问题描述
昨天做基于yolov5的缺陷检测,用labeliimg标注文件,直接用YOLO格式的标注方式了,最终生成了txt的文档。下一步要做的就是划分训练验证测试集了,看了各种博客,所以做个总结,以便自己以后使用。
labelimg
可视化标注工具。具体的下载和使用其他博主已经介绍的很详细了。
ubuntu版:labelimg
window版:labelimg
YOLO转VOC --(txt转xml)
import os
import glob
from PIL import Image
voc_annotations = "要保存的xml文件地址"
yolo_txt = "标注后的txt存放文件"
img_path = "标注的图片文件"
labels = ['class'] # label for datasets
# 图像存储位置
src_img_dir = img_path # 添加你的路径
# 图像的txt文件存放位置
src_txt_dir = yolo_txt
src_xml_dir = voc_annotations
img_Lists = glob.glob(src_img_dir + '/*.jpg')
img_basenames = []
for item in img_Lists:
img_basenames.append(os.path.basename(item))
img_names = []
for item in img_basenames:
temp1, temp2 = os.path.splitext(item)
img_names.append(temp1)
for img in img_names:
im = Image.open((src_img_dir + '/' + img + '.jpg'))
width, height = im.size
# 打开txt文件
gt = open(src_txt_dir + '/' + img + '.txt').read().splitlines()
print(gt)
if gt:
# 将主干部分写入xml文件中
xml_file = open((src_xml_dir + '/' + img + '.xml'), 'w')
xml_file.write('<annotation>\n')
xml_file.write(' <folder>VOC2007</folder>\n')
xml_file.write(' <filename>' + str(img) + '.jpg' + '</filename>\n')
xml_file.write(' <size>\n')
xml_file.write(' <width>' + str(width) + '</width>\n')
xml_file.write(' <height>' + str(height) + '</height>\n')
xml_file.write(' <depth>3</depth>\n')
xml_file.write(' </size>\n')
# write the region of image on xml file
for img_each_label in gt:
spt = img_each_label.split(' ') # 这里如果txt里面是以逗号‘,’隔开的,那么就改为spt = img_each_label.split(',')。
print(f'spt:{spt}')
xml_file.write(' <object>\n')
xml_file.write(' <name>' + str(labels[int(spt[0])]) + '</name>\n')
xml_file.write(' <pose>Unspecified</pose>\n')
xml_file.write(' <truncated>0</truncated>\n')
xml_file.write(' <difficult>0</difficult>\n')
xml_file.write(' <bndbox>\n')
center_x = round(float(spt[1].strip()) * width)
center_y = round(float(spt[2].strip()) * height)
bbox_width = round(float(spt[3].strip()) * width)
bbox_height = round(float(spt[4].strip()) * height)
xmin = str(int(center_x - bbox_width / 2))
ymin = str(int(center_y - bbox_height / 2))
xmax = str(int(center_x + bbox_width / 2))
ymax = str(int(center_y + bbox_height / 2))
xml_file.write(' <xmin>' + xmin + '</xmin>\n')
xml_file.write(' <ymin>' + ymin + '</ymin>\n')
xml_file.write(' <xmax>' + xmax + '</xmax>\n')
xml_file.write(' <ymax>' + ymax + '</ymax>\n')
xml_file.write(' </bndbox>\n')
xml_file.write(' </object>\n')
xml_file.write('</annotation>')
如出现UnicodeDecodeError: ‘utf-8’ 的错误就是编码方式的问题,另存为 utf-8的格式即可。
VOC转YOLO–(xml转txt)
这个文件最好格式规范(应该都懂)。代码出自大神bubbliiiing(不得不说,别人才是大神,咱只是代码的搬运工)
import os
import random
import xml.etree.ElementTree as ET
from utils.utils import get_classes
#--------------------------------------------------------------------------------------------------------------------------------#
# annotation_mode用于指定该文件运行时计算的内容
# annotation_mode为0代表整个标签处理过程,包括获得VOCdevkit/VOC2007/ImageSets里面的txt以及训练用的2007_train.txt、2007_val.txt
# annotation_mode为1代表获得VOCdevkit/VOC2007/ImageSets里面的txt
# annotation_mode为2代表获得训练用的2007_train.txt、2007_val.txt
#--------------------------------------------------------------------------------------------------------------------------------#
annotation_mode = 2
#-------------------------------------------------------------------#
# 必须要修改,用于生成2007_train.txt、2007_val.txt的目标信息
# 与训练和预测所用的classes_path一致即可
# 如果生成的2007_train.txt里面没有目标信息
# 那么就是因为classes没有设定正确
# 仅在annotation_mode为0和2的时候有效
#-------------------------------------------------------------------#
classes_path = 'model_data/voc_classes.txt'
#--------------------------------------------------------------------------------------------------------------------------------#
# trainval_percent用于指定(训练集+验证集)与测试集的比例,默认情况下 (训练集+验证集):测试集 = 9:1
# train_percent用于指定(训练集+验证集)中训练集与验证集的比例,默认情况下 训练集:验证集 = 9:1
# 仅在annotation_mode为0和1的时候有效
#--------------------------------------------------------------------------------------------------------------------------------#
trainval_percent = 0.9
train_percent = 0.9
#-------------------------------------------------------#
# 指向VOC数据集所在的文件夹
# 默认指向根目录下的VOC数据集
#-------------------------------------------------------#
VOCdevkit_path = 'VOCdevkit'
VOCdevkit_sets = [('2007', 'train'), ('2007', 'val')]
classes, _ = get_classes(classes_path)
def convert_annotation(year, image_id, list_file):
in_file = open(os.path.join(VOCdevkit_path, 'VOC%s/Annotations/%s.xml'%(year, image_id)), encoding='utf-8')
tree=ET.parse(in_file)
root = tree.getroot()
for obj in root.iter('object'):
difficult = 0
if obj.find('difficult')!=None:
difficult = obj.find('difficult').text
cls = obj.find('name').text
if cls not in classes or int(difficult)==1:
continue
cls_id = classes.index(cls)
xmlbox = obj.find('bndbox')
b = (int(float(xmlbox.find('xmin').text)), int(float(xmlbox.find('ymin').text)), int(float(xmlbox.find('xmax').text)), int(float(xmlbox.find('ymax').text)))
list_file.write(" " + ",".join([str(a) for a in b]) + ',' + str(cls_id))
if __name__ == "__main__":
random.seed(0)
if annotation_mode == 0 or annotation_mode == 1:
print("Generate txt in ImageSets.")
xmlfilepath = os.path.join(VOCdevkit_path, 'VOC2007/Annotations')
saveBasePath = os.path.join(VOCdevkit_path, 'VOC2007/ImageSets/Main')
temp_xml = os.listdir(xmlfilepath)
total_xml = []
for xml in temp_xml:
if xml.endswith(".xml"):
total_xml.append(xml)
num = len(total_xml)
list = range(num)
tv = int(num*trainval_percent)
tr = int(tv*train_percent)
trainval= random.sample(list,tv)
train = random.sample(trainval,tr)
print("train and val size",tv)
print("train size",tr)
ftrainval = open(os.path.join(saveBasePath,'trainval.txt'), 'w')
ftest = open(os.path.join(saveBasePath,'test.txt'), 'w')
ftrain = open(os.path.join(saveBasePath,'train.txt'), 'w')
fval = open(os.path.join(saveBasePath,'val.txt'), 'w')
for i in list:
name=total_xml[i][:-4]+'\n'
if i in trainval:
ftrainval.write(name)
if i in train:
ftrain.write(name)
else:
fval.write(name)
else:
ftest.write(name)
ftrainval.close()
ftrain.close()
fval.close()
ftest.close()
print("Generate txt in ImageSets done.")
if annotation_mode == 0 or annotation_mode == 2:
print("Generate 2007_train.txt and 2007_val.txt for train.")
for year, image_set in VOCdevkit_sets:
image_ids = open(os.path.join(VOCdevkit_path, 'VOC%s/ImageSets/Main/%s.txt'%(year, image_set)), encoding='utf-8').read().strip().split()
list_file = open('%s_%s.txt'%(year, image_set), 'w', encoding='utf-8')
for image_id in image_ids:
list_file.write('%s/VOC%s/JPEGImages/%s.jpg'%(os.path.abspath(VOCdevkit_path), year, image_id))
convert_annotation(year, image_id, list_file)
list_file.write('\n')
list_file.close()
print("Generate 2007_train.txt and 2007_val.txt for train done.")
运行成功后直接划分了训练验证集,之后训练即可。
版权声明:本文为CSDN博主「城市黎明的烟火」的原创文章,遵循CC 4.0 BY-SA版权协议,转载请附上原文出处链接及本声明。
原文链接:https://blog.csdn.net/qingliange/article/details/122209657
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