[Tensorflow] Object Detection API - prepare your training data

From: TensorFlow Object Detection API

This chapter help you to train your own model to identify objects required.

1. Data

1.1 Get your own data

标准的范例，从ImageNet上获取数据集

Get your own data from ImageNet

Download tiny-imagenet-200.zip, which is smaller than original monster. (150G)

图片格式转化

We need .png but not .jpg here, so

cd ./images
ls -1 *.jpg | xargs -n 1 bash -c 'convert "$0" "${0%.jpg}.png"'

1.2 Create your Annotation.

获取标记记录

Sol 01:

# 找数据集上现成的对应的标记框记录

Find its xml version from: http://image-net.org/download-bboxes

Sol 02:

# 自己写标记记录

Write script to create your xml for Annotation from *_box.txt. This is not a complex structure as following.

<annotation>

    <folder>n02119789</folder>

    <filename>n02119789_122</filename>

    <source>

        <database>ImageNet database</database>

    </source>

    <size>

        <width>200</width>

        <height>191</height>

        <depth>3</depth>

    </size>

    <segmented>0</segmented>

    <object>

        <name>n02119789</name>

        <pose>Unspecified</pose>

        <truncated>0</truncated>

        <difficult>0</difficult>

        <bndbox>

            <xmin>17</xmin>

            <ymin>16</ymin>

            <xmax>181</xmax>

            <ymax>181</ymax>

        </bndbox>

    </object>

</annotation>

Sol 03:

# 通过工具辅助生成标记框记录

Label them manually. This is a crazy way to create your train data (100-500 images) if you have enough time.

sudo apt-get install pyqt5-dev-tools

sudo pip3 install lxml

git clone https://github.com/tzutalin/labelImg

unsw@unsw-UX303UB$ make qt5py3

unsw@unsw-UX303UB$ python3 labelImg.py

完整的数据集

Finally, this is what we need.

.csv 格式的数据集

Similarly, we need .csv but not .xml here, so

Download: https://raw.githubusercontent.com/datitran/raccoon_dataset/master/xml_to_csv.py

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():

    image_path = os.path.join(os.getcwd(), 'annotations')

    xml_df = xml_to_csv(image_path)

    xml_df.to_csv('raccoon_labels.csv', index=None)

    print('Successfully converted xml to csv.')

main()

xml_to_csv.py

unsw@unsw-UX303UB$ python xml_to_csv.py
Successfully converted xml to csv.

unsw@unsw-UX303UB$ ls

annotations  images  Others  raccoon_labels.csv  xml_to_csv.py

This is final bounding box info.

2. Cascade Classifier Training

接口

THE OPENCV TUTORIAL FOR TRAINING CASCADE CLASSIFIERS is a pretty good place to start. It explains the 2 binary utilities used in the process (opencv_createsamples and opencv_traincascade),

and all of their command line arguments and options, but it doesn’t really give an example of a flow to follow, nor does it explain all the possible uses for the opencv_createsamplesutility.

方案一

On the other hand, NAOTOSHI SEO’S TUTORIAL is actually quite thorough and explains the 4 different uses for the opencv_createsamples utility.

THORSTEN BALL WROTE A TUTORIAL using Naotoshi Seo’s scripts to train a classifier to detect bananas, but it requires running some perl scripts and compiling some C++… too much work…

方案二

Jeff also has some NICE NOTES about how he prepared his data, and a SCRIPT for automatically iterating over a couple of options for the 2 utilities.

The way we did it was inspired by all of these tutorials, with some minor modifications and optimizations.

二、Process

是什么

Ref: https://processing.org/download/

一种语言，处理图像，提供了更为亲和的方式。

/* implement */