本文将从以下三个方面介绍如何制作自己的数据集
- 数据标注
- 数据扩增
- 将数据转化为COCO的json格式
- 参考资料
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:
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
LabelImg适用于目标检测任务的数据集制作。它来自该项目:https://github.com/tzutalin/labelImg
应用界面如下图所示:
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。
考虑到篇幅问题,将这一部分单列出来,详细请参考本篇博客:https://www.cnblogs.com/lky-learning/p/11653861.html
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
原文地址:https://www.cnblogs.com/lky-learning/p/11640180.html