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https://github.com/Cartucho/OpenLabeling

Label images and video for Computer Vision applications
https://github.com/Cartucho/OpenLabeling

bounding-boxes darkflow darknet gui labeling-tool object-detection opencv pascal-voc training-yolo yolo

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Label images and video for Computer Vision applications

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# OpenLabeling: open-source image and video labeler



[![GitHub stars](https://img.shields.io/github/stars/Cartucho/OpenLabeling.svg?style=social&label=Stars)](https://github.com/Cartucho/OpenLabeling)



Image labeling in multiple annotation formats:

- PASCAL VOC (= [darkflow](https://github.com/thtrieu/darkflow))

- [YOLO darknet](https://github.com/pjreddie/darknet)

- ask for more (create a new issue)...








## Citation



This project was developed for the following paper, please consider citing it:



```bibtex

@INPROCEEDINGS{8594067,

  author={J. {Cartucho} and R. {Ventura} and M. {Veloso}},

  booktitle={2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, 

  title={Robust Object Recognition Through Symbiotic Deep Learning In Mobile Robots}, 

  year={2018},

  pages={2336-2341},

}

```



## Latest Features



- Jun 2019: Deep Learning Object Detection Model

- May 2019: [ECCV2018] Distractor-aware Siamese Networks for Visual Object Tracking

- Jan 2019: easy and quick bounding-boxe's resizing!

- Jan 2019: video object tracking with OpenCV trackers!

- TODO: Label photos via Google drive to allow "team online labeling".

[New Features Discussion](https://github.com/Cartucho/OpenLabeling/issues/3)



## Table of contents



- [Quick start](#quick-start)

- [Prerequisites](#prerequisites)

- [Run project](#run-project)

- [GUI usage](#gui-usage)

- [Authors](#authors)



## Quick start



To start using the YOLO Bounding Box Tool you need to [download the latest release](https://github.com/Cartucho/OpenLabeling/archive/v1.3.zip) or clone the repo:



```

git clone --recurse-submodules [email protected]:Cartucho/OpenLabeling.git

```



### Prerequisites



You need to install:



- [Python](https://www.python.org/downloads/)

- [OpenCV](https://opencv.org/) version >= 3.0

    1. `python -mpip install -U pip`

    1. `python -mpip install -U opencv-python`

    1. `python -mpip install -U opencv-contrib-python`

- numpy, tqdm and lxml:

    1. `python -mpip install -U numpy`

    1. `python -mpip install -U tqdm`

    1. `python -mpip install -U lxml`



Alternatively, you can install everything at once by simply running:



```

python -mpip install -U pip

python -mpip install -U -r requirements.txt

```

- [PyTorch](https://pytorch.org/get-started/locally/) 

    Visit the link for a configurator for your setup.

    

### Run project



Step by step:



  1. Open the `main/` directory

  2. Insert the input images and videos in the folder **input/**

  3. Insert the classes in the file **class_list.txt** (one class name per line)

  4. Run the code:

  5. You can find the annotations in the folder **output/**



         python main.py [-h] [-i] [-o] [-t] [--tracker TRACKER_TYPE] [-n N_FRAMES]



         optional arguments:

          -h, --help                Show this help message and exit

          -i, --input               Path to images and videos input folder | Default: input/

          -o, --output              Path to output folder (if using the PASCAL VOC format it's important to set this path correctly) | Default: output/

          -t, --thickness           Bounding box and cross line thickness (int) | Default: -t 1

          --tracker tracker_type    tracker_type being used: ['CSRT', 'KCF','MOSSE', 'MIL', 'BOOSTING', 'MEDIANFLOW', 'TLD', 'GOTURN', 'DASIAMRPN']

          -n N_FRAMES               number of frames to track object for

  To use DASIAMRPN Tracker:

  1. Install the [DaSiamRPN](https://github.com/foolwood/DaSiamRPN) submodule and download the model (VOT) from [google drive](https://drive.google.com/drive/folders/1BtIkp5pB6aqePQGlMb2_Z7bfPy6XEj6H)

  2. copy it into 'DaSiamRPN/code/'

  3. set default tracker in main.py or run it with --tracker DASIAMRPN



#### How to use the deep learning feature



- Download one or some deep learning models from https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md

  and put it into `object_detection/models` directory (you need to create the `models` folder by yourself). The outline of `object_detection` looks like that:

  + `tf_object_detection.py`

  + `utils.py`

  + `models/ssdlite_mobilenet_v2_coco_2018_05_09`



Download the pre-trained model by clicking this link http://download.tensorflow.org/models/object_detection/ssdlite_mobilenet_v2_coco_2018_05_09.tar.gz and put it into `object_detection/models`. Create the `models` folder if necessary. Make sure to extract the model.



  **Note**: Default model used in `main_auto.py` is `ssdlite_mobilenet_v2_coco_2018_05_09`. We can

  set `graph_model_path` in file `main_auto.py` to change the pretrain model

- Using `main_auto.py` to automatically label data first



  TODO: explain how the user can 



### GUI usage



Keyboard, press: 






| Key | Description |

| --- | --- |

| a/d | previous/next image |

| s/w | previous/next class |

| e | edges |

| h | help |

| q | quit |



Video:



| Key | Description |

| --- | --- |

| p | predict the next frames' labels |



Mouse:

  - Use two separate left clicks to do each bounding box

  - **Right-click** -> **quick delete**!

  - Use the middle mouse to zoom in and out

  - Use double click to select a bounding box



## Authors



* **João Cartucho**



    Feel free to contribute



    [![GitHub contributors](https://img.shields.io/github/contributors/Cartucho/OpenLabeling.svg)](https://github.com/Cartucho/OpenLabeling/graphs/contributors)