%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

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. teaser

## 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

## 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 * Tue May 30 2023 Python_Bot - 0.4.3-1 - Package Spec generated