' ，在research目录下执行
export PYTHONPATH=$PYTHONPATH:`pwd`:`pwd`/slim
python setup.py build
python setup.py install如果没有问题，加载配置后会开始训练，中间过程生成的文件和model都存在刚刚运行训练脚本时设置的vien_train_gpu_models目录中
可以查看图形化训练状态数据(修改logdir==training:后面的路径为执行训练脚本设置的vien_train_gpu_models目录)：
tensorboard --logdir==training:/home/zheshi/tensorflow/models/research/object_detection/vien_train_gpu_models --host=127.0.0.1然后浏览器访问http://127.0.0.1:6006即可