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labelme2coco.py
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import os
import sys
import glob
import json
import shutil
import argparse
import numpy as np
from tqdm import tqdm
from labelme import utils
from sklearn.model_selection import train_test_split
class Labelme2coco():
def __init__(self, args):
self.classname_to_id = {args.class_name: 1}
self.images = []
self.annotations = []
self.categories = []
self.ann_id = 0
def save_coco_json(self, instance, save_path):
json.dump(instance, open(save_path, 'w', encoding='utf-8'), ensure_ascii=False, indent=1)
def read_jsonfile(self, path):
with open(path, "r", encoding='utf-8') as f:
return json.load(f)
def _get_box(self, points):
min_x = min_y = np.inf
max_x = max_y = 0
for x, y in points:
min_x = min(min_x, x)
min_y = min(min_y, y)
max_x = max(max_x, x)
max_y = max(max_y, y)
return [min_x, min_y, max_x - min_x, max_y - min_y]
def _get_keypoints(self, points, keypoints, num_keypoints):
if points[0] == 0 and points[1] == 0:
visable = 0
else:
visable = 2
num_keypoints += 1
keypoints.extend([points[0], points[1], visable])
return keypoints, num_keypoints
def _image(self, obj, path):
image = {}
img_x = utils.img_b64_to_arr(obj['imageData'])
image['height'], image['width'] = img_x.shape[:-1]
self.img_id = int(os.path.basename(path).split(".json")[0])
image['id'] = self.img_id
image['file_name'] = os.path.basename(path).replace(".json", ".jpg")
return image
def _annotation(self, bboxes_list, keypoints_list, json_path):
if len(keypoints_list) != args.join_num * len(bboxes_list):
print('you loss {} keypoint(s) with file {}'.format(args.join_num * len(bboxes_list) - len(keypoints_list), json_path))
print('Please check ！！！')
sys.exit()
i = 0
for object in bboxes_list:
annotation = {}
keypoints = []
num_keypoints = 0
label = object['label']
bbox = object['points']
annotation['id'] = self.ann_id
annotation['image_id'] = self.img_id
annotation['category_id'] = int(self.classname_to_id[label])
annotation['iscrowd'] = 0
annotation['area'] = 1.0
annotation['segmentation'] = [np.asarray(bbox).flatten().tolist()]
annotation['bbox'] = self._get_box(bbox)
for keypoint in keypoints_list[i * args.join_num: (i + 1) * args.join_num]:
point = keypoint['points']
annotation['keypoints'], num_keypoints = self._get_keypoints(point[0], keypoints, num_keypoints)
annotation['num_keypoints'] = num_keypoints
i += 1
self.ann_id += 1
self.annotations.append(annotation)
def _init_categories(self):
for name, id in self.classname_to_id.items():
category = {}
category['supercategory'] = name
category['id'] = id
category['name'] = name
category['keypoint'] = ['left_top', 'right_top', 'right_bottom', 'left_bottom']
# category['keypoint'] = [str(i + 1) for i in range(args.join_num)]
self.categories.append(category)
def to_coco(self, json_path_list):
self._init_categories()
for json_path in tqdm(json_path_list):
obj = self.read_jsonfile(json_path)
self.images.append(self._image(obj, json_path))
shapes = obj['shapes']
bboxes_list, keypoints_list = [], []
for shape in shapes:
if shape['shape_type'] == 'rectangle':
bboxes_list.append(shape)
elif shape['shape_type'] == 'point':
keypoints_list.append(shape)
self._annotation(bboxes_list, keypoints_list, json_path)
keypoints = {}
keypoints['info'] = {'description': 'Monitor Dataset', 'version': 1.0, 'year': 2020}
keypoints['license'] = ['Acer']
keypoints['images'] = self.images
keypoints['annotations'] = self.annotations
keypoints['categories'] = self.categories
return keypoints
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument("--class_name", "--n", help="class name", type=str, required=True)
parser.add_argument("--input", "--i", help="json file path (labelme)", type=str, required=True)
parser.add_argument("--output", "--o", help="output file path (coco format)", type=str, required=True)
parser.add_argument("--join_num", "--j", help="number of join", type=int, required=True)
parser.add_argument("--ratio", "--r", help="train and test split ratio", type=float, default=0.12)
args = parser.parse_args()
labelme_path = args.input
saved_coco_path = args.output
if not os.path.exists("%scoco/annotations/"%saved_coco_path):
os.makedirs("%scoco/annotations/"%saved_coco_path)
if not os.path.exists("%scoco/train/"%saved_coco_path):
os.makedirs("%scoco/train"%saved_coco_path)
if not os.path.exists("%scoco/val/"%saved_coco_path):
os.makedirs("%scoco/val"%saved_coco_path)
json_list_path = glob.glob(labelme_path + "/*.json")
train_path, val_path = train_test_split(json_list_path, test_size=args.ratio)
print('{} for training'.format(len(train_path)),
'\n{} for testing'.format(len(val_path)))
print('Start transform please wait ...')
l2c_train = Labelme2coco(args)
train_keypoints = l2c_train.to_coco(train_path)
l2c_train.save_coco_json(train_keypoints, '%scoco/annotations/keypoints_train.json' % saved_coco_path)
for file in train_path:
shutil.copy(file.replace("json", "jpg"), "%scoco/train/" % saved_coco_path)
for file in val_path:
shutil.copy(file.replace("json", "jpg"), "%scoco/val/" % saved_coco_path)
#
l2c_val = Labelme2coco(args)
val_instance = l2c_val.to_coco(val_path)
l2c_val.save_coco_json(val_instance, '%scoco/annotations/keypoints_val.json' % saved_coco_path)