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
```

To compile on Mac OSX type in console 3 commands:

brew install cmake
cmake .
make
./osx_mark.sh

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

To test, simply run

on Windows: x64/Release/yolo_mark.cmd
on Linux: ./linux_mark.sh
on Mac OSX: ./osx_mark.sh

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

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)

Sauraus / Yolo_mark

Yolo_mark

How to get frames from videofile:

Here are:

About

Languages