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voc_annotation.py
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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 = 0
# -------------------------------------------------------------------#
# 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/DOTA_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)
# -------------------------------------------------------#
# 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_DOTA_path = 'dataset'
VOCdevkit_DOTA_sets = [('2007', 'train'), ('2007', 'val')]
def convert_annotation(year, image_id, list_file,image_set):
# in_file = open(os.path.join(VOCdevkit_path, 'VOC%s/Annotations/%s.xml' % (year, image_id)), encoding='utf-8')
in_file = open(os.path.join(VOCdevkit_DOTA_path, 'DOTA_V1.0/%s/labelTxt-v1.0/xml/%s.xml' % (image_set, 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')
# 生成dota的txt
train_xmlfilepath = os.path.join(VOCdevkit_DOTA_path,'DOTA_V1.0/train/labelTxt-v1.0/xml')
val_xmlfilepath = os.path.join(VOCdevkit_DOTA_path,'DOTA_V1.0/val/labelTxt-v1.0/xml')
test_imgnamepath = os.path.join(VOCdevkit_DOTA_path,'DOTA_V1.0/test/images')
# xmlfilepath = os.path.join(VOCdevkit_DOTA_path, 'DOTA_V1.0/train/labelTxt-v1.0/xml')
saveBasePath = os.path.join(VOCdevkit_path, 'VOC2007/ImageSets/Main')
temp_train_xml = os.listdir(train_xmlfilepath)
train_xml = []
for xml in temp_train_xml:
if xml.endswith(".xml"):
train_xml.append(xml)
temp_val_xml = os.listdir(val_xmlfilepath)
val_xml = []
for xml in temp_val_xml:
if xml.endswith(".xml"):
val_xml.append(xml)
temp_test_name = os.listdir(test_imgnamepath)
test_imgname = []
for img in temp_test_name:
if img.endswith(".png"):
test_imgname.append(img)
num_train = len(train_xml)
list_train = range(num_train)
num_val = len(val_xml)
list_val = range(num_val)
num_test = len(test_imgname)
list_test = range(num_test)
num_trainval = num_train+num_val
# 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_train:
name = train_xml[i][:-4] + '\n'
ftrain.write(name)
for i in list_val:
name = val_xml[i][:-4] + '\n'
fval.write(name)
for i in list_test:
name = test_imgname[i][:-4] + '\n'
ftest.write(name)
for i in list_train:
name = train_xml[i][:-4] + '\n'
ftrainval.write(name)
for i in list_val:
name = val_xml[i][:-4] + '\n'
ftrainval.write(name)
# 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))
list_file.write('%s/DOTA_V1.0/%s/images/%s.png' % (os.path.abspath(VOCdevkit_DOTA_path), image_set, image_id))
convert_annotation(year, image_id, list_file,image_set)
list_file.write('\n')
list_file.close()
print("Generate 2007_train.txt and 2007_val.txt for train done.")