mcquin/resize.ipynb

## resize.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy\n",
    "import scipy\n",
    "import skimage.io\n",
    "import skimage.transform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "scikit-image==0.13.0\n",
      "scipy==0.19.0\n",
      "numpy==1.12.1\n"
     ]
    }
   ],
   "source": [
    "print(\"scikit-image=={}\".format(skimage.__version__))\n",
    "print(\"scipy=={}\".format(scipy.__version__))\n",
    "print(\"numpy=={}\".format(numpy.__version__))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "labels = numpy.zeros((20, 20), dtype=numpy.uint8)\n",
    "\n",
    "labels[2:8, 2:8] = 1\n",
    "\n",
    "labels[0:8, 12:18] = 2\n",
    "\n",
    "labels[12:18, 0:8] = 3\n",
    "\n",
    "labels[12:20, 12:20] = 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/site-packages/skimage/io/_plugins/matplotlib_plugin.py:74: UserWarning: Low image data range; displaying image with stretched contrast.\n",
      "  warn(\"Low image data range; displaying image with \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fb9d6d8f550>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kQEZEVEiAjIjYl8gUOyKiWgJkRESJJGkiIqaRABkRUSEBcjj7/6uX8bqrDm67G/Pe4yc8\n23YXGnHT4xvb7sK8t+wDv9pnW6bYERFlDNR4nGvbEiAjohXjMILMl1VERDsa/EZxSWsk7ZB0f8X+\nEyXt6npm9sV1upgRZES0ouER5JXApcDV05T5ke3TBqk0I8iIaEeDI0jbtwKNP+8qATIiZt8gwbET\nIBdL2tC1rBqi1XdJulfSjZLeWucNmWJHxKxTsQxgp+2lIzR5D/AG289JWgF8Gzim35sygoyIdszi\nY19tP2P7uWJ9HbBQ0uJ+7+sbIMuyQ5L+WtJDkjZJukHSIRXvfVTSfUXWaMMAxxMRE06uv4zclvRa\nSSrWl9GJfU/2e1+dEeSVwPKebeuBt9n+N8BPgYumef97bR834vA4IiZNs5f5XAv8GDhW0lZJ50o6\nT9J5RZEzgfsl3Qt8GVhpu2/Nfc9B2r5V0tE9227uenl70XhERH0NXuZj+6w++y+lcxnQQJo4B/lR\n4MaKfQZulnR3v6yTpFVTGarnf/F8A92KiDlrgOl1m3fcjJTFlvQZYDdwTUWR99jeJuk1wHpJDxXX\nK+3D9mpgNcDif714DG5CioiRjMFf+dAjSEnnAKcBf1w1l7e9rfi5A7gBWDZsexExWcZhBDlUgJS0\nHPg0cLrtfb/HqFPmIEkHT60DpwCl90lGxDw0i5f5DKvOZT77ZIfonOw8mM60eaOky4qyr5O0rnjr\n4cBtRdboTuAfbH93Ro4iIsbOOIwg62Sxy7JDV1SUfRxYUaw/Arx9pN5FxGRqeWRYV241jIh2JEBG\nROwrz8WOiJhOAmRERDn1v9OvdQmQETH7kqSJiKiWc5ARERWUx75GRFTICDIiokTLd8jUlQAZEe1I\ngIyI2FcuFI+ImE6ug4yIKJcRZEREmVwoHhFRbRyug2zioV0REYNr9rGvayTtkFT61AJ1fFnSFkmb\nJB1fp4sJkBHRioa/UfxKYPk0+08FjimWVcBX61SaABkRs890sth1l37VdZ6W+tQ0Rc4ArnbH7cAh\nko7oV2/OQUZEKwbMYi+WtKHr9eriUdF1HQk81vV6a7Ft+3RvSoCMiHYMFiB32l46Qz2plAAZEbOu\nhTtptgFLul4fVWybVp3Hvu6THZL0OUnbike+bpS0ouK9yyX9pMgcXVjjICJiPhjk/GMzd9ysBT5c\nZLNPAHbZnnZ6DfVGkFfSeQ721T3bv2T7f1S9SdIC4CvAyXTm+3dJWmv7gRptRsSEa3IEKela4EQ6\n5yq3Ap8FFgLYvgxYR+eR1FuAXwEfqVNvnedi3yrp6CH6vAzYUjwfG0nfoJNJSoCMiEbvpLF9Vp/9\nBj4xaL2jXOZzfnHB5RpJh5bsr8oaRUQ0fR3kjBg2QH4V+G3gODpp8i+M2hFJqyRtkLTh+V88P2p1\nETGXGdjr+ktLhgqQtp+wvcf2XuBrdKbTvQbKGtlebXup7aWLDlk0TLciYpw0eKvhTBkqQPZcgf5B\noOz+x7uAYyS9UdL+wEo6maSIiLGYYvdN0lRkh06UdByd2P4o8PGi7OuAy22vsL1b0vnATcACYI3t\nzTNyFBExfibhC3MrskNXVJR9nE4qfer1Ojrp9YiIf+Hx+Lqz3EkTEbOucyfNBIwgIyJmREaQERHl\nMoKMiCiTZ9JERFRp7EsoZlQCZES0Io99jYiokhFkRESJXAcZETGNjCAjIirM/fiYABkR7ch1kBER\nVRIgIyJKmNxqGBFRRjhT7IiISgmQEREVxiBAjvJUw4iI4Uydg6y79CFpuaSfSNoi6cKS/edI+idJ\nG4vlY3W6mRFkRLSiqXOQkhYAXwFOpvN46bskrbX9QE/R62yfP0jdGUFGRDvs+sv0lgFbbD9i+9fA\nN4AzmuhiAmREtGCA4NgJkIslbehaVnVVdiTwWNfrrcW2Xn8gaZOk6yUtKdm/j0yxI2L2mUGTNDtt\nLx2hxb8HrrX9gqSPA1cB7+v3powgI6IdzSVptgHdI8Kjim0vsv2k7ReKl5cDv1Oni3Wei70GOA3Y\nYfttxbbrgGOLIocAv7B9XMl7HwWeBfYAu0f8HyAiJoj2NnYrzV3AMZLeSCcwrgT+40vako6wvb14\neTrwYJ2K60yxrwQuBa6e2mD7P3Q1/AVg1zTvf6/tnXU6ExHzhIG9zWSxbe+WdD5wE7AAWGN7s6RL\ngA221wJ/Jul0YDfwFHBOnbr7Bkjbt0o6umyfJAF/RI25fETEv2j2mTS21wHrerZd3LV+EXDRoPWO\nmqT5XeAJ2w9X7DdwsyQD/8v26qqKiqzUKoBFHMjjJzw7YtciYk4bgztpRg2QZwHXTrP/Pba3SXoN\nsF7SQ7ZvLStYBM/VAK/QYXP/NxcRo5nkAClpP+D3mSYbZHtb8XOHpBvoXNBZGiAjYh5p8BzkTBrl\nMp/3Aw/Z3lq2U9JBkg6eWgdOAe4fob2ImBgG762/tKRvgJR0LfBj4FhJWyWdW+xaSc/0WtLrJE2d\nKD0cuE3SvcCdwD/Y/m5zXY+IsdbcrYYzpk4W+6yK7eeUbHscWFGsPwK8fcT+RcQkGpMpdm41jIh2\nTHKSJiJiJAmQERFl2j23WFcCZETMPgPN3Ys9YxIgI6IdGUFGRFRIgIyIKONc5hMRUcrgFu+QqSsB\nMiLakRFkRESFnIOMiChh5zKfiIhKGUFGRJRzRpAREWVyq2FERLl83VlERDkD3rOn7W70NcojFyIi\nhuNmH7kgabmkn0jaIunCkv0HSLqu2H9H1aOseyVARkQrvNe1l+lIWgB8BTgVeAtwlqS39BQ7F3ja\n9puBLwGfr9PHBMiIaEdzI8hlwBbbj9j+NfAN4IyeMmcAVxXr1wMnSVK/iufkOchneXrn93z9P5bs\nWgzsnO3+tNjufG270XYXHNFe2wOaiN93hTd0v3iWp2/6nq9fPMD7F0na0PV6te3VxfqRwGNd+7YC\n7+x5/4tlbO+WtAt4FX2Oe04GSNuvLtsuaYPtpbPdn7bana9tz8djbrPtNtq1vXw22xtWptgRMe62\nAUu6Xh9VbCstI2k/4JXAk/0qToCMiHF3F3CMpDdK2h9YCaztKbMWOLtYPxP4gd3/SvU5OcWexur+\nRSaq3fna9nw85jbbbvOYR1acUzwfuAlYAKyxvVnSJcAG22uBK4CvS9oCPEUniPalGkE0ImJeyhQ7\nIqJCAmRERIU5FyBn6pahGu0ukXSLpAckbZb0yZIyJ0raJWljsVzcRNtF3Y9Kuq+od0PJfkn6cnHc\nmyQd31C7x3Ydz0ZJz0i6oKdMI8ctaY2kHZLu79p2mKT1kh4ufh5a8d6zizIPSzq7rMwQbf+1pIeK\n3+cNkg6peO+0n82QbX9O0rau3+mKivdO+/cwRLvXdbX5qKSNFe8d6Zgnhu05s9A5wfoz4E3A/sC9\nwFt6yvwpcFmxvhK4rqG2jwCOL9YPBn5a0vaJwHdm6NgfBRZPs38FcCMg4ATgjhn6/f8ceMNMHDfw\ne8DxwP1d2/47cGGxfiHw+ZL3HQY8Uvw8tFg/tIG2TwH2K9Y/X9Z2nc9myLY/B/xFjc9j2r+HQdvt\n2f8F4OKZOOZJWebaCHLGbhnqx/Z22/cU688CD9K5+n6uOAO42h23A4dIGuwekf5OAn5mu+wuppHZ\nvpVOBrFb9+d5FfDvS976AWC97adsPw2sBwa60Lisbds3295dvLydzvVzjas47jrq/D0M1W7xN/NH\nwLVD9GvemGsBsuyWod4g9ZJbhoCpW4YaU0zb3wHcUbL7XZLulXSjpLc22KyBmyXdLWlVyf46v5tR\nraT6D2amjvtw29uL9Z8Dh5eUmY1j/yidEXqZfp/NsM4vpvdrKk4tzORx/y7whO2HK/bP1DGPlbkW\nIFsn6eXAN4ELbD/Ts/seOtPPtwP/E/h2g02/x/bxdL6R5BOSfq/BuvsqLrA9Hfi/Jbtn8rhf5M7c\nbtavO5P0GWA3cE1FkZn4bL4K/DZwHLCdznR3Np3F9KPHVv89zhVzLUDO2C1DdUhaSCc4XmP7W737\nbT9j+7lifR2wUNIgN9xXsr2t+LkDuIHO9Kpbnd/NKE4F7rH9REnfZuy4gSemThUUP3eUlJmxY5d0\nDnAa8MdFgN5Hjc9mYLafsL3H9l7gaxV1zshxF383vw9cN03/Gj/mcTTXAuSM3TLUT3FO5grgQdtf\nrCjz2qnznZKW0fn9jRycJR0k6eCpdTrJg/t7iq0FPlxks08AdnVNTZtQOaKYqeMudH+eZwN/V1Lm\nJuAUSYcWU9FTim0jkbQc+DRwuu1fVZSp89kM03b3+eMPVtRZ5+9hGO8HHrK9taJvM3LMY6ntLFHv\nQidb+1M62bvPFNsuofOPGGARnWngFuBO4E0NtfseOtO7TcDGYlkBnAecV5Q5H9hMJ5t4O/BvG2r7\nTUWd9xb1Tx13d9ui86WgPwPuA5Y2+Ds/iE7Ae2XXtsaPm04A3g78hs75tHPpnD/+PvAw8D3gsKLs\nUuDyrvd+tPjMtwAfaajtLXTO8U193lNXR7wOWDfdZ9NA218vPsdNdILeEb1tV/09jNJusf3Kqc+2\nq2yjxzwpS241jIioMNem2BERc0YCZEREhQTIiIgKCZARERUSICMiKiRARkRUSICMiKjw/wFqRh3+\nR16mZAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb9d6df6910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "skimage.io.imshow(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "downsized = skimage.transform.resize(\n",
    "    labels,\n",
    "    (10, 5),\n",
    "    order=0,\n",
    "    mode=\"edge\",\n",
    "    preserve_range=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/site-packages/skimage/io/_plugins/matplotlib_plugin.py:77: UserWarning: Float image out of standard range; displaying image with stretched contrast.\n",
      "  warn(\"Float image out of standard range; displaying \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fb9d6b79510>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3pdXfCWVlieKI2BURB8u3dwFfrGrUwW1JaWKibavhOeBsSWdKOp7i8o5NR7V79GNCVlNj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\nbdlycFu2HNyWrf8DVFnj3Ryq7YoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb9d7bb9b90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "skimage.io.imshow(downsized)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
	{
	"cells": [
	{
	"cell_type": "code",
	"execution_count": 1,
	"metadata": {
	"collapsed": true
	},
	"outputs": [],
	"source": [
	"%matplotlib inline"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 2,
	"metadata": {
	"collapsed": true
	},
	"outputs": [],
	"source": [
	"import numpy\n",
	"import scipy\n",
	"import skimage.io\n",
	"import skimage.transform"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 3,
	"metadata": {
	"collapsed": false
	},
	"outputs": [
	{
	"name": "stdout",
	"output_type": "stream",
	"text": [
	"scikit-image==0.13.0\n",
	"scipy==0.19.0\n",
	"numpy==1.12.1\n"
	]
	}
	],
	"source": [
	"print(\"scikit-image=={}\".format(skimage.__version__))\n",
	"print(\"scipy=={}\".format(scipy.__version__))\n",
	"print(\"numpy=={}\".format(numpy.__version__))"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 4,
	"metadata": {
	"collapsed": true
	},
	"outputs": [],
	"source": [
	"labels = numpy.zeros((20, 20), dtype=numpy.uint8)\n",
	"\n",
	"labels[2:8, 2:8] = 1\n",
	"\n",
	"labels[0:8, 12:18] = 2\n",
	"\n",
	"labels[12:18, 0:8] = 3\n",
	"\n",
	"labels[12:20, 12:20] = 4"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 5,
	"metadata": {
	"collapsed": false
	},
	"outputs": [
	{
	"name": "stderr",
	"output_type": "stream",
	"text": [
	"/usr/local/lib/python2.7/site-packages/skimage/io/_plugins/matplotlib_plugin.py:74: UserWarning: Low image data range; displaying image with stretched contrast.\n",
	" warn(\"Low image data range; displaying image with \"\n"
	]
	},
	{
	"data": {
	"text/plain": [
	"<matplotlib.image.AxesImage at 0x7fb9d6d8f550>"
	]
	},
	"execution_count": 5,
	"metadata": {},
	"output_type": "execute_result"
	},
	{
	"data": {
	"image/png": 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kQEZEVEiAjIjYl8gUOyKiWgJkRESJJGkiIqaRABkRUSEBcjj7/6uX8bqrDm67G/Pe4yc8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\nR16mZAAAAABJRU5ErkJggg==\n",
	"text/plain": [
	"<matplotlib.figure.Figure at 0x7fb9d6df6910>"
	]
	},
	"metadata": {},
	"output_type": "display_data"
	}
	],
	"source": [
	"skimage.io.imshow(labels)"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 6,
	"metadata": {
	"collapsed": false
	},
	"outputs": [],
	"source": [
	"downsized = skimage.transform.resize(\n",
	" labels,\n",
	" (10, 5),\n",
	" order=0,\n",
	" mode=\"edge\",\n",
	" preserve_range=True\n",
	")"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 7,
	"metadata": {
	"collapsed": false
	},
	"outputs": [
	{
	"name": "stderr",
	"output_type": "stream",
	"text": [
	"/usr/local/lib/python2.7/site-packages/skimage/io/_plugins/matplotlib_plugin.py:77: UserWarning: Float image out of standard range; displaying image with stretched contrast.\n",
	" warn(\"Float image out of standard range; displaying \"\n"
	]
	},
	{
	"data": {
	"text/plain": [
	"<matplotlib.image.AxesImage at 0x7fb9d6b79510>"
	]
	},
	"execution_count": 7,
	"metadata": {},
	"output_type": "execute_result"
	},
	{
	"data": {
	"image/png": 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	"text/plain": [
	"<matplotlib.figure.Figure at 0x7fb9d7bb9b90>"
	]
	},
	"metadata": {},
	"output_type": "display_data"
	}
	],
	"source": [
	"skimage.io.imshow(downsized)"
	]
	},
	{
	"cell_type": "code",
	"execution_count": null,
	"metadata": {
	"collapsed": true
	},
	"outputs": [],
	"source": []
	}
	],
	"metadata": {
	"kernelspec": {
	"display_name": "Python 2",
	"language": "python",
	"name": "python2"
	},
	"language_info": {
	"codemirror_mode": {
	"name": "ipython",
	"version": 2
	},
	"file_extension": ".py",
	"mimetype": "text/x-python",
	"name": "python",
	"nbconvert_exporter": "python",
	"pygments_lexer": "ipython2",
	"version": "2.7.13"
	}
	},
	"nbformat": 4,
	"nbformat_minor": 2
	}