{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Close Op"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Added new repo: scijava.public\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "method": "display_data"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "55424458-bc09-41d8-8ade-cd50ca504b20",
       "version_major": 2,
       "version_minor": 0
      },
      "method": "display_data"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "net.imagej.ImageJ@3baf5137"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "//load ImageJ\n",
    "%classpath config resolver scijava.public https://maven.scijava.org/content/groups/public\n",
    "%classpath add mvn net.imagej imagej 2.0.0-rc-67\n",
    "\n",
    "//create ImageJ object\n",
    "ij = new net.imagej.ImageJ()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This op performs [morphological closing](https://en.wikipedia.org/wiki/Closing_(morphology)) on any binary image. Let's see how the `Op` is called:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Available operations:\n",
       "\t(IterableInterval out?) =\n",
       "\tnet.imagej.ops.morphology.close.ListClose(\n",
       "\t\tIterableInterval out?,\n",
       "\t\tRandomAccessibleInterval in1,\n",
       "\t\tList in2)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ij.op().help(\"close\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note the parameters here:\n",
    "\n",
    "* `IterableInterval out`: the output image\n",
    "* `RandomAccessibleInterval in1`: the input image\n",
    "* `List in2`: this parameter contains all of the [`Shape`](http://javadoc.scijava.org/ImgLib2/net/imglib2/algorithm/neighborhood/Shape.html)s that the `Op` will use to close (i.e. for each `Shape` in `in2` close the input (or output of the last `close` if there are 2+ `Shape`s in `in2`)).\n",
    "\n",
    "We need to get a binary image to perform any morphological operations. Let's get a grayscale image and then binarize it using [the Huang Threshold Op](../threshold/threshold.ipynb#Huang-Thresholding):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] Verifying GIF format\n",
      "[INFO] Reading dimensions\n",
      "[INFO] Reading data blocks\n"
     ]
    },
    {
     "data": {
      "image/png": "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"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_full = ij.scifio().datasetIO().open(\"http://imagej.net/images/blobs.gif\")\n",
    "\n",
    "//input_full has 3 channels. We only need one.\n",
    "input = ij.op().run(\"hyperSliceView\", input_full, 2, 0)\n",
    "\n",
    "//invert the image so that the blobs are \"on\" and the background \"off\"\n",
    "inverted = ij.op().run(\"create.img\", input)\n",
    "ij.op().run(\"invert\", inverted, input)\n",
    "\n",
    "binaryInput = ij.op().run(\"threshold.huang\", inverted)\n",
    "\n",
    "ij.notebook().display(binaryInput)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As it states in the Wikipedia article, closing an image will bridge small gaps between objects. The smaller the gap, the wider the bridge:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import net.imglib2.algorithm.neighborhood.HyperSphereShape\n",
    "\n",
    "//create the shape that we are going to dilate the image with\n",
    "shape = new HyperSphereShape(6)\n",
    "\n",
    "output = ij.op().run(\"close\", null, binaryInput, [shape])\n",
    "\n",
    "ij.notebook().display(output)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To show the difference, let's [stack](../transform/stackView.ipynb) the images to see the difference:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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gVFDxQM/ub8a/9qz3dAAAAKQA8LdO/9WAPxr9CQRIRQUCAKDSID7IWj/++3OFFHLRAUC7oCdPABw489YtfwIA4DnQPBowXg8gEQKpzkSMHwAAAAXF0m9OPIyf4i4OQWDSyUAaksUHXnwAYFnebYrxy2SFqjvDkMaeJ8M1yfgj+lxSGKRzfDwAAACN5wAA8NUmA6lBwGkuZGoDogw7EwDdVIL0SnddipQFAHwRqdxd5MLU5yTweRsAfGkTEKg3gjH1E2NjW7j/q2XluQRfSR2HH7bb8nUIAAgd5QYB4E9GJXsnfpELYj0vUwCg5pQaEWGobkATjT927QEAAQprXEUcDGj67j/tBPAGAsbHMA/DWuYQAACeAKjQqU+eBA9DEm9z26xQAPBQVahAoKRrkI/67j8mOzT+JcN3QNbOMxZnCOxGb+aTksOSTr0ch+SfHr88UTaMlJtSODW5TwCgVehx01DZKdWTC/rFm6iSIk4LlZlO+fLNTzj/As763gQHyY2zAAAAAElFTkSuQmCC"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import net.imglib2.img.Img\n",
    "\n",
    "list = new ArrayList<Img>()\n",
    " \n",
    "list.add(binaryInput)\n",
    "list.add(output)\n",
    "\n",
    "stacked = ij.op().run(\"stackView\", list)\n",
    "\n",
    "ij.notebook().display(stacked)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note the yellow regions denoting the original blobs as well as the green bridges between the blobs."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Groovy",
   "language": "groovy",
   "name": "groovy"
  },
  "language_info": {
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   "file_extension": ".groovy",
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  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
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   "title_cell": "Table of Contents",
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   "toc_window_display": false
  }
 },
 "nbformat": 4,
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}