| import argparse |
| import numpy as np |
| import glob |
| import os |
| from xml.dom import minidom |
| import shutil |
|
|
| np.random.seed(42069) |
|
|
| parser = argparse.ArgumentParser() |
| parser.add_argument("--train", type=int, default=80, help="Percentage of training dataset") |
| parser.add_argument("--test", type=int, default=10, help="Percentage of test dataset") |
| parser.add_argument("--val", type=int, default=10, help="Percentage of validation dataset") |
| parser.add_argument("-m", "--move", action='store_true', default=False, help="Move files to dataset instead of copying") |
| parser.add_argument("input_dir") |
| parser.add_argument("output_dir") |
|
|
| args = parser.parse_args() |
|
|
| if not (args.train + args.test + args.val) == 100: |
| raise ValueError("Train, Test and Validation percentage should add up to 100") |
|
|
| class_names = os.listdir(args.input_dir) |
| os.mkdir(args.output_dir) |
|
|
| for class_name in class_names: |
| dataset_class_dir = os.path.join(args.input_dir, class_name) |
| dataset_class_dir_output = os.path.join(args.output_dir, class_name) |
|
|
| if not os.path.isdir(dataset_class_dir): |
| continue |
| dataset_class_dir = os.path.join(args.input_dir, class_name) |
| dataset_class_dir_output = os.path.join(args.output_dir, class_name) |
| |
| os.mkdir(dataset_class_dir_output) |
| annotations = {} |
| doc = minidom.parse(os.path.join(dataset_class_dir, "annotations.xml")) |
| images = doc.getElementsByTagName('image') |
|
|
| for image in images: |
| image_name = image.attributes['name'].value |
| annotations[image_name] = image |
| |
| image_names = list(annotations.keys()) |
| np.random.shuffle(image_names) |
| |
| train_no = int(round((args.train / 100) * len(image_names))) |
| assert(train_no > 0) |
|
|
| valid_no = int(round((args.val / 100) * len(image_names))) |
| assert(valid_no > 0) |
|
|
| test_no = len(image_names) - (train_no + valid_no) |
| assert(test_no > 0) |
|
|
| train_files = image_names[:train_no] |
| valid_files = image_names[train_no:train_no + valid_no] |
| test_files = image_names[train_no + valid_no:] |
|
|
|
|
| for subset, arr in [ ("train", train_files), ("test", test_files), ("val", valid_files) ]: |
| os.mkdir(os.path.join(dataset_class_dir_output, subset)) |
| annotation_xml = minidom.Document() |
| annotations_root = annotation_xml.createElement('annotations') |
| annotation_xml.appendChild(annotations_root) |
|
|
| for f in arr: |
| annotations_root.appendChild(annotations[f]) |
| if not args.move: |
| shutil.copy2(os.path.join(dataset_class_dir, f), os.path.join(dataset_class_dir_output, subset, f)) |
| else: |
| shutil.move(os.path.join(dataset_class_dir, f), os.path.join(dataset_class_dir_output, subset, f)) |
| xml_str = annotation_xml.toprettyxml(indent="\t") |
|
|
| with open(os.path.join(dataset_class_dir_output, subset, "annotations.xml"), 'w') as f: |
| f.write(xml_str) |
