TensorFlow Object Detection API 超详细教程和踩坑过程（数据准备和训练）

1.准备数据

object detection的数据是需要tfrecord格式的，但是一般我们还是先制作voc格式的数据更加方便。

1.voc格式数据的准备：github上下载一个label-img：

然后选择VOC格式，开始漫长的数据标注过程，标注之后的数据会多一个xml文档，一般在标注之前就按照VOC2012的标准建立文件，你可以去下载VOC2012数据集看一下，如果不想我把数据文件的构成截图发出来，很详细了：

建立VOCdevkit文件，里面建立一个VOC2012文件夹，里面放三个文件：

这三个文件分别放XML、txt、和图像：

至此，VOC2012的数据已经准备完毕了，现在我们要把它转换成tfrecord。

当然如果你需要修改xml中的路径我这也有相应的代码：

# coding=utf-8
import os
import os.path
import xml.dom.minidom
path = "E:\VOCdevkit\VOC2012\Annotations"
files = os.listdir(path)  # 得到文件夹下所有文件名称
s = []
count = 0
for xmlFile in files:  # 遍历文件夹
    if not os.path.isdir(xmlFile):  # 判断是否是文件夹,不是文件夹才打开
            name1 = xmlFile.split('.')[0]
            dom = xml.dom.minidom.parse(path + '\\' + xmlFile)
            root = dom.documentElement
            newfolder = root.getElementsByTagName('folder')
            newpath = root.getElementsByTagName('path')
            newfilename = root.getElementsByTagName('filename')
            newfolder[0].firstChild.data = 'VOCdevkit\VOC2012\JPEGImages'
            newpath[0].firstChild.data = 'VOCdevkit\VOC2012\JPEGImages' + '\\' + name1 + '.jpg'
            newfilename[0].firstChild.data = name1 + '.jpg'
            with open(os.path.join(path, xmlFile), 'w') as fh:
                dom.writexml(fh)
                print('写入name/pose OK!')
            count = count + 1

2.数据格式转换：数据转换为tfrecord有两种办法。

第一种：先将xml转换为csv，然后把cvs转换为tfrecord

xml到csv的代码：

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']:
        project_path = '/home/lyf/tensorflow-tutorial/models/research/Annotations'
        image_path = os.path.join(project_path, directory)
        xml_df = xml_to_csv(image_path)
        xml_df.to_csv('project_data/{}_labels.csv'.format(directory), index=None)
        print('Successfully converted xml to csv.')


main()

修改path路径，运行代码，就可以进行转换：生成两个csv文件。这两个csv一个是train，一个是test，然后把他们转换为tfrecord，运行代码是每次需要在pycharm里设置参数，或者在linux中执行如下命令：

# Create train data:
python generate_tfrecord.py --csv_input=data/train_labels.csv --output_path=train.record

# Create test data:
python generate_tfrecord.py --csv_input=data/test_labels.csv --output_path=test.record

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

  # Create test data:
  python generate_tfrecord.py --csv_input=data/test_labels.csv  --output_path=test.record
"""
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 research.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 == 'Y' or row_label== 'N':
        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()

也可以用object detection 里内置的函数把csv转换为tfrecord，调用generate_tfrecord.py，注意要改变--csv_input与--output_path这两个参数：

    python generate_tfrecord.py --csv_input=sunglasses_test_labels.csv --output_path=sunglass_test.record

第二种：

执行：

    mkdir object_detection/ssd_model

把数据放到这个路径中：

./object_detection/ssd_model/VOCdevkit/

执行这个配置文件，这个是github的官方教程给的：

python ./object_detection/dataset_tools/create_pascal_tf_record.py --label_map_path=object_detection/data/pascal_label_map.pbtxt --data_dir=object_detection/ssd_model/VOCdevkit/ --year=VOC2012 --set=train --output_path=object_detection/ssd_model/pascal_train.record

python ./object_detection/dataset_tools/create_pascal_tf_record.py --label_map_path=object_detection/data/pascal_label_map.pbtxt --data_dir=object_detection/ssd_model/VOCdevkit/ --year=VOC2012 --set=val --output_path=object_detection/ssd_model/pascal_val.record

至此，所需要的数据已经全部准备好。

3.修改配置文件：

首先选择你所用的模型，我测了一下，带fpn层的模型用起来有点点问题，我用的ssd_mobilenet_v1_coco模型，你可以自己选择模型：

下载的模型解压后放到刚刚建立的ssd-model文件中，然后还得拷贝两个配置文件到ssd-model中：

    cp object_detection/data/pascal_label_map.pbtxt object_detection/ssd_model/
    cp object_detection/samples/configs/ssd_mobilenet_v1_pets.config object_detection/ssd_model/

ssd-model中就是这样：

pascal_label_map.pbtxt中的信息需要根据自己的类别更改：

然后在ssd_mobilenet_v1_pets.config文件中将一些信息修改：

fine_tune_checkpoint: "/home/lyf/models/research/object_detection/ssd_model/model.ckpt"

train_input_reader: {
  tf_record_input_reader {
    input_path: "/home/lyf/models/research/object_detection/ssd_model/pascal_train.record"
  }
  label_map_path: "/home/lyf/models/research/object_detection/ssd_model/pascal_label_map.pbtxt"
}

eval_input_reader: {
  tf_record_input_reader {
    input_path: "/home/lyf/models/research/object_detection/ssd_model/pascal_val.record"
  }
  label_map_path: "/home/lyf/models/research/object_detection/ssd_model/pascal_label_map.pbtxt"
  shuffle: false
  num_readers: 1
}

至于其他的batch和迭代次数也要根据数据量调整一下。

4.开始训练：

执行下列命令：

    python object_detection/legacy/train.py --train_dir object_detection/train --pipeline_config_path object_detection/ssd_model/ssd_mobilenet_v1_pets.config

训练过程中可以打开tensorborder检测训练情况：

tensorboard --logdir=/home/lyf/models/research/object_detection/ssd_model

训练完成之后把训练文件转化为pb文件：

    python object_detection/export_inference_graph.py --input_type image_tensor --pipeline_config_path    object_detection/ssd_model/ssd_mobilenet_v1_pets.config --trained_checkpoint_prefix object_detection/train/model.ckpt-10000 --output_directory object_detection/ssd_model/model/

5.模型测试：

建立一个test.py，就可以用于测试了，记得pip intall python-opencv。

import cv2
import numpy as np
import tensorflow as tf
from object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as vis_util


class TOD(object):
    def __init__(self):
        self.PATH_TO_CKPT = '/home/lyf/models/research/object_detection/ssd_model/model/frozen_inference_graph.pb'
        self.PATH_TO_LABELS = '/home/lyf/models/research/object_detection/ssd_model/pascal_label_map.pbtxt'
        self.NUM_CLASSES = 1
        self.detection_graph = self._load_model()
        self.category_index = self._load_label_map()

    def _load_model(self):
        detection_graph = tf.Graph()
        with detection_graph.as_default():
            od_graph_def = tf.GraphDef()
            with tf.gfile.GFile(self.PATH_TO_CKPT, 'rb') as fid:
                serialized_graph = fid.read()
                od_graph_def.ParseFromString(serialized_graph)
                tf.import_graph_def(od_graph_def, name='')
        return detection_graph

    def _load_label_map(self):
        label_map = label_map_util.load_labelmap(self.PATH_TO_LABELS)
        categories = label_map_util.convert_label_map_to_categories(label_map,
                                                                    max_num_classes=self.NUM_CLASSES,
                                                                    use_display_name=True)
        category_index = label_map_util.create_category_index(categories)
        return category_index

    def detect(self, image):
        with self.detection_graph.as_default():
            with tf.Session(graph=self.detection_graph) as sess:
                # Expand dimensions since the model expects images to have shape: [1, None, None, 3]
                image_np_expanded = np.expand_dims(image, axis=0)
                image_tensor = self.detection_graph.get_tensor_by_name('image_tensor:0')
                boxes = self.detection_graph.get_tensor_by_name('detection_boxes:0')
                scores = self.detection_graph.get_tensor_by_name('detection_scores:0')
                classes = self.detection_graph.get_tensor_by_name('detection_classes:0')
                num_detections = self.detection_graph.get_tensor_by_name('num_detections:0')
                # Actual detection.
                (boxes, scores, classes, num_detections) = sess.run(
                    [boxes, scores, classes, num_detections],
                    feed_dict={image_tensor: image_np_expanded})
                # Visualization of the results of a detection.
                vis_util.visualize_boxes_and_labels_on_image_array(
                    image,
                    np.squeeze(boxes),
                    np.squeeze(classes).astype(np.int32),
                    np.squeeze(scores),
                    self.category_index,
                    use_normalized_coordinates=True,
                    line_thickness=8)

        cv2.namedWindow("detection", cv2.WINDOW_NORMAL)
        cv2.imshow("detection", image)
        cv2.waitKey(0)

if __name__ == '__main__':
    image = cv2.imread('/home/lyf/test/000084.jpg')
    detecotr = TOD()
    detecotr.detect(image)

TensorFlow Object Detection API 超详细教程和踩坑过程（数据准备和训练）

1.准备数据

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