SEG.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = 10,10\n",
    "from segtools.segtools.scores_dense import seg\n",
    "import tifffile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def ellipsoid(sx=128, sy=128, loc=(64,64),a=10,coeff=2, ax1=np.array([3,2])):\n",
    "    q = np.zeros((sx,sy))\n",
    "    ax1n = ax1/np.sqrt(np.dot(ax1,ax1))\n",
    "\n",
    "    ax2n = np.array([ax1n[1],-ax1n[0]])\n",
    "    b=coeff*a\n",
    "\n",
    "    for i in range(sx):\n",
    "        for j in range(sy):\n",
    "            vec = np.array(loc) - np.array([i,j])\n",
    "            pr1 = np.dot(vec,ax1n)\n",
    "            pr2 = np.dot(vec,ax2n)\n",
    "\n",
    "            if (((pr1/a)**2 + (pr2/b)**2) <= 1):\n",
    "                q[i,j] = 1\n",
    "                \n",
    "    return q.astype(np.int32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ellipsoid_color(sx=128, sy=128, loc=(64,64), a=10,coeff=2, ax1=np.array([3,2]), color=[255,0,0,255]):\n",
    "    q = np.zeros((sx,sy,4))\n",
    "    ax1n = ax1/np.sqrt(np.dot(ax1,ax1))\n",
    "\n",
    "    ax2n = np.array([ax1n[1], -ax1n[0]])\n",
    "    b=coeff*a\n",
    "\n",
    "    for i in range(sx):\n",
    "        for j in range(sy):\n",
    "            vec = np.array(loc) - np.array([i,j])\n",
    "            pr1 = np.dot(vec,ax1n)\n",
    "            pr2 = np.dot(vec,ax2n)\n",
    "\n",
    "            if (((pr1/a)**2 + (pr2/b)**2) <= 1):\n",
    "                q[i,j,:] = np.array(color)\n",
    "                \n",
    "    return q.astype(np.uint8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "ell1 = ellipsoid()\n",
    "ell2 = ellipsoid(loc=(64,70))\n",
    "\n",
    "ell1_c = ellipsoid_color(color=[255,0,0,200])\n",
    "ell2_c = ellipsoid_color(color=[0,255,0,200], loc=[64,65])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x11ad44080>"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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BWAIAGIglAICBWAIAGIglAICBWAIAGIglAICBWAIAGIglAICBWAIAGIglAICBWAIAGIgl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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11abaa828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = (0.5*ell1_c + 0.5*ell2_c).astype(np.uint8)\n",
    "plt.imshow(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x11c0e0588>"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GUlW9p6oerqovbNp2flXdXlVfXb4+fdleVfWHVXWkqj5XVS/YycUDAOy0J3Jl6b1Jrjxm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\ngVgCABiIJQCAwf8H8lxlcWmEFIoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112ea87f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(ell1_c[:,:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/dibrov/Documents/PhD/software/segtools/segtools/scores_dense.py:29: RuntimeWarning: invalid value encountered in true_divide\n",
      "  return psg / (rsum + csum - psg)\n",
      "/Users/dibrov/Documents/PhD/software/segtools/segtools/scores_dense.py:41: RuntimeWarning: invalid value encountered in true_divide\n",
      "  m0 = np.where(tmp==0,0,psg / tmp)\n",
      "/Users/dibrov/Documents/PhD/software/segtools/segtools/scores_dense.py:43: RuntimeWarning: invalid value encountered in true_divide\n",
      "  m1 = np.where(tmp==0,0,psg / tmp)\n"
     ]
    }
   ],
   "source": [
    "score = seg(ell1_c[:,:,0],ell2_c[:,:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.916923076923077"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c9b2ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rang = 50\n",
    "score = np.zeros(rang)\n",
    "for dist in range(rang):\n",
    "    ell1_c = ellipsoid_color(color=[255,0,0,200])\n",
    "    ell2_c = ellipsoid_color(color=[0,255,0,200], loc=[64,64+dist])\n",
    "    img = (0.5*ell1_c + 0.5*ell2_c).astype(np.uint8)\n",
    "#     tifffile.imsave(\"../../data/seg/img_\" + str(dist).zfill(2)+\".tif\",img)\n",
    "    score[dist] = seg((ell1_c[:,:,0]/255).astype(np.int32), (ell2_c[:,:,1]/255).astype(np.int32))\n",
    "    plt.imshow(img)\n",
    "    plt.text(64,10,str(score[dist]), fontsize=20)\n",
    "    plt.axis('off')\n",
    "    plt.savefig(\"../../data/seg/img_\" + str(dist).zfill(2)+\".tif\")\n",
    "    plt.clf()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.        , 0.91692308, 0.84047267, 0.76988636, 0.70451436,\n",
       "       0.64379947, 0.58726115, 0.53448276, 0.48510131, 0.43879908,\n",
       "       0.39529675, 0.35729847, 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ])"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c52d3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(ell1_c)\n",
    "plt.text(64,10,\"sth\", fontsize=20)\n",
    "plt.axis('off')\n",
    "plt.savefig(\"../../data/bla.png\")"
   ]
  },
  {
   "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": "3.5.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}