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|
%global _empty_manifest_terminate_build 0
Name: python-mpl-image-labeller
Version: 1.1.2
Release: 1
Summary: Use interactive matplotlib to label images for classification
License: BSD-3-Clause
URL: https://mpl-image-labeller.rtfd.io
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/b2/a4/147e4bc6074e648908c10cf6b39ebd6dcd176752715825be2056c86efc01/mpl_image_labeller-1.1.2.tar.gz
BuildArch: noarch
Requires: python3-matplotlib
Requires: python3-black
Requires: python3-flake8
Requires: python3-flake8-docstrings
Requires: python3-ipython
Requires: python3-isort
Requires: python3-jedi
Requires: python3-mypy
Requires: python3-pre-commit
Requires: python3-pydocstyle
Requires: python3-pytest
Requires: python3-Sphinx
Requires: python3-jupyter-sphinx
Requires: python3-myst-nb
Requires: python3-numpydoc
Requires: python3-sphinx-book-theme
Requires: python3-sphinx-copybutton
Requires: python3-sphinx-panels
Requires: python3-sphinx-thebe
Requires: python3-sphinx-togglebutton
Requires: python3-pytest
%description
# mpl-image-labeller
[](https://mybinder.org/v2/gh/ianhi/mpl-image-labeller/main?urlpath=lab/tree/docs/examples)
[](https://mpl-image-labeller.readthedocs.io/en/stable/?badge=stable)
[](https://github.com/ianhi/mpl-image-labeller/raw/master/LICENSE)
[](https://pypi.org/project/mpl-image-labeller)
[](https://python.org)
Use Matplotlib to label images for classification. Works anywhere Matplotlib does - from the notebook to a standalone gui!
For more see the [documentation](https://mpl-image-labeller.readthedocs.io/en/stable/?badge=stable).
## Install
```bash
pip install mpl-image-labeller
```
## Key features
- Simple interface
- Uses keys instead of mouse
- Only depends on Matplotlib
- Works anywhere - from inside Jupyter to any supported GUI framework
- Displays images with correct aspect ratio
- Easily configurable keymap
- Smart interactions with default Matplotlib keymap
- Callback System (see `examples/callbacks.py`)
**single class per image**
**multiple classes per image**
## Usage
```python
import matplotlib.pyplot as plt
import numpy as np
from mpl_image_labeller import image_labeller
images = np.random.randn(5, 10, 10)
labeller = image_labeller(
images, classes=["good", "bad", "meh"], label_keymap=["a", "s", "d"]
)
plt.show()
```
**accessing the axis**
You can further modify the image (e.g. add masks over them) by using the plotting methods on
axis object accessible by `labeller.ax`.
**Lazy Loading Images**
If you want to lazy load your images you can provide a function to give the images. This function should take
the integer `idx` as an argument and return the image that corresponds to that index. If you do this then you
must also provide `N_images` in the constructor to let the object know how many images it should expect. See `examples/lazy_loading.py` for an example.
### Controls
- `<-` move one image back
- `->` move one image forward
To label images use the keys defined in the `label_keymap` argument - default 0, 1, 2...
Get the labels by accessing the `labels` property.
### Overwriting default keymap
Matplotlib has default keybindings that it applied to all figures via `rcparams.keymap` that allow for actions such as `s` to save or `q` to quit. If you inlcude one of these keys as a shortcut for labelling as a class then that default keymap will be disabled for that figure.
## Related Projects
This is not the first project to implement easy image labelling but seems to be the first to do so entirely in Matplotlib. The below
projects implement varying degrees of complexity and/or additional features in different frameworks.
- https://github.com/wbwvos/pidgey
- https://github.com/agermanidis/pigeon
- https://github.com/Serhiy-Shekhovtsov/tkteach
- https://github.com/robertbrada/PyQt-image-annotation-tool
- https://github.com/Cartucho/OpenLabeling
%package -n python3-mpl-image-labeller
Summary: Use interactive matplotlib to label images for classification
Provides: python-mpl-image-labeller
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-mpl-image-labeller
# mpl-image-labeller
[](https://mybinder.org/v2/gh/ianhi/mpl-image-labeller/main?urlpath=lab/tree/docs/examples)
[](https://mpl-image-labeller.readthedocs.io/en/stable/?badge=stable)
[](https://github.com/ianhi/mpl-image-labeller/raw/master/LICENSE)
[](https://pypi.org/project/mpl-image-labeller)
[](https://python.org)
Use Matplotlib to label images for classification. Works anywhere Matplotlib does - from the notebook to a standalone gui!
For more see the [documentation](https://mpl-image-labeller.readthedocs.io/en/stable/?badge=stable).
## Install
```bash
pip install mpl-image-labeller
```
## Key features
- Simple interface
- Uses keys instead of mouse
- Only depends on Matplotlib
- Works anywhere - from inside Jupyter to any supported GUI framework
- Displays images with correct aspect ratio
- Easily configurable keymap
- Smart interactions with default Matplotlib keymap
- Callback System (see `examples/callbacks.py`)
**single class per image**
**multiple classes per image**
## Usage
```python
import matplotlib.pyplot as plt
import numpy as np
from mpl_image_labeller import image_labeller
images = np.random.randn(5, 10, 10)
labeller = image_labeller(
images, classes=["good", "bad", "meh"], label_keymap=["a", "s", "d"]
)
plt.show()
```
**accessing the axis**
You can further modify the image (e.g. add masks over them) by using the plotting methods on
axis object accessible by `labeller.ax`.
**Lazy Loading Images**
If you want to lazy load your images you can provide a function to give the images. This function should take
the integer `idx` as an argument and return the image that corresponds to that index. If you do this then you
must also provide `N_images` in the constructor to let the object know how many images it should expect. See `examples/lazy_loading.py` for an example.
### Controls
- `<-` move one image back
- `->` move one image forward
To label images use the keys defined in the `label_keymap` argument - default 0, 1, 2...
Get the labels by accessing the `labels` property.
### Overwriting default keymap
Matplotlib has default keybindings that it applied to all figures via `rcparams.keymap` that allow for actions such as `s` to save or `q` to quit. If you inlcude one of these keys as a shortcut for labelling as a class then that default keymap will be disabled for that figure.
## Related Projects
This is not the first project to implement easy image labelling but seems to be the first to do so entirely in Matplotlib. The below
projects implement varying degrees of complexity and/or additional features in different frameworks.
- https://github.com/wbwvos/pidgey
- https://github.com/agermanidis/pigeon
- https://github.com/Serhiy-Shekhovtsov/tkteach
- https://github.com/robertbrada/PyQt-image-annotation-tool
- https://github.com/Cartucho/OpenLabeling
%package help
Summary: Development documents and examples for mpl-image-labeller
Provides: python3-mpl-image-labeller-doc
%description help
# mpl-image-labeller
[](https://mybinder.org/v2/gh/ianhi/mpl-image-labeller/main?urlpath=lab/tree/docs/examples)
[](https://mpl-image-labeller.readthedocs.io/en/stable/?badge=stable)
[](https://github.com/ianhi/mpl-image-labeller/raw/master/LICENSE)
[](https://pypi.org/project/mpl-image-labeller)
[](https://python.org)
Use Matplotlib to label images for classification. Works anywhere Matplotlib does - from the notebook to a standalone gui!
For more see the [documentation](https://mpl-image-labeller.readthedocs.io/en/stable/?badge=stable).
## Install
```bash
pip install mpl-image-labeller
```
## Key features
- Simple interface
- Uses keys instead of mouse
- Only depends on Matplotlib
- Works anywhere - from inside Jupyter to any supported GUI framework
- Displays images with correct aspect ratio
- Easily configurable keymap
- Smart interactions with default Matplotlib keymap
- Callback System (see `examples/callbacks.py`)
**single class per image**
**multiple classes per image**
## Usage
```python
import matplotlib.pyplot as plt
import numpy as np
from mpl_image_labeller import image_labeller
images = np.random.randn(5, 10, 10)
labeller = image_labeller(
images, classes=["good", "bad", "meh"], label_keymap=["a", "s", "d"]
)
plt.show()
```
**accessing the axis**
You can further modify the image (e.g. add masks over them) by using the plotting methods on
axis object accessible by `labeller.ax`.
**Lazy Loading Images**
If you want to lazy load your images you can provide a function to give the images. This function should take
the integer `idx` as an argument and return the image that corresponds to that index. If you do this then you
must also provide `N_images` in the constructor to let the object know how many images it should expect. See `examples/lazy_loading.py` for an example.
### Controls
- `<-` move one image back
- `->` move one image forward
To label images use the keys defined in the `label_keymap` argument - default 0, 1, 2...
Get the labels by accessing the `labels` property.
### Overwriting default keymap
Matplotlib has default keybindings that it applied to all figures via `rcparams.keymap` that allow for actions such as `s` to save or `q` to quit. If you inlcude one of these keys as a shortcut for labelling as a class then that default keymap will be disabled for that figure.
## Related Projects
This is not the first project to implement easy image labelling but seems to be the first to do so entirely in Matplotlib. The below
projects implement varying degrees of complexity and/or additional features in different frameworks.
- https://github.com/wbwvos/pidgey
- https://github.com/agermanidis/pigeon
- https://github.com/Serhiy-Shekhovtsov/tkteach
- https://github.com/robertbrada/PyQt-image-annotation-tool
- https://github.com/Cartucho/OpenLabeling
%prep
%autosetup -n mpl-image-labeller-1.1.2
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-mpl-image-labeller -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Mon May 15 2023 Python_Bot <Python_Bot@openeuler.org> - 1.1.2-1
- Package Spec generated
|
