Segmentor_tester.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Segmentor Exploration\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "<a name=\"Import_Image_Files\"></a>\n",
    "\n",
    "## Import Image Files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First import the following packages:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pylab as plt\n",
    "import imageio\n",
    "from see import Segmentors\n",
    "from see import JupyterGUI"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, read in the image to be segmented, and the ground truth segmentation mask of the image:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = JupyterGUI.pickimage()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "<a name=\"Manual_Search\"></a>\n",
    "\n",
    "## Manual Search"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Manual searching of parameters can easily be done using the provided GUI. Pre-established parameters can be put into the widget, or the parameter values can be changed using the sliders. To change the algorithm, simply change the `alg` input. For a list of available inputs print `Segmentors.algorithmspace`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "alg = JupyterGUI.picksegment(list(Segmentors.algorithmspace.keys()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "img, params = colorselect(colorspace, channel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "img, params = preprocess(img, params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "### Example of input for params\n",
    "params = JupyterGUI.segmentwidget(data.img, data.mask, params = None, alg = alg.value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With this code, make sure to import skimage, along with any input images this algorithm will be applied to."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mask = binarymorphcleaner(mask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "seg = Segmentors.algoFromParams(params.tolist())\n",
    "mask = seg.evaluate(data.img)\n",
    "print('Fitness Value: ', Segmentors.FitnessFunction(mask, data.mask)[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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