json, export_dir + 'val.Coco is a complete code coverage tool chain for Tcl, QML, C# and C/C++ programs, which runs on macOS, Linux, and Windows.Ĭoco analyzes the way an application runs, as part of a test suite, for example. json_categories ) # export val coco json save_json ( val_coco. json, export_dir + 'train.json' ) # create val coco object val_coco = get_coco_from_labelme_folder ( labelme_val_folder, coco_category_list = train_coco. convert ( labelme_folder, export_dir, train_split_rate ) # import functions from labelme2coco import get_coco_from_labelme_folder, save_json # set labelme training data directory labelme_train_folder = 'tests/data/labelme_annot' # set labelme validation data directory labelme_val_folder = 'tests/data/labelme_annot' # set path for coco json to be saved export_dir = 'tests/data/' # create train coco object train_coco = get_coco_from_labelme_folder ( labelme_train_folder ) # export train coco json save_json ( train_coco. Getting started Installation pip install -U labelme2cocoīasic Usage labelme2coco path / to / labelme / dir labelme2coco path / to / labelme / dir - train_split_rate 0.85 Advanced Usage # import package import labelme2coco # set directory that contains labelme annotations and image files labelme_folder = 'tests/data/labelme_annot' # set export dir export_dir = 'tests/data/' # set train split rate train_split_rate = 0.85 # convert labelme annotations to coco labelme2coco. You can use this package to convert labelme annotations to COCO format. However, widely used frameworks/models such as Yolact/Solo, Detectron, MMDetection etc. Labelme is a widely used is a graphical image annotation tool that supports classification, segmentation, instance segmentation and object detection formats. A lightweight package for converting your labelme annotations into COCO object detection format.Ĭonvert LabelMe annotations to COCO format in one step

Free instals Coco