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labelme2yolo.py
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labelme2yolo.py
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'''
Created on Aug 18, 2021
@author: xiaosonh
'''
import os
import sys
import argparse
import shutil
import math
from collections import OrderedDict
import json
import cv2
import PIL.Image
from sklearn.model_selection import train_test_split
from labelme import utils
class Labelme2YOLO(object):
def __init__(self, json_dir, to_seg=False):
self._json_dir = json_dir
self._label_id_map = self._get_label_id_map(self._json_dir)
self._to_seg = to_seg
i = 'YOLODataset'
i += '_seg/' if to_seg else '/'
self._save_path_pfx = os.path.join(self._json_dir, i)
def _make_train_val_dir(self):
self._label_dir_path = os.path.join(self._save_path_pfx, 'labels/')
self._image_dir_path = os.path.join(self._save_path_pfx, 'images/')
for yolo_path in (os.path.join(self._label_dir_path + 'train/'),
os.path.join(self._label_dir_path + 'val/'),
os.path.join(self._image_dir_path + 'train/'),
os.path.join(self._image_dir_path + 'val/')):
if os.path.exists(yolo_path):
shutil.rmtree(yolo_path)
os.makedirs(yolo_path)
def _get_label_id_map(self, json_dir):
label_set = set()
for file_name in os.listdir(json_dir):
if file_name.endswith('json'):
json_path = os.path.join(json_dir, file_name)
data = json.load(open(json_path))
for shape in data['shapes']:
label_set.add(shape['label'])
return OrderedDict([(label, label_id) \
for label_id, label in enumerate(label_set)])
def _train_test_split(self, folders, json_names, val_size):
if len(folders) > 0 and 'train' in folders and 'val' in folders:
train_folder = os.path.join(self._json_dir, 'train/')
train_json_names = [train_sample_name + '.json' \
for train_sample_name in os.listdir(train_folder) \
if os.path.isdir(os.path.join(train_folder, train_sample_name))]
val_folder = os.path.join(self._json_dir, 'val/')
val_json_names = [val_sample_name + '.json' \
for val_sample_name in os.listdir(val_folder) \
if os.path.isdir(os.path.join(val_folder, val_sample_name))]
return train_json_names, val_json_names
train_idxs, val_idxs = train_test_split(range(len(json_names)),
test_size=val_size)
train_json_names = [json_names[train_idx] for train_idx in train_idxs]
val_json_names = [json_names[val_idx] for val_idx in val_idxs]
return train_json_names, val_json_names
def convert(self, val_size):
json_names = [file_name for file_name in os.listdir(self._json_dir) \
if os.path.isfile(os.path.join(self._json_dir, file_name)) and \
file_name.endswith('.json')]
folders = [file_name for file_name in os.listdir(self._json_dir) \
if os.path.isdir(os.path.join(self._json_dir, file_name))]
train_json_names, val_json_names = self._train_test_split(folders, json_names, val_size)
self._make_train_val_dir()
# convert labelme object to yolo format object, and save them to files
# also get image from labelme json file and save them under images folder
for target_dir, json_names in zip(('train/', 'val/'),
(train_json_names, val_json_names)):
for json_name in json_names:
json_path = os.path.join(self._json_dir, json_name)
json_data = json.load(open(json_path))
print('Converting %s for %s ...' % (json_name, target_dir.replace('/', '')))
img_path = self._save_yolo_image(json_data,
json_name,
self._image_dir_path,
target_dir)
yolo_obj_list = self._get_yolo_object_list(json_data, img_path)
self._save_yolo_label(json_name,
self._label_dir_path,
target_dir,
yolo_obj_list)
print('Generating dataset.yaml file ...')
self._save_dataset_yaml()
def convert_one(self, json_name):
json_path = os.path.join(self._json_dir, json_name)
json_data = json.load(open(json_path))
print('Converting %s ...' % json_name)
img_path = self._save_yolo_image(json_data, json_name,
self._json_dir, '')
yolo_obj_list = self._get_yolo_object_list(json_data, img_path)
self._save_yolo_label(json_name, self._json_dir,
'', yolo_obj_list)
def _get_yolo_object_list(self, json_data, img_path):
yolo_obj_list = []
img_h, img_w, _ = cv2.imread(img_path).shape
for shape in json_data['shapes']:
# labelme circle shape is different from others
# it only has 2 points, 1st is circle center, 2nd is drag end point
if shape['shape_type'] == 'circle':
yolo_obj = self._get_circle_shape_yolo_object(shape, img_h, img_w)
else:
yolo_obj = self._get_other_shape_yolo_object(shape, img_h, img_w)
yolo_obj_list.append(yolo_obj)
return yolo_obj_list
def _get_circle_shape_yolo_object(self, shape, img_h, img_w):
label_id = self._label_id_map[shape['label']]
obj_center_x, obj_center_y = shape['points'][0]
radius = math.sqrt((obj_center_x - shape['points'][1][0]) ** 2 +
(obj_center_y - shape['points'][1][1]) ** 2)
if self._to_seg:
retval = [label_id]
n_part = radius / 10
n_part = int(n_part) if n_part > 4 else 4
n_part2 = n_part << 1
pt_quad = [None for i in range(0, 4)]
pt_quad[0] = [[obj_center_x + math.cos(i * math.pi / n_part2) * radius,
obj_center_y - math.sin(i * math.pi / n_part2) * radius]
for i in range(1, n_part)]
pt_quad[1] = [[obj_center_x * 2 - x1, y1] for x1, y1 in pt_quad[0]]
pt_quad[1].reverse()
pt_quad[3] = [[x1, obj_center_y * 2 - y1] for x1, y1 in pt_quad[0]]
pt_quad[3].reverse()
pt_quad[2] = [[obj_center_x * 2 - x1, y1] for x1, y1 in pt_quad[3]]
pt_quad[2].reverse()
pt_quad[0].append([obj_center_x, obj_center_y - radius])
pt_quad[1].append([obj_center_x - radius, obj_center_y])
pt_quad[2].append([obj_center_x, obj_center_y + radius])
pt_quad[3].append([obj_center_x + radius, obj_center_y])
for i in pt_quad:
for j in i:
j[0] = round(float(j[0]) / img_w, 6)
j[1] = round(float(j[1]) / img_h, 6)
retval.extend(j)
return retval
obj_w = 2 * radius
obj_h = 2 * radius
yolo_center_x= round(float(obj_center_x / img_w), 6)
yolo_center_y = round(float(obj_center_y / img_h), 6)
yolo_w = round(float(obj_w / img_w), 6)
yolo_h = round(float(obj_h / img_h), 6)
return label_id, yolo_center_x, yolo_center_y, yolo_w, yolo_h
def _get_other_shape_yolo_object(self, shape, img_h, img_w):
label_id = self._label_id_map[shape['label']]
if self._to_seg:
retval = [label_id]
for i in shape['points']:
i[0] = round(float(i[0]) / img_w, 6)
i[1] = round(float(i[1]) / img_h, 6)
retval.extend(i)
return retval
def __get_object_desc(obj_port_list):
__get_dist = lambda int_list: max(int_list) - min(int_list)
x_lists = [port[0] for port in obj_port_list]
y_lists = [port[1] for port in obj_port_list]
return min(x_lists), __get_dist(x_lists), min(y_lists), __get_dist(y_lists)
obj_x_min, obj_w, obj_y_min, obj_h = __get_object_desc(shape['points'])
yolo_center_x= round(float((obj_x_min + obj_w / 2.0) / img_w), 6)
yolo_center_y = round(float((obj_y_min + obj_h / 2.0) / img_h), 6)
yolo_w = round(float(obj_w / img_w), 6)
yolo_h = round(float(obj_h / img_h), 6)
return label_id, yolo_center_x, yolo_center_y, yolo_w, yolo_h
def _save_yolo_label(self, json_name, label_dir_path, target_dir, yolo_obj_list):
txt_path = os.path.join(label_dir_path,
target_dir,
json_name.replace('.json', '.txt'))
with open(txt_path, 'w+') as f:
for yolo_obj_idx, yolo_obj in enumerate(yolo_obj_list):
yolo_obj_line = ""
for i in yolo_obj:
yolo_obj_line += f'{i} '
yolo_obj_line = yolo_obj_line[:-1]
if yolo_obj_idx != len(yolo_obj_list) - 1:
yolo_obj_line += '\n'
f.write(yolo_obj_line)
def _save_yolo_image(self, json_data, json_name, image_dir_path, target_dir):
img_name = json_name.replace('.json', '.png')
img_path = os.path.join(image_dir_path, target_dir,img_name)
if not os.path.exists(img_path):
img = utils.img_b64_to_arr(json_data['imageData'])
PIL.Image.fromarray(img).save(img_path)
return img_path
def _save_dataset_yaml(self):
yaml_path = os.path.join(self._save_path_pfx, 'dataset.yaml')
with open(yaml_path, 'w+') as yaml_file:
yaml_file.write('train: %s\n' % \
os.path.join(self._image_dir_path, 'train/'))
yaml_file.write('val: %s\n\n' % \
os.path.join(self._image_dir_path, 'val/'))
yaml_file.write('nc: %i\n\n' % len(self._label_id_map))
names_str = ''
for label, _ in self._label_id_map.items():
names_str += "'%s', " % label
names_str = names_str.rstrip(', ')
yaml_file.write('names: [%s]' % names_str)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--json_dir',type=str,
help='Please input the path of the labelme json files.')
parser.add_argument('--val_size',type=float, nargs='?', default=0.1,
help='Please input the validation dataset size, for example 0.1 ')
parser.add_argument('--json_name',type=str, nargs='?', default=None,
help='If you put json name, it would convert only one json file to YOLO.')
parser.add_argument('--seg', action='store_true',
help='Convert to YOLOv5 v7.0 segmentation dataset')
args = parser.parse_args(sys.argv[1:])
convertor = Labelme2YOLO(args.json_dir, to_seg=args.seg)
if args.json_name is None:
convertor.convert(val_size=args.val_size)
else:
convertor.convert_one(args.json_name)