Metadata-Version: 2.1
Name: boxdetect
Version: 1.0.0
Summary: boxdetect is a Python package based on OpenCV which allows you to easily detect rectangular shapes like characters boxes on scanned forms.
Home-page: http://github.com/karolzak/boxdetect
Author: Karol Zak
Author-email: karol.zak@hotmail.com
License: MIT
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        **BoxDetect** is a Python package based on OpenCV which allows you to easily detect rectangular shapes like character or checkbox boxes on scanned forms.
        
        Main purpose of this library is to provide helpful functions for processing document images like bank forms, applications, etc. and extract regions where character boxes or tick/check boxes are present.
        
        ![](https://raw.githubusercontent.com/karolzak/boxdetect/master/images/example1.png)
        
        ## Features
        - `boxdetect.pipelines.get_boxes` - basic pipeline for boxes extraction
        - `boxdetect.pipelines.get_checkboxes` - pipeline which returns only the checkboxes with simple estimation of the state (checked/unchecked)
        - `boxdetect.config.PipelinesConfig` - advanced config class used to run pipelines
        - `boxdetect.config.PipelinesConfig.save_yaml/load_yaml` - allows to save and load configs into and from yaml files
        - `boxdetect.config.PipelinesConfig.autoconfigure` - simple mechanism to automatically set config based on box sizes list you're looking for
        - `boxdetect.config.PipelinesConfig.autoconfigure_from_vott` - automatically sets up config based on ground truth/annotation json files from [VoTT](https://github.com/microsoft/VoTT)
        - `boxdetect.img_proc` and `boxdetect.rect_proc` - utility functions which can be used to build custom pipelines
        
        ## Getting Started
        
        Checkout [usage examples below](#Usage-examples) to get a better understanding of how it works or go to [get-started-pipelines.ipynb](https://github.com/karolzak/boxdetect/blob/master/notebooks/get-started-pipelines.ipynb) and [get-started-autoconfig.ipynb](https://github.com/karolzak/boxdetect/blob/master/notebooks/get-started-autoconfig.ipynb) notebooks which holds step by step examples for using **BoxDetect** with premade `boxdetect.pipelines` functions.
        
        ## Installation
        
        **BoxDetect** can be installed directly from this repo using `pip`:
        
        ```
        pip install git+https://github.com/karolzak/boxdetect
        ```
        
        or through [PyPI](https://pypi.org/project/boxdetect/)
        
        ```
        pip install boxdetect
        ```
        
        # Usage examples
        
        You can use `BoxDetect` either by leveraging one of the pre-made pipelines or by treating `BoxDetect` functions as your toolbox to compose your own pipelines that fits your needs perfectly.
        
        - [Using pre-made pipelines](#using-boxdetectpipelines)  
            - [Detecting boxes and grouping them together with `boxdetect.pipelines.get_boxes`](#Detect-character-boxes-and-group-them-together)  
            - [Changing `group_size_range` param to highlight checkboxes](#highlighting-just-the-checkboxes)  
            - [Using `boxdetect.pipelines.get_checkboxes` to retrieve checkboxes and their values](#using-boxdetectpipelinesget_checkboxes-to-retrieve-and-recognize-just-the-checkboxes)  
            - [Using `boxdetect.config.PipelinesConfig.autoconfigure` to setup a config params based on the box sizes list](#using-boxdetectconfigpipelinesconfigautoconfigure-to-quickly-and-easily-setup-a-config-params-based-on-the-box-sizes-list)  
            - [Using `boxdetect.config.PipelinesConfig.autoconfigure_from_vott` to setup a config params based on the annotated ground truth](#using-boxdetectconfigpipelinesconfigautoconfigure_from_vott-to-quickly-and-easily-setup-a-config-params-based-on-the-annotated-ground-truth)  
            - [Saving and loading configuration to and from `yaml` files](#Saving-and-loading-configuration-to-and-from-yaml-files)  
        
        
        ## Using `boxdetect.pipelines`
        
        ### Detect character boxes and group them together
        [[back to usage examples]](#usage-examples)
        
        Start with getting the default `PipelinesConfig` and adjusting it for your requirements and data:
        ```python
        from boxdetect import config
        
        file_name = 'form_example1.png'
        
        cfg = config.PipelinesConfig()
        
        # important to adjust these values to match the size of boxes on your image
        cfg.width_range = (30,55)
        cfg.height_range = (25,40)
        
        # the more scaling factors the more accurate the results but also it takes more time to processing
        # too small scaling factor may cause false positives
        # too big scaling factor will take a lot of processing time
        cfg.scaling_factors = [0.7]
        
        # w/h ratio range for boxes/rectangles filtering
        cfg.wh_ratio_range = (0.5, 1.7)
        
        # group_size_range starting from 2 will skip all the groups
        # with a single box detected inside (like checkboxes)
        cfg.group_size_range = (2, 100)
        
        # num of iterations when running dilation tranformation (to engance the image)
        cfg.dilation_iterations = 0
        ```
        
        As a second step simply run:
        ```python
        from boxdetect.pipelines import get_boxes
        
        rects, grouping_rects, image, output_image = get_boxes(
            file_name, cfg=cfg, plot=False)
        ```
        
        Each of the returned elements in `grouping_rects` are rectangular bounding boxes representing grouped character boxes (x, y, w, h)
        ```python
        print(grouping_rects)
        
        OUT:
        # (x, y, w, h)
        [(276, 276, 1221, 33),
         (324, 466, 430, 33),
         (384, 884, 442, 33),
         (985, 952, 410, 32),
         (779, 1052, 156, 33),
         (253, 1256, 445, 33)]
        ```
        
        Display output image with bounding rectangles drawn on it
        ```python
        plt.figure(figsize=(20,20))
        plt.imshow(output_image)
        plt.show()
        ```
        
        ![](https://raw.githubusercontent.com/karolzak/boxdetect/master/images/example1.png)
        
        ### Highlighting just the checkboxes
        [[back to usage examples]](#usage-examples)
        
        And if you would like to highlight only the checkboxes you just need to change a single parameter:
        ```python
        # limit down the grouping algorithm to just singular boxes (e.g. checkboxes)
        cfg.group_size_range = (1, 1)
        ```
        
        ![](https://raw.githubusercontent.com/karolzak/boxdetect/master/images/checkbox-example.jpg)
        
        ### Using `boxdetect.pipelines.get_checkboxes` to retrieve and recognize just the checkboxes
        [[back to usage examples]](#usage-examples)
        
        Assuming we're using the same image and config is already adjusted ([look above](#Detect-character-boxes-and-group-them-together)) we just need to run:
        
        ```python
        from boxdetect.pipelines import get_checkboxes
        
        checkboxes = get_checkboxes(
            file_path, cfg=cfg, px_threshold=0.1, plot=False, verbose=True)
        ```
        If `verbose=True` it will print out bunch of details of checkboxes being detected and estimation being made about their state:
        ```
        Processing file:  ../images/form_example1.png
        ----------------------------------
        nonzero_px_count:  3
        all_px_count:  858
        nonzero_px_count / all_px_count =  0.0034965034965034965
        ----------------------------------
        ----------------------------------
        nonzero_px_count:  363
        all_px_count:  858
        nonzero_px_count / all_px_count =  0.4230769230769231
        ----------------------------------
        ```
        
        Now looking into the details of our results:
        ```python
        print("Output object type: ", type(checkboxes))
        for checkbox in checkboxes:
            print("Checkbox bounding rectangle (x,y,width,height): ", checkbox[0])
            print("Result of `contains_pixels` for the checkbox: ", checkbox[1])
            print("Display the cropout of checkbox:")
            plt.figure(figsize=(1,1))
            plt.imshow(checkbox[2])
            plt.show()
        ```
        We should see the following:
        
        ![](https://raw.githubusercontent.com/karolzak/boxdetect/master/images/checkboxes-details.jpg)
        
        
        ### Using `boxdetect.config.PipelinesConfig.autoconfigure` to quickly and easily setup a config params based on the box sizes list
        [[back to usage examples]](#usage-examples)
        
        BoxDetect allows you to provide a list of sizes (h, w) of boxes which you are interested in and based on that list it would automatically set up the config to detect those.
        
        ```python
        from boxdetect import config
        
        cfg = config.PipelinesConfig()
        
        # The values I'm providing below is a list of box sizes I'm interested in and want to focus on
        # [(h, w), (h, w), ...]
        cfg.autoconfigure([(46, 46), (44, 43)])
        ```
        
        And after doing that you can use any of the `boxdetect.pipelines` functions as bellow:
        
        ```python
        from boxdetect.pipelines import get_checkboxes
        
        checkboxes = get_checkboxes(file_path, cfg=cfg, plot=False)
        ```
        
        ### Using `boxdetect.config.PipelinesConfig.autoconfigure_from_vott` to quickly and easily setup a config params based on the annotated ground truth
        [[back to usage examples]](#usage-examples)
        
        Another option is to use ground truth annotations from VoTT.  
        Check VoTT repo and docs on how to create a new project and start labelling your data: https://github.com/microsoft/VoTT
        
        For this example I used VoTT to label my input image and my VoTT project looks somewhat like that:
        
        ![](https://raw.githubusercontent.com/karolzak/boxdetect/master/images/vott1.JPG)
        
        In principle you only need to mark a single box for each different size however the more boxes you will annotate - the more accurate the results should be.
        
        ```python
        from boxdetect import config
        
        cfg = config.PipelinesConfig()
        
        cfg.autoconfigure_from_vott(
            vott_dir="../tests/data/autoconfig_simple", class_tags=["box"])
        ```
        
        And after doing that you can use any of the `boxdetect.pipelines` functions as bellow:
        
        ```python
        from boxdetect.pipelines import get_checkboxes
        
        checkboxes = get_checkboxes(file_path, cfg=cfg, plot=False)
        ```
        
        ### Saving and loading configuration to and from `yaml` files
        [[back to usage examples]](#usage-examples)
        
        If you want to save a specific configuration for later reusability or automation purposes you can do so by using `PipelinesConfig` functions: `save_yaml` and `load_yaml` like below:
        
        ```python
        from boxdetect import config
        
        cfg = config.PipelinesConfig()
        cfg.morph_kernels_thickness = 10
        cfg.save_yaml('test_cfg.yaml')
        
        cfg2.load_yaml('test_cfg.yaml')
        ```
        
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
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
Description-Content-Type: text/markdown
