# Yolo_mark

**Repository Path**: Python_Ai_Road/Yolo_mark

## Basic Information

- **Project Name**: Yolo_mark
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Unlicense
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-11-12
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## 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 | 
--- | --- |
<kbd>→</kbd> | Next image |
<kbd>←</kbd> | Previous image |
<kbd>r</kbd> | Delete selected box (mouse hovered) |
<kbd>c</kbd> | Clear all marks on the current image |
<kbd>p</kbd> | Copy previous mark |
<kbd>o</kbd> | Track objects |
<kbd>ESC</kbd> | Close application |
<kbd>n</kbd> | One object per image |
<kbd>0-9</kbd> | Object id |
<kbd>m</kbd> | Show coords |
<kbd>w</kbd> | Line width |
<kbd>k</kbd> | Hide object name |
<kbd>h</kbd> | Help |