{ "info": { "author": "Avinash Kak", "author_email": "kak@purdue.edu", "bugtrack_url": null, "classifiers": [ "Programming Language :: Python :: 2.7", "Programming Language :: Python :: 3.6", "Topic :: Scientific/Engineering :: Image Recognition" ], "description": "\n\nConsult the module API page at\n\n https://engineering.purdue.edu/kak/distWatershed/Watershed-2.2.1.html\n\nfor all information related to this module, including information related\nto the latest changes to the code. The page at the URL shown above lists\nall of the module functionality you can invoke in your own code. That page\nalso describes how you can directly access the segmented blobs in your own\ncode and how you can apply a color filter to an image before its segmentation.\n\nWith regard to the basic purpose of the module, it is a Python\nimplementation of the watershed algorithm for image segmentation. This implementation\nallows for both fully automatic and marker-assisted segmentation of an image.\n\nTypical usage syntax:\n\n::\n\n from Watershed import *\n shed = Watershed(\n data_image = \"orchid0001.jpg\",\n binary_or_gray_or_color = \"color\",\n size_for_calculations = 128,\n sigma = 1,\n gradient_threshold_as_fraction = 0.1,\n level_decimation_factor = 16,\n padding = 20,\n )\n shed.extract_data_pixels()\n shed.display_data_image()\n shed.mark_image_regions_for_gradient_mods() #(A)\n shed.compute_gradient_image()\n shed.modify_gradients_with_marker_minima() #(B)\n shed.compute_Z_level_sets_for_gradient_image()\n shed.propagate_influence_zones_from_bottom_to_top_of_Z_levels()\n shed.display_watershed()\n shed.display_watershed_in_color()\n shed.extract_watershed_contours_seperated()\n shed.display_watershed_contours_in_color()\n\n The statements in lines (A) and (B) are needed only for marker-assisted\n segmentation with the module. 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