tensorflow目標檢測API之建立自己的數據集

 

1 收集數據

　　為了方便，我找了11張月兒的照片做數據集，如圖1，當然這在實際應用過程中是遠遠不夠的

 

2 labelImg軟件的安裝

　　使用labelImg軟件（下載地址：https://github.com/tzutalin/labelImg）為圖片做標簽

下載下來之后解壓縮，用Anaconda Prompt cd到解壓縮后的labelImg文件目錄下，例如  cd C:\Users\admin\Desktop\labelImg-master

然后安裝pyqt，輸入命令  conda install pyqt=5（注意：一定要使用管理員方式運行命令）

完成后輸入命令   pyrcc5 -o resources.py resources.qrc，這個命令沒有返回

最后執行   python labelImg.py，如果提示缺少包則安裝就行

運行結果如圖2

3 labelImg軟件的使用

點擊Open Dir打開數據集所在的文件夾，將圖片導入。如圖3所示。

 

在界面中按下w鍵，選擇你的目標，然后在彈出的框中為你的目標確定一個名字。如圖4

標記完之后每張圖片都有一個對應的xml文件，如圖5所示

4 標簽文件的格式轉換（一定要將這一步中的代碼放在object_detection文件夾下）

（1）xml轉csv

代碼(xml_to_csv.py)

# -*- coding: utf-8 -*-

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

os.chdir('C:/Code/models-master/research/object_detection/my_train_images/train')  # 這個是我文件夾的目錄，改成你自己的
path = 'C:/Code/models-master/research/object_detection/my_train_images/train'     # 訓練圖片的路徑，改成你自己的


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('gaoyue_train.csv', index=None)                      # 輸出xsv文件的名字，改成你自己的
    print('Successfully converted xml to csv.')


main()

運行之后可以看到train文件夾下多了一個gaoyue.csv文件，重復上面的代碼，更改文件夾，將test數據也生成一個.csv文件。

（2）csv轉tfrecord

代碼（csv_to_tfrecord.py）

 

# -*- coding: utf-8 -*-


"""
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('C:/Code/models-master/research/object_detection')                 # 當前工作目錄

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 == 'gaoyue':
        return 1
    # elif row_label == 'vehicle':
    #     return 2
    else:
        return 0


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/train')              
    path = os.path.join(os.getcwd(), 'my_train_images/train')       # 當前路徑加上你圖片存放的路徑
    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()

然后打開Anaconda Prompt cd到你csv_to_tfrecord.py文件所在的地方

輸入命令

 python csv_to_tfrecord.py --csv_input=my_train_images/test/gaoyue_test.csv  --output_path=gaoyue_train.record   （csv_to_tfrecord.py為轉換的代碼文件，csv_input是你要轉換的csv文件所在的路徑，output_path是你輸出tfrecord文件的路徑）

 運行結果如圖所示

生成 gaoyue_train.csv文件