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packaging, tests and major refactorization (#2)
* update ignored extensions * initial code upload * major refactorization * add tests
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‎.gitignore

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*.pyc
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*.swp
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*.pkl
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*.pth
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result*
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weights*
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.vscode
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# Byte-compiled / optimized / DLL files
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__pycache__/
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*.py[cod]
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*.py,cover
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.hypothesis/
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.pytest_cache/
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cover/
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# Translations
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*.mo
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docs/_build/
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# PyBuilder
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.pybuilder/
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target/
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# Jupyter Notebook
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ipython_config.py
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# pyenv
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.python-version
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# For a library or package, you might want to ignore these files since the code is
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# intended to run in multiple environments; otherwise, check them in:
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# .python-version
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# pipenv
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# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
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# Pyre type checker
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.pyre/
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# pytype static type analyzer
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.pytype/
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# Cython debug symbols
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cython_debug/

‎LICENSE

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MIT License
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Copyright (c) 2020 fcakyon
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Copyright (c) 2019 NAVER Corp., 2020 Fatih C Akyon
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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The above copyright notice and this permission notice shall be included in
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all copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
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THE SOFTWARE.

‎README.md

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# craft-text-detector
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Cross-platform CRAFT text detection package on Pytorch
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[![PyPI version](https://badge.fury.io/py/craft-text-detector.svg)](https://badge.fury.io/py/craft-text-detector)
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[![Conda version](https://anaconda.org/fcakyon/craft-text-detector/badges/version.svg)](https://anaconda.org/fcakyon/craft-text-detector)
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[![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)
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## CRAFT: Character-Region Awareness For Text detection
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Packaged, Pytorch-based, easy to use, cross-platform version of the CRAFT text detector | [Paper](https://arxiv.org/abs/1904.01941) |
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### Overview
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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.
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<img width="1000" alt="teaser" src="./figures/craft_example.gif">
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## Getting started
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### Installation
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- Install using conda for Linux, Mac and Windows (preferred):
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```console
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conda install -c fcakyon craft-text-detector
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```
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- Install using pip for Linux and Mac:
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```console
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pip install craft-text-detector
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```
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### Basic Usage
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```python
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# import package
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import craft_text_detector as craft
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# set image path and export folder directory
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image_path = 'figures/idcard.png'
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output_dir = 'outputs/'
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# apply craft text detection and export detected regions to output directory
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prediction_result = craft.detect_text(image_path, output_dir, crop_type="poly", cuda=False)
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```
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### Advanced Usage
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```python
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# import package
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import craft_text_detector as craft
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# set image path and export folder directory
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image_path = 'figures/idcard.png'
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output_dir = 'outputs/'
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# read image
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image = craft.read_image(image_path)
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# load models
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refine_net = craft.load_refinenet_model()
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craft_net = craft.load_craftnet_model()
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# perform prediction
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prediction_result = craft.get_prediction(image=image,
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craft_net=craft_net,
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refine_net=refine_net,
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text_threshold=0.7,
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link_threshold=0.4,
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low_text=0.4,
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cuda=True,
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long_size=1280,
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show_time=True)
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# export detected text regions
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exported_file_paths = craft.export_detected_regions(image_path=image_path,
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image=image,
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regions=prediction_result["boxes"],
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output_dir=output_dir,
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rectify=True)
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# export heatmap, detection points, box visualization
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craft.export_extra_results(image_path=image_path,
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image=image,
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regions=prediction_result["boxes"],
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heatmaps=prediction_result["heatmaps"],
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output_dir=output_dir)
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```

‎craft_text_detector/__init__.py

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from __future__ import absolute_import
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__version__ = "0.1.8"
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from craft_text_detector.imgproc import read_image
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from craft_text_detector.file_utils import (export_detected_regions,
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export_extra_results)
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from craft_text_detector.predict import (load_craftnet_model,
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load_refinenet_model,
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get_prediction)
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# load craft model
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craft_net = load_craftnet_model()
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# detect texts
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def detect_text(image_path,
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output_dir=None,
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rectify=True,
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export_extra=True,
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text_threshold=0.7,
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link_threshold=0.4,
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low_text=0.4,
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cuda=False,
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long_size=1280,
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show_time=False,
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refiner=True,
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crop_type="poly"):
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"""
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Arguments:
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image_path: path to the image to be processed
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output_dir: path to the results to be exported
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rectify: rectify detected polygon by affine transform
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export_extra: export heatmap, detection points, box visualization
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text_threshold: text confidence threshold
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link_threshold: link confidence threshold
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low_text: text low-bound score
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cuda: Use cuda for inference
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long_size: desired longest image size for inference
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show_time: show processing time
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refiner: enable link refiner
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crop_type: crop regions by detected boxes or polys ("poly" or "box")
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Output:
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{"masks": lists of predicted masks 2d as bool array,
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"boxes": list of coords of points of predicted boxes,
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"boxes_as_ratios": list of coords of points of predicted boxes as ratios of image size,
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"polys_as_ratios": list of coords of points of predicted polys as ratios of image size,
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"heatmaps": visualization of the detected characters/links,
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"text_crop_paths": list of paths of the exported text boxes/polys}
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"""
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# load image
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image = read_image(image_path)
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# load refiner if required
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if refiner:
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refine_net = load_refinenet_model()
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else:
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refine_net = None
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# perform prediction
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prediction_result = get_prediction(image=image,
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craft_net=craft_net,
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refine_net=refine_net,
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text_threshold=text_threshold,
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link_threshold=link_threshold,
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low_text=low_text,
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cuda=cuda,
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long_size=long_size,
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show_time=show_time)
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# arange regions
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if crop_type == "box":
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regions = prediction_result["boxes"]
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elif crop_type == "poly":
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regions = prediction_result["polys"]
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else:
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raise TypeError("crop_type can be only 'polys' or 'boxes'")
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# export if output_dir is given
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prediction_result["text_crop_paths"] = []
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if output_dir is not None:
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# export detected text regions
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exported_file_paths = export_detected_regions(image_path=image_path,
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image=image,
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regions=regions,
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output_dir=output_dir,
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rectify=rectify)
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prediction_result["text_crop_paths"] = exported_file_paths
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# export heatmap, detection points, box visualization
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if export_extra:
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export_extra_results(image_path=image_path,
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image=image,
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regions=regions,
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heatmaps=prediction_result["heatmaps"],
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output_dir=output_dir)
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# return prediction results
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return prediction_result

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