def convert_annotation(image_id):
in_file open( H:/phone/VOCdevkit/VOC2007/Annotations/%s.xml %image_id)
out_file open( H:/phone/VOCdevkit/VOC2007/YOLOLabels/%s.txt %image_id, 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 ):
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 (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 )
in_file.close()
out_file.close()
wd os.getcwd()
wd os.getcwd()
data_base_dir os.path.join(wd, VOCdevkit/ )
if not os.path.isdir(data_base_dir):
os.mkdir(data_base_dir)
work_sapce_dir os.path.join(data_base_dir, VOC2007/ )
if not os.path.isdir(work_sapce_dir):
os.mkdir(work_sapce_dir)
annotation_dir os.path.join(work_sapce_dir, Annotations/ )
if not os.path.isdir(annotation_dir):
os.mkdir(annotation_dir)
clear_hidden_files(annotation_dir)
image_dir os.path.join(work_sapce_dir, JPEGImages/ )
if not os.path.isdir(image_dir):
os.mkdir(image_dir)
clear_hidden_files(image_dir)
yolo_labels_dir os.path.join(work_sapce_dir, YOLOLabels/ )
if not os.path.isdir(yolo_labels_dir):
os.mkdir(yolo_labels_dir)
clear_hidden_files(yolo_labels_dir)
yolov5_images_dir os.path.join(data_base_dir, images/ )
if not os.path.isdir(yolov5_images_dir):
os.mkdir(yolov5_images_dir)
clear_hidden_files(yolov5_images_dir)
yolov5_labels_dir os.path.join(data_base_dir, labels/ )
if not os.path.isdir(yolov5_labels_dir):
os.mkdir(yolov5_labels_dir)
clear_hidden_files(yolov5_labels_dir)
yolov5_images_train_dir os.path.join(yolov5_images_dir, train/ )
if not os.path.isdir(yolov5_images_train_dir):
os.mkdir(yolov5_images_train_dir)
clear_hidden_files(yolov5_images_train_dir)
yolov5_images_test_dir os.path.join(yolov5_images_dir, val/ )
if not os.path.isdir(yolov5_images_test_dir):
os.mkdir(yolov5_images_test_dir)
clear_hidden_files(yolov5_images_test_dir)
yolov5_labels_train_dir os.path.join(yolov5_labels_dir, train/ )
if not os.path.isdir(yolov5_labels_train_dir):
os.mkdir(yolov5_labels_train_dir)
clear_hidden_files(yolov5_labels_train_dir)
yolov5_labels_test_dir os.path.join(yolov5_labels_dir, val/ )
if not os.path.isdir(yolov5_labels_test_dir):
os.mkdir(yolov5_labels_test_dir)
clear_hidden_files(yolov5_labels_test_dir)
train_file open(os.path.join(wd, yolov5_train.txt ), w )
test_file open(os.path.join(wd, yolov5_val.txt ), w )
train_file.close()
test_file.close()
train_file open(os.path.join(wd, yolov5_train.txt ), a )
test_file open(os.path.join(wd, yolov5_val.txt ), a )
list_imgs os.listdir(image_dir) # list image files
probo random.randint(1, 100)
print( Probobility: %d % probo)
for i in range(0,len(list_imgs)):
path os.path.join(image_dir,list_imgs[i])
if os.path.isfile(path):
image_path image_dir list_imgs[i]
voc_path list_imgs[i]
(nameWithoutExtention, extention) os.path.splitext(os.path.basename(image_path))
(voc_nameWithoutExtention, voc_extention) os.path.splitext(os.path.basename(voc_path))
annotation_name nameWithoutExtention .xml
annotation_path os.path.join(annotation_dir, annotation_name)
label_name nameWithoutExtention .txt
label_path os.path.join(yolo_labels_dir, label_name)
probo random.randint(1, 100)
print( Probobility: %d % probo)
if(probo 80): # train dataset
if os.path.exists(annotation_path):
train_file.write(image_path n )
convert_annotation(nameWithoutExtention) # convert label
copyfile(image_path, yolov5_images_train_dir voc_path)
copyfile(label_path, yolov5_labels_train_dir label_name)
else: # test dataset
if os.path.exists(annotation_path):
test_file.write(image_path n )
convert_annotation(nameWithoutExtention) # convert label
copyfile(image_path, yolov5_images_test_dir voc_path)
copyfile(label_path, yolov5_labels_test_dir label_name)
train_file.close()
test_file.close()
参考文章
