frame_difference_vs_motion_estimation_plus_residual.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Frame difference vs motion estimation + residual"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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9gE8CK2u2afd7slAl5H3ZOQ/m/WbM+4LzPqVU6g24Dfh6zf0AHgJOK6j/54G3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\nsvkNMzrpBmYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f837f59e828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# loading the frames\n",
    "frame_0 = mpimg.imread('i/smw_background_ball_1.png')\n",
    "frame_1 = mpimg.imread('i/smw_background_ball_2.png')\n",
    "# showing the frames\n",
    "f, (plt1, plt2) = plt.subplots(1, 2)\n",
    "plt1.set_title('frame_0');plt1.imshow(frame_0,interpolation='nearest');\n",
    "plt2.set_title('frame_1');plt2.imshow(frame_1,interpolation='nearest');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Frame difference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f837c47a748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(np.subtract(frame_0[:,:,0], frame_1[:,:,0]),interpolation='nearest',cmap=plt.cm.binary);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8360131d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# the block we want to motion estimate\n",
    "ball = frame_0[27:37,1:11,]\n",
    "\n",
    "block_size = 10\n",
    "plt.imshow(ball,interpolation='nearest');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Motion estimation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f83600a20b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's pretend we had motion estimation (full scan, reduced scan(lfp)...) \n",
    "# and that the motion vectors were:\n",
    "x = 26 \n",
    "y = 10\n",
    "predicted_frame = np.array(frame_1)\n",
    "\n",
    "# applying the motion compensation\n",
    "predicted_frame[x:(x+block_size),y:(y+block_size),] = ball\n",
    "\n",
    "plt.imshow(predicted_frame,interpolation='nearest');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Frame difference vs motion estimation + residual"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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0JPAGYqJnbQmsCSEsy7QvAqZVHp6ZNZpz3qw1VFTwJW1NPF43M4TwYiVPBcJw\nHebMmUN7e3tJW1dXF11dXZWEaJYbPT099PT0lLStXr26quuoZc4DzJ07l7a2tpK2GTNm0NnZWVGc\nZnnQ29vL/PnzS9oGBgZG/fxK9/B3B7YA7pekpG09YB9JJwAHAW2SpmS2+KcSt/iH1N3dTUdHR4Xh\nmOVXuQ3ivr4+Zs2aVc3V1CznAWbOnMn06dOrGa9Zy+rs7By0Mdzf38/s2bNH9fxKC/7NwK6Ztv8C\nHgIuAJ4BXgQOAH4OIGknYBvgzgrXZWaN55w3axEVFfwQwgrgwXSbpBXAcyGEh5LHs4GLJT0PLAcu\nBW4PIdxTnZDNrF6c82ato+KT9srIHqc7GVgLXAO0ATcCx1dhPWbWHJzzZhPQuAt+CGH/zOMB4MTk\nz8xajHPebGLyvfTNzMxywAXfzMwsB1zwzczMcsAF38zMLAdc8M3MzHLABd/MzCwHXPDNzMxywAXf\nzMwsB1zwzczMcsAF38zMLAdc8M3MzHLABd/MzCwHXPDNzMxywAXfzMwsB1zwzczMcsAF38zMLAdc\n8M3MzHLABd/MzCwHXPDNzMxywAXfzMwsB1zwzczMcsAF38zMLAcqKviSzpK0LvP3YGp+m6TLJC2R\ntFzSNZKmVj9sM6sX571ZaxjLHn4vsCUwLfl7e2reJcAhwOHAPkAHcO04YzSzxnPem01w64/hOS+F\nEBZnGyVNAT4JHBlC+E3S9gngIUl7hBDuGV+oZtZAznuzCW4se/g7SnpG0gJJV0p6TdK+O3ED4pZC\nxxDCw8CTwF7jD9XMGsh5bzbBVVrw7wI+DhwIHAtsB/xW0mTiMN+aEMKyzHMWJfPMbGJy3pu1gIqG\n9EMIN6Ue9kq6B3gC+CCweoinCQgjLXvOnDm0t7eXtHV1ddHV1VVJiGa50dPTQ09PT0nb6tVDpeHY\n1TLv586dS1tbW0nbjBkz6OzsHGO0Zq2rt7eX+fPnl7QNDAyM+vljOYZfFEJYKukRYAfgZmBDSVMy\nW/tTiVv7w+ru7qajo2M84ZjlSrkN4r6+PmbNmlXT9VYz72fOnMn06dNrFKlZa+ns7By0Mdzf38/s\n2bNH9fxxXYcvaRPgdUAfcD/wEnBAav5OwDbAneNZj5k1D+e92cRU0R6+pK8BNxCH87YCvkpM9p+E\nEJZJmg1cLOl5YDlwKXC7z9Q1m7ic92atodIh/a2Bq4DNgMXA74C3hhCeS+afDKwFrgHagBuB46sT\nqpk1iPOqSRq0AAAJPElEQVTerAVUetLeh0aYPwCcmPyZWQtw3pu1Bt9L38zMLAdc8M3MzHLABd/M\nzCwHXPDNzMxywAXfzMwsB1zwzczMcsAF38zMLAdc8M3MzHLABd/MzCwHXPDNzMxywAXfzMwsB1zw\nzczMcsAF38zMLAdc8M3MzHLABd/MzCwHXPDNzMxywAXfzMwsB1zwzczMcsAF38zMLAdc8M3MzHLA\nBd/MzCwHXPDNzMxyoOKCL6lD0g8lLZG0UtIDkt6U6XO2pL5k/lxJO1QvZDOrN+e92cRXUcGXtClw\nOzAAHAjsApwCPJ/qcxpwAnAMsAewArhJ0oZVitnM6sh5b9Ya1q+w/xeBJ0MIR6fansj0OQk4J4Rw\nA4CkjwKLgPcAV481UDNrGOe9WQuodEj/UOA+SVdLWiTp95KKXwKStgOmAbcU2kIIy4C7gb2qEbCZ\n1Z3z3qwFVFrwtweOAx4G3gl8F7hU0lHJ/GlAIG7Zpy1K5pnZxOO8N2sBlQ7pTwLuCSF8OXn8gKQZ\nxC+DK4d5nohfCEOaM2cO7e3tJW1dXV10dXVVGKJZPvT09NDT01PStnr16lqsqmZ5P3fuXNra2kra\nZsyYQWdn5zjCNWtNvb29zJ8/v6RtYGBg1M+vtOD3Aw9l2h4C3pdMP0tM8i0p3dqfCvxhuAV3d3fT\n0dFRYThm+VVug7ivr49Zs2ZVe1U1y/uZM2cyffr0KoVp1to6OzsHbQz39/cze/bsUT2/0iH924Gd\nM207k5zAE0J4nJj8BxRmSpoC7AncUeG6zKw5OO/NWkCle/jfAG6XdDrxzNs9gaOBT6X6XAKcKelR\nYCFwDvA0cN24ozWzRnDem7WAigp+COE+Se8FLgC+DDwOnBRC+Emqz4WSNgYuBzYFbgMODiGsqV7Y\nZlYvznuz1lDxnfZCCHNCCF0hhI1DCDNCCFeU6fOVEEJH0ufAEMKjo1l29gSkRnAMjsExDFarvF+w\nYEFtAq5Ab29vo0MAmiMOx9DaMTTVvfSb4YvNMTgGx1A/jz32WKNDGHTWc6M0QxyOobVjaKqCb2Zm\nZrXhgm9mZpYDLvhmZmY5UOllebXQDrB48WJWr15NX19fQ4NxDI5hIsewePHiwmT7cP2aQDvAmjVr\n6O/vb2ggAwMDDY+hWeJwDBMvhiVLlhQmR8x5hTDsnS9rTtKHgR81NAiz1vOREMJVjQ5iKM57s6ob\nMeeboeBvRvyN7YVATW4EbpYj7cBrgZtCCM81OJYhOe/NqmbUOd/wgm9mZma155P2zMzMcsAF38zM\nLAdc8M3MzHLABd/MzCwHXPDNzMxyoCkKvqTjJT0uaZWkuyS9pcbr21vS9ZKekbRO0mFl+pwtqU/S\nSklzJe1QxfWfLukeScskLZL0c0k7Zfq0SbpM0hJJyyVdI2lqFWM4VtIDkpYmf3dIOqhe6x8iptOT\n/4+L6xWHpLOSdab/HqzX+lPr6ZD0w2Q9K5P/mzdl+tTsM9kI9cz7Rud8snznffmYnPd1yvuGF3xJ\nRwAXAWcBbwQeAG6StHkNVzsZ+CNwPDDoukRJpwEnAMcAewArkpg2rNL69wa+BewJvAPYAPiVpI1S\nfS4BDgEOB/YBOoBrq7R+gKeA04Ddk795wHWSdqnT+kskX/afIv7/p9Ujjl5gS2Ba8vf2eq5f0qbA\n7cAA8dr0XYBTgOdTfWr9mayrBuR9o3MenPeDOO/rnPchhIb+AXcB30w9FvA0cGqd1r8OOCzT1gec\nnHo8BVgFfLBGMWyexPH21PoGgPem+uyc9Nmjhu/Fc8An6r1+YBPgYWB/4Fbg4nq9D8SC8/sh5tXl\nfQAuAH4zQp+6fiZr/dfIvG+GnE/W4bx33tc17xu6hy9pA+JW5i2FthBf1c3AXg2KaTvi1l46pmXA\n3TWMaVPiXsffkse7E3/nIB3Dw8CTtYhB0iRJRwIbA3fWe/3AZcANIYR5mfY31ymOHZOh3gWSrpT0\nmqS9Xu/DocB9kq5Ohnp/L+nowswGfSZrptnyvoHvr/PeeV/XvG/0kP7mwHrAokz7IuILbYRpxCSs\nS0ySRBw++l0IoXAMaRqwJvnPrVkMkjolLSduzX6HuEX753qtP4nhSOANwOllZm9ZhzjuAj5OHFI7\nFtgO+K2kydTvfdgeOI64t/NO4LvApZKOSubX9TNZB82W93V/f533znsakPfN8Gt55Ygyx9karFYx\nfQd4PaXHj+oVw5+B3Yh7GocDP5C0T73WL2lr4pfezBDCi5U8tVpxhBBuSj3slXQP8ATwQYa+x3u1\n/x8mAfeEEL6cPH5A0gzil8GVwzyvGfNkPJrt9dQyHue9877ued/oPfwlwFriFl3aVAZv1dTLs8Q3\ntOYxSfo20A38Uwgh/funzwIbSppSyxhCCC+FEB4LIfw+hHAG8cSZk+q1fuLQ2RbA/ZJelPQisC9w\nkqQ1ybra6hBHUQhhKfAIsAP1ex/6gYcybQ8B2yTTdftM1kmz5X1d31/nvfM+Ufe8b2jBT7bu7gcO\nKLQlQ10HAHc0KKbHiW90OqYpxDNrqxZTkvTvBvYLITyZmX0/8FImhp2IH4Q7qxVDGZOAtjqu/2Zg\nV+LQ3m7J333ErdvC9It1iKNI0ibA64gny9TrfbideFJQ2s7EPY66fSbrpdnyvp7vr/MecN4X1D/v\nq3XG4TjOVPwg8azDjwL/AFxOPGt0ixquczLxg/UG4pmXn00evyaZf2oSw6HED+b/An8BNqzS+r9D\nvPRib+LWW+GvPdPnceCfiFvEtwO3VfE9OI84nLgt0An8G/FDvn891j9MXMWzdev0PnyNeNnNtsDb\ngLnErefN6vU+EE9SGiAez3wd8GFgOXBkqk9NP5P1/qt33jc651OfJed9+bic93XI+5r+J1bwwj9N\n/F3sVcQtqDfXeH37Jkm/NvN3RarPV4hbeyuBm4Adqrj+cuteC3w01aeNeM3ukuRD8FNgahVj+A/g\nseQ9fxb4VSHp67H+YeKal0n8Wr8PPyZeDraKeBbuVcB29X4fiEO8PcnnbT7wyTJ9avaZbMRfPfO+\n0TmfLN95P3Rczvs65L2SBZqZmVkLa/RJe2ZmZlYHLvhmZmY54IJvZmaWAy74ZmZmOeCCb2ZmlgMu\n+GZmZjnggm9mZpYDLvhmZmY54IJvZmaWAy74ZmZmOeCCb2ZmlgP/Hxb9AvI7a1cbAAAAAElFTkSu\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f835a50a5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, (plt1, plt2) = plt.subplots(1, 2)\n",
    "plt1.set_title('Frame difference');plt1.imshow(np.subtract(frame_0[:,:,0], frame_1[:,:,0]),interpolation='nearest',cmap=plt.cm.binary);\n",
    "plt2.set_title('Motion estimation');plt2.imshow(np.subtract(predicted_frame[:,:,0], frame_1[:,:,0]),interpolation='nearest',cmap=plt.cm.binary);"
   ]
  }
 ],
 "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"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}