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https://github.com/AlexeyAB/Yolo_mark

GUI for marking bounded boxes of objects in images for training neural network Yolo v3 and v2
https://github.com/AlexeyAB/Yolo_mark

darknet dnn labeling marking-bounded-boxes object-detection training-yolo yolo

Last synced: 2 months ago
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GUI for marking bounded boxes of objects in images for training neural network Yolo v3 and v2

Host: GitHub
URL: https://github.com/AlexeyAB/Yolo_mark
Owner: AlexeyAB
License: unlicense
Created: 2016-12-17T21:25:07.000Z (about 8 years ago)
Default Branch: master
Last Pushed: 2020-12-11T00:29:20.000Z (about 4 years ago)
Last Synced: 2024-10-29T17:42:08.322Z (2 months ago)
Topics: darknet, dnn, labeling, marking-bounded-boxes, object-detection, training-yolo, yolo
Language: C++
Homepage: https://github.com/AlexeyAB/darknet
Size: 4.14 MB
Stars: 1,807
Watchers: 71
Forks: 681
Open Issues: 147
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

awesome-object-detection-datasets - AlexeyAB/Yolo_mark
awesome-dataset-tools - Yolo_mark - GUI for marking bounded boxes of objects in images (Labeling Tools / Images)
awesome-yolo-object-detection - AlexeyAB/Yolo_mark
awesome-yolo-object-detection - AlexeyAB/Yolo_mark

README

        # Yolo_mark

**Windows** & **Linux** GUI for marking bounded boxes of objects in images for training Yolo v3 and v2

* To compile on **Windows** open `yolo_mark.sln` in MSVS2013/2015, compile it **x64 & Release** and run the file: `x64/Release/yolo_mark.cmd`. Change paths in `yolo_mark.sln` to the OpenCV 2.x/3.x installed on your computer:

    * (right click on project) -> properties -> C/C++ -> General -> Additional Include Directories: `C:\opencv_3.0\opencv\build\include;`

        

    * (right click on project) -> properties -> Linker -> General -> Additional Library Directories: `C:\opencv_3.0\opencv\build\x64\vc14\lib;`

* To compile on **Linux** type in console 3 commands:

    ```

    cmake .

    make

    ./linux_mark.sh

    ```

Supported both: OpenCV 2.x and OpenCV 3.x

--------

1. To test, simply run 

  * **on Windows:** `x64/Release/yolo_mark.cmd`

  * **on Linux:** `./linux_mark.sh`

2. To use for labeling your custom images:

 * delete all files from directory `x64/Release/data/img`

 * put your `.jpg`-images to this directory `x64/Release/data/img`

 * change numer of classes (objects for detection) in file `x64/Release/data/obj.data`: https://github.com/AlexeyAB/Yolo_mark/blob/master/x64/Release/data/obj.data#L1

 * put names of objects, one for each line in file `x64/Release/data/obj.names`: https://github.com/AlexeyAB/Yolo_mark/blob/master/x64/Release/data/obj.names

 * run file: `x64\Release\yolo_mark.cmd`

3. To training for your custom objects, you should change 2 lines in file `x64/Release/yolo-obj.cfg`:

 * set number of classes (objects): https://github.com/AlexeyAB/Yolo_mark/blob/master/x64/Release/yolo-obj.cfg#L230

 * set `filter`-value 

   * For Yolov2 `(classes + 5)*5`: https://github.com/AlexeyAB/Yolo_mark/blob/master/x64/Release/yolo-obj.cfg#L224

   * For Yolov3 `(classes + 5)*3`

 3.1 Download pre-trained weights for the convolutional layers (76 MB): http://pjreddie.com/media/files/darknet19_448.conv.23 

 

 3.2 Put files: `yolo-obj.cfg`, `data/train.txt`, `data/obj.names`, `data/obj.data`, `darknet19_448.conv.23` and directory `data/img` near with executable `darknet`-file, and start training: `darknet detector train data/obj.data yolo-obj.cfg darknet19_448.conv.23`

For a detailed description, see: https://github.com/AlexeyAB/darknet#how-to-train-to-detect-your-custom-objects

----

#### How to get frames from videofile:

To get frames from videofile (save each N frame, in example N=10), you can use this command:

* on Windows: `yolo_mark.exe data/img cap_video test.mp4 10`

* on Linux: `./yolo_mark x64/Release/data/img cap_video test.mp4 10`

Directory `data/img` should be created before this. Also on Windows, the file `opencv_ffmpeg340_64.dll` from `opencv\build\bin` should be placed near with `yolo_mark.exe`.

As a result, many frames will be collected in the directory `data/img`. Then you can label them manually using such command: 

* on Windows: `yolo_mark.exe data/img data/train.txt data/obj.names`

* on Linux: `./yolo_mark x64/Release/data/img x64/Release/data/train.txt x64/Release/data/obj.names`

----

#### Here are:

* /x64/Release/

  * `yolo_mark.cmd` - example hot to use yolo mark: `yolo_mark.exe data/img data/train.txt data/obj.names`

  * `train_obj.cmd` - example how to train yolo for your custom objects (put this file near with darknet.exe): `darknet.exe detector train data/obj.data yolo-obj.cfg darknet19_448.conv.23`

  * `yolo-obj.cfg` - example of yoloV3-neural-network for 2 object

* /x64/Release/data/

  * `obj.names` - example of list with object names

  * `obj.data` - example with configuration for training Yolo v3

  * `train.txt` - example with list of image filenames for training Yolo v3

  

* /x64/Release/data/img/`air4.txt` - example with coordinates of objects on image `air4.jpg` with aircrafts (class=0)

![Image of Yolo_mark](https://habrastorage.org/files/229/f06/277/229f06277fcc49279342b7edfabbb47a.jpg)

### Instruction manual

#### Mouse control

Button | Description | 

--- | --- |

Left | Draw box

Right | Move box

#### Keyboard Shortcuts

Shortcut | Description | 

--- | --- |

→ | Next image |

← | Previous image |

r | Delete selected box (mouse hovered) |

c | Clear all marks on the current image |

p | Copy previous mark |

o | Track objects |

ESC | Close application |

n | One object per image |

0-9 | Object id |

m | Show coords |

w | Line width |

k | Hide object name |

h | Help |