File size: 3,993 Bytes
609dae8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 | import os , os.path, glob, shutil, json, argparse
import pathlib
"""
script to sort images of dataset, depending on what you want to do.
to run this script you have to give the split-type and a output directory.
you can create the following splits:
- train_test_split | used training test split from the paper
- wear_dev_split | ideal for analyse wear developments
- type_split | split by BSD type
Example:
python sort_dataset.py --split_type=train_test_split \
--output_dir=BSD_split
"""
def mogli(split_type, output_dir):
# paths
path_data = './data/'
path_label = './label/'
image_data = 'image_data.json'
path_data = pathlib.Path(path_data)
path_label = pathlib.Path(path_label)
path_output_dir = pathlib.Path(output_dir)
path_image_data = pathlib.Path(image_data)
data_paths_sorted = sorted(list(path_data.glob("*.jpg")))
label_paths_sorted = sorted(list(path_label.glob("*.json")))
# open json
with open(path_image_data, 'r') as f:
dict_data = json.load(f)
# check output
if not str(path_output_dir).endswith('/'):
path_output_dir = str(path_output_dir) + '/'
else:
path_output_dir = str(path_output_dir)
# run through image-data
for i in range(len(dict_data['images'])):
filename = dict_data['images'][i]['filename']
split = dict_data['images'][i]['train_test_split']
type = dict_data['images'][i]['type']
wear_dev = dict_data['images'][i]['wear_dev']
annotations = dict_data['images'][i]['annotations']
# search for filename in path_data
for file in data_paths_sorted:
filename_data = os.path.basename(file)
if filename_data == filename:
if split_type == 'wear_dev_split':
wear_dev_split(file, path_output_dir, wear_dev)
elif split_type == 'train_test_split':
train_test_split(file, path_output_dir, split)
elif split_type == 'type_split':
type_split(file, path_output_dir, type)
# search for label in path_label
for file in label_paths_sorted:
filename_data = os.path.basename(file)
if filename_data == annotations:
if split_type == 'wear_dev_split':
wear_dev_split(file, path_output_dir, wear_dev)
elif split_type == 'train_test_split':
train_test_split(file, path_output_dir, split)
elif split_type == 'type_split':
type_split(file, path_output_dir, type)
def wear_dev_split(input, output, wear_dev):
for i in range(len(wear_dev)):
folder = output + wear_dev[i]
if not os.path.exists(folder):
os.makedirs(folder)
shutil.copy2(input, folder)
def train_test_split(input, output, split):
folder1 = str(output) + 'train'
folder2 = str(output) + 'test'
if not os.path.exists(folder1):
os.makedirs(folder1)
if not os.path.exists(folder2):
os.makedirs(folder2)
if split == 'train':
shutil.copy2(input, folder1)
elif split == 'test':
shutil.copy2(input, folder2)
def type_split(input, output, type):
folder1 = str(output) + 'BSD_type_1'
folder2 = str(output) + 'BSD_type_2'
if not os.path.exists(folder1):
os.makedirs(folder1)
if not os.path.exists(folder2):
os.makedirs(folder2)
if type == 1:
shutil.copy2(input, folder1)
elif type == 2:
shutil.copy2(input, folder2)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--split_type', help='split type: wear_dev_split | train_test_split | type_split', type=str, required=True)
parser.add_argument('--output_dir', help='ouput_dir', type=str, required=True)
opt = parser.parse_args()
mogli(opt.split_type, opt.output_dir)
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