前言
目前用于**图像分割的**数据集，我目前接触到的用的比较多的有：
1 PASCAL VOC
2 COCO
3 YOLO
4 Halcon自己的格式（其实就是Halcon字典类型）
当前我涉及到计算机视觉中的数据集格式有，PASCAL VOC、COCO 和 YOLO 用于不同的目标检测和图像分割任务。以下是这三种数据集格式的介绍：
1. PASCAL VOC 格式：
PASCAL VOC（Visual Object Classes）是一个广泛使用的目标检测和图像分割数据集，其标注格式以XML文件的形式提供。以下是一个PASCAL VOC格式的示例（针对单个物体）：
<annotation>
<folder>images</folder>
<filename>example.jpg</filename>
<source>
<database>PASCAL VOC</database>
</source>
<size>
<width>800</width>
<height>600</height>
<depth>3</depth>
</size>
<object>
<name>cat</name>
<pose>Unspecified</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>200</xmin>
<ymin>150</ymin>
<xmax>400</xmax>
<ymax>450</ymax>
</bndbox>
</object>
</annotation>
2. COCO 格式：
COCO（Common Objects in Context）是一个用于目标检测、分割和关键点估计的大规模数据集，其标注格式以JSON文件的形式提供。以下是一个COCO格式的示例（针对单个物体）：
{
"info": {},
"images": [
{
"id": 1,
"file_name": "example.jpg",
"width": 800,
"height": 600,
"depth": 3
}
],
"annotations": [
{
"id": 1,
"image_id": 1,
"category_id": 1,
"bbox": [200, 150, 200, 300],
"area": 60000,
"iscrowd": 0
}
],
"categories": [
{
"id": 1,
"name": "cat"
}
]
}
3. YOLO 格式：
YOLO（You Only Look Once）是一个目标检测算法，同时也有其特定的数据集格式。YOLO格式通常需要一个文本文件，其中每行描述了一张图像中的目标。以下是一个YOLO格式的示例（每行表示单个物体）：
0 0.45 0.35 0.2 0.5
在此示例中，每行包含了类别索引和目标的归一化坐标信息（中心点坐标和宽高相对于图像尺寸的比例）。
请注意，这些示例仅为了演示目的，实际数据集文件可能包含更多图像和目标的标注信息。不同的数据集格式适用于不同的任务和算法，您在使用特定数据集时需要了解其相应的标注格式。
这几种格式，都是描述图片中的某个框框的位置，以及这个框框对应的类别。
背景
我现在手头有一个PASCAL VOC 格式的数据集，每张图片都有对应好的标记图片，我现在想用halcon去读取整个数据集。但是，halcon是有自己的标注工具的：MVTec Deep Learning Tool
有这个软件标注的图片，导出的数据集格式是：.hdict
那有没有办法，把 PASCAL VOC 直接转为 .hdict 格式呢？
PASCAL VOC 转 .hdict
PASCAL VOC 的格式类型，我们已经看到了，就是个XML解析这个XML不在话下，但是 .hdict这个文件是个二进制的文件，看不到其中的内容。
于是，我搜索全网，发现了一个 PASCAL VOC 转 .hdict 的一个halcon脚本，然后花了一块大洋买了下来，下载下来一看，问题不大，稍微改改果然能用：
*read_dict ('C:/Users/12820/Desktop/数据/分割.hdict', [], [], DictHandle)
* Image Acquisition 01: Code generated by Image Acquisition 01
*read_dl_dataset_from_coco
*read_dl
create_dict (NEWDictHandle1)
class_ids:=[0,1,2,3,4,5]
class_names:=['crazing', 'inclusion', 'patches', 'pitted_surface', 'rolled-in_scale', 'scratches']
image_dir:='images/'
set_dict_tuple (NEWDictHandle1, 'class_ids', class_ids)
set_dict_tuple (NEWDictHandle1, 'class_names', class_names)
set_dict_tuple (NEWDictHandle1, 'image_dir', image_dir)
list_files ('images/', ['files','follow_links','recursive'], ImageFiles)
tuple_regexp_select (ImageFiles, ['\\.(tif|tiff|gif|bmp|jpg|jpeg|jp2|png|pcx|pgm|ppm|pbm|xwd|ima|hobj)$','ignore_case'], ImageFiles)
list_files ('labels/', ['files','follow_links','recursive'], xmladdress)
samples:=[]
for Index := 0 to |ImageFiles| - 1 by 1
read_image (Image, ImageFiles[Index])
open_file (xmladdress[Index], 'input', FileHandle)
IsEof := false
bbox_row1:=[]
bbox_col1:=[]
bbox_row2:=[]
bbox_col2:=[]
bbox_label_id:=[]
while (not(IsEof))
fread_line (FileHandle, XmlElement, IsEof)
if (IsEof)
break
endif
tuple_split (XmlElement, '<''>', Substrings)
create_dict (image)
if (Substrings[1]=='folder')
floder:= Substrings[2]
endif
if (Substrings[1]=='filename')
filename:= Substrings[2]
endif
*class_names:=['crazing', 'inclusion', 'patches', 'pitted_surface', 'rolled-in_scale', 'scratches']
if (Substrings[1]=='name')
if (Substrings[2]== class_names[0] )
bbox_label_id:=[bbox_label_id,0]
elseif (Substrings[2]==class_names[1])
bbox_label_id:=[bbox_label_id,1]
elseif (Substrings[2]==class_names[2])
bbox_label_id:=[bbox_label_id,2]
elseif (Substrings[2]==class_names[3])
bbox_label_id:=[bbox_label_id,3]
elseif (Substrings[2]==class_names[4])
bbox_label_id:=[bbox_label_id,4]
elseif (Substrings[2]==class_names[5])
bbox_label_id:=[bbox_label_id,5]
endif
endif
if (Substrings[1]=='xmin')
bbox_col1:= [bbox_col1,Substrings[2]]
tuple_number (bbox_col1, bbox_col1)
endif
if (Substrings[1]=='ymin')
bbox_row1:= [bbox_row1,Substrings[2] ]
tuple_number (bbox_row1, bbox_row1)
endif
if (Substrings[1]=='xmax')
bbox_col2:=[bbox_col2, Substrings[2] ]
tuple_number (bbox_col2, bbox_col2)
endif
if (Substrings[1]=='ymax')
bbox_row2:=[bbox_row2, Substrings[2] ]
tuple_number (bbox_row2, bbox_row2)
endif
endwhile
* gen_rectangle1 (Rectangle,bbox_row1 , bbox_col1,bbox_row2 , bbox_col2)
set_dict_tuple (image, 'image_id', Index+1)
set_dict_tuple (image, 'image_file_name', floder+'/'+filename)
set_dict_tuple (image, 'bbox_label_id', bbox_label_id)
set_dict_tuple (image, 'bbox_row1', bbox_row1)
set_dict_tuple (image, 'bbox_col1', bbox_col1)
set_dict_tuple (image, 'bbox_row2', bbox_row2)
set_dict_tuple (image, 'bbox_col2', bbox_col2)
samples:=[samples,image]
*stop()
endfor
set_dict_tuple (NEWDictHandle1, 'samples', samples)
write_dict (NEWDictHandle1, '数据test.hdict', [], [])
看到最后一句：write_dict 才意识到，原来所谓的.hdict文件就是halcon里的字典格式啊！
虽然，这个脚本文件可以用，但是1800条数据转换下来，花费了将近半个小时，这能忍？
还有就是PASCAL VOC标注文件有点地方图片名称没带后缀导致，导入后图片无法在
Deep Learning Tool 中显示！所以，搞清楚原理之后，我还是自己写个工具才更省心啊：
HTuple NEWDict;
HOperatorSet.CreateDict(out NEWDict);
List<int> class_ids = new List<int> { 0, 1, 2, 3, 4, 5 };
List<string> class_names = new List<string> { "crazing", "inclusion", "patches", "pitted_surface", "rolled-in_scale", "scratches" };
string image_dir = "F:\\temp\\数据集格式转换测试\\images";
//图片字典
HTuple hv_image = new HTuple();
HTuple hv_samples = new HTuple();
HTuple hv_class_ids = new HTuple(class_ids.ToArray());
HTuple hv_class_names = new HTuple(class_names.ToArray());
HTuple hv_image_dir = new HTuple(image_dir);
HOperatorSet.SetDictTuple(NEWDict, "class_ids", hv_class_ids);
HOperatorSet.SetDictTuple(NEWDict, "class_names", hv_class_names);
HOperatorSet.SetDictTuple(NEWDict, "image_dir", hv_image_dir);
string[] imageFiles = Directory.GetFiles(image_dir, "*.*", SearchOption.AllDirectories);
List<Dictionary<string, object>> samples = new List<Dictionary<string, object>>();
int index = 0;
string extension = "";
foreach (string imagePath in imageFiles)
{
HOperatorSet.CreateDict(out hv_image);
string xmlPath = "D:/DATASET/yolo/NEU-DET/ANNOTATIONS/" + Path.GetFileNameWithoutExtension(imagePath) + ".xml";
XDocument xdoc;
using (StreamReader reader = new StreamReader(xmlPath))
{
string xmlContent = reader.ReadToEnd();
xdoc = XDocument.Parse(xmlContent);
// 现在可以使用xdoc进行XML解析操作
}
XElement xroot = xdoc.Root;//根节点
List<int> bbox_label_ids = new List<int>();
List<int> bbox_col1 = new List<int>();
List<int> bbox_row1 = new List<int>();
List<int> bbox_col2 = new List<int>();
List<int> bbox_row2 = new List<int>();
//----folder
var folder = xroot.Element("folder").Value;
//----filename
var filename = xroot.Element("filename").Value;
if(Path.GetExtension(filename) != "")
{
extension = Path.GetExtension(filename);
}
else
{
if (extension != "")
{
filename += extension;
}
}
//----获取object节点(一个xml中可能会有多个)
var objectNodes = xroot.Descendants("object");
foreach (var objectNode in objectNodes)
{
//bndbox节点，包含xmin,ymin,xmax,ymax
XElement bndboxNode = objectNode.Element("bndbox");
XElement xminNode = bndboxNode.Element("xmin");
XElement yminNode = bndboxNode.Element("ymin");
XElement xmaxNode = bndboxNode.Element("xmax");
XElement ymaxNode = bndboxNode.Element("ymax");
// 解析坐标值并添加到相应列表
bbox_col1.Add(int.Parse(xminNode.Value));
bbox_row1.Add(int.Parse(yminNode.Value));
bbox_col2.Add(int.Parse(xmaxNode.Value));
bbox_row2.Add(int.Parse(ymaxNode.Value));
// 获取类别名称对应的编号，并添加到相应列表
string className = objectNode.Element("name").Value;
int id = class_names.IndexOf(className);
bbox_label_ids.Add(id);
}
HOperatorSet.SetDictTuple(hv_image, "image_id", index + 1);
HOperatorSet.SetDictTuple(hv_image, "image_file_name", (folder + "/") + filename);
HOperatorSet.SetDictTuple(hv_image, "bbox_label_id", bbox_label_ids.ToArray());
HOperatorSet.SetDictTuple(hv_image, "bbox_row1", bbox_row1.ToArray());
HOperatorSet.SetDictTuple(hv_image, "bbox_col1", bbox_col1.ToArray());
HOperatorSet.SetDictTuple(hv_image, "bbox_row2", bbox_row2.ToArray());
HOperatorSet.SetDictTuple(hv_image, "bbox_col2", bbox_col2.ToArray());
// hv_image添加到samples
using (HDevDisposeHelper dh = new HDevDisposeHelper())
{
HTuple ExpTmpLocalVar_samples = hv_samples.TupleConcat(hv_image);
hv_samples.Dispose();
hv_samples = ExpTmpLocalVar_samples;
}
index++;
}
HOperatorSet.SetDictTuple(NEWDict, "samples", hv_samples);
HOperatorSet.WriteDict(NEWDict, "数据Csharp.hdict", new HTuple(), new HTuple());
MessageBox.Show("转换完成");
这次使用XDocument方式解析，弹指间，转换就完成了！再次用Deep Learning Tool打开转换好的Csharp.hdict，这次就成功了
读取.hdict 格式的数据集
有了.hdict 这个格式的数据集，怎么用呢？
*读取数据集！！！这个就是深度学习工具标记的字典
read_dict (“xxxxx.hdict”, [], [], DLDataset)
应为它就是一个字典，所以直接使用read_dict就能读取数据集了！
还有，halcon除了自家的数据集之外，其实可以直接读取coco数据集：
read_dl_dataset_from_coco (FileExists, [], [], DLDataset1)
是不是很方便！
具体如何训练数据这些内容，后续持续输出，我们下一篇文件见！
附录
附送一个PASCAL VOC 转 YOLO的 python脚本！
import xml.etree.ElementTree as ET
import pickle
import os
from os import listdir, getcwd
from os.path import join
import glob
classes = ["crazing", "inclusion", "patches", "pitted_surface", "rolled-in_scale", "scratches"]
def convert(size, box):
dw = 1./size[0]
dh = 1./size[1]
x = (box[0] + box[1])/2.0
y = (box[2] + box[3])/2.0
w = box[1] - box[0]
h = box[3] - box[2]
x = x*dw
w = w*dw
y = y*dh
h = h*dh
return (x,y,w,h)
def convert_annotation(image_name):
in_file = open('./ANNOTATIONS/'+image_name[:-3]+'xml')
out_file = open('./LABELS/'+image_name[:-3]+'txt','w')
tree=ET.parse(in_file)
root = tree.getroot()
size = root.find('size')
w = int(size.find('width').text)
h = int(size.find('height').text)
for obj in root.iter('object'):
cls = obj.find('name').text
if cls not in classes:
print(cls)
continue
cls_id = classes.index(cls)
xmlbox = obj.find('bndbox')
b = (float(xmlbox.find('xmin').text), float(xmlbox.find('xmax').text), float(xmlbox.find('ymin').text), float(xmlbox.find('ymax').text))
bb = convert((w,h), b)
out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n')
wd = getcwd()
if __name__ == '__main__':
for image_path in glob.glob("./IMAGES/*.jpg"):
image_name = image_path.split('\\')[-1]
#print(image_path)
convert_annotation(image_name)
