Metadata-Version: 2.1
Name: craft-text-detector
Version: 0.1.8
Summary: Fast and accurate text detection library built on CRAFT implementation
Home-page: https://github.com/fcakyon/craft_text_detector
Author: Fatih Cagatay Akyon
License: MIT
Description: [![PyPI version](https://badge.fury.io/py/craft-text-detector.svg)](https://badge.fury.io/py/craft-text-detector)
        [![Conda version](https://anaconda.org/fcakyon/craft-text-detector/badges/version.svg)](https://anaconda.org/fcakyon/craft-text-detector)
        [![CI](https://github.com/fcakyon/craft-text-detector/workflows/CI/badge.svg)](https://github.com/fcakyon/craft-text-detector/actions?query=event%3Apush+branch%3Amaster+is%3Acompleted+workflow%3ACI)
        
        
        ## CRAFT: Character-Region Awareness For Text detection
        Packaged Version of the Official Pytorch implementation of CRAFT text detector | [Paper](https://arxiv.org/abs/1904.01941) |  [Supplementary](https://youtu.be/HI8MzpY8KMI) |
        
        **[Youngmin Baek](mailto:youngmin.baek@navercorp.com), Bado Lee, Dongyoon Han, Sangdoo Yun, Hwalsuk Lee.**
        
         **Package maintainer: Fatih Cagatay Akyon**
        
        ### 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">
        
        ## Updates
        **6 April, 2020**: Conda package release
        
        **1 April, 2020**: Python 3.8 support, removed skimage dependency
        
        **24 March, 2020**: Polygon rectification support
        
        **23 March, 2020**: Python 3.5 support
        
        **21 March, 2020**: Initial package release
        
        
        ## Getting started
        ### Installation
        - Install using conda for Linux, Mac and Windows (preferred):
        ```console
        conda install -c fcakyon craft-text-detector
        ```
        - Install using pip for Linux and Mac:
        ```console
        pip install craft-text-detector
        ```
        
        ### Basic Usage
        ```python
        # import package
        import craft_text_detector as craft
        
        # set image path and export folder directory
        image_path = 'figures/idcard.png'
        output_dir = 'outputs/'
        
        # apply craft text detection and export detected regions to output directory
        prediction_result = craft.detect_text(image_path, output_dir, crop_type="poly", cuda=False)
        ```
        
        ### Advanced Usage
        ```python
        # import package
        import craft_text_detector as craft
        
        # set image path and export folder directory
        image_path = 'figures/idcard.png'
        output_dir = 'outputs/'
        
        # read image
        image = craft.read_image(image_path)
        
        # load models
        refine_net = craft.load_refinenet_model()
        craft_net = craft.load_craftnet_model()
        
        # perform prediction
        prediction_result = craft.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,
        				         mag_ratio=0.8,
        				         show_time=True)
        
        # export detected text regions
        exported_file_paths = craft.export_detected_regions(image_path=image_path,
                                                            image=image,
                                                            regions=prediction_result["boxes"],
                                                            output_dir=output_dir,
                                                            rectify=True)
        
        # export heatmap, detection points, box visualization
        craft.export_extra_results(image_path=image_path,
            	                   image=image,
                                   regions=prediction_result["boxes"],
                                   heatmap=prediction_result["heatmap"],
                                   output_dir=output_dir)
        ```
        
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.5
Description-Content-Type: text/markdown
