def prepare_multi_scale_data(src_path, dst_path, gap=200, subsize=1024, scales=[0.5, 1.0, 1.5], num_process=32):
dst_trainval_path = osp.join(dst_path, 'trainval_split')
dst_test_base_path = osp.join(dst_path, 'test_split')
dst_test_path = osp.join(dst_path, 'test_split/images')
# make dst path if not exist
mkdir_if_not_exists(dst_path)
mkdir_if_not_exists(dst_trainval_path)
mkdir_if_not_exists(dst_test_base_path)
mkdir_if_not_exists(dst_test_path)
# split train data
print('split train data')
split_train = splitbase_trainval(osp.join(src_path, 'train'), dst_trainval_path,
gap=gap, subsize=subsize, num_process=num_process)
for scale in scales:
split_train.splitdata(scale)
print('split val data')
# split val data
split_val = splitbase_trainval(osp.join(src_path, 'val'), dst_trainval_path,
gap=gap, subsize=subsize, num_process=num_process)
for scale in scales:
split_val.splitdata(scale)
# split test data
print('split test data')
split_test = splitbase_test(osp.join(src_path, 'test/images'), dst_test_path,
gap=gap, subsize=subsize, num_process=num_process)
for scale in scales:
split_test.splitdata(scale)
convert_dota_to_mmdet(dst_trainval_path,
osp.join(dst_trainval_path, 'trainval_s2anet.pkl'))
convert_dota_to_mmdet(dst_test_base_path,
osp.join(dst_test_base_path, 'test_s2anet.pkl'), trainval=False)
print('done!')
def prepare_multi_scale_data(src_path,
dst_path,
gap=200,
subsize=1024,
scales=[0.5, 1.0, 1.5],
num_process=32):
"""Prepare DOTA split data and labels
Args:
src_path: dataset path
dst_path: output path
gap: overlapping area
subsize: size of chip image
scales: multi-scale settings
num_process: num of processer
"""
dst_train_path = osp.join(dst_path, 'train_split')
dst_val_path = osp.join(dst_path, 'val_split')
dst_trainval_path = osp.join(dst_path, 'trainval_split')
dst_test_base_path = osp.join(dst_path, 'test_split')
dst_test_path = osp.join(dst_path, 'test_split/images')
# make dst path if not exist
mkdir_if_not_exists(dst_path)
mkdir_if_not_exists(dst_train_path)
mkdir_if_not_exists(dst_val_path)
mkdir_if_not_exists(dst_test_base_path)
mkdir_if_not_exists(dst_test_path)
# split train data
print('split train data')
split_train = splitbase_trainval(osp.join(src_path, 'train'),
dst_train_path,
gap=gap,
subsize=subsize,
num_process=num_process)
for scale in scales:
split_train.splitdata(scale)
print('split val data')
# split val data
split_val = splitbase_trainval(osp.join(src_path, 'val'),
dst_val_path,
gap=gap,
subsize=subsize,
num_process=num_process)
for scale in scales:
split_val.splitdata(scale)
# split test data
print('split test data')
split_test = splitbase_test(osp.join(src_path, 'test/images'),
dst_test_path,
gap=gap,
subsize=subsize,
num_process=num_process)
for scale in scales:
split_test.splitdata(scale)
# prepare trainval data
print('move train val to trainval')
mkdir_if_not_exists(dst_trainval_path)
os.system('mv {}/* {}'.format(dst_train_path, dst_trainval_path))
os.system('find ' + dst_val_path + '/images/ -name "*.png" -exec mv {} ' +
dst_trainval_path + '/images/ \\;')
os.system('find ' + dst_val_path +
'/labelTxt/ -name "*.txt" -exec mv {} ' + dst_trainval_path +
'/labelTxt/ \\;')
print('generate labels with json format')
generate_json_labels(dst_trainval_path,
osp.join(dst_trainval_path, 'trainval.json'))
generate_json_labels(dst_test_base_path,
osp.join(dst_test_base_path, 'test.json'),
trainval=False)
print('generate labels with coco format')
DOTA2COCOTrain(dst_trainval_path,
osp.join(dst_trainval_path, 'trainval_coco.json'),
wordname_15)
DOTA2COCOTest(dst_test_base_path,
osp.join(dst_test_base_path, 'test_coco.json'), wordname_15)