Metadata-Version: 1.1
Name: Watershed
Version: 2.0.4
Summary: An image segmentation algorithm based on the watershed paradigm
Home-page: https://engineering.purdue.edu/kak/distWatershed/Watershed-2.0.4.html
Author: Avinash Kak
Author-email: kak@purdue.edu
License: Python Software Foundation License
Download-URL: https://engineering.purdue.edu/kak/distWatershed/Watershed-2.0.4.tar.gz
Description: 
        
        Consult the module API page at
        
              https://engineering.purdue.edu/kak/distWatershed/Watershed-2.0.4.html
        
        for all information related to this module, including information related
        to the latest changes to the code.  The page at the URL shown above lists
        all of the module functionality you can invoke in your own code.  That page
        also describes how you can directly access the segmented blobs in your own
        code and how you can apply a color filter to an image before its segmentation.
        
        With regard to the basic purpose of the module, it 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,
                           padding = 20,
                       )
                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_seperated()
                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
Classifier: Programming Language :: Python :: 3.5
