Copy_of_EECS_504_PS3_Motion Magnification and Texture Synthesis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Uqh1kK1LeS2U"
   },
   "source": [
    "#EECS 504 PS3: Motion Magnification and Texture Synthesis\n",
    "\n",
    "Please provide the following information \n",
    "(e.g. Andrew Owens, ahowens):\n",
    "\n",
    "[Mohammadreza] [Tavasoli Naeini], [69124256]\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Wug5j9jHeeAx"
   },
   "source": [
    "# Starting\n",
    "\n",
    "Run the following code to import the modules you'll need. After your finish the assignment, remember to run all cells and save the notebook to your local machine as a .ipynb file for Canvas submission."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "loxyYKR1ZBsG"
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "from numpy.fft import fft2, ifft2, fftshift, ifftshift\n",
    "from numpy import angle, real\n",
    "from numpy import exp, abs, pi, sqrt\n",
    "import matplotlib.pyplot as plt\n",
    "import cv2\n",
    "import math\n",
    "\n",
    "import random\n",
    "import sys\n",
    "from scipy import ndimage\n",
    "\n",
    "# cv2.imshow does not work in Colab, we use cv2_imshow instead\n",
    "from google.colab.patches import cv2_imshow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "-O7p5RhASRaB"
   },
   "outputs": [],
   "source": [
    "%%capture\n",
    "# download some images\n",
    "# should take a second to run\n",
    "\n",
    "# for motion magnification problem\n",
    "! wget -O bill.avi \"https://drive.google.com/uc?export=download&id=1QR2dCShn-fYYEvcAQ8J4mnIo_ISqrjc7\"\n",
    "\n",
    "# for texture synthesis problem\n",
    "! wget -O texture.jpg \"https://drive.google.com/uc?export=download&id=11IlTZNLwfYsXQt2L1GbKdNqcj6JNkQik\"\n",
    "! wget -O rings.jpg \"https://drive.google.com/uc?export=download&id=1fYtmQe_a40BWg1OU178SFbcU4KRZuphn\"\n",
    "! wget -O jigsaw.jpg \"https://drive.google.com/uc?export=download&id=1SlYUw6vT9_PNP8XNhQfAu36AtPJ-PMpk\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "RpV5DTNmaBDk"
   },
   "source": [
    "## **Problem 3.1 (a)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ijhkAqcdZM3v"
   },
   "outputs": [],
   "source": [
    "def imshow(im, cmap='gray'):\n",
    "    # clip image from 0-1\n",
    "    im = np.clip(im, 0, 1)\n",
    "    plt.imshow(im, cmap=cmap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Mo2f0-Y6ZQyf"
   },
   "outputs": [],
   "source": [
    "# 9x9 images\n",
    "im_size = 9\n",
    "\n",
    "# we would like to magnify the change between im1 and im2 by 4x\n",
    "magnification_factor = 4;\n",
    "\n",
    "# horizontal movement from (0, 0) to (0, 1)\n",
    "im1 = np.zeros([im_size, im_size])\n",
    "im2 = np.zeros([im_size, im_size])\n",
    "im1[0,0] = 1\n",
    "im2[0,1] = 1\n",
    "\n",
    "ff1 = fftshift(fft2(im1))\n",
    "ff2 = fftshift(fft2(im2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "USjr94uIXJ1Y"
   },
   "source": [
    "### This is a quick tutorial for those who are not familiar with complex numbers in python."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 102
    },
    "colab_type": "code",
    "id": "KOZCXoSWZV2D",
    "outputId": "335f46a7-8acf-487c-afbe-f5b66b5b458a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x = (1+1j)\n",
      "x.real 1.0 x.imag 1.0\n",
      "Magnitude 1.4142135623730951\n",
      "Phase 0.7853981633974483\n",
      "y = (1.0000000000000002+1j)\n"
     ]
    }
   ],
   "source": [
    "# create a complex number\n",
    "x = 1 + 1j\n",
    "print(\"x =\", x)\n",
    "print(\"x.real\", x.real, \"x.imag\", x.imag)\n",
    "\n",
    "# magnitude and phase of complex number\n",
    "mag = abs(x)\n",
    "phase = angle(x)\n",
    "\n",
    "print(\"Magnitude\", mag)\n",
    "print(\"Phase\", phase)\n",
    "\n",
    "# Euler's formula\n",
    "y = mag * exp(phase * 1j)\n",
    "print(\"y =\", y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "uWusq5kFZSEw"
   },
   "outputs": [],
   "source": [
    "def magnify_change(im1, im2, magnification_factor):\n",
    "    \n",
    "    # find phase shift in frequency domain\n",
    "    im1_dft = fft2(im1)\n",
    "    im2_dft = fft2(im2)\n",
    "    phase_shift = angle(im2_dft) - angle(im1_dft)\n",
    "    \n",
    "    # magnify the phase change in frequency domain \n",
    "    magnified_dft = abs(im2_dft)*exp((angle(im1_dft) + magnification_factor * phase_shift) * 1j)\n",
    "    \n",
    "    # what does the magnified phase change cause in image space?\n",
    "    magnified = ifft2(magnified_dft).real\n",
    "    \n",
    "    return magnified"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 261
    },
    "colab_type": "code",
    "id": "YulC7V6nZW2u",
    "outputId": "ba5d74b7-976c-48ce-a7d4-5dedac5915bc"
   },
   "outputs": [
    {
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gsC36CuOysLP6CqtZOHK7+54kd1TVM+bvujDJTYOmAlairzAuOgvDWfbZFV6f5EPzR33e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\nC5h7S5wHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x2592 with 3 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# magnify position change\n",
    "magnified = magnify_change(im1, im2, magnification_factor);\n",
    "\n",
    "plt.figure(figsize=(12,36))\n",
    "plt.subplot(131)\n",
    "imshow(im1); plt.title('im1');\n",
    "\n",
    "plt.subplot(132)\n",
    "imshow(im2); plt.title('im2');\n",
    "\n",
    "plt.subplot(133)\n",
    "imshow(magnified); plt.title('magnified');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "nQAF4he7Za_8"
   },
   "source": [
    "## **Problem 3.1 (b)**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 261
    },
    "colab_type": "code",
    "id": "1LPwI_TsZfDR",
    "outputId": "a7bc0469-0548-4905-a1cd-d85a2b5c412e"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Sm5JckeSUxrkkLce+SuNiZ6VG5hlydwLnA++tqvOAR4G3HrlRkr1J1pKsDZxR0vzsqzQu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\nzv8HxGMomUzMkJQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x2592 with 3 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 9x9 images\n",
    "im_size = 9\n",
    "\n",
    "# we would like to magnify the change between im1 and im2 by 4x\n",
    "magnification_factor = 4\n",
    "\n",
    "# horizontal movement from (0, 0) to (0, 1)\n",
    "# additional vertical movement from (8, 8) to (7, 8)\n",
    "im1 = np.zeros([im_size, im_size])\n",
    "im2 = np.zeros([im_size, im_size])\n",
    "im1[0,0] = 1\n",
    "im2[0,1] = 1\n",
    "im1[8,8] = 1\n",
    "im2[7,8] = 1\n",
    "\n",
    "# magnify position change\n",
    "magnified = magnify_change(im1, im2, magnification_factor)\n",
    "\n",
    "\n",
    "plt.figure(figsize=(12,36))\n",
    "plt.subplot(131)\n",
    "imshow(im1); plt.title('im1');\n",
    "\n",
    "plt.subplot(132)\n",
    "imshow(im2); plt.title('im2');\n",
    "\n",
    "plt.subplot(133)\n",
    "imshow(magnified); plt.title('magnified');\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "XyIt4H8MZnno"
   },
   "source": [
    "## **Problem 3.1 (c)**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 278
    },
    "colab_type": "code",
    "id": "NomQMElTZYiq",
    "outputId": "60d5dae2-5c35-45e6-9a8d-7eb9c5c31ae5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'magnified')"
      ]
     },
     "execution_count": 9,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ObzyXpNWYV2lazKzUyJCSuxv4QeB9VXUR8CjwlqM3SrIvyUaSjZFnlDSceZWmZWFmzau0\nmiEl927g7qq6fn77amaB/A5Vtb+q9lbV3jEHlLQU8ypNy8LMmldpNQtLblXdB9yV5FnzD10K3NJ0\nKkkrMa/StJhZqZ2hZ1d4A/Dh+as+bwd+pt1Ikk6SeZWmxcxKDQwquVV1E+DTJNIEmFdpWsys1IZX\nPJMkSVJ3LLmSJEnqjiVXkiRJ3bHkSpIkqTuWXEmSJHXHkitJkqTuDD1PriRJ20pVNdlvkib73c4O\nHz7cZL979uxpst9W86ovHsmVJElSdyy5kiRJ6o4lV5IkSd2x5EqSJKk7llxJkiR1x5IrSZKk7iws\nuUmeleSmTW8PJ3nzVgwnaTnmVZoWMyu1s/A8uVX1JeB5AEl2AfcA1zaeS9IKzKs0LWZWamfZ5QqX\nAl+uqq+0GEbSqMyrNC1mVhrRsiX3NcCVLQaRNDrzKk2LmZVGNLjkJnkS8CrgI8e5f1+SjSQbYw0n\naTXmVZqWE2XWvEqrWbgmd5OXAzdU1deOdWdV7Qf2AyRpc0FxSUOZV2lajptZ8yqtZpnlCq/Fp1Gk\nqTCv0rSYWWlkg0puktOBlwDXtB1H0skyr9K0mFmpjUHLFarqUeC7G88iaQTmVZoWMyu14RXPJEmS\n1B1LriRJkrpjyZUkSVJ3LLmSJEnqjiVXkiRJ3bHkSpIkqTvLXPFM6l5Vm4sJJWmyX2knM1fb3+HD\nh9c9gnYwj+RKkiSpO5ZcSZIkdceSK0mSpO5YciVJktQdS64kSZK6Y8mVJElSdwaV3CQ/n+RgkpuT\nXJnk1NaDSVqNeZWmxcxKbSwsuUnOAd4I7K2q5wC7gNe0HkzS8syrNC1mVmpn6HKF3cCeJLuB04Cv\nthtJ0kkyr9K0mFmpgYUlt6ruAd4F3AncC3y9qj7VejBJyzOv0rSYWamdIcsVngpcDlwAPB04PclP\nHmO7fUk2kmyMP6akIcyrNC1DMmtepdUMWa5wGfBnVfVAVX0LuAb4oaM3qqr9VbW3qvaOPaSkwcyr\nNC0LM2tepdUMKbl3Ai9MclqSAJcCh9qOJWlF5lWaFjMrNTJkTe71wNXADcAX55+zv/FcklZgXqVp\nMbNSO6mq8XeajL9TaQu0yANAkgPb9alG8yo9gXmVpuO4efWKZ5IkSeqOJVeSJEndseRKkiSpO5Zc\nSZIkdceSK0mSpO5YciVJktSd3Y32+yDwlYHbnjXffgqmNCs479Jm52IfbJl5n7H8NFum17zCtOad\n0qzQ97zmdT2ct50pzQoj5bXJeXKXkWRju56P8GhTmhWct7WpzTuGqX3PU5p3SrOC807B1L5n521n\nSrPCePO6XEGSJEndseRKkiSpO9uh5E7pGt1TmhWct7WpzTuGqX3PU5p3SrOC807B1L5n521nSrPC\nSPOufU2uJEmSNLbtcCRXkiRJGtXaSm6SlyX5UpLbkrxlXXMMkeS8JJ9JckuSg0netO6ZFkmyK8mN\nST627lkWSXJmkquT3JrkUJIXrXumE0ny8/Ofg5uTXJnk1HXP1Jp5bc/MtrET8wpmtjXz2s6YmV1L\nyU2yC3gv8HLgQuC1SS5cxywDPQ78QlVdCLwQ+OfbfF6ANwGH1j3EQO8BPllVzwaeyzaeO8k5wBuB\nvVX1HGAX8Jr1TtWWed0yZnZkOzGvYGa3iHltYOzMrutI7guA26rq9qp6DLgKuHxNsyxUVfdW1Q3z\n9x9h9gNyznqnOr4k5wI/Clyx7lkWSfIU4IeB9wNU1WNV9dB6p1poN7AnyW7gNOCra56nNfPamJlt\naqflFcxsU+a1udEyu66Sew5w16bbd7ONf6A3S3I+cBFw/XonOaF3A78IfHvdgwxwAfAA8Ovzp36u\nSHL6uoc6nqq6B3gXcCdwL/D1qvrUeqdqzry2Z2Yb2KF5BTPbmnltZOzM+sKzJSQ5A/ht4M1V9fC6\n5zmWJK8A7q+qA+ueZaDdwA8C76uqi4BHgW27fizJU5kdEbkAeDpwepKfXO9UOpYp5BXMbEvmdVqm\nkFnz2tbYmV1Xyb0HOG/T7XPnH9u2kpzCLHwfrqpr1j3PCVwMvCrJHcyeorokyYfWO9IJ3Q3cXVVH\n/mq/mlkgt6vLgD+rqgeq6lvANcAPrXmm1sxrW2a2nZ2YVzCzLZnXtkbN7LpK7heAZya5IMmTmC0q\n/uiaZlkoSZitZzlUVb+67nlOpKreWlXnVtX5zP5d/6Cqtu2Ri6q6D7grybPmH7oUuGWNIy1yJ/DC\nJKfNfy4uZRsv4h+JeW3IzDa1E/MKZrYZ89rcqJndPdpYS6iqx5O8HriO2SvnPlBVB9cxy0AXA68D\nvpjkpvnH3lZVH1/jTD15A/Dh+YPx7cDPrHme46qq65NcDdzA7BXBNzK9K8ksxbzqGCaR2Z2YVzCz\neoJJ5BXGz6xXPJMkSVJ3fOGZJEmSumPJlSRJUncsuZIkSeqOJVeSJEndseRKkiSpO5ZcSZIkdceS\nK0mSpO5YciVJktSd/wdBPzJ2FkSdaQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x2592 with 3 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 9x9 images\n",
    "im_size = 9\n",
    "\n",
    "# we would like to magnify the change between im1 and im2 by 4x\n",
    "magnification_factor = 4\n",
    "\n",
    "# width of our Gaussian window\n",
    "sigma = 2\n",
    "\n",
    "# horizontal movement from (0, 0) to (0, 1)\n",
    "# additional vertical movement from (8, 8) to (7, 8)\n",
    "im1 = np.zeros([im_size, im_size])\n",
    "im2 = np.zeros([im_size, im_size])\n",
    "im1[0,0] = 1\n",
    "im2[0,1] = 1\n",
    "im1[8,8] = 1\n",
    "im2[7,8] = 1\n",
    "\n",
    "# we will magnify windows of the image and aggregate the results\n",
    "magnified = np.zeros([im_size, im_size])\n",
    "\n",
    "# meshgrid for computing Gaussian window\n",
    "X, Y = np.meshgrid(np.arange(im_size), np.arange(im_size))\n",
    "\n",
    "for y in range(0, im_size, 2*sigma):\n",
    "    for x in range(0, im_size, 2*sigma):\n",
    "        gaussian_mask = exp(-((X - x)**2 + (Y - y)**2)/(2*sigma**2))\n",
    "        window_magnified = magnify_change(gaussian_mask * im1, gaussian_mask * im2,\n",
    "                                          magnification_factor)\n",
    "        \n",
    "        magnified = magnified + window_magnified\n",
    "        \n",
    "plt.figure(figsize=(12,36))\n",
    "plt.subplot(131)\n",
    "imshow(im1); plt.title('im1');\n",
    "\n",
    "plt.subplot(132)\n",
    "imshow(im2); plt.title('im2');\n",
    "\n",
    "plt.subplot(133)\n",
    "imshow(magnified); plt.title('magnified')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "9Kzn223xZsN3"
   },
   "source": [
    "## **Problem 3.1 (d) - Motion Magnification**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "0l47cx31ZvpO",
    "outputId": "05b2a3eb-a8c3-4f7d-db4a-8438b5ff273b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "frames size: (60, 200, 200, 3) # (nb_frames, height, width, channel)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import cv2\n",
    "\n",
    "cap = cv2.VideoCapture('bill.avi')\n",
    "\n",
    "# list of video frames\n",
    "frames = []\n",
    "\n",
    "while(cap.isOpened()):\n",
    "    # read frame from the video\n",
    "    ret, frame = cap.read()\n",
    "    \n",
    "    if ret is False:\n",
    "        break\n",
    "\n",
    "    frame = cv2.resize(frame, (200, 200), interpolation=cv2.INTER_CUBIC)\n",
    "    frames.append(frame)\n",
    "\n",
    "cap.release()\n",
    "\n",
    "# scale frame to 0-1\n",
    "frames = np.array(frames) / 255.\n",
    "print(\"frames size:\", frames.shape, \"# (nb_frames, height, width, channel)\")\n",
    "\n",
    "# get height, width\n",
    "num_frames = frames.shape[0]\n",
    "height = frames.shape[1]\n",
    "width = frames.shape[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 289
    },
    "colab_type": "code",
    "id": "RbB0d5fDZ1DQ",
    "outputId": "1f72fec1-9b02-48cc-cc60-0b2548bf5d2c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/16\n",
      "2/16\n",
      "3/16\n",
      "4/16\n",
      "5/16\n",
      "6/16\n",
      "7/16\n",
      "8/16\n",
      "9/16\n",
      "10/16\n",
      "11/16\n",
      "12/16\n",
      "13/16\n",
      "14/16\n",
      "15/16\n",
      "16/16\n"
     ]
    }
   ],
   "source": [
    "# 10x magnification of motion\n",
    "magnification_factor = 10\n",
    "\n",
    "# width of Gaussian window\n",
    "sigma = 26\n",
    "\n",
    "# alpha for moving average\n",
    "alpha = 0.5\n",
    "\n",
    "# we will magnify windows of the video and aggregate the results\n",
    "magnified = np.zeros_like(frames)\n",
    "\n",
    "# meshgrid for computing Gaussian window\n",
    "X, Y = np.meshgrid(np.arange(width), np.arange(height))\n",
    "\n",
    "# iterate over windows of the frames\n",
    "x_range = list(range(0, width, 2*sigma))\n",
    "y_range = list(range(0, height, 2*sigma))\n",
    "num_windows = len(x_range) * len(y_range)\n",
    "window_index = 1\n",
    "\n",
    "for y in y_range:\n",
    "    for x in x_range:\n",
    "        for channel_index in range(3): # RGB channels\n",
    "            for frame_index in range(num_frames):\n",
    "                \n",
    "                # create windowed frames\n",
    "                gaussian_mask = exp(-((X - x)**2 + (Y - y)**2)/(2*sigma**2))\n",
    "                windowed_frames = gaussian_mask * frames[frame_index,:,:,channel_index]\n",
    "            \n",
    "                # initialize moving average of phase for current window/channel\n",
    "                if frame_index == 0:\n",
    "                    window_average_phase = angle(fft2(windowed_frames))\n",
    "                \n",
    "                window_dft = fft2(windowed_frames)\n",
    "                \n",
    "                # compute phase shift and constrain to [-pi, pi] since\n",
    "                # angle space wraps around\n",
    "                window_phase_shift = angle(window_dft) - window_average_phase\n",
    "                window_phase_shift[window_phase_shift > pi] = window_phase_shift[window_phase_shift > pi] - 2 * pi\n",
    "                window_phase_shift[window_phase_shift < -pi] = window_phase_shift[window_phase_shift < -pi] + 2 * pi\n",
    "                \n",
    "                # magnify phase shift\n",
    "                window_magnified_phase = window_average_phase + magnification_factor * window_phase_shift\n",
    "                 \n",
    "                # go back to image space\n",
    "                window_magnified_dft =  abs(window_dft) * exp(window_magnified_phase * 1j)\n",
    "                window_magnified = abs(ifft2(window_magnified_dft))\n",
    "                \n",
    "                # update moving average\n",
    "                window_phase_unwrapped = window_average_phase + window_phase_shift\n",
    "                window_average_phase = alpha * window_average_phase + (1 - alpha) * window_phase_unwrapped\n",
    "                \n",
    "                # aggregate\n",
    "                magnified[frame_index,:,:,channel_index] = magnified[frame_index,:,:,channel_index] + window_magnified\n",
    "        \n",
    "        # print progress\n",
    "        print('{}/{}'.format(window_index, num_windows))\n",
    "        window_index += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "T0LAo9tGZ3IL"
   },
   "outputs": [],
   "source": [
    "outputs = magnified / np.max(magnified)\n",
    "for channel_index in range(3):\n",
    "    original_frame = frames[0,:,:,channel_index]\n",
    "    magnified_frame = outputs[0,:,:,channel_index]\n",
    "    scale = np.std(original_frame[:]) / np.std(magnified_frame[:])\n",
    "    original_mean = np.mean(original_frame[:])\n",
    "    magnified_mean = np.mean(magnified_frame[:])\n",
    "    outputs[:,:,:,channel_index] = magnified_mean + scale * (outputs[:,:,:,channel_index] - magnified_mean)\n",
    "\n",
    "outputs = np.clip(outputs, 0, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "eGwdxkFrZ4K9"
   },
   "outputs": [],
   "source": [
    "# create output video\n",
    "fourcc = cv2.VideoWriter_fourcc('M','J','P','G')\n",
    "# fourcc = cv2.VideoWriter_fourcc(*'XVID')\n",
    "out = cv2.VideoWriter('bill_magnified.avi',fourcc, 30.0, (height, width))\n",
    "\n",
    "for i in range(frames.shape[0]):\n",
    "    # scale the frame back to 0-255\n",
    "    frame = (np.clip(outputs[i], 0, 1) * 255).astype(np.uint8)\n",
    "    \n",
    "    # write frame to output video\n",
    "    out.write(frame)\n",
    "\n",
    "out.release()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8QgaekEWZ6lE"
   },
   "outputs": [],
   "source": [
    "# Only for colab downloading videos\n",
    "try:\n",
    "    from google.colab import files\n",
    "    files.download('bill_magnified.avi')\n",
    "except:\n",
    "    print(\"Only for google colab\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "NPrGFZZ-aGcx"
   },
   "source": [
    "## **Problem 3.2 - Texture Synthesis**\n",
    "\n",
    "Note that the functions provided below are templates. Feel free to add your own helper functions as needed.\n",
    "\n",
    "You can reference the pseudocode provided here:  https://people.eecs.berkeley.edu/~efros/research/NPS/alg.html."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "DxMJ7MolbtZa"
   },
   "source": [
    "### Find matches function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "aay0xFu3b0rS"
   },
   "outputs": [],
   "source": [
    "def find_matches(template, sample, gauss_mask):\n",
    "    ### parameters, as used by Efros and Leung\n",
    "    # Size constants\n",
    "    n, _, nCh = sample.shape \n",
    "    w, _, _ = template.shape\n",
    "    dw = int(w/2)\n",
    "    size = n - w + 1\n",
    "\n",
    "    # Param for candidate pixels\n",
    "    epsilon = 0.1\n",
    "  #  delta=0.3\n",
    "\n",
    "    # Preallocate\n",
    "    SSD = np.zeros((size*size))\n",
    "\n",
    "    # Filter kernel for NaN values\n",
    "    valid_mask =(~np.isnan(template[: , : , 0]))\n",
    "    gauss_mask = np.multiply(gauss_mask, valid_mask.astype(np.float64))\n",
    "    gauss_mask= gauss_mask.reshape(w*w, 1) / np.sum(gauss_mask)\n",
    "\n",
    "    # Filter template\n",
    "    template = np.nan_to_num(template).reshape(w*w, 1, nCh)\n",
    "\n",
    "    # Stride params\n",
    "    stride_shape = w, w, size, size, nCh\n",
    "    s0, s1, s2 = sample.strides\n",
    "    stride_steps = s0, s1, s0, s1, s2\n",
    "\n",
    "    # Set up efficient view of source image\n",
    "    out_view = np.lib.stride_tricks.as_strided(\n",
    "        sample, \n",
    "        shape=stride_shape, \n",
    "        strides=stride_steps)\n",
    "    columns = out_view.reshape(w*w, size*size, nCh)\n",
    "\n",
    "    # Get distances for all patches and channels, sum channels for each patch and apply mask,\n",
    "    # then sum for every pixel in each patch\n",
    "    dists = (template - columns)**2\n",
    "    weighted_diffs = np.sum(dists, axis=2)*gauss_mask\n",
    "    SSD = np.sum(weighted_diffs, axis=0)\n",
    "\n",
    "    # Return candidates\n",
    "    min_ssd = np.min(SSD)\n",
    "\n",
    "    SSD = SSD.reshape(size, size)\n",
    "    idx = np.where(SSD <= min_ssd*(1 + epsilon))\n",
    "\n",
    "    return sample[idx[0] + dw, idx[1] + dw], SSD[idx]\n",
    "\n",
    "\n",
    "    \n",
    "    #### TODO:\n",
    "    # valid_mask is a square mask of width window_size that is 1 where template is filled\n",
    "\n",
    "    #### TODO:\n",
    "    # vectorized code that calculates SSD(template, sample)*mask for all patches"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "dL_bp42ob1AT"
   },
   "source": [
    "### Synthesize texture function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "EOOIwV57b-eT"
   },
   "outputs": [],
   "source": [
    "def im2double(im):\n",
    "    info = np.iinfo(im.dtype) \n",
    "    return im.astype(np.float) / info.max\n",
    "def synth_texture(sample, window_size, out_size):\n",
    "    # normalize pixel intensity\n",
    "    \n",
    "    #sample = sample.astype(np.float64)\n",
    "    #sample = sample / 255.0\n",
    "    sample = im2double(sample)\n",
    "    seed_size = 3\n",
    "    [sheight, swidth, channels] = sample.shape\n",
    "    synth_img = np.zeros((*out_size, channels))\n",
    "    winwidth = int(window_size/2)\n",
    "\n",
    "\n",
    "    synth_img = np.full((*out_size, channels), np.nan)\n",
    "\n",
    "    # initialization: pick a random 3x3 patch from sample and place in the middle of the synthesized image\n",
    "    rh = round(seed_size + np.random.uniform(0,1) * (sheight - 2 * seed_size))\n",
    "    rw = round(seed_size + np.random.uniform(0,1) * (swidth - 2 * seed_size))\n",
    "    center = [round(.5 * x) for x in out_size] # center\n",
    "    synth_img[center[0]: center[0] + seed_size, center[1]: center[1] + seed_size ,:] = sample[rh: rh + seed_size , rw: rw + seed_size,:]\n",
    "\n",
    "    # bitmap indicating filled pixels, the seed patch is initially filled\n",
    "    filled = np.zeros(out_size)\n",
    "    filled[center[0]: center[0] + seed_size , center[1]: center[1] + seed_size] = 1\n",
    "    num_filled = int(np.sum(filled))\n",
    "    n_pixels = out_size[0]*out_size[1]\n",
    "\n",
    "    # TODO\n",
    "    # intialize 2D zero-mean gaussian w/ variance = (window_size / 6.4)\n",
    "    G = np.zeros((window_size, window_size))\n",
    "    G[int(window_size/2), int(window_size/2)] = 1\n",
    "    G = ndimage.gaussian_filter(G, window_size/6.4, mode=\"wrap\")\n",
    "    G = G/np.sum(G)\n",
    "    delta_start_value = 0.3\n",
    "    delta = delta_start_value\n",
    "    # main loop: repeat until all pixels are filled\n",
    "    next_p = n_pixels / 10\n",
    "    while(num_filled < n_pixels):\n",
    "        # report progress\n",
    "        if(num_filled > next_p):\n",
    "            print(round(100 * num_filled / n_pixels), '% complete')\n",
    "            next_p += n_pixels / 10\n",
    "\n",
    "        # TODO:\n",
    "        # get list of unfilled pixels as (x, y) coordinates\n",
    "        # hint: use dilation and masking to efficiently find unfilled neighboring pixels\n",
    "        boundary = ndimage.binary_dilation(filled).astype(filled.dtype) - filled\n",
    "        #print(boundary)\n",
    "        (ii, jj) = np.ix_(*np.nonzero(boundary))\n",
    "       # print(*np.nonzero(boundary))\n",
    "       # print(ii)\n",
    "       # print(pixelslist.shape)\n",
    "\n",
    "        # TODO:\n",
    "        # (optional, but recommended) randomly permute and sort list of pixels decreasing number of filled neighbor pixels\n",
    "         \n",
    "\n",
    "        # TODO:\n",
    "        # iterate over the list of pixels\n",
    "       # for x, y in pixelslist:\n",
    "        for i in range(len(ii)):\n",
    "          # TODO:\n",
    "          # place window with center as (x, y) and use as template\n",
    "            y, x = int(ii[i,0]), int(jj[0,i])\n",
    "          #  print(x)\n",
    "            x_max = x + winwidth + 1\n",
    "            x_min = x - winwidth\n",
    "            y_max = y + winwidth + 1\n",
    "            y_min = y - winwidth\n",
    "            sx_max = min(x_max, out_size[1])\n",
    "           # print(sx_max)\n",
    "            sx_min = max(x_min,    0)\n",
    "            #print(sx_min)\n",
    "            sy_max = min(y_max, out_size[0])\n",
    "            sy_min = max(y_min,    0)\n",
    "            tx_max = w - (x_max - sx_max)\n",
    "            #print(tx_max)\n",
    "            tx_min = 0 - (x_min - sx_min)\n",
    "            #print(tx_min)\n",
    "            ty_max = w - (y_max - sy_max)\n",
    "            ty_min = 0 - (y_min - sy_min)\n",
    "            template = np.full((w, w, channels), np.nan)\n",
    "            template[ty_min : ty_max, tx_min : tx_max, : ] = synth_img[sy_min : sy_max, sx_min : sx_max, :]\n",
    "           # print(template)\n",
    "            #print(sample)\n",
    "            pixels, errors = find_matches(template, sample, G)\n",
    "           # print(pixels)\n",
    "\n",
    "            assert pixels.shape[0] != 0\n",
    "            rand_pix_idx = np.random.choice(np.arange(pixels.shape[0]))\n",
    "            #print(rand_pix_idx)\n",
    "\n",
    "           # if errors[rand_pix_idx] <= delta:\n",
    "                #delta = delta_start_value\n",
    "            filled[y,x] = 1\n",
    "            synth_img[y,x,:] = pixels[rand_pix_idx, :]\n",
    "        if not (int(np.sum(filled)) - num_filled> 0):\n",
    "\n",
    "              delta = delta * 1.1\n",
    "              print(\"Not making progress, increasing delta to\", delta)\n",
    "             # print(int(np.sum(filled)))\n",
    "        num_filled = int(np.sum(filled))\n",
    "        \n",
    "\n",
    "          # TODO:\n",
    "          # find the best matching windows from sample image\n",
    "          # randomly sample one from best matches\n",
    "   # print(synth_img[:,:,::-1].shape)\n",
    "   # print(synth_img.shape)\n",
    "    synth_img=synth_img*255.0\n",
    "    synth_img=synth_img.astype(np.int64)\n",
    "    return synth_img"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ar9WGk44b_Aw"
   },
   "source": [
    "### Main function\n",
    "\n",
    "Run the texture synthesis algorithm to generate texture of new shape. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "cE0a6bhJcBbp",
    "outputId": "5ed3e8f4-813f-4a2b-9b5c-ab77b70f5788"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Source Image\n"
     ]
    },
    {
     "data": {
      "image/png": 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+fkffvA+ilLtkpIxKJbQ2PSCPs1kwa63l2qZSta0dSa6FCpNZto4b11hrZN+M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\n12oJSoq+IU1R9d2Yt8OxzKUc1+v1/wfoSU5emSLvQQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=68x68 at 0x7F8430BE5278>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 % complete\n",
      "21 % complete\n",
      "31 % complete\n",
      "41 % complete\n",
      "51 % complete\n",
      "60 % complete\n",
      "70 % complete\n",
      "80 % complete\n",
      "91 % complete\n",
      "Target Image\n"
     ]
    },
    {
     "data": {
      "image/png": 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Vrbevvt3wNep7+wU3yXRod4I4Mi/U5qrM19ndTXa1a1vPPZiOT//tB5B8j2qW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\ns6fZYr3vyWUC+Z7rHwz8Jx6yA386CA9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      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=100x100 at 0x7F8430BE5588>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 % complete\n",
      "21 % complete\n",
      "31 % complete\n",
      "41 % complete\n",
      "51 % complete\n",
      "60 % complete\n",
      "70 % complete\n",
      "80 % complete\n",
      "91 % complete\n",
      "Target Image\n"
     ]
    },
    {
     "data": {
      "image/png": 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fpggAWPlDh5xww1oTDL6u9Yo4lqHfygSJdJuH19+v3v23q9u/e4MCZ/jqFe4u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\nLC2aIdYIKSayi/rifb6+SEHIwXCvfzj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NtlfT3TIGm+chtQBji1q1ElIrlv/Am2VB96Ln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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=100x100 at 0x7F8430BE5208>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 % complete\n",
      "21 % complete\n",
      "31 % complete\n",
      "41 % complete\n",
      "51 % complete\n",
      "60 % complete\n",
      "70 % complete\n",
      "80 % complete\n",
      "91 % complete\n",
      "Target Image\n"
     ]
    },
    {
     "data": {
      "image/png": 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4ribjpEoWQTAvM67Q4Uc7B18eTz52C7zakzeO1oouLlXSs+3yo+Pv/AXteaQR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\nPd7AcXyiKFSzX8sm3T/Y3yu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      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=100x100 at 0x7F8430BE5588>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=68x68 at 0x7F8430BE5550>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 % complete\n",
      "21 % complete\n",
      "31 % complete\n",
      "41 % complete\n",
      "51 % complete\n",
      "60 % complete\n",
      "70 % complete\n",
      "80 % complete\n",
      "91 % complete\n"
     ]
    },
    {
     "data": {
      "image/png": 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VLi13+70BNTsajSI/IYamrnOcO9COWI0BF8pFyBw8J/rwf79jlq0AVOBnDzsV\ndIP3/vJsam2sF8Oo13stjeew99JUvHkSAo8uZiwiCWo/trzgn+1okZ5qhpVSpjNBRIbhMB6OiUVQ\nV1u0Y7SjSvVog/DcdyM3gMC3K4tSVw0GA2vkJdLUDW8fd+/eLyyHRqOBkMrRqM2bflchw5HwEO9F\nyferKtaeheyoBcvHmCAOUOdmvRfbldjvq6bpei6oTd0oiMYDd+hGgz7n5MwGhZCnpQPlD57TzFNp\nW0lAeoOJG+O0q3bl6o/3JHbtwPKDSVPLdLWqiz13CJ0GB4zCshGAPFheAeYAgAHDP9vRQsQIjGzl\nS8hMw3SFRSMPc6WUQ4EMZ6ZXVBMkBFTLfnvbGmNefXZ29GJqDYbGwU25X91l5bLkTdO836RlZjQR\noETbUIZbjFPbY4CQlFrKWvjJJHAD6fikR1A0Ik3lxz6+7QeeRyjmKOqpXQZItcZPvvfM8feeEz06\nGntFlbXdNqt53iKGd6u9INiZx8kwvB86spflrl9e1aJph3AUJL5l+T03EMDB8jrIe/8CR4sYFHg2\npa4xassrTtLbtfXNfeCyyV+ePtK+tylSg65Fe06X9+vV/Q6nZcKGk4vp7GLqH4c2taQeX5zus7ts\nd98tlgvrPqlFWXRCekghrSRkm+zga5w0bZb5s7SiAbVsLKmWmhT1n/j4ketEXkDDyLCu7eWcSaK0\n1j7Q4vCmDp4TPT0+AXgglB5oVGqMaMpsi2++fGsRYhvpzAcYYHA6KVa3tksAoO9ry45+JGl/LKj/\nvKPlRi6LPGFZNkJKaCn6dFeob64BgDBWvrC2QWwFaLdD64f08v++X3x964L74mJ69snRyy9O/RhM\nGHqYN5lbTmff2ffagJLLelk8mcEd1VLJ/T7FsNvs4+kmWkaxlzhx7CCCEPVE3n3s41PKlE8tmzp2\nd5hbxqJDVi0NosYgAKosri169Fmg0GC9yDO/ZBIJC2uLMFvv364eHKZlOfrs1TBxvwOw7MGhLSzL\nhz+9Dt7Mk6D+847WyI/syLMDBwFuPDuXqq66TVZ9++/+kO4WR5/Og2QyHg2QLL77Zvvd33/V3ucv\nx9Nnx/PxbDA7jgNPa6c1SBDDjCbDPM4alPfKKvdPZnAUjjzLqqq60tW+K7P78obcD+Iw9qfjwLe8\nVrXyyceXGNkuacD4jgoE8hwTyp6xyjKGGvPxZ08nZ2N76Nm/e+gBLdVCM+E4rj12u7td+m63udt/\nnvXx6+dANAAIlqBaG/2hTynWdsae6rpCRCEkATFjWvSfcLTiZHh6zDzftixaibjr+8WC++tyudut\nvrxfv9k4Qzx2pwjBZrdtVqvEwt7IC48Yi3zf0i4RSEpNDVAkgyg5ssJrbWB1MIOj02k4PkvOxtYg\nFA1vRVE8FAequtwVfIWqsB45FkYgeNPmtWklidDB9WhbCh9pONT8MHSYIapF1PMtlE+bhH1bLSLH\n4KNn4XjYi6rdiB4J3Tbf/oevTpAEALsylmvpSAMAGGMM6hEokAiAGtMj3CCCkAGAn3e0hsfRfICf\nz6Oq74RDmk4OZ+2LbXR9Ez6utstttb+q8q6UfeeazgrIYDCwB6KiLXE6zUPOG98zgUWlgUKBNqZE\nVVU2BzP4dDC3f31xcjGN5yNkQOdFvak21yfZJt/+4S7d7eubXU+xH7sASPbSSIM0O7geXZ913AEP\nbEqQpAb1h4GiASpkb4hD312J5uG767t1bgjyZ7Pjo1efPWu32wcS123b8Y1ou6v/63fHv35hD41L\n1dF8EEUUd8ghSCO/QqiwagB46OcAYBsjQf+8ozWIw2cjdfScWMzPOsRbNJ0E+a4Jhk54rQdv1d1G\n1qpQfbfpOUVuibRfYbNX/LIriaEWA+21HAQHs9fLh1Q3Zlc3rU9mp8fB69MXL+aD4yGauYFLnb41\ndbj9wv/620oPAvHmdnu3yu+bdrP1LV9IDgBWh+0dMR0BAMtCGItHZB9J5LItALQIp8RpjeviiP6x\na7+7fMPTlkftq1/91eu/ef2vfjFE4vz+uu/1/n6VrR/T9U2VLrayYvZDlV/XSK4rL1LPYufZALuO\no1lLu5U9AgBHua1GP+9ozefIiZkVdT52ARR2bU8hFoIKSA89qxUYfVNWNULEJl0v07LBiypoFeOC\nt9g/9h2fuh7rql4WWXW33G6Xu25HXGs0Gw4uZicXyeQYcJAFNHe9Wmui69nzkNmJR3yCPFLUf8wW\nrQFJAPFeaiHsHpqipto7nDdJZ4F1BWLMbCWhBEAHW4e++7s/8Ie76HQan5y+/m9f/vY3w/MThQV9\n/pLulfW8Odql+ua2eP9/Xl/B15vy8e7/dDbPRpNnk6xpttL6/MQTzAW23xIbADyJudY/72jFAxaN\nFWUceu14NgIUgwXcNo6QQV/5FUboMNApw8pyuHCW6aavYb1dzS82wdVgfHZqASVZL3W2fWzSdarb\nDjljMv1hY0lx4WM+QkWJg1eOGZ86hA4AjM7a5rx0+fW+apghXCjHw82+ZGl5fbMRwcnJ2WwQow3S\nzNsMec4tCzhDjWhTRfvLx8NQj744mn3mPb9Qn+hF7ApqIGf1ks8W4byBkP8aE4I3l4/fvbvHj6vu\ndkZPR62YUnPGYx3Fg9wKAKDCBIiRbfUzjlbodZRUJuBGClwqgm3dAEVgWmiyBOs63b2rC4LG02nk\nPXl2Hd/cte3dd1cB2wyv80kS6E5aosq7Kl0WfddHnhU5lhPLp43lDKqJqnrV7SXC7vGroemSaH86\njpbbMkmwFqITzpDJXqzyIlp7i3ePbuhOp76sUTVAB9mvACuDSUWmm11DqY2j0+Pg9enwfHBh0agu\nfcrnbAkImEla065tdDQNxaReIba7WaH9lpTRUupyuVrny+pBfu+m9Iw6gDEykuc/52i5tFdGK6Qt\nKpRDZcW1dKWAfUq2q90frm63PXRHwfyz0489u8OEsLx8zO/TdPOwRNQhlBjFoCvr2jHUczxjO9IQ\nxTthJMa1bfpE9z1SoDthssweHj3z6pW/G8Z25PfbDUIM2Rgbyet9+s4AgEVI7HrmN+Oum+R4xE0N\nLTXGfVi2j48pZVE8Gg8GF7PXnyXByGK0Cag4LAGHy9F9iHTa9Mt3t+2qtA1yMPdrW/Ji+X/Vapfv\nHuYHN4UyDxOjjexy8TOOFsLoR1Guf45n97w5Wi3F5dvV8j8u7r68LbKaVy3qa5vpvtNA0cFtrbaJ\nOR1Ar3vvw0rNQDkep3VBieNFCM9CJ/GYywDZBtc29mjEeNE1vMyuyZ1ju5FHJo5FvDYy6nvnaXm9\nePfNNXUH7vhFnDw/gllUBEDYYyOIbSyGwUgMAB1ju0b3ZUN8Rl0rIjCLB5PRTIjp/d06v1q9u2mK\n12WznAXHp3bkSi6affUzjlZnlIcNKRzBHWnYkxP1855dlLg4nsfxaDU8ZaPB8n//Zim3uDDQSQCQ\nWO02pXW9Gh0NlwM7cR2L2nvmFbhmxkijjQ1IcGNNXCONBmoMJswyTuhakePVhLlt31RGroribp3e\nTl0XIomA9saI3bbefvN+/eYd9eMJ4SzdFydd7BBUIneLjSO1tFUDogcEAJZLrdCzPRcs7FqWN3SC\nyMI0dBz7YZc/7rP0ctM01SgVTuQBBpnzn3C0XHpwtBBiKicO1i1nUtmblX682X31++uf9+xuzHTD\npnrkJmIwX43LiwnO8xrahFLLYxSgFVDd7e7ZpW31Dj5REOO58kmIAIi2Kmr1KGKtDQgxajkt8iT1\nbWtgudQhUTAqKr7ZyN26kJd3LA5sI9sJNghpLvPr5vq7Yned0fTy0WB0htDC9X7YDQBjWBZYNspD\nyA+RBoC2qAAQjlkSueEAe77lDDxqE4XgMX/MLnfNXvqjkFhE1vJHjlZf1yILihBZltsiSRQUnBRl\n33Xk8aH5+qvH/6Rnl+Aupra0ATOsPCaMBgxewDzLCie+bYK80ZWU24eN+jvTibbDr7k1giEwIm1F\nGu31AG27U1wCF7xoAg3DxB+OAtu1MEZxGFMo0bYqb7MH572WZXQ+TSguu35/vTkg33SXNfDugZY9\nH3hZM33aDTD2ga3IOd2W8inUEjmDIA5mZwNs4whBmHgkQOZdc7l8aG/SdpGTwDaNJCW3oUPgGQ0f\n+OJl42O04nHkGy79KsP1vl2W6dXN9v3b+/+kZ2cQSGMJJbJK1Ouiyoq22Xk2Hh3FR0mCAv9IOPus\n3mzz+6vHNc9Krlr+Kv9idDaQTAFRSUsCXokDgFc0zciRvj9wfHs0DG3HbgXBYIPFngbKs6yvJnGf\n1tWqOiDfdETYLm/S/HbeNfUvLj7aDXxgK+oO1b08hFrGjuuGoXsSD2dBfBISitdvd8x2uVKgeb4q\nDkCeantpjGtZxhilTF1zsgJsJDIC78sFsgNWy4Ysbqttub9Zru/uHzsBg2N3ejz+c57dTRfvFC/3\nbb0s04fH/e4eKXE8H80nw7PnI2oxBtMC0fHt481y+1CU7373ZrvLf6P+FfwyGdMxAcgqvdpVBwBP\nae2Glh9Yw2EUT0PX9fwWUedPBkpbtsPYMy3t86ZYpcduTJ+/PBrl1aZtl/fbXdv+6W4AMFFGqx9C\nLQDDQTAIJ8lpMD3HdqPwF1P4Zg2fTgBggx+8oirrthao6/Uh+2JW2sBDWUQ9H9VlpREyXuCboOZd\nmu4fN/k23RVVgx3HC1zbDkSvNrlWLsvlNGMsMAMkM21gnVkPZV9d1/e/u91+/Q62xdhznk8mLz6Z\nnrw+CiyiIME6uTofuZcL6+3l1fby9vfvwEDz8OlnM2l7UUvU4nK1ecz2qx11exROrJnvnEXxMAqj\nBDQaqGKb8KeBwpe7fVoAeKQHRC0v8ujLv5jw2vXy0r1ZLdPF+qv2492Am1DSu6vLdXqX6XXpuKFP\nbPdkEkSOK2rC1NlQO1+M7YemyRrcJswl9sYXZtcXXDK9UcanZbupPKesmgwBgIvBjQjGslWl4HWV\nV0UjTe8Y9ynsgPG2zjwvchrigy4r45myL7p+99iuv7vb/MfvqqtlAOz5ZPLs1TS5GM3P48Cllu2p\nXkXCP5qOj4dm8nv59Wpx+9UlX8vdeT49SnzHKZtm+/au3m4ZA3/KomE8mI6Gg9EgspFFtaS21E8D\nZbMptpsKyRYxx7Pt2mj6xSfDRsbzpruM/dNFXi6yx6vV5UO2XzbF9jQYJYTR9fVNsU7BmGAQPDt+\nORonw8h1rdaxRZGzyRAU8bM01l2vpBJVQwsjLKwtpJnkjt+DLnW3ylsDQFZgYCVth2BDueBaCi6B\nIG23+8fNIeywvlrEvm+Ng8D3nkDDQ4p/ffNQ32yCDobJ9GQ+HI+iyTjybBpG4AdgkAwRHQycARv4\n+AX9Ev27m/3izUN2u2snE+vTI/7mcZfuZV1FMy+JQ38WOiN/OA6HEQaAzgzLlvuVAFd0DijbGMcy\nBGvW9cJkLaInr0ZgxKrSwTxpL3fbwR5/eXNf58XtbZFtwsHAH0TNuq5vl4jDMHLJQB2Db1ml5zAb\naXfAW87qPpyfJPlDE4TNAWD8mIR4Avaf8HxTpKjsqlYaLgAAeQi42Gdlnndwcz+IhwBgR57xnSfQ\nEADSzZ7va68UyWB0dOrFUeANp2E8ZmFl+8ZJaiwaJogdUGfOlEzAPO+V82V7J7b1d2DMbhPue1U3\nFoOp5VPP9pPoJBx6MY2oxi7sCwhZUpDukAgGYQDg43dBT49cycFL0HBPa4Rd4mGCw6vlfb6/uVsX\n91svGBulTJZNXTuiNEZIsw1GjkU5BcRYJzTyXRMGfjh0dquQww4AnChILo6SZ4P5y/kTsP+E5zff\neQKKst1KihyH6CBGlrEBNCV+g7xaVqbIsnVrh9+zhoZIJLCitXJd4gaUEIIQAgA/whaLdJhi0dlI\nMqQxU/WIvLRcl9Gi52XXLuUq3zaiECvU24pPh+6BGA48wgI2Io7vNcaIkLLUFm1fHRLBVQHO8E/e\nBfWwMSzyndZFUJChbcVu5Fie7V47utfrXdF2W9ybgBo/dIaJsbzctoljl1xojI1GjGFsWThKqEU8\nDUtR9cqYeJC8+OUno89Ojz+bH4D9j/H8vlXrPReOjzmOJkk4HJqhRxEQAM8gH9H1alfrour0gTUU\nXIC0XeUZoxBgAAAMgBAy+pDpQAYBKJ/1zBG8c6Gzy8DYYzo/G77MW2mq/R/TBrijeh8g8L3ItUJM\n/d5x1OFoZyhVUjBh+sMOqWtlGFNnOjn/6F1Qy92r3kbgJZHb98icQMpadz8bdc65hiAI001roA1P\nvUk8tY8iZUdAYt0YpTPpEQQAHdYt6zZ9U8IBYDSjYfR6evzrk08/HYXPGHIkgHnC84usXkWuhTEW\nOoi9yTQZHY+9MbYjx0O+Ma3MpJugkof5Xh9YQ0MbUYKWDcWUCNQ0ddcEWulmvyuHOBw5uMaRi9rW\nIa7zVMXAeDQf+EFgDeNgOM5xp5RArmVz3gsuJOcYNUb7BAEArGu976oq56mSOyUybBCxhxeDj98F\nNVGDSyUx5g3HXmRXMDJBSYqSGteKLV7ylCPcqQ0Ft0UcqkZ97PECU1LQAzfAW3MAGJOL4cVfn88+\n8169LEO2dZ22A2wALdkcnxB7qQK7W/dCWM3gdD47D05PHH/qD4ahwWCMVxWdVzrBoiYWN0qxcJen\nBnRXg9JaIanyFdV84/lUeWBiFo/HTkR+VI7FZ6an5EclHGiH2pbbjmU0dBVP02I4YXsYcsTqGqWl\nKNatbGXbtkhyFEfReBR7cRxFx25vWw21KFcO1VXHhAUAjCJkC+EK7eBDTJqRXhpACBE/7quf8HjV\n99zAPl2tVzvskNnL+dmr2RdzNkfVGa2FbjSgJQ4T1mbaPfjbrcyc2JscD+fPpmfP4nDie6HFsMO5\n4FGfps1jpw4xvm6/51w3hrdKU+QqRDvF27YVD49z6IjNBrFnkeBH5Vh6iRECLPlTCQcQRkqtJWTb\nIgjd3bZ2Bul26DiOiw3bpLzNK1406V4UDe8IGvypQOaiispd3GmcZo4k0Z+4Wz1X1QeIQUjItgW6\nL3qBjjn72OP1RiQv1PY2zZfZ4mG9UYV7Njg7mX4+956x5kxVM5Uy07fKNXbUSBdJnK74drttjTk9\njg4I6+QsjGKP2AAI7M5uFZQtZ45LfYP2+0bLTlcSEzQPceBZFtNcdKJ/aPp8uyHvMQP0Pbz6oRwL\nQbY0oq3xxyUcqgIwcgiDTqrb5a4QXc80th0EHjzuyl4Vu7q43x16FkOh4wY/Esho1dhFRyWJrAAh\nBRjphNLEtVqbdBHzVrQGX5vWohbp9iAjWbaQxQ2tpOiiAG82UPaqvssWi/Qm3x0CbcmzwZM0xvBh\nysSoA8sAr0hbyiY32FbJKEzG4WAcREPP9mysuaJgMNgVhCzBNFc9r2vxoQ7Cn1YhybdZkW6arPrm\n8p4hagM9wKuHciyIAe/QoULM8na7WmXFXlHH9c8nrkWZRReLh0q0zSqrJUpzFQ4xFO3Dpmz3zWK/\nL3aVPz52BgHzHc8JvFEXyHaKO5qKIRCwEUIKtIHWoGjSnVqDKmt3q7DRS01Mr/lhpijpBhBo9VhW\nKIjdNebcsw5Pc323WJU1GnjRNKFDu3ModzEH5wDBd8opsbvZQ5XW+Xb3lBQeJCyIHMuhzOYSA1Eg\nxYepAAB6aLM++6EOwkdVSJp9kT6MH7+5rTa7t99u+Qb1vf2Sm2Q6tDtBHJkXantb5pvs8T67TdvW\nc4+m4/O//gQSHxDyF/P18qZc5V8/7BZ7cREFQldZXlfrTGLWcfLSDyevTl+8GIQnfYAtx0Kh6KkV\noA/muwIbDLgi1rruvMM+AABU2yJgjbE6xYN297Cs6m04GIVkqREGZPlFV2R53TzWrdLxqW95rG/J\nx0kaabqasbV0trt++fZu8fbuR0lhjaU2YEvDATEjKg7C9FrpJuuKrP64DsKhCokb2G01iGaDJIxW\nb+4fvvrm6qY1AK02s0r5sWFEPvX3q8067XB8PHz23//6+NfPI0p6Y85+Pd7en9z98erx2/vdtuqW\nBXYr3FFZdW2PSCycSTw6Dg/JPKTLA9VBsfoQZOk50gwM4hpCilDbVy3nXdO0SDZKWkQBtvaoL3fN\njhfOilkEEduyqd1oqYzmjQHPJsjjjeBNu7zrzcw1hG4IV4pohZZ79e7d6vL9MrtNGf8hKUzA6BYZ\nbTQDatpGuT3Pq5znDd83XVHXvYEkSYLzyQ9VSDAOnZivvXrkp7H3H7r+7ubm3fqu/lIc70eDxLUi\n9NTfb8ssGJ29/ttf//LffP6v3RePF7sY99zQ6sK9eRm9OZnd/sP1+o8PdG8rllNh8Ji5jdXuy/Yy\nWw4Gp1ubHuvWanuDqemgB0SZoQAyK0LPgP0nHMNhZnVno5iS8QgORxZWSbnfNOuyxBxRmytFTacL\n3eyKxbd3Soiu6stNwseu1XiNzQ3I23V9d7l+CpIxA4iDqXstjARNEKEAvDdIqHLfPS3hTSewHR0+\n5x/TnpTUfnLL0C9spP/37uar2+/uV63oj+LEtXDRFas0XbaVttn0s+Nnv704+jxp8ObU2sWuMDJv\nHTqKojB+7gVRPBne/v6dbmy52yVYFjXOrzb3gXuG0DI5O4jUa+bTugWbIArAM1N3rOs83Yub++Iw\nL9ZG2DGZHh0dRrs3MocjS/W4uftdnprW1T3WDkVNEFgg3Ooh71b77Hq/v09n0XCRhOBg4TtaQ1uX\nizd3Pxkki+eRJXuOadMqLao201lRPS3hSRL/OdpzSewjihtntNx9XoBKv1ktb++M30SeIwx0aVtT\nMfjkKD4Nhok7oDrwti/th0AX2NIdxkuqPqbBV1/f1dDShWAk2nOO3z3QsndH8CRSU1krC2uOabrL\nwZjrTdV26upmu7jh71aLDuP5F58/+6vXx79+HoxdN3DavN0uss16rZlm6MDMFA4jg8Fg7AaAScOr\npgP5bve+vXeBaZfyyAKAvu77svnJIFldyyA0jm2BMbud2pd1l1ayaQ5LuOt77vDD9OGY/QSqSOeg\ngdvtiHqVMxx03vDCy9Nn9aYQV2297VCMu7ztkKExGod+MgqtsaTextOZY8oQWgyK6MCXveNyJ/aG\nifsd+aBT+5blO57N9UETTWn/JFLTr/7+jwBgkONbSClY3dMP6uXiWgs6unj+4vOzFy+OBpGPBm4Y\ns8u0zm/TqtjUHWcMOdoKz5N55B69mFIaYZ8SbjjPyy1fL7Oal41qZWqyXqgCAVeesZ6qIz4Fycqm\n9l3mubZSUNU62+b7RyjKal8yyrQzCLTD/ik8pQ2u2NSvmXccDy4m5b5OF2F917ZVAWnfKo3AODgM\niY0hcDpH17x2SIMCajRDTBhRMQKd0+tumzYia590am84HHE6yupN26Zv3/x9sTiI1PTqm/VPqpdZ\nXvUtVoP8m2/fNXXvv5zPXs8dRjaXm2KZpVlfaOnaZmx50ziZPI9nF9Pn8zGzXddBdal63my3xXaR\nrVfbTdFsNnUrTa94L81TdcTmSvp+vp8mSVdFLIg924Ax2vCmbaq+KsB0yjgGEWT5jqsxM8hxUCg+\nwFMVwgE0pc+gjuOh60euHwyVt+UZa5V2CVbIMjWq96ha18WyKY6tAIUlMoA1ADRqUPXxsjHL+zy9\nvtov8yed2n828SxXNJmXl+m3bb7tDiI1/fIfrn5SvezbNrLHhzmoXNSj/RKZ39iRazASvGeIHTKv\nNA7CEy88TaZH41e/nVKEsQCpueq9WeYsJ3b0YLm3ewy4giorETfmUOyUK73d72meufnWXrkxtY7j\noYci13OMQcaYjteSd0ApIQSUxp6w5Q9gJjUGkPkenQOjDUIEad5ogYlwwaXEIgBtrfKrjTsIl7OI\nxSP80m350LNDJBEYvKnsu2+3N/9wf/v+QReLJ53ams39xMF0Ot/uLmM/X+RfrVa3v39Hl9vUDoOo\ntqQBZZTKUyQ5osHw82eJ5zpVXelKlov1V63uzPjs6CCQlPuasThk+iDOJsGART7GeDjqheRK6QaQ\n47KhP6yMcZV5imgrLg/FTktbG6NFU3YM2qretUWWqbFTJHZ8WIXbQhleIHcABqxGHEizQ05dadQC\nBgAEcMh4oyJta/kh4+1CElBGQq3MSKk17/N/eL8rsu328yr9vvgsIobj+/vt2z9cXv3D+25XeyJ6\nfhoedOpBOBxENrPkZkgPmqj3jf31akG33HjdD7h1Jrgd2fPjk/O//kTHvlN2d8V2d7Mxt3fF7W1f\n9cQi+Kboe07aPZ6MD+Ksd+K9iHVIco81xtJCGyqpSNze/ESUxX95KHbqGYZ0r0VTbjab4n6Z102x\naY+YMNrYgW3q3miwieBV3XCu+6rjtHMQB2oAxAGb+x4G+smMNzOO7CUs9oXI0rdv2mLxVHw2pKSu\n+fp6dfn1ZbNbdDuTaIuQocQIxckHoZnpQNpDv6wRdmyHfoloYpy8yS1ZG7BQb/QxjT/57LD8RZS0\nXW8uwxdUlErc5/ubr+5JYJNMUp5Phu5wEI2GA8fCY0sfOAbDIQhb3lsCGZsZhBkyPxVluTi2jodR\n6Gkhy7KdP2zX45Py8nHTP+QtJ5JrqRQCMNAL3ffpvsjyKlFN0gZYIpMhlyCNNHqCgVbX62qV/yjj\n7Vmu6fR4QsrSfXjY5NvuqfhsMoxNyze3G77PdQmeId7Y8wIbVcirmfcCRwywi5Gr3JweNFEwhvoh\nqiWtLcIt7VoquDg9+u3FYbNbv1yUFf8U1Vt8XjDPvXZ0/3D/WIiGjygOfG+gqCPcgzV/4Bg02GVl\nGo1bRdseNUVX77p8V38cZXG/+GT22Yk38oeOrYWRxpSvZj/U2Xm/lKkyyjDKLUJF29bbim92Yn+0\nqDXp/Dt2FFoZABiMnmCgzXeL1dUDlfxoyH7IeCcOtP0h5j1OJuvr7VPx2QpvNJdIgSWR1MomtDa6\nq3sjGZi+LTUMcV/lvjZDO0YekrFIkoCCjYWFpS9ty40Gx8dfXJz/5uVhsxuy3HZR/NKxWKCgGVXB\n8X5QZl1PCA4cZMe2Pep7hSskF/phl9FnIxhGnQddp4SmxV6nV7v6MVP7/cdRluNfXUxfsGTYAXQR\ndBQ5W/6hzk4nTnupc7wtl7uIoZCiQqB203UP7XLJrY3FBsPKQYHS3NSHGPoBBrp9e4uy1dhyPx9N\nzs9Gpy8Gf1JU+/uYN7tfkLsr3jT7tFO1cRnD1PExVEbIBl2tNizs6I2LMKDKi0IAnWAGRnxgx6jQ\nynI0oQ70Fon8eHD0tNmdk60k0O9HU2cog15PumyttfNIgVg5VdY+ryKbkTqLMM0ch1BdZg13o7AV\nXVY2+0W1WdXp5e7u9uHjKEswD/zYDFk5YCVSAFq6Cg51dniBRal1zfNNZjqFXMSAkBp9HPr4SRio\nKteM4Ol8MDkZnL0Yn57Of7Ko9lyNlKke7tO+k0pqCcTCQJPELVtu9CZv2kV1EG2eTZNR4k3PHHCt\nA4+5Xt/T4IvXZFe2213nEGcQMIsdNrsndBc0TbsJkWqiJNCfBVkd4SC1NGtkJ2T7E4rNZs2uvCgK\nFJJNJ5arrNiU6/v0erUUlokG/lNFUIuSCd0PDA5kqXu8xy52MR9F2TAMZ1GWhFtKhVYx8T3GSkDZ\nXfYU+vhJGEjt20Hojo7j4XEUDFwash8X1eYsv9clNZby+uo2qxrbC/1h7AW+J0DGvjbQ5NXuUfxj\n+rDbd8VkPDkZ5gI1HTzxmPRfv4zfmOL3Vz2J7cP2z+kcnpk+GphaS4mVNNtVurzKHt48rh8/lLNo\njP2k2GQrC7mY+EnHMwAYeb7v2wrjru7Sotls93nDk0HkzeaHKMsh95OAHIKIcY+YtklnOqcJUDQN\n3DAAioBijbAf+l4SOkW3022xyJ5CH/8UBgKpxpOj0el4dDLyJ74dUcu1DFQWgMJdh0DKhvrQ9CJT\n6c4Ia+qGJ0dn52feIB6GbrzKAIDz/OHd/qFdbbvN7n13WuyXD9sDpHbgMWn313/heoMJwZvf3/bX\n6Xr6cBNYoX/6SHvDXW7k3ZZvF8VBRWtKwzDpe6fzuWZCIJ2nDaPkB5+Z6JXaRZb7JN30tUAKMcd6\nqt31gaGXiAjJWMUYKB0UrCB4PBm4D5FzoOY8Ao5PRlEYua5l0Z+HgY6TeBCM42DkjBLsUGWDRB1W\n/LB1lVV7KM+53j5epxsRkdnLl89/++zik/loFkc2suETAECim/wf16/u5v/w92/uRfb2PlupwiEU\nAA48Jj39fI5w03QXIkfpff6UoUmGsAlkXqi76/1BRdtsizbv6r7riGK+9ZNus1NJtd+UH0k3tUYY\n231Lnmp3/VlqjpifoeZ+HgYa+1Hs+o5ta9QZFMhOWFZrOsSRhYUq9zxdVHc3+z/e3u+1TI4mF//q\n+ee/vfj802DiG6xaQCUAgHKTdLAJbCn78Gr5sG3qsigKRXpA1CZ2S58Pq/7Zh5l1lf0xu/sQS332\nynNd0jJnV6imY2nJemVkL/tOOoPwz7nNP5JuGKauAtBYZP1/RWruJ2Agz8GAtOicMkYjoFojCA2B\nJoMyyzar7Pp68eb+saia5OXJp//ml5//9uI3XwSnbjoj3QHNBoDGog/PfCWt+NELlkNRrzdZSXFE\nLHAF4qWkc7c6zKzuyL04G7//ZpHdbW0X/f/20dFRQoZ9eV0fAlRd02y7yo5s/8+4zT+SbggGK3Jt\nY0TVcWP+K1Jz/xQG8gIbAExhqqLSt5aIgELXSpFn9v27bfHw+HDbFlcZ8sbzT87PfvPi+FXseirE\n3YAUU9gfAm1ZP9wwO3CG/jjHQcrBID8Mz+YHjwMA6Ahk56q//YV1TD8TgT30w3fvVrus+t3jZraY\nHE+TpwCVqY3bG22TP3WbhdaxbuFB8HK9fJJuPJf5gedGoVBaJsGHtea/EjX3T2Ggru57qzlsYsqC\nRgHWDO3qbpda28e0ve1ur5fbBg9fzUaz0yAcEnfgWhlhmBkMCgBAIKSwtFyFJQepOmIybFjsvJzE\n6PmRNQgxRvTUvx2HeBnN5q23hgEkx97x4v0f3t4CXi0e7VIcAlR9oXjfAQAr0JAEXaVWGe6YisYS\nYMdrU92WD2/uNvcr0wg/tubTySicQsgcINrBvBV1Gf6XUHPe9xSYrMe600/f1dEAQA9Xqx/bTgJg\nW7VN5+0XebEp8qyhHvNnkZ/4gJDhsPWQJ3wCXUM+ZCc1QEg+pMIqyTvgg3AavD6d/vJ5PB/1XUdD\naA6aoSIDrEyQPoZtGRFaXaZ2rRd0dQhQcV61UjSePxsPxK5Kv7nnXB7PgsMq3pX4/ne3928vzb4J\nXPvseDw5PkqSxE9swhxqCyH6shH/JdTcEwXWlnWTtXXJW4G5Nhrqwm9ZK35kO1VMdZ0oKrbfpH2W\nt5JNojBGDAGYTvdSVSJcWppTskYcGwAAiupimCjeKvGhyVzfI6H/6Ythm3iBG9GyGHHuJAZrJrwT\nE6KoUvWZHd7A+Y8DVBos1tbbsuCCrNfBYpG/Og1Gie052XKz+Y/fwUMeUPtiPjs9mQ2PB/HQ8yeh\nGzm26VslVMX/S6g5EORAgZWrKlvrWzs1DgIkietpyn9kOykPawtJKUWji6LBgIjtEIyfPuaqbqMZ\n2Y1ITFzGfIwBABA/MqpRRZ0qyYvWZcFBpM0UCmPsBQ21mPadvG1HFk4j4j5vguHnL77eomPv9kcB\nqkZp3IJyBW2QbFOtiifaLX/M65tNUKnp0WwyOxkM4+FpnIy8cOD6MfNc38i6aYL/EmpOMHOgwPxx\nhINUaKUcxxnE7sRzKFZa8qzRUsvO1B2CvpG95r2CVunagAeu+eFjPlSHjKZeNAjcxHc8OzI+AECf\nAgC7f8xuNkXDDajDLr1sZWiMAoseMB2b6RFggBZOYHsW6cwcvzs/BKhoeoWNktSEvuM5nhM4LKBU\nWbXmT7RbX/ADkDeMo3gQe9MwjuNkHkWDjlLuMuMQbxjDfzY1h5Fmkh0oMN+hT7NvPJkdfT6wAte1\nWVXJw3dkNHnV7sqiqVG+Vh30TAGAbPXTx5zWO1joZeBQ37Hc0EeB57m+xoehZ9Vd0W7MYzU4TWav\njiavouMLfRS3Y1pThbwnTIcQsKdiLLpaRztL06Hv2O7hmOYZO5x/MCaseGh17cPj+ol2YyVzXYJ9\nz4sCwBqIUFYjPZsFiGENAphomOX+51FziPUMyd4QGxuKaCHMYfY9PT6Z//LixfnUHjhOHGiuay6r\nvK12ZXGztpa7XEpU7TgD23WfPmYAZgj0UpV5rfZN2TW53gLD+EO+GxLKHFPXvjd9dnw4nwWhPLhK\nH0qNM4sTiw+J9AlGwv6nx7TA8aZxMjkPZhfTIA7lbtJp9xNjXMvaXFetVlQQ1e6r3BrNEwCwIxoQ\nJDlmLgCA5xJk7ZH0tZMnHsWhR3wgww/cjjLGtaxsVxW5iYZ4Mp86E1fZkXEQF4CNFsjukRGNBfBD\nsXKt2SiJo8/O3IgdpDEAkMaURbM7Hl5fPxqXFFXldmY4GDDXOx4OnlbnbFd5eCOx4IXZyu/hQgCo\nCWN2Q+3wxI1PTi/Ox7MTiazx0P46gJL2uA4I96wCABj+s8e0wZE/Ow/mz6bHU8+fDOsob2oFMlyt\ne6m2PrYVM8QdBPEAAKhFAGMAcN2GCAgsgrG2EOC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      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=100x100 at 0x7F8430BE52E8>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Source Image\n"
     ]
    },
    {
     "data": {
      "image/png": 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rHRwcfP/99wMCAl588UUOh1NTU/Pxxx9zOJzU1NScnJzDhw9fu3YtJydnamrq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\nRU5ErkJggg==\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=136x136 at 0x7F8430BE5390>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 % complete\n",
      "21 % complete\n",
      "31 % complete\n",
      "41 % complete\n",
      "51 % complete\n",
      "60 % complete\n",
      "70 % complete\n",
      "80 % complete\n",
      "91 % complete\n",
      "Target Image\n"
     ]
    },
    {
     "data": {
      "image/png": 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doiiSJH0+n8lkslgsLBaLz+cHBQWh+C4oKIjP59fV1UEcqtPpdu/ePTExERYWptPp5s2b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\n07TRaGT+qif4cUw/CfP/Et7/B4Vkj8/6/KB7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=100x100 at 0x7F8430BE5390>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Texture synthesis on rings\n",
    "source = cv2.imread('texture.jpg')\n",
    "window_sizes = [5, 7, 13] \n",
    "out_size = [100, 100]\n",
    "print('Source Image')\n",
    "cv2_imshow(source)\n",
    "for w in window_sizes:\n",
    "  target = synth_texture(source, w, out_size)\n",
    "  print('Target Image')\n",
    "  cv2_imshow(target)\n",
    "w = 5\n",
    "# Texture synthesis on colored image\n",
    "source = cv2.imread('texture.jpg')\n",
    "cv2_imshow(source)\n",
    "target = synth_texture(source, w, out_size)\n",
    "#print('Target Image')\n",
    "cv2_imshow(target)\n",
    "# Don't need to submit this one\n",
    "source = cv2.imread('jigsaw.jpg')\n",
    "print('Source Image')\n",
    "cv2_imshow(source)\n",
    "target = synth_texture(source, w, out_size)\n",
    "print('Target Image')\n",
    "cv2_imshow(target)"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "Copy of EECS 504 Pset 3: Motion Magnification and Texture Synthesis.ipynb",
   "provenance": []
  },
  "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}