def get_stats_pb(img_dir):
val = process_block.ValueComputeProcess(
DS_NAME, os.path.join(os.path.dirname(__file__), '../stats/builtin'),
os.path.join(os.path.dirname(__file__), '../stats/builtin/{}.npy').format(DS_NAME), func=get_stats).\
run(img_dir=img_dir).val
val_test = process_block.ValueComputeProcess(
DS_NAME+'_test', os.path.join(os.path.dirname(__file__), '../stats/builtin'),
os.path.join(os.path.dirname(__file__), '../stats/builtin/{}.npy'.format(DS_NAME)), func=get_stats).\
run(img_dir=img_dir, is_test=True).val
return val, val_test
def get_dataset_stats(ds_name,
img_dir,
load_func=None,
file_list=None,
mean_val=([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])):
if ds_name == 'inria':
from data.inria import preprocess
val = preprocess.get_stats_pb(img_dir)[0]
print('Use {} mean std stats: {}'.format(ds_name, val))
elif ds_name == 'deepglobe':
from data.deepglobe import preprocess
val = preprocess.get_stats_pb(img_dir)[0]
print('Use {} mean std stats: {}'.format(ds_name, val))
elif ds_name == 'deepgloberoad':
from data.deepgloberoad import preprocess
val = preprocess.get_stats_pb(img_dir)[0]
print('Use {} mean std stats: {}'.format(ds_name, val))
elif ds_name == 'deepglobeland':
from data.deepglobeland import preprocess
val = preprocess.get_stats_pb(img_dir)[0]
print('Use {} mean std stats: {}'.format(ds_name, val))
elif ds_name == 'mnih':
from data.mnih import preprocess
val = preprocess.get_stats_pb(img_dir)[0]
print('Use {} mean std stats: {}'.format(ds_name, val))
elif ds_name == 'spca':
from data.spca import preprocess
val = preprocess.get_stats_pb(img_dir)[0]
print('Use {} mean std stats: {}'.format(ds_name, val))
elif load_func:
try:
val = process_block.ValueComputeProcess(
ds_name, os.path.join(os.path.dirname(__file__), '../data/stats/custom'),
os.path.join(os.path.dirname(__file__), '../data/stats/custom/{}.npy'.format(ds_name)),
func=load_func). \
run(img_dir=img_dir, file_list=file_list).val
print('Use {} mean std stats: {}'.format(ds_name, val))
except ValueError:
print(
'Dataset {} is not supported, use default mean stats instead'.
format(ds_name))
return np.array(mean_val)
else:
try:
val = misc_utils.load_file(
os.path.join(
os.path.realpath(
os.path.join(os.path.dirname(__file__), '..')),
'data/stats/custom/{}.npy'.format(ds_name)))
print('Use {} mean std stats: {}'.format(ds_name, val))
except (FileNotFoundError, OSError):
print(
'Dataset {} is not supported, use default mean stats instead {}'
.format(ds_name, mean_val))
return np.array(mean_val)
return val[0, :], val[1, :]
val = process_block.ValueComputeProcess(
DS_NAME, os.path.join(os.path.dirname(__file__), '../stats/builtin'),
os.path.join(os.path.dirname(__file__), '../stats/builtin/{}.npy'.format(DS_NAME)), func=get_stats).\
run(img_dir=img_dir).val
val_test = val
return val, val_test
def get_images(data_dir):
record_file_valid = os.path.join(data_dir, 'file_list_valid.txt')
file_list = misc_utils.load_file(record_file_valid)
rgb_files, gt_files = [], []
for line in file_list:
rgb_file, gt_file = line.strip().split(' ')
rgb_files.append(os.path.join(data_dir, 'patches', rgb_file))
gt_files.append(os.path.join(data_dir, 'patches', gt_file))
return rgb_files, gt_files
if __name__ == '__main__':
# save_dir = os.path.join(r'/hdd/mrs/deepglobe', 'pd{}_ol{}'.format(0, 0))
# train, valid = get_image_gt(r'/hdd/deepglobe', ['Vegas', 'Paris', 'Shanghai', 'Khartoum'], valid_percent=0.4)
# make_dataset(train, valid, save_dir)
val = process_block.ValueComputeProcess(
'DeepGlobe_cmatch', os.path.join(os.path.dirname(__file__), '../stats/custom'),
os.path.join(os.path.dirname(__file__), '../stats/custom/{}_cmatch.npy'.format(DS_NAME)), func=get_stats). \
run(img_dir='/hdd/mrs/deepglobe_cmatch/14p_pd0_ol0/patches').val
val_test = val
print(val)
if city_name in VAL_CITY and tile_id in VAL_IDS:
rgb_files.append(rgb_filename)
gt_files.append(gt_filename)
else:
# FIXME adhoc solution to make rgb and gt files have the same length
rgb_files.append(rgb_filename)
gt_files.append(rgb_filename)
return rgb_files, gt_files
if __name__ == '__main__':
'''ps = 512
pd = 0
ol = 0
save_dir = os.path.join(r'/hdd/mrs/inria', 'ps{}_pd{}_ol{}'.format(ps, pd, ol))
misc_utils.make_dir_if_not_exist(save_dir)
patch_inria(data_dir=r'/media/ei-edl01/data/remote_sensing_data/inria',
save_dir=save_dir,
patch_size=(ps, ps),
pad=pd, overlap=ol)'''
func = get_stats
from mrs_utils import process_block
pb = process_block.ValueComputeProcess(DS_NAME,
'../stats'.format(DS_NAME),
'../stats/{}.npy'.format(DS_NAME),
func=func)
mean_val = pb.run(
img_dir=r'/media/ei-edl01/data/remote_sensing_data/inria/images').val
print(mean_val)