Metadata-Version: 1.1
Name: Watershed
Version: 1.1.2
Summary: An image segmentation algorithm based on the watershed paradigm
Home-page: https://engineering.purdue.edu/kak/distWatershed/Watershed-1.1.2.html
Author: Avinash Kak
Author-email: kak@purdue.edu
License: Python Software Foundation License
Download-URL: https://engineering.purdue.edu/kak/distWatershed/Watershed-1.1.2.tar.gz
Description: 
        
        **version 1.1.2** fixes the module packaging errors that had crept into the previous version. 
        
        **version 1.1.1** presents cleaned-up documentation.  The implementation code remains unchanged.
        
        **Version 1.1** fixes a bug in the dilate() and erode()
        methods of the module that caused these methods to misbehave
        for non-square images.  Version 1.1 also includes
        improvements in the explanatory comments included in the
        scripts in the Examples directory.
        
        This module is a Python implementation of the Watershed
        algorithm for image segmentation.  The goal of this module
        is not to compete with the popular OpenCV implementation of
        the watershed algorithm.  On the other hand, the goal here
        is to provide an alternative framework that is more amenable
        to experimentation with the logic of watershed segmentation.
        
        Typical usage syntax:
        
        ::
        
                from Watershed import *
                shed = Watershed(
                           data_image = "orchid0001.jpg",
                           binary_or_gray_or_color = "color",
                           size_for_calculations = 128,
                           sigma = 1,
                           gradient_threshold_as_fraction = 0.1,
                           level_decimation_factor = 16,
                       )
                shed.extract_data_pixels()
                shed.display_data_image()
                shed.mark_image_regions_for_gradient_mods()                     #(A)
                shed.compute_gradient_image()
                shed.modify_gradients_with_marker_minima()                      #(B)
                shed.compute_Z_level_sets_for_gradient_image()
                shed.propagate_influence_zones_from_bottom_to_top_of_Z_levels()
                shed.display_watershed()
                shed.display_watershed_in_color()
                shed.extract_watershed_contours()
                shed.display_watershed_contours_in_color()
        
            The statements in lines (A) and (B) are needed only for marker-assisted
            segmentation with the module.  For a fully automated implemented of the
            BLM algorithm, you would need to delete those two statements.
                  
Keywords: image processing,image segmentation,computer vision
Platform: All platforms
Classifier: Topic :: Scientific/Engineering :: Image Recognition
Classifier: Programming Language :: Python :: 2.7
