{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Open 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": "8500594d-d570-4436-8810-ac4ea971c8ac",
       "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 opening](https://en.wikipedia.org/wiki/Opening_(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.open.ListOpen(\n",
       "\t\tIterableInterval out?,\n",
       "\t\tRandomAccessibleInterval in1,\n",
       "\t\tList in2)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ij.op().help(\"open\")"
   ]
  },
  {
   "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 open (i.e. for each `Shape` in `in2` open the input (or output of the last `open` 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": "iVBORw0KGgoAAAANSUhEUgAAAQAAAAD+CAYAAADVndu7AAAMnUlEQVR42u2dyXIkNwwF6/9/enxyeDxhya1uEnhLdgSPquKCl0UCIPQ8z/PrVDvxU+nHjX5N93mq76f7P9HftPbBXGeJ3xEAt39u8ELQc/MvA4Bmo5z4AQAAcA0AigsOAGb673xsaRe/BADaz6RTPwAAAK4AoEFYACDXedks/o8B0OCZdvb+A4DPxwQATMXfBCkAcH8cAEDc6XchPhoDAPIXzvQ/TfwVAFBfZADgd/yqBkDCuc6pX60AMPCeAwA8u/gAmrMXE2zmaZk0AEAmIAAAAJXiBwDXts89AED8AAAAsAMAAAAAAAAAAAAAfEXlDoC1KAAA6APAq89xEtRGX0+P1w4ANxcNCJyf+3ee6fJxmJzTrfWJAUD6DgMAeGZfnnzPKgBOD2ZL/EBg1rhOjcX0Dv36h+soAF7phKIRAgA9ANz68il8XJQAcGwnc2tQm4QDArsAOClaFZHefG40ABovarjH01PCdVtCBQAAwDpG3ZRXsC3+bQg8N4kJADyvqqaI/8SHzeHoOgYAF+dGQk2B7bE7b/0/hYCb3X70LqXkBjUAqMBHAXqNzlAnu337HTe8s+7FPJuPH4RD845u3/7dyfNOwhbYxdgAgN66WNqOqtEqb6MAAAAAAIKT2VbWCvFnHQNW5l7ZWJ0SKgAAAAAApvX8nQ0OAACAD5u/cTR7ngFABgAW597XSAg9AQC1tTC0E4wO8QMAR98VAAAArIXwepjYCAYHAKiJMDkmsXnfyfTD6UQjH0OiEK7ebbCUctwIGgCMXuw5BYB0wwYAFEVpT377EgANhg0A9M+xiH9srF0AmPw1zFNqVl3RWOcWXUEkAMAnkUXxRmjgbmennrtbnnYDABKu2TpWXooEgLshtQHAPc/e+cgDAMS3lHz9s30e22O0AoCzMQEAjTLcAEAUACecIMrGhPgBgOrlHRkAfJoFqC4mhE/2nertPRkAJF/mQPwAgLBnMQCoAch8KEer1lOBW7bWCB8AJISrAUBfDTeOROHlu4b7gnOtUfgAwBsAUhWBUsJqbeJvBoDDuIbeDwC4ew8Aiu0QAACBrjXDDo0AgEDJA0D8AKBWiADAN93WxIYBQHv1HQBwbjyG8wIAyEN/EP+BMZnOD55kPNEAYEL8Csc6AFB6Hm2uxHt7TG5raJEJiPhJjf67P8oA2J6vA+8HAABAa0t7wxOvBoBtsP3WB86R1N7T8WxPeuW3tv8n5u6g1jhHuiXcMD+/rLf/n67j4Z022VYAIDccmgaAC0ftmGuNFN4kJDr6QZpex0uRttnFJd8eADgAQPG4awsALtsQIVGIpTsdLwBAUN49ANBJpnGKMACAkLRbAKB1J0JhSw4AipJuVABww+BdL0WpJxgBgLC8hE3x3/Z2NxZmdY//SwDA5cvrHAF49/nKc9RaXeiSHeU64lTO3Vv9mPy5F0Vx8SvZA8B8u2QDgOlfa1l2x6PklevAasRMBIDq1/+2kbv7msT9D3lnJkUDBwA94WYzLWl6ehWN3yUasfUDAIbHaRcHRkKJMvcdEOIPLC/v8hVMqVHomjACAEKb01cppUipowMU8YfekE0EgFP+vXr+A+IPr4/h9FXmAg5l2re97y53HaQAkBAHp/iImPPqQgXhhn8AcxwAUxCgCk9u5eGf9sltDaX7CwAyz7pp4k/a1Uj11cU731SHv6Uyc3rCkkVfHQDA/yoIczwtin9yrABAzCjbwlzK40wHgA2olD30TV/ETWNMClWqrC8AELo+6V54IjEDDQDs9fFfzycj7CGrDQBU5GH85/NJC6UBgHwAfPl8AEADANlHgG+fDwBojeJ3AMCIAxgA0ABAZhbgS+8AADQAkJeO/fJ7AIC+MABATzbgeLgbAJCuixMwKyPzR+9D/NzYa4VA6v8OAABFxs88YK8AoNzomRfsdRUAiHrf0Jkz5msFAPFlkynYSbmz4Hl6VCaSM69u3jjOUwCwHq9MWUhFADjONwBYAkBVwcQCADjPNeI/sq47X4C0s9yrfVMCQIp4EP9H8zKfipro/HICQOJuC/G/XQ7OexsMAH42F6oOX8LDO7p73M/ASsb3yvNcAXBinlq36eIftQ4AuF4OURH/hKORzMaVechPglE0PgCQDQKjI25++EvV8KYNRD3SQL7HyhwAAFcIOIReyfWQB4H/La+EsJdKxp/7joNkLwBgf/5MSb5qBMDEDwAMACAxLAUAcmoaAAAAAAAoaPJWHwEAAAAAhQBYywTEB9CZmGIUC7esb3io5DwAAAAAAAAQBgQAoqFHlzVxAkBMIlDb3fdmAKivkTIA3ooCnHyRCwAQ//k5MYuLAwAXQ6BohMeFHMPkmF4AuHwNUivepl5JbYCAPQCmXt6YIUb5bMs8+bhEoC8BMN0JxN9ZiAIACAJguyOIv6sKDX4AoXEqdAbxd/2fBQAgNEYHJxHizvOrAAGRsSl1KlX8LrF2RJJT/fplfeGNf2SEWVCAUqI6clth0G+f3QwANWG2hVXTAeDgYK8FwGbW4en5BADe9riZfPUw4efHOT2nAACH8du7WwBwfqwAwNtZ1hQRehD/2fFuGLKzaADA8tEWAAAA/oMOAMD5VwoAkpi6k8EexO8PAPfah4h/1a/VcxnntqFtbmcbk2YQ/pEd4Fy6KgC4b9ju4kH04zbdM/ENAEi5WHWrzzizvwAAtPTd/tO6dhaHbQ1DAACst8u8X8ibwCgAQOaxIg28l1Kn+TKopAA7h+YAAACoL9SRAICknUsSAG79AACXciIz8tJ8LwDA4D62s4DS/BdJALj5e5S+oBTm9L+XDwD+eX8kAAhp5eW0AwDt6tYyACCuzdcfAMznGwAAgzROR/FcMqg6AEy86+I6aW9vHYzOUTwXvyhVWYBT83RxjXS/bk3hs+lxNADg9gdkcp4uro2ecXOGtg4rVVwCmp6ni+/TAkD7ORoAeNTeV98R/+BdWsbdvo0GAIR8hx2OWgbeLiIAAACGjzY6OwBERN8dyp+FgTLr6z81wQDAK4QJAAAA4g8AgEo/AQAAqPuKJvTZobwcABA2zBQHWiuwFHYBlQBIyahL8p4n93NiHLEA2JxwZUGlhc9cjFZ5zgPCpACgPX6ubKwOc37zfQNj0NqOAwDa1JxP5ggI784AAADoFL/ivC+MQ8tT3AgAxA4AFkuya3WoMQcAsQOAxbF411dzBwBCBwDLY9GofOoUYwUAACDIlrIWnzRaAOCyDiJjyVl8/gcAAAAAIQBQz1RD/DkAKE9xxtDSc+kBAAAAAOX59JsOXQAAAOpgkPZFBQCxSWUIl11KpqMVAAAAxG8sGKIAAIBWdJOuLRFIZCwIBQAAAABAQ/xlEOAeAAAAAKW+gPZbgL/9LWJB/ACgLTwLAACAlZAabmBOjeGP5yAYANCTIHTj/W4FWgEAAOBKc2Dq9JvwQjAAAACcmle3/gMAAAAADs4pAPj6RbShsyUA2AVqLQAwGI1zJQAAAD9wLnIvPtGoAABFX0Z3ABiNXggL8QOAEQC4X8FMzfsGAACgHgCpKa0N9QA4/wcDQPUmWVo+OwAAAJUAUDPuTSECAABwNRMwUWxJhTkQPwC4ehdAyZgUDV0hHIf4u7MAr14HTv3aJhbnQPwkuB2vCKQiNtWzrnpSDqKvniONr5y7o43U3D4QhIwJAAAAWm1TEV9yBVkAQJMGwO1zZIKzjV0ALfLYccqA2AEAAJph6PHUgwEAV3Rphlm1px6qHs5qAkASBJpgt7Lmpx4KANgFJN/lSLsqDgCC7wa4A6AJkutjOvlQAJDZ19SKxttzJjGWJAC43X8HABri35o3ibGcnhgA4H93oVH80/MnM44bE5MYc489A4rUUFT8Vfg4bk1GEgBiPcBC4TfVHwAwBIBqXYDECkbJ4k+A3TEATB4Dkr+s6rUVU45s7WFgWwBEZFeZ1qBLc9wCADMAxKRXmlahAQAAYM0PkL61PjGG1PRbfAAGAHAuqNgUTwcAAEAKAP83aEXj5ioqEAAAwlvhG8cT7qIDgLQ8gCgA8PXOBgDZgEt9dxEUAMhPRgIAYgBwNU4EzT93UQeA/G1Ad+NE1J75Ei3il7m8pi6chBg+YUA/CDhfh175xyAAAAAAAMs1oEwyLS+F21H8S0l0fJFomg7XZvEPNgBA086aRPgAQC5+CwR+YR8AoO/832Q8riJC+MUAYEHvrwXzAQDIAqTRAAAASC4QQgMAXASi0QAADikafgoAEFIMBIH476aajoWXx+M7IcaTXnnTj9RwyV0vX8bU0GNaplxLtt/wmBB9WmZZYqpsS8rvwrgQvlJFnIgqM+xmbHY4fwGozMRYCXU/OwAAAABJRU5ErkJggg=="
     },
     "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, opening is able to remove any object smaller than our `Shape`, while (as best it can) leaving objects larger than that element the same. Let's open our image with a somewhat large `Shape` to remove the smallest blobs in the image:"
   ]
  },
  {
   "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(\"open\", 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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AQiAabTgA3oEAE0nbizhFLAAUVIWSDOS3Rl90+zoWAJV38UmLaD9sCX1JnLexAFBbYKmGr9bHJJAJeTcY/YkFlmr4Kv1LApmYh4PhL3ZJ+f7tARDo6Nc/NPBPwM/Hqa4AAAAASUVORK5CYII="
     },
     "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 where the largest blobs remained (mostly) intact from the original in red to the output of the `Op` in green. Note the more [eccentric](https://en.wikipedia.org/wiki/Eccentricity_(mathematics)) the object the poorer it was restored (due to our `Shape` being a `HyperSphere`). However the smallest objects (shown in red) are removed from the image."
   ]
  }
 ],
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   "language": "groovy",
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  "toc": {
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