实现功能
将分割的label图转换为目标检测boundingbox标注文件（VOC格式）。
注：
1.分割样本里一张图片只有同一类别的多个目标。
2.转换为boundingbox标注通过连通域实现，所以重叠的目标处理不了，会标为1个。
数据集格式
其中，语义分割数据集格式如下：
原图片在JPEGImages文件夹中，命名格式为ImageID.jpg
Label图在labelimage文件夹中，命名格式为ImageID_classname.png
生成的boundingbox标注命名格式为ImageID.xml
XML标注格式
<annotation>
<folder>road_dataset</folder> #文件名
<filename>3425.jpg</filename> #原图片名
<path>D:\road_dataset\JPEGImages\3425.jpg</path> #原图片地址
<source>
<database>Unknown</database>
</source>
<size> #图片尺寸
<width>512</width>
<height>512</height>
<depth>3</depth>
</size>
<segmented>0</segmented> #是否用于分割，0为否
<object> #目标
<name>butt</name> #类别名称
<pose>Unspecified</pose> #拍摄角度
<truncated>0</truncated> #是否被截断
<difficult>0</difficult> #是否为困难样本
<bndbox> #boundingbox坐标（左下、右上）
<xmin>327</xmin>
<ymin>38</ymin>
<xmax>394</xmax>
<ymax>69</ymax>
</bndbox>
</object>
<object> #多个目标
<name>Cigarette butts</name>
<pose>Unspecified</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>139</xmin>
<ymin>279</ymin>
<xmax>214</xmax>
<ymax>318</ymax>
</bndbox>
</object>
</annotation>
其中<pose> <truncated> <difficult> 全是默认值。
得到label图中的连通域
使用skimage的morphology, measure通过连通域得到每一副一幅图片上的目标数量和boundingbox。
import os
import numpy as np
from itertools import groupby
from skimage import morphology,measure
from PIL import Image
from scipy import misc
# 因为一张图片里只有一种类别的目标，所以label图标记只有黑白两色
rgbmask = np.array([[0,0,0],[255,255,255]],dtype=np.uint8)
# 从label图得到 boundingbox 和图上连通域数量 object_num
def getboundingbox(image):
# mask.shape = [image.shape[0], image.shape[1], classnum]
mask = np.zeros((image.shape[0], image.shape[1]), dtype=np.uint8)
mask[np.where(np.all(image == rgbmask[1],axis=-1))[:2]] = 1
# 删掉小于10像素的目标
mask_without_small = morphology.remove_small_objects(mask,min_size=10,connectivity=2)
# 连通域标记
label_image = measure.label(mask_without_small)
#统计object个数
object_num = len(measure.regionprops(label_image))
boundingbox = list()
for region in measure.regionprops(label_image): # 循环得到每一个连通域bbox
boundingbox.append(region.bbox)
return object_num, boundingbox
在label图片上显示boundingbox，查看结果：
import matplotlib.pyplot as plt
import matplotlib.patches as patch
# 输出成图片查看得到boundingbox效果
imagedir = r‘D:\test_dataset\labelimage‘
if ~os.path.exists(r‘D:\test_dataset\test_getbbox‘):
os.mkdir(r‘D:\test_dataset\test_getbbox‘)
for root, _, fnames in sorted(os.walk(imagedir)):
for fname in sorted(fnames):
imagepath = os.path.join(root, fname)
image = misc.imread(imagepath)
objectnum, bbox = getboundingbox(image)
ImageID = fname.split(‘.‘)[0]
fig,ax = plt.subplots(1)
ax.imshow(image)
for box in bbox:
rect = patch.Rectangle((box[1], box[0]), box[3]-box[1], box[2]-box[0],edgecolor = ‘r‘, linewidth = 1,fill = False)
ax.add_patch(rect)
plt.savefig(‘D:/test_dataset/test_getbbox/‘+ImageID+‘.png‘)
输出图像为：
生成XML标注文件
createXMLlabel： 根据标注信息生成XML标注文件
import xml.etree.ElementTree as ET
def createXMLlabel(savedir,objectnum, bbox, classname, foldername=‘0‘,filename=‘0‘, path=‘0‘, database=‘road‘, width=‘400‘, height=‘600‘,depth=‘3‘, segmented=‘0‘, pose="Unspecified", truncated=‘0‘, difficult=‘0‘):
# 创建根节点
root = ET.Element("annotation")
# 创建子节点
folder_node = ET.Element("folder")
folder_node.text = foldername
# 将子节点数据添加到根节点
root.append(folder_node)
file_node = ET.Element("filename")
file_node.text = filename
root.append(file_node)
path_node = ET.Element("path")
path_node.text = path
root.append(path_node)
source_node = ET.Element("source")
# 也可以使用SubElement直接添加子节点
db_node = ET.SubElement(source_node, "database")
db_node.text = database
root.append(source_node)
size_node = ET.Element("size")
width_node = ET.SubElement(size_node, "width")
height_node = ET.SubElement(size_node, "height")
depth_node = ET.SubElement(size_node, "depth")
width_node.text = width
height_node.text = height
depth_node.text = depth
root.append(size_node)
seg_node = ET.Element("segmented")
seg_node.text = segmented
root.append(seg_node)
for i in range(objectnum):
newEle = ET.Element("object")
name = ET.Element("name")
name.text = classname
newEle.append(name)
pose_node = ET.Element("pose")
pose_node.text = pose
newEle.append(pose_node)
trunc = ET.Element("truncated")
trunc.text = truncated
newEle.append(trunc)
dif = ET.Element("difficult")
dif.text = difficult
newEle.append(dif)
boundingbox = ET.Element("bndbox")
xmin = ET.SubElement(boundingbox, "xmin")
ymin = ET.SubElement(boundingbox, "ymin")
xmax = ET.SubElement(boundingbox, "xmax")
ymax = ET.SubElement(boundingbox, "ymax")
xmin.text = str(bbox[i][1])
ymin.text = str(bbox[i][0])
xmax.text = str(bbox[i][3])
ymax.text = str(bbox[i][2])
newEle.append(boundingbox)
root.append(newEle)
ImageID = filename.split(‘.‘)[0]
# 创建elementtree对象，写入文件
tree = ET.ElementTree(root)
tree.write(savedir + ‘/‘+ ImageID + ".xml")
imagedir = r‘D:\test_dataset\labelimage‘
saveXMLdir = r‘D:\test_dataset\Annotations‘
if os.path.exists(saveXMLdir) is False:
os.mkdir(saveXMLdir)
for root, _, fnames in sorted(os.walk(imagedir)):
for fname in sorted(fnames):
labelpath = os.path.join(root, fname)
labelimage = misc.imread(labelpath)
# 得到label图上的boundingingbox和数量
objectnum, bbox = getboundingbox(labelimage)
# label图 命名格式为 ImgeID_classname.png
labelfilename = labelpath.split(‘\\‘)[-1]
ImageID = labelfilename.split(‘.‘)[0].split(‘_‘)[0]
classname = labelfilename.split(‘.‘)[0].split(‘_‘)[1]
origin_image_name = ImageID +‘.jpg‘
# 一些图片信息
foldername = ‘test_dataset‘
path =‘\\‘.join(imagedir.split(‘\\‘)[:-1]) + ‘\\JPEGImage\\‘+ origin_image_name
database = ‘Unknown‘
width = str(labelimage.shape[0])
height = str(labelimage.shape[1])
depth = str(labelimage.shape[2])
createXMLlabel(saveXMLdir,objectnum, bbox, classname, foldername=foldername,filename=origin_image_name, path=path,
database=database, width=width, height=height,depth=depth, segmented=‘0‘, pose="Unspecified",
truncated=‘0‘, difficult=‘0‘)
原文地址：https://www.cnblogs.com/smartweed/p/12153744.html
时间： 2024-10-09 16:23:08