/trackingImgLabel

TrackingImgLabel is an automatic graphical image annotation tool with a video as input, which uses tracking algorithm to track the object. You can only label the first frame of the video, then the tracking algorithm will automaticlly tracking the objects.

Primary LanguagePythonMIT LicenseMIT

trackingImgLabel

TrackingImgLabel is an automatic image annotation tool with video input, which uses tracking algorithm to track the object. You can label the first frame of the video, then the tracking algorithm will automatically track the objects.
Written in python3.5, using opencv3 and PyQt5.
Annotations can be both saved as XML files in PASCAL VOC format**(object detection task)** and classification images**(classification task)** The tracking method we used is KCF(if you want to improve the result, change a better tracking algorithm)

Installation

Ubuntu

tested in ubuntu16.04, anaconda3-4.1.1.
link:[download anaconda]
after you installed anaconda(PyQt5 included), you must continue install opencv3 by the following commands
PATH_TO_CONDA/pip install opencv-python
PATH_TO_CONDA/pip install numpy --upgrade
PATH_TO_CONDArefer to anaconda3/bin path

if you don't use conda environment, you must install some frequently-used libs, especially PyQt5、opencv
sudo apt-get install python3-pyqt5
pip install opencv-python
pip install numpy --upgrade

Windows

tested in Windows10, annaconda3-4.1.1
link:[download opencv3]
then in anaconda env, use pip to install opencv3, and upgrade numpy
pip install numpy --upgrade

Download prebuilt binaries

tested in windows10, use the software directly
link:[download exe]
password: 2f07

Notes

  1. make sure not to contain the Chinese path

Usage

  1. replace label.txt with your own classes

  2. open a video

  3. choose the output folder

  4. in the video play dock

click left button to choose object(you can choose several objects at the same time)
  1. info dock

current frame:current operation frame, displayed in left

tracking frames:the number of frames to track

save current label:save current frame's label to memory

erase current label:erase current frame's label then you can re-label current frame

start tracking:track start from current frame to current frame + tracking frames after finishing tracking, you can change current frame value to check annotation, if not satisfying you can erase current frame's label and continue to re-label

  1. video info

show video's infomation
  1. save option

start frame:start frame when saving

end frame:end frame when saving

save gap:save a frame every gap frames because of redundancy of the video

detection:saving mode, if selected it will save XML format for object detection(label_detection folder, generate JPEGImages for images storage and Annotations for xml storage), otherwise save classification frames for classification(label_class folder, generate Images for images storage and a label_train.txt)

start saving:start saving the result
object detection result

classification result

License

Free software: license

Contact

if you have any question you can contact me:
186368@zju.edu.cn or zhangzjn@qq.com