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|
%global _empty_manifest_terminate_build 0
Name: python-craft-text-detector
Version: 0.4.3
Release: 1
Summary: Fast and accurate text detection library built on CRAFT implementation
License: MIT
URL: https://github.com/fcakyon/craft_text_detector
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/7d/60/474d6ebd09c6db746a49af2dee0ac48547c6df35c3eee48056193677c794/craft-text-detector-0.4.3.tar.gz
BuildArch: noarch
Requires: python3-torch
Requires: python3-torchvision
Requires: python3-opencv-python
Requires: python3-scipy
Requires: python3-gdown
%description
# CRAFT: Character-Region Awareness For Text detection
<p align="center">
<a href="https://pepy.tech/project/craft-text-detector"><img src="https://pepy.tech/badge/craft-text-detector" alt="downloads"></a>
<a href="https://pypi.org/project/craft-text-detector"><img src="https://img.shields.io/pypi/pyversions/craft-text-detector" alt="downloads"></a>
<a href="https://twitter.com/fcakyon"><img src="https://img.shields.io/twitter/follow/fcakyon?color=blue&logo=twitter&style=flat" alt="fcakyon twitter">
<br>
<a href="https://github.com/fcakyon/craft-text-detector/actions"><img alt="Build status" src="https://github.com/fcakyon/craft-text-detector/actions/workflows/ci.yml/badge.svg"></a>
<a href="https://badge.fury.io/py/craft-text-detector"><img src="https://badge.fury.io/py/craft-text-detector.svg" alt="PyPI version" height="20"></a>
<a href="https://github.com/fcakyon/craft-text-detector/blob/main/LICENSE"><img alt="License: MIT" src="https://img.shields.io/pypi/l/craft-text-detector"></a>
</p>
Packaged, Pytorch-based, easy to use, cross-platform version of the CRAFT text detector | [Paper](https://arxiv.org/abs/1904.01941) |
## Overview
PyTorch implementation for CRAFT text detector that effectively detect text area by exploring each character region and affinity between characters. The bounding box of texts are obtained by simply finding minimum bounding rectangles on binary map after thresholding character region and affinity scores.
<img width="1000" alt="teaser" src="./figures/craft_example.gif">
## Getting started
### Installation
- Install using pip:
```console
pip install craft-text-detector
```
### Basic Usage
```python
# import Craft class
from craft_text_detector import Craft
# set image path and export folder directory
image = 'figures/idcard.png' # can be filepath, PIL image or numpy array
output_dir = 'outputs/'
# create a craft instance
craft = Craft(output_dir=output_dir, crop_type="poly", cuda=False)
# apply craft text detection and export detected regions to output directory
prediction_result = craft.detect_text(image)
# unload models from ram/gpu
craft.unload_craftnet_model()
craft.unload_refinenet_model()
```
### Advanced Usage
```python
# import craft functions
from craft_text_detector import (
read_image,
load_craftnet_model,
load_refinenet_model,
get_prediction,
export_detected_regions,
export_extra_results,
empty_cuda_cache
)
# set image path and export folder directory
image = 'figures/idcard.png' # can be filepath, PIL image or numpy array
output_dir = 'outputs/'
# read image
image = read_image(image)
# load models
refine_net = load_refinenet_model(cuda=True)
craft_net = load_craftnet_model(cuda=True)
# perform prediction
prediction_result = get_prediction(
image=image,
craft_net=craft_net,
refine_net=refine_net,
text_threshold=0.7,
link_threshold=0.4,
low_text=0.4,
cuda=True,
long_size=1280
)
# export detected text regions
exported_file_paths = export_detected_regions(
image=image,
regions=prediction_result["boxes"],
output_dir=output_dir,
rectify=True
)
# export heatmap, detection points, box visualization
export_extra_results(
image=image,
regions=prediction_result["boxes"],
heatmaps=prediction_result["heatmaps"],
output_dir=output_dir
)
# unload models from gpu
empty_cuda_cache()
```
%package -n python3-craft-text-detector
Summary: Fast and accurate text detection library built on CRAFT implementation
Provides: python-craft-text-detector
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-craft-text-detector
# CRAFT: Character-Region Awareness For Text detection
<p align="center">
<a href="https://pepy.tech/project/craft-text-detector"><img src="https://pepy.tech/badge/craft-text-detector" alt="downloads"></a>
<a href="https://pypi.org/project/craft-text-detector"><img src="https://img.shields.io/pypi/pyversions/craft-text-detector" alt="downloads"></a>
<a href="https://twitter.com/fcakyon"><img src="https://img.shields.io/twitter/follow/fcakyon?color=blue&logo=twitter&style=flat" alt="fcakyon twitter">
<br>
<a href="https://github.com/fcakyon/craft-text-detector/actions"><img alt="Build status" src="https://github.com/fcakyon/craft-text-detector/actions/workflows/ci.yml/badge.svg"></a>
<a href="https://badge.fury.io/py/craft-text-detector"><img src="https://badge.fury.io/py/craft-text-detector.svg" alt="PyPI version" height="20"></a>
<a href="https://github.com/fcakyon/craft-text-detector/blob/main/LICENSE"><img alt="License: MIT" src="https://img.shields.io/pypi/l/craft-text-detector"></a>
</p>
Packaged, Pytorch-based, easy to use, cross-platform version of the CRAFT text detector | [Paper](https://arxiv.org/abs/1904.01941) |
## Overview
PyTorch implementation for CRAFT text detector that effectively detect text area by exploring each character region and affinity between characters. The bounding box of texts are obtained by simply finding minimum bounding rectangles on binary map after thresholding character region and affinity scores.
<img width="1000" alt="teaser" src="./figures/craft_example.gif">
## Getting started
### Installation
- Install using pip:
```console
pip install craft-text-detector
```
### Basic Usage
```python
# import Craft class
from craft_text_detector import Craft
# set image path and export folder directory
image = 'figures/idcard.png' # can be filepath, PIL image or numpy array
output_dir = 'outputs/'
# create a craft instance
craft = Craft(output_dir=output_dir, crop_type="poly", cuda=False)
# apply craft text detection and export detected regions to output directory
prediction_result = craft.detect_text(image)
# unload models from ram/gpu
craft.unload_craftnet_model()
craft.unload_refinenet_model()
```
### Advanced Usage
```python
# import craft functions
from craft_text_detector import (
read_image,
load_craftnet_model,
load_refinenet_model,
get_prediction,
export_detected_regions,
export_extra_results,
empty_cuda_cache
)
# set image path and export folder directory
image = 'figures/idcard.png' # can be filepath, PIL image or numpy array
output_dir = 'outputs/'
# read image
image = read_image(image)
# load models
refine_net = load_refinenet_model(cuda=True)
craft_net = load_craftnet_model(cuda=True)
# perform prediction
prediction_result = get_prediction(
image=image,
craft_net=craft_net,
refine_net=refine_net,
text_threshold=0.7,
link_threshold=0.4,
low_text=0.4,
cuda=True,
long_size=1280
)
# export detected text regions
exported_file_paths = export_detected_regions(
image=image,
regions=prediction_result["boxes"],
output_dir=output_dir,
rectify=True
)
# export heatmap, detection points, box visualization
export_extra_results(
image=image,
regions=prediction_result["boxes"],
heatmaps=prediction_result["heatmaps"],
output_dir=output_dir
)
# unload models from gpu
empty_cuda_cache()
```
%package help
Summary: Development documents and examples for craft-text-detector
Provides: python3-craft-text-detector-doc
%description help
# CRAFT: Character-Region Awareness For Text detection
<p align="center">
<a href="https://pepy.tech/project/craft-text-detector"><img src="https://pepy.tech/badge/craft-text-detector" alt="downloads"></a>
<a href="https://pypi.org/project/craft-text-detector"><img src="https://img.shields.io/pypi/pyversions/craft-text-detector" alt="downloads"></a>
<a href="https://twitter.com/fcakyon"><img src="https://img.shields.io/twitter/follow/fcakyon?color=blue&logo=twitter&style=flat" alt="fcakyon twitter">
<br>
<a href="https://github.com/fcakyon/craft-text-detector/actions"><img alt="Build status" src="https://github.com/fcakyon/craft-text-detector/actions/workflows/ci.yml/badge.svg"></a>
<a href="https://badge.fury.io/py/craft-text-detector"><img src="https://badge.fury.io/py/craft-text-detector.svg" alt="PyPI version" height="20"></a>
<a href="https://github.com/fcakyon/craft-text-detector/blob/main/LICENSE"><img alt="License: MIT" src="https://img.shields.io/pypi/l/craft-text-detector"></a>
</p>
Packaged, Pytorch-based, easy to use, cross-platform version of the CRAFT text detector | [Paper](https://arxiv.org/abs/1904.01941) |
## Overview
PyTorch implementation for CRAFT text detector that effectively detect text area by exploring each character region and affinity between characters. The bounding box of texts are obtained by simply finding minimum bounding rectangles on binary map after thresholding character region and affinity scores.
<img width="1000" alt="teaser" src="./figures/craft_example.gif">
## Getting started
### Installation
- Install using pip:
```console
pip install craft-text-detector
```
### Basic Usage
```python
# import Craft class
from craft_text_detector import Craft
# set image path and export folder directory
image = 'figures/idcard.png' # can be filepath, PIL image or numpy array
output_dir = 'outputs/'
# create a craft instance
craft = Craft(output_dir=output_dir, crop_type="poly", cuda=False)
# apply craft text detection and export detected regions to output directory
prediction_result = craft.detect_text(image)
# unload models from ram/gpu
craft.unload_craftnet_model()
craft.unload_refinenet_model()
```
### Advanced Usage
```python
# import craft functions
from craft_text_detector import (
read_image,
load_craftnet_model,
load_refinenet_model,
get_prediction,
export_detected_regions,
export_extra_results,
empty_cuda_cache
)
# set image path and export folder directory
image = 'figures/idcard.png' # can be filepath, PIL image or numpy array
output_dir = 'outputs/'
# read image
image = read_image(image)
# load models
refine_net = load_refinenet_model(cuda=True)
craft_net = load_craftnet_model(cuda=True)
# perform prediction
prediction_result = get_prediction(
image=image,
craft_net=craft_net,
refine_net=refine_net,
text_threshold=0.7,
link_threshold=0.4,
low_text=0.4,
cuda=True,
long_size=1280
)
# export detected text regions
exported_file_paths = export_detected_regions(
image=image,
regions=prediction_result["boxes"],
output_dir=output_dir,
rectify=True
)
# export heatmap, detection points, box visualization
export_extra_results(
image=image,
regions=prediction_result["boxes"],
heatmaps=prediction_result["heatmaps"],
output_dir=output_dir
)
# unload models from gpu
empty_cuda_cache()
```
%prep
%autosetup -n craft-text-detector-0.4.3
%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-craft-text-detector -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Mon May 15 2023 Python_Bot <Python_Bot@openeuler.org> - 0.4.3-1
- Package Spec generated
|
