Update README.md and setup.py
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tzutalin
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@ -1,7 +1,12 @@
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=======
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History
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=======
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1.8.2 (2018-12-02)
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------------------
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* Fix pip depolyment issue
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1.8.1 (2018-12-02)
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------------------
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12
Makefile
12
Makefile
@ -1,6 +1,6 @@
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# ex: set ts=8 noet:
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all: qt5
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all: qt5 test
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test: testpy3
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@ -24,6 +24,12 @@ qt5py3:
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pyrcc5 -o libs/resources.py resources.qrc
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clean:
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rm -f ~/.labelImgSettings.pkl resources.pyc
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rm -rf ~/.labelImgSettings.pkl *.pyc dist labelImg.egg-info __pycache__ build
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.PHONY: test
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pip_upload:
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python3 setup.py upload
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long_description:
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restview --long-description
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.PHONY: all
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59
README.rst
59
README.rst
@ -16,7 +16,7 @@ LabelImg is a graphical image annotation tool.
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It is written in Python and uses Qt for its graphical interface.
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Annotations are saved as XML files in PASCAL VOC format, the format used
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by `ImageNet <http://www.image-net.org/>`__. Besdies, it also supports YOLO format
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by `ImageNet <http://www.image-net.org/>`__. Besides, it also supports YOLO format
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.. image:: https://raw.githubusercontent.com/tzutalin/labelImg/master/demo/demo3.jpg
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:alt: Demo Image
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@ -29,19 +29,14 @@ by `ImageNet <http://www.image-net.org/>`__. Besdies, it also supports YOLO for
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Installation
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------------------
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Download prebuilt binaries
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~~~~~~~~~~~~~~~~~~~~~~~~~~
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- `Windows <https://github.com/tzutalin/labelImg/releases>`__
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- macOS. Binaries for macOS are not yet available. Help would be appreciated. At present, it must be `built from source <#macos>`__.
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Build from source
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~~~~~~~~~~~~~~~~~
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Linux/Ubuntu/Mac requires at least `Python
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2.6 <https://www.python.org/getit/>`__ and has been tested with `PyQt
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4.8 <https://www.riverbankcomputing.com/software/pyqt/intro>`__.
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4.8 <https://www.riverbankcomputing.com/software/pyqt/intro>`__. However, `Python
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3 or above <https://www.python.org/getit/>`__ and `PyQt5 <https://pypi.org/project/PyQt5/>`__ are strongly recommended.
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Ubuntu Linux
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@ -56,7 +51,7 @@ Python 2 + Qt4
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python labelImg.py
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python labelImg.py [IMAGE_PATH] [PRE-DEFINED CLASS FILE]
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Python 3 + Qt5 (Recommanded)
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Python 3 + Qt5 (Recommended)
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.. code:: shell
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@ -78,14 +73,14 @@ Python 2 + Qt4
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python labelImg.py
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python labelImg.py [IMAGE_PATH] [PRE-DEFINED CLASS FILE]
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Python 3 + Qt5 (Recommanded)
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Python 3 + Qt5 (Recommended)
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.. code:: shell
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brew install qt # Install qt-5.x.x by Homebrew
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brew install libxml2
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or
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or using pip
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pip3 install pyqt5 lxml # Install qt and lxml by pip
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@ -94,23 +89,21 @@ Python 3 + Qt5 (Recommanded)
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python3 labelImg.py [IMAGE_PATH] [PRE-DEFINED CLASS FILE]
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**NEW** Python 3 Virtualenv + Binary
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Python 3 Virtualenv (Recommended)
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This can avoid a lot of the QT / Python version issues, and gives you a nice .app file with a new SVG Icon
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in your /Applications folder. You can consider this script: build-tools/build-for-macos.sh
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Virtualenv can avoid a lot of the QT / Python version issues
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.. code:: shell
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brew install python3
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pip install pipenv
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pipenv --three
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pipenv shell
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pip install py2app
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pip install PyQt5 lxml
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make qt5py3
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rm -rf build dist
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python setup.py py2app -A
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mv "dist/labelImg.app" /Applications
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pip3 install pipenv
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pipenv --three # or pipenv install pyqt5 lxml
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pipenv run pip install pyqt5 lxml
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pipenv run make qt5py3
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python3 labelImg.py
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[Optional] rm -rf build dist; python setup.py py2app -A;mv "dist/labelImg.app" /Applications
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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
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Windows
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@ -142,15 +135,14 @@ Open the Anaconda Prompt and go to the `labelImg <#labelimg>`__ directory
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python labelImg.py
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python labelImg.py [IMAGE_PATH] [PRE-DEFINED CLASS FILE]
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Get from PyPI
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~~~~~~~~~~~~~~~~~
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Get from PyPI but only python3.0 or above
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. code:: shell
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pip install labelImg
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pip3 install labelImg
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labelImg
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labelImg [IMAGE_PATH] [PRE-DEFINED CLASS FILE]
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I tested pip on Ubuntu 14.04 and 16.04. However, I didn't test pip on macOS and Windows
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Use Docker
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~~~~~~~~~~~~~~~~~
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@ -198,15 +190,15 @@ Steps (YOLO)
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2. Build and launch using the instructions above.
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3. Right below "Save" button in toolbar, click "PascalVOC" button to switch to YOLO format.
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3. Right below "Save" button in the toolbar, click "PascalVOC" button to switch to YOLO format.
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4. You may use Open/OpenDIR to process single or multiple images. When finished with single image, click save.
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4. You may use Open/OpenDIR to process single or multiple images. When finished with a single image, click save.
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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.
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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.
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Note:
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- Your label list shall not change in the middle of processing a list of images. When you save a image, classes.txt will also get updated, while previous annotations will not be updated.
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- 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.
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- You shouldn't use "default class" function when saving to YOLO format, it will not be referred.
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@ -255,7 +247,7 @@ This is used when creating a dataset automatically, the user can then through al
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**Difficult:**
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The difficult field being 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.
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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.
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According to your deep neural network implementation, you can include or exclude difficult objects during training.
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How to contribute
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@ -276,6 +268,7 @@ Related
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download image, create a label text for machine learning, etc
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2. `Use Docker to run labelImg <https://hub.docker.com/r/tzutalin/py2qt4>`__
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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>`__
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4. `App Icon based on Icon by Nick Roach (GPL)` <https://www.elegantthemes.com/> <https://www.iconfinder.com/icons/1054978/shop_tag_icon> __
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4. `App Icon based on Icon by Nick Roach (GPL) <https://www.elegantthemes.com/>`__
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5. `Setup python development in vscode <https://tzutalin.blogspot.com/2019/04/set-up-visual-studio-code-for-python-in.html>`__
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@ -1,2 +1,2 @@
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__version_info__ = ('1', '8', '1')
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__version_info__ = ('1', '8', '2')
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__version__ = '.'.join(__version_info__)
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64
setup.py
64
setup.py
@ -9,6 +9,8 @@ import os
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here = os.path.abspath(os.path.dirname(__file__))
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NAME = 'labelImg'
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REQUIRES_PYTHON = '>=3.0.0'
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REQUIRED_DEP = ['pyqt5', 'lxml']
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about = {}
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with open(os.path.join(here, 'libs', '__init__.py')) as f:
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@ -21,14 +23,6 @@ with open('HISTORY.rst') as history_file:
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history = history_file.read()
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REQUIRED = []
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if (sys.version_info > (3, 0)):
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REQUIRED = ['pyqt5', 'lxml']
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else:
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print('\033[93m For py2, you are unable to installl pyqt4 by pip \033[0m')
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REQUIRED = ['lxml']
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# OS specific settings
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SET_REQUIRES = []
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if _platform == "linux" or _platform == "linux2":
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@ -47,6 +41,47 @@ OPTIONS = {
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'iconfile': 'resources/icons/app.icns'
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}
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class UploadCommand(Command):
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"""Support setup.py upload."""
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description=readme + '\n\n' + history,
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user_options = []
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@staticmethod
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def status(s):
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"""Prints things in bold."""
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print('\033[1m{0}\033[0m'.format(s))
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def initialize_options(self):
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pass
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def finalize_options(self):
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pass
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def run(self):
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try:
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self.status('Removing previous builds…')
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rmtree(os.path.join(here, 'dist'))
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except OSError:
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self.status('Fail to remove previous builds..')
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pass
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self.status('Building Source and Wheel (universal) distribution…')
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os.system(
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'{0} setup.py sdist bdist_wheel --universal'.format(sys.executable))
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self.status('Uploading the package to PyPI via Twine…')
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os.system('twine upload dist/*')
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self.status('Pushing git tags…')
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os.system('git tag -d v{0}'.format(about['__version__']))
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os.system('git tag v{0}'.format(about['__version__']))
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# os.system('git push --tags')
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sys.exit()
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setup(
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app=APP,
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name=NAME,
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@ -56,6 +91,7 @@ setup(
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author="TzuTa Lin",
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author_email='tzu.ta.lin@gmail.com',
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url='https://github.com/tzutalin/labelImg',
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python_requires=REQUIRES_PYTHON,
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package_dir={'labelImg': '.'},
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packages=required_packages,
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entry_points={
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@ -64,7 +100,7 @@ setup(
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]
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},
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include_package_data=True,
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install_requires=REQUIRED,
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install_requires=REQUIRED_DEP,
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license="MIT license",
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zip_safe=False,
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keywords='labelImg labelTool development annotation deeplearning',
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@ -73,16 +109,18 @@ setup(
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'Intended Audience :: Developers',
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'License :: OSI Approved :: MIT License',
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'Natural Language :: English',
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"Programming Language :: Python :: 2",
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'Programming Language :: Python :: 2.6',
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'Programming Language :: Python :: 2.7',
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'Programming Language :: Python :: 3',
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'Programming Language :: Python :: 3.3',
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'Programming Language :: Python :: 3.4',
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'Programming Language :: Python :: 3.5',
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'Programming Language :: Python :: 3.6',
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'Programming Language :: Python :: 3.7',
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],
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package_data={'data/predefined_classes.txt': ['data/predefined_classes.txt']},
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options={'py2app': OPTIONS},
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setup_requires= SET_REQUIRES
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setup_requires=SET_REQUIRES,
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# $ setup.py publish support.
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cmdclass={
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'upload': UploadCommand,
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}
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)
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