code/test_groundtruth.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt, argparse, numpy as np, math, sys, copy\n",
    "from skimage.segmentation import slic, mark_boundaries, felzenszwalb\n",
    "from skimage.util import img_as_float\n",
    "from scipy.cluster import hierarchy\n",
    "from skimage import io\n",
    "from collections import defaultdict\n",
    "import numpy as np\n",
    "import SPHC as sphc\n",
    "import superpixels as sp\n",
    "import plot as pl\n",
    "import process_ground_truth as pgt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "imagePath = 'images/'\n",
    "segmPath = 'images/segmentation/'\n",
    "borderPath = 'images/borders/'\n",
    "ultraPath = 'images/ultrametric/'\n",
    "groundPath = 'BSDS500/data/groundTruth/train/'\n",
    "trainPath = 'BSDS500/data/images/train/'\n",
    "imageFiles = ['3063.jpg'] #, '71076.jpg', '100007.jpg', '101084.jpg', '385039.jpg']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABIgAAADRCAYAAACn4onvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ncx0rrxgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f55eca3f6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ground_base = pgt.get_groundTruth((groundPath + '2092.mat'))\n",
    "ground_shape = (ground_base.shape[0], ground_base.shape[1], 3)\n",
    "\n",
    "img = np.zeros(ground_shape,dtype=np.uint8) #create blank image to save\n",
    "for i in range(3):\n",
    "    img[:, :, i] = ground_base[:,:, 0]\n",
    "\n",
    "image = img_as_float(io.imread((trainPath + '2092.jpg')))\n",
    "fs_image, fs_borders, ult = sp.process_image(image\n",
    "                                      , slic_segments = 1024\n",
    "                                      , felz_scale = 2048\n",
    "                                      , felz_min_size = 30\n",
    "                                      , ultrametric = True\n",
    "                                      , save=False\n",
    "                                      , filename = file\n",
    "                                      , paths=[segmPath, borderPath, ultraPath])\n",
    "\n",
    "#plot images\n",
    "lult = int(len(ult) / 3)\n",
    "segult = []\n",
    "labels = []\n",
    "segult.append(img)\n",
    "labels.append('Ground truth')\n",
    "for i in range(0,3):\n",
    "    index = i*lult\n",
    "        \n",
    "    segult.append(ult[index])\n",
    "    labels.append('Segments: ' + str(len(ult) - index))\n",
    "\n",
    "pl.plot_compare_4(segult, labels, True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision is: 0.373065435786\n",
      "Recall is: 0.392420173083\n"
     ]
    }
   ],
   "source": [
    "import eval_boundary as ev\n",
    "\n",
    "fs_ultrametric = ult[0][:, :, 0:1]\n",
    "precision, recall = ev.eval_bound(fs_ultrametric, ground_base, 4)\n",
    "print(\"Precision is: \" + str(precision))\n",
    "print(\"Recall is: \" + str(recall))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  },
  "widgets": {
   "state": {},
   "version": "1.1.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}