{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Erode 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": "e33c8d93-d958-4986-8312-613337cfcfa9",
       "version_major": 2,
       "version_minor": 0
      },
      "method": "display_data"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "net.imagej.ImageJ@7b00c550"
      ]
     },
     "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 erosion](https://en.wikipedia.org/wiki/Erosion_(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.erode.DefaultErode(\n",
       "\t\tIterableInterval out?,\n",
       "\t\tRandomAccessibleInterval in1,\n",
       "\t\tShape in2,\n",
       "\t\tboolean isFull?,\n",
       "\t\tOutOfBoundsFactory f?)\n",
       "\t(IterableInterval out?) =\n",
       "\tnet.imagej.ops.morphology.erode.ListErode(\n",
       "\t\tIterableInterval out?,\n",
       "\t\tRandomAccessibleInterval in1,\n",
       "\t\tList in2,\n",
       "\t\tboolean isFull?)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ij.op().help(\"erode\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note the parameters here:\n",
    "\n",
    "* `IterableInterval out`: the output image\n",
    "* `RandomAccessibleInterval in1`: the input image\n",
    "* `Shape in2`: the [`Shape`](http://javadoc.scijava.org/ImgLib2/net/imglib2/algorithm/neighborhood/Shape.html) that the `Op` will use to dilate each edge point of a structure. Note that in the Wikipedia article linked in the previous cell a circle was used; there are many other options (such as a rectangle) that are also available in ImageJ-Ops.\n",
    "* `List in2`: this parameter, only used in `ListErode`, contains a list of `Shape`s each used once in a series of erosions (i.e. for each `Shape` in the `List` call `DefaultErode` using the input (from the last `erode` if not the first) and that `Shape`)\n",
    "* `boolean isFull`: an **optional** parameter that the user can set to tell the `Op` if the **dimensions of the output** are bigger than the dimensions of the input **plus** the radius of the `Shape` parameter. This parameter should **never** be set to true if **no output is given**, since this will cause the output to be translated and therefore inaccurate.\n",
    "* `OutOfBoundsFactory f`: an **optional** parameter that tells the `Op` how to extend the input (so that the `Shape` can be applied outside each of the input's edge values without error). If no `OutOfBoundsFactory` is given all values outside the input `Interval` are set to the \"off\" value of the binary image.\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": [
    "Now that we have our image, let's erode it :"
   ]
  },
  {
   "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(4)\n",
    "\n",
    "output = ij.op().run(\"erode\", null, binaryInput, shape, false)\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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"
     },
     "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 red original image and the green eroded image are the same, as well as the red borders that show where the erosion occurred."
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Groovy",
   "language": "groovy",
   "name": "groovy"
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  "language_info": {
   "codemirror_mode": "groovy",
   "file_extension": ".groovy",
   "mimetype": "",
   "name": "Groovy",
   "nbconverter_exporter": "",
   "version": "2.4.3"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": false,
   "sideBar": false,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": false,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}