Commit 3708814d authored by maad.al-anni's avatar maad.al-anni
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resources/icons/.DS_Store
resources.py
.idea*
labelImg.egg-info*
*.pyc
.*.swp
build/
dist/
tags
cscope*
.ycm_extra_conf.py
.subvimrc
.vscode
*.pkl
# MacOS System-Generated
.DS_Store
.DS_Store?
._*
.Spotlight-V100
.Trashes
ehthumbs.db
Thumbs.db
# vim: set ts=2 et:
# run xvfb with 32-bit color
# xvfb-run -s '-screen 0 1600x1200x24+32' command_goes_here
jobs:
include:
# Python 3 + QT5
- os: linux
dist: focal
language: generic
python: "3.6"
env:
- QT=5
- CONDA=4.2.0
addons:
apt:
packages:
- cmake
- xvfb
before_install:
# ref: https://repo.anaconda.com/archive/
- curl -O https://repo.anaconda.com/archive/Anaconda3-2020.02-Linux-x86_64.sh
# ref: http://conda.pydata.org/docs/help/silent.html
- /bin/bash Anaconda3-2020.02-Linux-x86_64.sh -b -p $HOME/anaconda3
- export PATH="$HOME/anaconda3/bin:$PATH"
# ref: http://stackoverflow.com/questions/21637922/how-to-install-pyqt4-in-anaconda
- conda create -y -n labelImg-py3qt5 python=3.6
- source activate labelImg-py3qt5
- conda install -y pyqt=5
- conda install -y lxml
- make qt5py3
- xvfb-run make testpy3
# Pipenv Python 3.7.5 + QT5 - Build .app
- os: osx
language: generic
env:
- PIPENV_VENV_IN_PROJECT=1
- PIPENV_IGNORE_VIRTUALENVS=1
install:
- python3 --version
- pip3 install pipenv
- pipenv install pyqt5 lxml
- pipenv run pip install pyqt5==5.13.2 lxml
- pipenv run make qt5py3
- rm -rf build dist
- pipenv run python setup.py py2app
- open dist/labelImg.app
script:
- exit 0
TzuTa Lin
[LabelMe](http://labelme2.csail.mit.edu/Release3.0/index.php)
Ryan Flynn
History
=======
1.8.6 (2021-10-10)
------------------
* Display box width and height
1.8.5 (2021-04-11)
------------------
* Merged a couple of PRs
* Fixed issues
* Support CreateML format
1.8.4 (2020-11-04)
------------------
* Merged a couple of PRs
* Fixed issues
1.8.2 (2018-12-02)
------------------
* Fix pip depolyment issue
1.8.1 (2018-12-02)
------------------
* Fix issues
* Support zh-Tw strings
1.8.0 (2018-10-21)
------------------
* Support drawing sqaure rect
* Add item single click slot
* Fix issues
1.7.0 (2018-05-18)
------------------
* Support YOLO
* Fix minor issues
1.6.1 (2018-04-17)
------------------
* Fix issue
1.6.0 (2018-01-29)
------------------
* Add more pre-defined labels
* Show cursor pose in status bar
* Fix minor issues
1.5.2 (2017-10-24)
------------------
* Assign different colors to different lablels
1.5.1 (2017-9-27)
------------------
* Show a autosaving dialog
1.5.0 (2017-9-14)
------------------
* Fix the issues
* Add feature: Draw a box easier
1.4.3 (2017-08-09)
------------------
* Refactor setting
* Fix the issues
1.4.0 (2017-07-07)
------------------
* Add feature: auto saving
* Add feature: single class mode
* Fix the issues
1.3.4 (2017-07-07)
------------------
* Fix issues and improve zoom-in
1.3.3 (2017-05-31)
------------------
* Fix issues
1.3.2 (2017-05-18)
------------------
* Fix issues
1.3.1 (2017-05-11)
------------------
* Fix issues
1.3.0 (2017-04-22)
------------------
* Fix issues
* Add difficult tag
* Create new files for pypi
1.2.3 (2017-04-22)
------------------
* Fix issues
1.2.2 (2017-01-09)
------------------
* Fix issues
Copyright (c) <2015-Present> Tzutalin
Copyright (C) 2013 MIT, Computer Science and Artificial Intelligence Laboratory. Bryan Russell, Antonio Torralba, William T. Freeman
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
# ex: set ts=8 noet:
all: qt5 test
test: testpy3
testpy2:
python -m unittest discover tests
testpy3:
python3 -m unittest discover tests
qt4: qt4py2
qt5: qt5py3
qt4py2:
pyrcc4 -py2 -o libs/resources.py resources.qrc
qt4py3:
pyrcc4 -py3 -o libs/resources.py resources.qrc
qt5py3:
pyrcc5 -o libs/resources.py resources.qrc
clean:
rm -rf ~/.labelImgSettings.pkl *.pyc dist labelImg.egg-info __pycache__ build
pip_upload:
python3 setup.py upload
long_description:
restview --long-description
.PHONY: all
# LabelImge Extended Tool
Extended Open-Source Label Image Annotation Tool called Labelimge "labelImg.py" with variant cross-platforms required at least Python 3.6 and has been tested with PyQt 4.8, additionally achieves Detection & Multi-Tracking Objects.
## Dependencies
requires at least `Python 3+` and has been tested with `PyQt 4.8`. However, `opencv-contrib-python` and `lxml` are strongly recommended to run the extended LabelImg on Amphora Detection & MultiTracking Approches.
###### Windows
- [ ] install [python](https://www.python.org/downloads)
- [ ] install [ PyQt5](https://www.riverbankcomputing.com/software/pyqt/download)
- [ ] install [lxml](https://lxml.de/installation.html)
- [ ] install [opencv-contrib-python](https://pypi.org/project/opencv-contrib-python/)
if items were added in files in the resources/strings folder:
```
conda install pyqt=5
conda install -c anaconda lxml
pyrcc5 -o libs/resources.py resources.qrc
python labelImg.py
python labelImg.py [IMAGE_PATH] [PRE-DEFINED CLASS FILE]
```
## Getting the .CFG File, .Weights File, Obj.NAMES File
- [ ] [obtaining .cfg](https://github.com/AlexeyAB/darknet#datasets)
- [ ] [obtaining .weights](https://github.com/AlexeyAB/darknet#datasets)
- [ ] [obtaining .names](https://github.com/AlexeyAB/darknet#datasets)
Store the files in place where you will be able to uploaded to the Labelimage Extended Tool later on.
## Detection Phase
> YOLO (You Only Look Once) is an object detection algorithm that allows to detect objects in an images in near real-time. YOLOv4 is 4th version of YOLO which introduced in April 2020.
- [YoloV4] - [YoloV4](https://github.com/AlexeyAB)
## Tracking phase
> OpenCV’s multi-object tracking API implemented using the MultiTracker class in Python, in order to implement its featurized parametric obligedly needs OpenCV Library with latest Version.
- [MultiTracking] - [MultiTracking](https://github.com/adipandas/multi-object-tracker)
subsidaries are as describe herebelow.
| Function name | Description |
| ------------- | ------------------------------ |
| `o` or `O` | Upload Prerequisites Files |
| `y` or `Y` | apply the Detection Mode |
| `n` or `N` | apply the Tracking Mode |
| `p` or `P` | help |
| ctrl+shift+T | Activate the single tracking mode|
## Contact Info
<maad.alanni@gitlab.lis-lab.fr>
LabelImg
========
.. image:: https://img.shields.io/pypi/v/labelimg.svg
:target: https://pypi.python.org/pypi/labelimg
.. image:: https://img.shields.io/travis/tzutalin/labelImg.svg
:target: https://travis-ci.org/tzutalin/labelImg
.. image:: https://img.shields.io/badge/lang-en-blue.svg
:target: https://github.com/tzutalin/labelImg/blob/master/README.zh.rst
.. image:: https://img.shields.io/badge/lang-zh-green.svg
:target: https://github.com/tzutalin/labelImg/blob/master/readme/README.zh.rst
.. image:: https://img.shields.io/badge/lang-zh--TW-green.svg
:target: (https://github.com/jonatasemidio/multilanguage-readme-pattern/blob/master/README.pt-br.md
LabelImg is a graphical image annotation tool.
It is written in Python and uses Qt for its graphical interface.
Annotations are saved as XML files in PASCAL VOC format, the format used
by `ImageNet <http://www.image-net.org/>`__. Besides, it also supports YOLO and CreateML formats.
.. image:: https://raw.githubusercontent.com/tzutalin/labelImg/master/demo/demo3.jpg
:alt: Demo Image
.. image:: https://raw.githubusercontent.com/tzutalin/labelImg/master/demo/demo.jpg
:alt: Demo Image
`Watch a demo video <https://youtu.be/p0nR2YsCY_U>`__
Installation
------------------
Get from PyPI but only python3.0 or above
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
This is the simplest (one-command) install method on modern Linux distributions such as Ubuntu and Fedora.
.. code:: shell
pip3 install labelImg
labelImg
labelImg [IMAGE_PATH] [PRE-DEFINED CLASS FILE]
Build from source
~~~~~~~~~~~~~~~~~
Linux/Ubuntu/Mac requires at least `Python
2.6 <https://www.python.org/getit/>`__ and has been tested with `PyQt
4.8 <https://www.riverbankcomputing.com/software/pyqt/intro>`__. However, `Python
3 or above <https://www.python.org/getit/>`__ and `PyQt5 <https://pypi.org/project/PyQt5/>`__ are strongly recommended.
Ubuntu Linux
^^^^^^^^^^^^
Python 3 + Qt5
.. code:: shell
sudo apt-get install pyqt5-dev-tools
sudo pip3 install -r requirements/requirements-linux-python3.txt
make qt5py3
python3 labelImg.py
python3 labelImg.py [IMAGE_PATH] [PRE-DEFINED CLASS FILE]
macOS
^^^^^
Python 3 + Qt5
.. code:: shell
brew install qt # Install qt-5.x.x by Homebrew
brew install libxml2
or using pip
pip3 install pyqt5 lxml # Install qt and lxml by pip
make qt5py3
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
.. code:: shell
brew install python3
pip3 install pipenv
pipenv run pip install pyqt5==5.15.2 lxml
pipenv run make qt5py3
pipenv run 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 <https://www.python.org/downloads/windows/>`__,
`PyQt5 <https://www.riverbankcomputing.com/software/pyqt/download5>`__
and `install lxml <http://lxml.de/installation.html>`__.
Open cmd and go to the `labelImg <#labelimg>`__ directory
.. code:: shell
pyrcc4 -o libs/resources.py resources.qrc
For pyqt5, pyrcc5 -o libs/resources.py resources.qrc
python labelImg.py
python labelImg.py [IMAGE_PATH] [PRE-DEFINED CLASS FILE]
Windows + Anaconda
^^^^^^^^^^^^^^^^^^
Download and install `Anaconda <https://www.anaconda.com/download/#download>`__ (Python 3+)
Open the Anaconda Prompt and go to the `labelImg <#labelimg>`__ directory
.. code:: shell
conda install pyqt=5
conda install -c anaconda lxml
pyrcc5 -o libs/resources.py resources.qrc
python labelImg.py
python labelImg.py [IMAGE_PATH] [PRE-DEFINED CLASS FILE]
Use Docker
~~~~~~~~~~~~~~~~~
.. code:: shell
docker run -it \
--user $(id -u) \
-e DISPLAY=unix$DISPLAY \
--workdir=$(pwd) \
--volume="/home/$USER:/home/$USER" \
--volume="/etc/group:/etc/group:ro" \
--volume="/etc/passwd:/etc/passwd:ro" \
--volume="/etc/shadow:/etc/shadow:ro" \
--volume="/etc/sudoers.d:/etc/sudoers.d:ro" \
-v /tmp/.X11-unix:/tmp/.X11-unix \
tzutalin/py2qt4
make qt4py2;./labelImg.py
You can pull the image which has all of the installed and required dependencies. `Watch a demo video <https://youtu.be/nw1GexJzbCI>`__
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.
Create pre-defined classes
~~~~~~~~~~~~~~~~~~~~~~~~~~
You can edit the
`data/predefined\_classes.txt <https://github.com/tzutalin/labelImg/blob/master/data/predefined_classes.txt>`__
to load pre-defined classes
Annotation visualization
~~~~~~~~~~~~~~~~~~~~~~~~
1. Copy the existing lables file to same folder with the images. The labels file name must be same with image file name.
2. Click File and choose 'Open Dir' then Open the image folder.
3. Select image in File List, it will appear the bounding box and label for all objects in that image.
(Choose Display Labels mode in View to show/hide lablels)
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 |
+--------------------+--------------------------------------------+
| Ctrl + Shift + d | Delete the current image |
+--------------------+--------------------------------------------+
| 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.
How to reset the settings
~~~~~~~~~~~~~~~~~~~~~~~~~
In case there are issues with loading the classes, you can either:
1. From the top menu of the labelimg click on Menu/File/Reset All
2. Remove the `.labelImgSettings.pkl` from your home directory. In Linux and Mac you can do:
`rm ~/.labelImgSettings.pkl`
How to contribute
~~~~~~~~~~~~~~~~~
Send a pull request
License
~~~~~~~
`Free software: MIT license <https://github.com/tzutalin/labelImg/blob/master/LICENSE>`_
Citation: Tzutalin. LabelImg. Git code (2015). https://github.com/tzutalin/labelImg
Related and additional tools
~~~~~~~~~~~~~~~~~~~~~~~~~~~~
1. `ImageNet Utils <https://github.com/tzutalin/ImageNet_Utils>`__ to
download image, create a label text for machine learning, etc
2. `Use Docker to run labelImg <https://hub.docker.com/r/tzutalin/py2qt4>`__
3. `Generating the PASCAL VOC TFRecord files <https://github.com/tensorflow/models/blob/4f32535fe7040bb1e429ad0e3c948a492a89482d/research/object_detection/g3doc/preparing_inputs.md#generating-the-pascal-voc-tfrecord-files>`__
4. `App Icon based on Icon by Nick Roach (GPL) <https://www.elegantthemes.com/>`__
5. `Setup python development in vscode <https://tzutalin.blogspot.com/2019/04/set-up-visual-studio-code-for-python-in.html>`__
6. `The link of this project on iHub platform <https://code.ihub.org.cn/projects/260/repository/labelImg>`__
7. `Convert annotation files to CSV format or format for Google Cloud AutoML <https://github.com/tzutalin/labelImg/tree/master/tools>`__
Stargazers over time
~~~~~~~~~~~~~~~~~~~~
.. image:: https://starchart.cc/tzutalin/labelImg.svg
import cv2
\ No newline at end of file
*.spec
build
dist
pyinstaller
python-2.*
pywin32*
virtual-wine
venv_wine
PyQt4-*
lxml-*
windows_v*
linux_v*
### Deploy to PyPI
```
cd [ROOT]
sh build-tools/build-for-pypi.sh
```
### Build for Ubuntu
```
cd build-tools
sh run-in-container.sh