用MATLAB如何制作自己的数据集,Windows10+YOLOv3实现检测自己的数据集(1)——制作自己的数据集...

一、数据标注

在深度学习的目标检测任务中,首先要使用训练集进行模型训练。训练的数据集好坏决定了任务的上限。下面介绍两种常用的图像目标检测标注工具:Labelme和LabelImg。

(1)Labelme

Labelme适用于图像分割任务和目标检测任务的数据集制作,它来自该项目:https://github.com/wkentaro/labelme 。

按照项目中的教程安装完毕后,应用界面如下图所示:

用MATLAB如何制作自己的数据集,Windows10+YOLOv3实现检测自己的数据集(1)——制作自己的数据集..._第1张图片

它能够提供多边形、矩形、圆形、直线和点的图像标注,并将结果保存为 JSON 文件。

(2)LabelImg

应用界面如下图所示:

用MATLAB如何制作自己的数据集,Windows10+YOLOv3实现检测自己的数据集(1)——制作自己的数据集..._第2张图片

它能够提供矩形的图像标注,并将结果保存为txt(YOLO)或xml(PascalVOC)格式。如果需要修改标签的类别内容,则在主目录data文件夹中的predefined_classes.txt文件中修改。

我使用的就是这一个标注软件,标注结果保存为xml格式,后续还需要进行标注格式的转换。

操作快捷键:

Ctrl + u  加载目录中的所有图像,鼠标点击Open dir同功能

Ctrl + r  更改默认注释目标目录(xml文件保存的地址)

Ctrl + s  保存

Ctrl + d  复制当前标签和矩形框

space     将当前图像标记为已验证

w         创建一个矩形框

d         下一张图片

a         上一张图片

del       删除选定的矩形框

Ctrl++    放大

Ctrl--    缩小

↑→↓←        键盘箭头移动选定的矩形框

二、数据扩增

在某些场景下的目标检测中,样本数量较小,导致检测的效果比较差,这时就需要进行数据扩增。本文介绍常用的6类数据扩增方式,包括裁剪、平移、改变亮度、加入噪声、旋转角度以及镜像。

三、将数据转换至COCO的json格式

首先让我们明确一下几种格式,参考自【点此处】:

3.1 csv

csv/

labels.csv

images/

image1.jpg

image2.jpg

...

labels.csv 的形式:

/path/to/image,xmin,ymin,xmax,ymax,label

例如:

/mfs/dataset/face/image1.jpg,450,154,754,341,face

/mfs/dataset/face/image2.jpg,143,154,344,341,face

3.2 voc

标准的voc数据格式如下:

VOC2007/

Annotations/

0d4c5e4f-fc3c-4d5a-906c-105.xml

0ddfc5aea-fcdac-421-92dad-144/xml

...

ImageSets/

Main/

train.txt

test.txt

val.txt

trainval.txt

JPEGImages/

0d4c5e4f-fc3c-4d5a-906c-105.jpg

0ddfc5aea-fcdac-421-92dad-144.jpg

...

3.3 COCO

coco/

annotations/

instances_train2017.json

instances_val2017.json

images/

train2017/

0d4c5e4f-fc3c-4d5a-906c-105.jpg

...

val2017

0ddfc5aea-fcdac-421-92dad-144.jpg

...

Json file 格式: (imageData那一块太长了,不展示了)

{

"version": "3.6.16",

"flags": {},

"shapes": [

{

"label": "helmet",

"line_color": null,

"fill_color": null,

"points": [

[

131,

269

],

[

388,

457

]

],

"shape_type": "rectangle"

}

],

"lineColor": [

0,

255,

0,

128

],

"fillColor": [

255,

0,

0,

128

],

"imagePath": "004ffe6f-c3e2-3602-84a1-ecd5f437b113.jpg",

"imageData": "" # too long ,so not show here

"imageHeight": 1080,

"imageWidth": 1920

}

在上一节中提到,经过标注后的结果保存为xml格式,我们首先要把这些xml标注文件整合成一个csv文件。

整合代码如下:

import os

import glob

import pandas as pd

import xml.etree.ElementTree as ET

## xml文件的路径

os.chdir('./data/annotations/scratches')

path = 'C:/Users/Admin/Desktop/data/annotations/scratches' # 绝对路径

img_path = 'C:/Users/Admin/Desktop/data/images'

def xml_to_csv(path):

xml_list = []

for xml_file in glob.glob(path + '/*.xml'): #返回所有匹配的文件路径列表。

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)

# )

value = (img_path +'/' + root.find('filename').text,

int(member[4][0].text),

int(member[4][1].text),

int(member[4][2].text),

int(member[4][3].text),

member[0].text

)

xml_list.append(value)

#column_name = ['filename', 'width', 'height', 'class', 'xmin', 'ymin', 'xmax', 'ymax']

column_name = ['filename', 'xmin', 'ymin', 'xmax', 'ymax', 'class']

xml_df = pd.DataFrame(xml_list, columns=column_name)

return xml_df

if __name__ == '__main__':

image_path = path

xml_df = xml_to_csv(image_path)

## 修改文件名称

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

print('Successfully converted xml to csv.')

当显示 Successfully converted xml to csv 后,我们就得到了整理后的标记文件。

在有些模型下,有了图像数据和csv格式的标注文件后,就可以进行训练了。但是在YOLOv3中,标记文件的类型为COCO的json格式,因此我们还得将其转换至json格式。

转换代码:

import os

import json

import numpy as np

import pandas as pd

import glob

import cv2

import shutil

from IPython import embed

from sklearn.model_selection import train_test_split

np.random.seed(41)

# 0为背景

classname_to_id = {"scratches": 1,"inclusion": 2}

class Csv2CoCo:

def __init__(self,image_dir,total_annos):

self.images = []

self.annotations = []

self.categories = []

self.img_id = 0

self.ann_id = 0

self.image_dir = image_dir

self.total_annos = total_annos

def save_coco_json(self, instance, save_path):

json.dump(instance, open(save_path, 'w'), ensure_ascii=False, indent=2) # indent=2 更加美观显示

# 由txt文件构建COCO

def to_coco(self, keys):

self._init_categories()

for key in keys:

self.images.append(self._image(key))

shapes = self.total_annos[key]

for shape in shapes:

bboxi = []

for cor in shape[:-1]:

bboxi.append(int(cor))

label = shape[-1]

annotation = self._annotation(bboxi,label)

self.annotations.append(annotation)

self.ann_id += 1

self.img_id += 1

instance = {}

instance['info'] = 'spytensor created'

instance['license'] = ['license']

instance['images'] = self.images

instance['annotations'] = self.annotations

instance['categories'] = self.categories

return instance

# 构建类别

def _init_categories(self):

for k, v in classname_to_id.items():

category = {}

category['id'] = v

category['name'] = k

self.categories.append(category)

# 构建COCO的image字段

def _image(self, path):

image = {}

img = cv2.imread(self.image_dir + path)

image['height'] = img.shape[0]

image['width'] = img.shape[1]

image['id'] = self.img_id

image['file_name'] = path

return image

# 构建COCO的annotation字段

def _annotation(self, shape,label):

# label = shape[-1]

points = shape[:4]

annotation = {}

annotation['id'] = self.ann_id

annotation['image_id'] = self.img_id

annotation['category_id'] = int(classname_to_id[label])

annotation['segmentation'] = self._get_seg(points)

annotation['bbox'] = self._get_box(points)

annotation['iscrowd'] = 0

annotation['area'] = 1.0

return annotation

# COCO的格式: [x1,y1,w,h] 对应COCO的bbox格式

def _get_box(self, points):

min_x = points[0]

min_y = points[1]

max_x = points[2]

max_y = points[3]

return [min_x, min_y, max_x - min_x, max_y - min_y]

# segmentation

def _get_seg(self, points):

min_x = points[0]

min_y = points[1]

max_x = points[2]

max_y = points[3]

h = max_y - min_y

w = max_x - min_x

a = []

a.append([min_x,min_y, min_x,min_y+0.5*h, min_x,max_y, min_x+0.5*w,max_y, max_x,max_y, max_x,max_y-0.5*h, max_x,min_y, max_x-0.5*w,min_y])

return a

if __name__ == '__main__':

## 修改目录

csv_file = "data/annotations/scratches/scratches.csv"

image_dir = "data/images/"

saved_coco_path = "./"

# 整合csv格式标注文件

total_csv_annotations = {}

annotations = pd.read_csv(csv_file,header=None).values

for annotation in annotations:

key = annotation[0].split(os.sep)[-1]

value = np.array([annotation[1:]])

if key in total_csv_annotations.keys():

total_csv_annotations[key] = np.concatenate((total_csv_annotations[key],value),axis=0)

else:

total_csv_annotations[key] = value

# 按照键值划分数据

total_keys = list(total_csv_annotations.keys())

train_keys, val_keys = train_test_split(total_keys, test_size=0.2)

print("train_n:", len(train_keys), 'val_n:', len(val_keys))

## 创建必须的文件夹

if not os.path.exists('%ssteel/annotations/'%saved_coco_path):

os.makedirs('%ssteel/annotations/'%saved_coco_path)

if not os.path.exists('%ssteel/images/train/'%saved_coco_path):

os.makedirs('%ssteel/images/train/'%saved_coco_path)

if not os.path.exists('%ssteel/images/val/'%saved_coco_path):

os.makedirs('%ssteel/images/val/'%saved_coco_path)

## 把训练集转化为COCO的json格式

l2c_train = Csv2CoCo(image_dir=image_dir,total_annos=total_csv_annotations)

train_instance = l2c_train.to_coco(train_keys)

l2c_train.save_coco_json(train_instance, '%ssteel/annotations/instances_train.json'%saved_coco_path)

for file in train_keys:

shutil.copy(image_dir+file,"%ssteel/images/train/"%saved_coco_path)

for file in val_keys:

shutil.copy(image_dir+file,"%ssteel/images/val/"%saved_coco_path)

## 把验证集转化为COCO的json格式

l2c_val = Csv2CoCo(image_dir=image_dir,total_annos=total_csv_annotations)

val_instance = l2c_val.to_coco(val_keys)

l2c_val.save_coco_json(val_instance, '%ssteel/annotations/instances_val.json'%saved_coco_path)

至此,我们的数据预处理工作就做好了

四、参考资料

https://blog.csdn.net/sty945/article/details/79387054

https://blog.csdn.net/saltriver/article/details/79680189

https://www.ctolib.com/topics-44419.html

https://www.zhihu.com/question/20666664

https://github.com/spytensor/prepare_detection_dataset#22-voc

https://blog.csdn.net/chaipp0607/article/details/79036312

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