使用Tensorflow object detection API——训练模型（Window10系统）

【数据标注处理】

　　1、先将下载好的图片训练数据放在models-master/research/images文件夹下，并分别为训练数据和测试数据创建train、test两个文件夹。文件夹目录如下

　　

　　2、下载LabelImg这款小软件对图片进行标注

　　3、下载完成后解压，直接运行。（注：软件目录最好不要存在中文，否则可能会报错）

　　4、设置图片目录，逐张打开图片，按快捷键W，然后通过鼠标拖拽实现目标物体框选，随后输入物体类别，单张图片多目标则重复操作，目标框选完成后，保存操作。

　　5、重复上述操作，直至所有图片完成选定。

【图片标注数据处理】

　　1、打开xml_to_csv.py，修改path 为对应train、test文件夹路径，并运行，在对应目录下将会生成csv文件,将生成的csv文件拷贝到models-master\research\object_detection\data文件夹下。

# -*- coding: utf-8 -*-
"""
Created on Sat Apr 14 10:01:27 2018

@author: Administrator
"""
# -*- coding: utf-8 -*-
"""
Created on Tue Jan 16 00:52:02 2018
@author: Xiang Guo
将文件夹内所有XML文件的信息记录到CSV文件中
"""  

import os
import glob
import pandas as pd
import xml.etree.ElementTree as ET  

#XML文件路径
pathStr='F:\\模型训练\\img\\train';

os.chdir(pathStr)
path = pathStr  

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('person.csv', index=None)
    print('Successfully converted xml to csv.')  

main()  

　　2、打开python generate_tfrecord.py，将对应的label改成自己的类别，python generate_tfrecord.py --csv_input=data/person_train.csv  --output_path=data/person_train.record，输入对应train、test.csv文件路径，生成对应tfrecord数据文件。

# -*- coding: utf-8 -*-
"""
Created on Sat Apr 14 10:04:27 2018

@author: Administrator
"""

# -*- coding: utf-8 -*-
"""
由CSV文件生成TFRecord文件
"""  

"""
Usage:
  # From tensorflow/models/
  # Create train data:
  python csv_to_TFRecords.py --csv_input=data/train_labels.csv  --output_path=data/person_train.record
  # Create test data:
  python csv_to_TFRecords.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  

#这改成object_detection路径
os.chdir('F:\\模型训练\\models-master\\research\\object_detection\\')  

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
#注意将对应的label改成自己的类别！！！！！！！！！！
def class_text_to_int(row_label):
    if row_label == 'person':
        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')
    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()  

　　3、打开或下载ssd_mobilenet_v1_coco.config配置文件，修改训练、测试数据路径、分类数、批次图片数量（避免超出显存，稍微小点），放置在models-master\research\object_detection\training文件夹下。

# 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 {
  #训练的数据类数
    num_classes: 1
    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: 1#训练批次
  optimizer {
    rms_prop_optimizer: {
      learning_rate: {
        exponential_decay_learning_rate {
          initial_learning_rate: 0.004
          decay_steps: 800720
          decay_factor: 0.95
        }
      }
      momentum_optimizer_value: 0.9
      decay: 0.9
      epsilon: 1.0
    }
  }
  #这两行注释
  #fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED/model.ckpt"
  #from_detection_checkpoint: true

  # 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: 200000
  data_augmentation_options {
    random_horizontal_flip {
    }
  }
  data_augmentation_options {
    ssd_random_crop {
    }
  }
}
#训练数据
train_input_reader: {
  tf_record_input_reader {
    input_path: "data/person_train.record"
  }
  label_map_path: "data/person.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
}
#测试数据
eval_input_reader: {
  tf_record_input_reader {
    input_path: "data/person_test.record"
  }
  label_map_path: "data/person.pbtxt"
  shuffle: false
  num_readers: 1
}

　　4、在data文件下创建对应.pbtxt文件，修改类型对应的ID序号，id序号注意与前面创建CSV文件时保持一致。

item {
  id: 1
  name: 'person'
}  

item {
  id: 2
  name: 'car'
}

【训练模型】

　　1、在models-master\research\object_detection目录下运行python train.py --logtostderr --train_dir=training/ --pipeline_config_path=training/ssd_mobilenet_v1_coco.config

　　

　　2、等待loss稳定在一个比较小的值之间，则可以停止训练。（直接关闭窗口以上即可）

　　3、可视化操作：在models-master\research\object_detection文件夹下，运行tensorboard --logdir='training' ，然后在浏览器中输入localhost:6006即可查看模型训练的各项参数情况。

4、Anaconda Prompt 定位到  models\research\object_detection 文件夹下，运行

python export_inference_graph.py \ --input_type image_tensor \ --pipeline_config_path training/ssd_mobilenet_v1_coco.config \  --trained_checkpoint_prefix training/model.ckpt-31012 \  --output_directory person_vehicle_inference_graph 

　　trained_checkpoint_prefix training/model.ckpt-31012 这个checkpoint（.ckpt-后面的数字）可以在training文件夹下找到你自己训练的模型的情况，填上对应的数字（如果有多个，选最大的）。
　　output_directory tv_vehicle_inference_graph 改成自己的名字

　　运行完后，可以在person_vehicle_inference_graph （这是我的名字）文件夹下发现若干文件，有saved_model、checkpoint、frozen_inference_graph.pb等。 .pb结尾的就是最重要的frozen model了，还记得第一大部分中frozen model吗？没错，就是我们在后面要用到的部分

【测试模型】

　　1、打开jupyter notebook，先复制object detection API自带的object_detection_tutorial.ipynb代码；

　　2、将模型修改为刚刚导出的模型地址，以及pbtxt文件位置；

　　3、设置测试图片路径

　　4、运行

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