Metadata-Version: 2.1
Name: craft-text-detector
Version: 0.3.5
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: # CRAFT: Character-Region Awareness For Text detection
        
        [![Downloads](https://pepy.tech/badge/craft-text-detector)](https://pepy.tech/project/craft-text-detector)
        [![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)
        
        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 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 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()
        ```
        
Keywords: machine-learning,deep-learning,ml,pytorch,text,text-detection,craft
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
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 :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Education
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Image Recognition
Requires-Python: >=3.6
Description-Content-Type: text/markdown
