update export data format: yolo polygon format
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greatx
parent
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commit
639cbcad0d
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@ -8,6 +8,11 @@
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Help converting LabelMe Annotation Tool JSON format to YOLO text file format.
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If you've already marked your segmentation dataset by LabelMe, it's easy to use this tool to help converting to YOLO format dataset.
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---------
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## New
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- export data as yolo polygon annotation (for YOLOv5 v7.0 segmentation)
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## Parameters Explain
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**--json_dir** LabelMe JSON files folder path.
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@ -1,4 +1,4 @@
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# SPDX-FileCopyrightText: 2022-present Wang Xin <xinwang614@gmail.com>
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#
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# SPDX-License-Identifier: MIT
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__version__ = '0.0.1'
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__version__ = '0.0.5'
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@ -123,214 +123,211 @@ def apply_exif_orientation(image):
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else:
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return image
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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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def _make_train_val_dir(self):
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self._label_dir_path = os.path.join(self._json_dir,
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self._label_dir_path = os.path.join(self._json_dir,
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'YOLODataset/labels/')
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self._image_dir_path = os.path.join(self._json_dir,
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self._image_dir_path = os.path.join(self._json_dir,
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'YOLODataset/images/')
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for yolo_path in (os.path.join(self._label_dir_path + 'train/'),
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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._label_dir_path + 'test/'),
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os.path.join(self._image_dir_path + 'train/'),
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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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os.path.join(self._image_dir_path + 'test/')):
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if os.path.exists(yolo_path):
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shutil.rmtree(yolo_path)
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os.makedirs(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 OrderedDict([(label, label_id) \
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return OrderedDict([(label, label_id)
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for label_id, label in enumerate(label_set)])
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def _train_test_split(self, folders, json_names, val_size, test_size):
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if len(folders) > 0 and 'train' in folders and 'val' in folders and 'test' 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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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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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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test_folder = os.path.join(self._json_dir, 'test/')
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test_json_names = [test_sample_name + '.json' \
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for test_sample_name in os.listdir(test_folder) \
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if os.path.isdir(os.path.join(test_folder, test_sample_name))]
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test_json_names = [test_sample_name + '.json'
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for test_sample_name in os.listdir(test_folder)
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if os.path.isdir(os.path.join(test_folder, test_sample_name))]
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return train_json_names, val_json_names, test_json_names
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train_idxs, val_idxs = train_test_split(range(len(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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tmp_train_len = len(train_idxs)
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test_idxs = []
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if test_size > 1e-8:
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train_idxs, test_idxs = train_test_split(range(tmp_train_len), test_size=test_size / (1 - val_size))
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train_idxs, test_idxs = train_test_split(
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range(tmp_train_len), test_size=test_size / (1 - 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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test_json_names = [json_names[test_idx] for test_idx in test_idxs]
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return train_json_names, val_json_names, test_json_names
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def convert(self, val_size, test_size):
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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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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, test_json_names = self._train_test_split(folders, json_names, val_size, test_size)
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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, test_json_names = self._train_test_split(
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folders, json_names, val_size, test_size)
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self._make_train_val_dir()
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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_names in zip(('train/', 'val/', 'test/'),
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for target_dir, json_names in zip(('train/', 'val/', 'test/'),
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(train_json_names, val_json_names, test_json_names)):
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pool = Pool(os.cpu_count() - 1)
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for json_name in json_names:
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pool.apply_async(self.covert_json_to_text, args=(target_dir, json_name))
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pool.apply_async(self.covert_json_to_text,
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args=(target_dir, json_name))
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pool.close()
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pool.join()
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print('Generating dataset.yaml file ...')
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self._save_dataset_yaml()
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def covert_json_to_text(self, target_dir, json_name):
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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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print('Converting %s for %s ...' % (json_name, target_dir.replace('/', '')))
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img_path = self._save_yolo_image(json_data,
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json_name,
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self._image_dir_path,
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target_dir)
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print('Converting %s for %s ...' %
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(json_name, target_dir.replace('/', '')))
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img_path = self._save_yolo_image(json_data,
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json_name,
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self._image_dir_path,
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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_name,
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self._label_dir_path,
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target_dir,
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yolo_obj_list)
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self._save_yolo_label(json_name,
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self._label_dir_path,
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target_dir,
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yolo_obj_list)
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def convert_one(self, json_name):
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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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print('Converting %s ...' % json_name)
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img_path = self._save_yolo_image(json_data, json_name,
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img_path = self._save_yolo_image(json_data, json_name,
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self._json_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_name, self._json_dir,
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self._save_yolo_label(json_name, self._json_dir,
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'', yolo_obj_list)
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def _get_yolo_object_list(self, json_data, img_path):
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yolo_obj_list = []
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img_h, img_w, _ = cv2.imread(img_path).shape
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for shape in json_data['shapes']:
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# labelme circle shape is different from others
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# it only has 2 points, 1st is circle center, 2nd is drag end point
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if shape['shape_type'] == 'circle':
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yolo_obj = self._get_circle_shape_yolo_object(shape, img_h, img_w)
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yolo_obj = self._get_circle_shape_yolo_object(
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shape, img_h, img_w)
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else:
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yolo_obj = self._get_other_shape_yolo_object(shape, img_h, img_w)
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yolo_obj = self._get_other_shape_yolo_object(
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shape, img_h, img_w)
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yolo_obj_list.append(yolo_obj)
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return yolo_obj_list
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def _get_circle_shape_yolo_object(self, shape, img_h, img_w):
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obj_center_x, obj_center_y = shape['points'][0]
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radius = math.sqrt((obj_center_x - shape['points'][1][0]) ** 2 +
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radius = math.sqrt((obj_center_x - shape['points'][1][0]) ** 2 +
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(obj_center_y - shape['points'][1][1]) ** 2)
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obj_w = 2 * radius
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obj_h = 2 * radius
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yolo_center_x= round(float(obj_center_x / img_w), 6)
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yolo_center_x = round(float(obj_center_x / img_w), 6)
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yolo_center_y = round(float(obj_center_y / 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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return label_id, yolo_center_x, yolo_center_y, yolo_w, yolo_h
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def _get_other_shape_yolo_object(self, shape, img_h, img_w):
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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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obj_x_min, obj_w, obj_y_min, obj_h = __get_object_desc(shape['points'])
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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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point_list = shape['points']
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points = np.zeros(2 * len(point_list))
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points[::2] = [float(point[0]) / img_w for point in point_list]
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points[1::2] = [float(point[1]) / img_h for point in point_list]
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label_id = self._label_id_map[shape['label']]
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return label_id, yolo_center_x, yolo_center_y, yolo_w, yolo_h
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return (label_id, points.tolist())
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def _save_yolo_label(self, json_name, label_dir_path, target_dir, yolo_obj_list):
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txt_path = os.path.join(label_dir_path,
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target_dir,
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txt_path = os.path.join(label_dir_path,
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target_dir,
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json_name.replace('.json', '.txt'))
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with open(txt_path, 'w+') as f:
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for yolo_obj_idx, yolo_obj in enumerate(yolo_obj_list):
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yolo_obj_line = '%s %s %s %s %s\n' % yolo_obj \
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if yolo_obj_idx + 1 != len(yolo_obj_list) else \
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'%s %s %s %s %s' % yolo_obj
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for yolo_obj in yolo_obj_list:
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label, points = yolo_obj
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points = [str(item) for item in points]
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yolo_obj_line = f"{label} {' '.join(points)}\n"
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f.write(yolo_obj_line)
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def _save_yolo_image(self, json_data, json_name, image_dir_path, target_dir):
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img_name = json_name.replace('.json', '.png')
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img_path = os.path.join(image_dir_path, target_dir,img_name)
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img_path = os.path.join(image_dir_path, target_dir, img_name)
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if not os.path.exists(img_path):
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img = img_b64_to_arr(json_data['imageData'])
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PIL.Image.fromarray(img).save(img_path)
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return img_path
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def _save_dataset_yaml(self):
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yaml_path = os.path.join(self._json_dir, 'YOLODataset/', 'dataset.yaml')
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yaml_path = os.path.join(
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self._json_dir, 'YOLODataset/', 'dataset.yaml')
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with open(yaml_path, 'w+') as yaml_file:
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yaml_file.write('train: %s\n' % \
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yaml_file.write('train: %s\n' %
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os.path.join(self._image_dir_path, 'train/'))
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yaml_file.write('val: %s\n\n' % \
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yaml_file.write('val: %s\n' %
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os.path.join(self._image_dir_path, 'val/'))
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yaml_file.write('test: %s\n\n' % \
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yaml_file.write('test: %s\n' %
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os.path.join(self._image_dir_path, 'test/'))
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yaml_file.write('nc: %i\n\n' % len(self._label_id_map))
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yaml_file.write('nc: %i\n' % len(self._label_id_map))
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names_str = ''
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for label, _ in self._label_id_map.items():
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names_str += "'%s', " % label
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