/labelImg-TRK

This repository is an extension of famous labelImg annotation tool(for object detection). This improved version contains various image level tracking algorithms for faster and efficient data annotation.

Primary LanguagePythonMIT LicenseMIT

LabelImg-TRK

LabelImg-TRK is a graphical image annotation tool.

It is written in Python and uses Qt for its graphical interface.

Tracking has been recently added to LabelImg-TRK to fasten the data annotation process. Consecutive images should have same dimensions.

Annotations are saved as XML files in PASCAL VOC format, the format used by ImageNet. Besides, it also supports YOLO format

Demo Image

Demo Image

Watch a demo video

Installation

Build from source

Linux/Ubuntu/Mac requires at least Python 2.6 and has been tested with PyQt 4.8. However, Python 3 or above and PyQt5 are strongly recommended.

Ubuntu Linux

Python 2 + Qt4

sudo apt-get install pyqt4-dev-tools gcc
sudo pip3 install -r requirements.txt
make qt4py2
bash initialize_siamMask.sh
python labelImg.py
python labelImg.py [IMAGE_PATH] [PRE-DEFINED CLASS FILE]

Python 3 + Qt5 (Recommended)

sudo apt-get install pyqt5-dev-tools gcc
sudo pip3 install -r requirements.txt
make qt5py3
bash initialize_siamMask.sh
python3 labelImg.py
python3 labelImg.py [IMAGE_PATH] [PRE-DEFINED CLASS FILE]

macOS

Python 3 + Qt5 (Recommended)

pip3 install -r requirements.txt
make qt5py3
bash initialize_siamMask.sh
python3 labelImg.py
python3 labelImg.py [IMAGE_PATH] [PRE-DEFINED CLASS FILE]

Python 3 Virtualenv (Recommended)

Virtualenv can avoid a lot of the QT / Python version issues

brew install python3
pip3 install pipenv
pipenv --three # or pipenv install pyqt5 lxml
pipenv run pip install -r install requirements.txt
pipenv run make qt5py3
bash initialize_siamMask.sh
python3 labelImg.py
[Optional] rm -rf build dist; python setup.py py2app -A;mv "dist/labelImg.app" /Applications

Note: The Last command gives you a nice .app file with a new SVG Icon in your /Applications folder. You can consider using the script: build-tools/build-for-macos.sh

Windows

Install Python, PyQt5 and install lxml.

Install opencv-python opencv-contrib-python (for windows)

Open cmd and go to the labelImg directory

Windows + Anaconda

Download and install Anaconda (Python 3+)

Open the Anaconda Prompt and go to the labelImg directory

conda install -r requirements.txt
pyrcc5 -o libs/resources.py resources.qrc
python labelImg.py
python labelImg.py [IMAGE_PATH] [PRE-DEFINED CLASS FILE]

You can pull the image which has all of the installed and required dependencies. Watch a demo video

Usage

Steps (PascalVOC)

  1. Build and launch using the instructions above.
  2. Click 'Change default saved annotation folder' in Menu/File
  3. Click 'Open Dir'
  4. Click 'Create RectBox'
  5. Click and release left mouse to select a region to annotate the rect box
  6. You can use right mouse to drag the rect box to copy or move it

The annotation will be saved to the folder you specify.

You can refer to the below hotkeys to speed up your workflow.

Steps (YOLO)

  1. In data/predefined_classes.txt define the list of classes that will be used for your training.
  2. Build and launch using the instructions above.
  3. Right below "Save" button in the toolbar, click "PascalVOC" button to switch to YOLO format.
  4. You may use Open/OpenDIR to process single or multiple images. When finished with a single image, click save.

A txt file of YOLO format will be saved in the same folder as your image with same name. A file named "classes.txt" is saved to that folder too. "classes.txt" defines the list of class names that your YOLO label refers to.

Note:

  • Your label list shall not change in the middle of processing a list of images. When you save an image, classes.txt will also get updated, while previous annotations will not be updated.
  • You shouldn't use "default class" function when saving to YOLO format, it will not be referred.
  • When saving as YOLO format, "difficult" flag is discarded.

Object Tracking

  1. Tag an bounding box in any image as describe above. Make sure tracking check-box is tick. Select preferred tracking algorithm.
  2. Now when you open the "next image" with keyboard short "d", the same bounding box is tracked automatically for you in the "next image" with the same annotation as previous image.
  3. Then you can also add more custom bounding boxes &/or alter tracked bounding boxes.
  4. Tracking process can be stopped either by un-ticking tracking check-box, or by deleting all the bounding boxes in the current image.
  5. Currently the LabelImg-TRK supports 6-7 different tracking algorithms. Some deep learning based tracking algorithms are coming soon.

Note:

  • Tracked bounding boxes for the "next image" will only be created if the dimensional of two images are same. ("current image with bounding box" & "next image")
  • Tracked bounding boxes for the "next image" will only be created if there are no previously saved bounding boxes for that "next image".
  • Tracked bounding boxes will be automatically saved to corresponding xml/txt, unless they are explicitly altered.
  • Bounding boxes are only tracked when you move to next image, moving backward (shortcut "a") will not trigger tracking.
  • Multiple bounding boxes can be tracked simultaneously.
  • If tracker has predicted bounding boxes then log will appear in the title of the "LabelImg-TRK" app, notifying some information.
  • While your on the "next image", all the bounding boxes those have been generated by tracking will have reddish highlight when you click on them. (general bounding boxes have blue highlights)
  • If you move to image after "next image" (with or without tracking) then reddish highlight will be replaced to conventional blue highlight.
  • Finally, when you have finished annotations. And are just cross checking the annotations, make sure to un-tick the tracking check-box.

Create pre-defined classes

You can edit the data/predefined_classes.txt to load pre-defined classes

Hotkeys

Ctrl + u Load all of the images from a directory
Ctrl + r Change the default annotation target dir
Ctrl + s Save
Ctrl + d Copy the current label and rect box
Space Flag the current image as verified
w Create a rect box
d Next image
a Previous image
del Delete the selected rect box
Ctrl++ Zoom in
Ctrl-- Zoom out
↑→↓← Keyboard arrows to move selected rect box

Verify Image:

When pressing space, the user can flag the image as verified, a green background will appear. This is used when creating a dataset automatically, the user can then through all the pictures and flag them instead of annotate them.

Difficult:

The difficult field is set to 1 indicates that the object has been annotated as "difficult", for example, an object which is clearly visible but difficult to recognize without substantial use of context. According to your deep neural network implementation, you can include or exclude difficult objects during training.

Thank you @tzutalin & License

This annotation tool was originally build by tzutalin. This is just an extension of his work.

Free software: MIT license

Citation: Tzutalin. LabelImg. Git code (2015). https://github.com/tzutalin/labelImg

Support

Feel free to open an issue on or send along a pull request. If you like the work, show your appreciation by "FORK", "STAR", or "SHARE".

Author: SanjyotZade