Create labelme2yolo.py
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rooneysh
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'''
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Created on Aug 18, 2021
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@author: xiaosonh
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'''
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import os
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import sys
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import argparse
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import json
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import cv2
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import PIL.Image
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from sklearn.model_selection import train_test_split
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from labelme import utils
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class Labelme2YOLO(object):
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def __init__(self, json_dir):
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self._json_dir = json_dir
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self._label_id_map = self._get_label_id_map(self._json_dir)
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self._label_dir_path = os.path.join(json_dir, 'YOLODataset/labels/')
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self._image_dir_path = os.path.join(json_dir, 'YOLODataset/images/')
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self._make_train_val_dir(self._json_dir)
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def _make_train_val_dir(self):
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for yolo_path in (os.path.join(self._label_dir_path + 'train/'),
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os.path.join(self._label_dir_path + 'val/'),
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os.path.join(self._image_dir_path + 'train/'),
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os.path.join(self._image_dir_path + 'val/')):
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if not os.path.exists(yolo_path):
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os.makedirs(yolo_path)
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def _get_label_id_map(self, json_dir):
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label_set = set()
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for file_name in os.listdir(json_dir):
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if file_name.endswith('json'):
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json_path = os.path.join(json_dir, file_name)
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data = json.load(open(json_path))
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for shape in data['shapes']:
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label_set.add(shape['label'])
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return {label: label_id for label_id, label in enumerate(label_set)}
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def _train_test_split(self, folders, json_names, val_size):
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if len(folders) > 0 and 'train' in folders and 'val' in folders:
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train_folder = os.path.join(self._json_dir, 'train/')
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train_json_names = [train_sample_name + '.json' \
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for train_sample_name in os.listdir(train_folder) \
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if os.path.isdir(os.path.join(train_folder, train_sample_name))]
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val_folder = os.path.join(self._json_dir, 'val/')
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val_json_names = [val_sample_name + '.json' \
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for val_sample_name in os.listdir(val_folder) \
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if os.path.isdir(os.path.join(val_folder, val_sample_name))]
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return train_json_names, val_json_names
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train_idxs, val_idxs = train_test_split(range(len(json_names)),
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test_size=val_size)
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train_json_names = [json_names[train_idx] for train_idx in train_idxs]
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val_json_names = [json_names[val_idx] for val_idx in val_idxs]
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return train_json_names, val_json_names
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def convert(self, val_size=0.2):
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json_names = [file_name for file_name in os.listdir(self._json_dir) \
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if os.path.isfile(os.path.join(self._json_dir, file_name)) and \
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file_name.endswith('.json')]
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folders = [file_name for file_name in os.listdir(self._json_dir) \
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if os.path.isdir(os.path.join(self._json_dir, file_name))]
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train_json_names, val_json_names = self._train_test_split(folders, json_names, val_size)
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# convert labelme object to yolo format object, and save them to files
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# also get image from labelme json file and save them under images folder
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for target_dir, json_name in zip(('train/', 'val/'), (train_json_names, val_json_names)):
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json_path = os.path.join(self._json_dir, json_name)
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json_data = json.load(open(json_path))
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img_path = self._save_yolo_image(json_data, json_name, target_dir)
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yolo_obj_list = self._get_yolo_object_list(json_data, img_path)
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self._save_yolo_label(json_path, target_dir, yolo_obj_list)
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def _get_yolo_object_list(self, json_data, img_path):
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def __get_object_desc(obj_port_list):
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__get_dist = lambda int_list: max(int_list) - min(int_list)
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x_lists = [port[0] for port in obj_port_list]
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y_lists = [port[1] for port in obj_port_list]
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return min(x_lists), __get_dist(x_lists), min(y_lists), __get_dist(y_lists)
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yolo_obj_list = []
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for shape in json_data['shapes']:
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obj_x_min, obj_w, obj_y_min, obj_h = __get_object_desc(shape['points'])
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img_h, img_w, _ = cv2.imread(img_path).shape
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yolo_center_x= round(float((obj_x_min + obj_w / 2.0) / img_w), 6)
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yolo_center_y = round(float((obj_y_min + obj_h / 2.0) / img_h), 6)
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yolo__w = round(float(obj_w / img_w), 6)
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yolo_h = round(float(obj_h / img_h), 6)
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label_id = self._label_id_map[shape['label']]
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yolo_obj_list.append((label_id, yolo_center_x, yolo_center_y, yolo__w, yolo_h))
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return yolo_obj_list
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def _save_yolo_label(self, json_path, yolo_obj_list):
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txt_path = json_path.replace('.json', '.text')
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with open(txt_path, 'w+') as f:
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for yolo_obj in yolo_obj_list:
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f.write('%s %s %s %s %s\n' % yolo_obj)
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def _save_yolo_image(self, json_data, json_name, target_dir):
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img = utils.img_b64_to_arr(json_data['imageData'])
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img_name = json_name.replace('.json', '.png')
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img_path = os.path.join(self._image_dir_path, target_dir,img_name )
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PIL.Image.fromarray(img).save(img_path)
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return img_path
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if __name__ == '__main__':
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argv = sys.argv[1:]
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parser = argparse.ArgumentParser()
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parser.add_argument('--json_dir',type=str,
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help='Please input the path of the labelme json files.')
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parser.add_argument('--val_size',type=str,
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help='Please input the validation dataset size, for example 0.1 ')
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json_dir, val_size = parser.parse_args(argv)
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convertor = Labelme2YOLO(json_dir)
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convertor.convert(val_size=val_size)
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