tensorflow+ssd_mobilenet实现目标检测的训练
本文在Ubuntu下使用tensorflow的object detection API来训练自己的数据集。所用模型为ssd_mobilenet，也可以使用其他的模型。当然也可以在windows下训练，代码上没有多大差别，主要是配置环境那里，比较麻烦（windows和linux下都一样麻烦）。
一、配置环境
1. 在GitHub上下载所需的models文件，地址：https://github.com/tensorflow/models。
2. 安装pillow、lxml。Jupyter Notebook和matplotlib这两个我就不说了，肯定早就装好了。
pip install pillow
pip install lxml
3. 编译protobuf，object detection API是使用protobuf来训练模型和配置参数，所以得先编译protobuf，下载地址：https://github.com/google/protobuf/releases
windows的配置参考：https://blog.csdn.net/dy_guox/article/details/79081499
Ubuntu下的配置为：
下载地址 https://github.com/google/protobuf/releases
下载protobuf-all-3.7.0.tar.gz
命令如下：
tar zxvf protobuf-all-3.7.0.tar.gz cd protobuf-3.7.0/ ./autogen.sh ./configure --prefix=/usr/local/protobuf make
make check 出现错误，不过没事
修改配置文件
（1）vim /etc/profile，添加
export PATH=$PATH:/usr/local/protobuf/bin/
export PKG_CONFIG_PATH=/usr/local/protobuf/lib/pkgconfig/
保存执行source /etc/profile
vim ~/.profile，添加
export PATH=$PATH:/usr/local/protobuf/bin/
export PKG_CONFIG_PATH=/usr/local/protobuf/lib/pkgconfig/ 否则会出现登陆用户找不到protoc命令
（2）配置动态链接库
vim /etc/ld.so.conf 在新的一行添加
/usr/local/protobuf/lib
执行命令 sudo ldconfig
安装完成
执行protoc --version,会出现当前libporoto的版本信息
libprotoc 2.6.1
二、在Ubuntu内先制作好自己的VOC数据集。制作过程如下：
在Ubuntu内制作自己的VOC数据集
三、将样本数据转换为TFRecord格式
1. 新建 train_test_split.py 把xml文件数据集分为了train、test、validation三部分，并存储在Annotations文件夹中，训练验证集占80%，测试集占20%。训练集占训练验证集的80%。代码如下：
import os import random import time import shutil xmlfilepath=r'./Annotations' saveBasePath=r"./Annotations" trainval_percent=0.8 train_percent=0.8 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(os.getcwd(), 'Annotations/{}'.format(directory)) 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(os.getcwd(), 'Annotations/{}'.format(directory)) 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: directory="test" xml_path = os.path.join(os.getcwd(), 'Annotations/{}'.format(directory)) 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.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，xml_to_csv.py 将生成的csv文件放在 object_detection/data/
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.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(): 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('whsyxt.csv', index=None) xml_df.to_csv('/home/z/work/models-master/research/object_detection/data/trafficlight_{}_labels.csv'.format(directory), index=None) print('Successfully converted xml to csv.') main()
3. 生成tfrecord文件，generate_tfrecord.py
#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Tue Mar 5 15:28:55 2019 @author: z """ """ Usage: # From tensorflow/models/ # Create train data: python generate_tfrecord.py --csv_input=data/tv_vehicle_labels.csv --output_path=train.record # Create test data: python generate_tfrecord.py --csv_input=data/test_labels.csv --output_path=test.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 os.chdir('/home/z/work/models-master/research/') 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): # 你的所有类别 if row_label == 'red pedestrian': 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(), 'object_detection/images/voc/VOCdevkit/VOC2018/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.app.run()
主要是在 row_label 这里要添加上你标注的类别，字符串 row_label 应于labelImg中标注的名称相同。同样 path 为图片的路径。
cd /home/z/work/models-master/research
python generate_tfrecord.py --csv_input=object_detection/data/trafficlight_train_labels.csv --output_path=object_detection/data/
trafficlight_train.tfrecord
generate_tfrecord.py 需要在research目录下，也就是object_detection的上级目录，因为在脚本中使用了 object_detection.utils，如果在 object_detection 下执行命令会报错（No module named object_detection）。
其实这句命令很好理解，其实就是根据脚本中提供的图片路径，找到图片所在。至于是哪些图片？由csv文件来决定。csv文件主要就是记录图片的名称、类别、以及标记框的坐标。如下图所示：
类似的，我们可以输入如下命令，将验证集和测试集也转换为tfrecord格式。
python generate_tfrecord.py --csv_input=object_detection/data/trafficlight_validation_labels.csv --output_path=object_detection/data/
trafficlight_validation.tfrecord
python generate_tfrecord.py --csv_input=object_detection/data/trafficlight_test_labels.csv --output_path=object_detection/data/
trafficlight_test.tfrecord
四、训练
1. 在object_detection/data文件夹下创建标签分类的配置文件（label_map.txt），需要检测几种目标，将创建几个id，代码如下：
item { id: 1 # id 从1开始编号 name: 'red pedestrian' } item { id: 2 name: 'green pedestrian' }
2. 配置管道配置文件，找到object_detection/samples/config/ssd_mobilenet_v1_coco.config，复制到data文件夹下。修改后的代码如下：
# SSD with Mobilenet v1 configuration for MSCOCO Dataset. # Users should configure the fine_tune_checkpoint field in the train config as # well as the label_map_path and input_path fields in the train_input_reader and # eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that # should be configured. model { ssd { num_classes: 17 box_coder { faster_rcnn_box_coder { y_scale: 10.0 x_scale: 10.0 height_scale: 5.0 width_scale: 5.0 } } matcher { argmax_matcher { matched_threshold: 0.5 unmatched_threshold: 0.5 ignore_thresholds: false negatives_lower_than_unmatched: true force_match_for_each_row: true } } similarity_calculator { iou_similarity { } } anchor_generator { ssd_anchor_generator { num_layers: 6 min_scale: 0.2 max_scale: 0.95 aspect_ratios: 1.0 aspect_ratios: 2.0 aspect_ratios: 0.5 aspect_ratios: 3.0 aspect_ratios: 0.3333 } } image_resizer { fixed_shape_resizer { height: 300 width: 300 } } box_predictor { convolutional_box_predictor { min_depth: 0 max_depth: 0 num_layers_before_predictor: 0 use_dropout: false dropout_keep_probability: 0.8 kernel_size: 1 box_code_size: 4 apply_sigmoid_to_scores: false conv_hyperparams { activation: RELU_6, regularizer { l2_regularizer { weight: 0.00004 } } initializer { truncated_normal_initializer { stddev: 0.03 mean: 0.0 } } batch_norm { train: true, scale: true, center: true, decay: 0.9997, epsilon: 0.001, } } } } feature_extractor { type: 'ssd_mobilenet_v1' min_depth: 16 depth_multiplier: 1.0 conv_hyperparams { activation: RELU_6, regularizer { l2_regularizer { weight: 0.00004 } } initializer { truncated_normal_initializer { stddev: 0.03 mean: 0.0 } } batch_norm { train: true, scale: true, center: true, decay: 0.9997, epsilon: 0.001, } } } loss { classification_loss { weighted_sigmoid { } } localization_loss { weighted_smooth_l1 { } } hard_example_miner { num_hard_examples: 3000 iou_threshold: 0.99 loss_type: CLASSIFICATION max_negatives_per_positive: 3 min_negatives_per_image: 0 } classification_weight: 1.0 localization_weight: 1.0 } normalize_loss_by_num_matches: true post_processing { batch_non_max_suppression { score_threshold: 1e-8 iou_threshold: 0.6 max_detections_per_class: 100 max_total_detections: 100 } score_converter: SIGMOID } } } train_config: { batch_size: 2 optimizer { rms_prop_optimizer: { learning_rate: { exponential_decay_learning_rate { initial_learning_rate: 0.00001 decay_steps: 800720 decay_factor: 0.95 } } momentum_optimizer_value: 0.9 decay: 0.9 epsilon: 1.0 } } # Note: The below line limits the training process to 200K steps, which we # empirically found to be sufficient enough to train the pets dataset. This # effectively bypasses the learning rate schedule (the learning rate will # never decay). Remove the below line to train indefinitely. num_steps: 6000 data_augmentation_options { random_horizontal_flip { } } data_augmentation_options { ssd_random_crop { } } } train_input_reader: { tf_record_input_reader { input_path: "data/trafficlight_train.tfrecord" } label_map_path: "data/trafficlight_label_map.pbtxt" } eval_config: { num_examples: 8000 # Note: The below line limits the evaluation process to 10 evaluations. # Remove the below line to evaluate indefinitely. max_evals: 10 } eval_input_reader: { tf_record_input_reader { input_path: "data/trafficlight_validation.tfrecord" } label_map_path: "data/trafficlight_label_map.pbtxt" shuffle: false num_readers: 1 }
可以修改的有好几处，在代码中都标红了。建议还是将配置文件看一下。主要包括：标注的类别数目、batch_size(建议设置小一点)、学习率和退化率、训练的总步数、训练集和验证集的tfrecord的路径、label_map的路径
3. 开始训练
python train.py --logtostderr --train_dir=training/ --pipeline_config_path=data/ssd_mobilenet_v1_coco.config
参考地址：
