optical_flow_attempt.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import cv2\n",
    "import time\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "------------------------\n",
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      "\n",
      " [[  84.64796448  106.0581131 ]]]\n",
      "------------------------\n"
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      "------------------------\n",
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      "------------------------\n",
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      "\n",
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      "------------------------\n",
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      "\n",
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      "------------------------\n",
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      "\n",
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      "------------------------\n",
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      "------------------------\n",
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      "\n",
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      "------------------------\n",
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      "\n",
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      "\n",
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      "------------------------\n",
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      "\n",
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      "\n",
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      "------------------------\n",
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      "\n",
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      "\n",
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      "------------------------\n",
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      "\n",
      " [[  93.49467468   64.0362854 ]]\n",
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      "\n",
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      "------------------------\n",
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      "\n",
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      "\n",
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      "------------------------\n",
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      "\n",
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      "\n",
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      "------------------------\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      " [[  94.25540924   99.40882111]]]\n",
      "------------------------\n",
      "points:  [[[  67.95623016   63.0586853 ]]\n",
      "\n",
      " [[  93.23290253   64.04824829]]\n",
      "\n",
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      "\n",
      " [[  75.97324371   58.08103943]]\n",
      "\n",
      " [[  94.75032043   99.29807281]]]\n",
      "------------------------\n",
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      "\n",
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      "\n",
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      "\n",
      " [[  95.34294128   98.85740662]]]\n",
      "------------------------\n"
     ]
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    {
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      "------------------------\n",
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      "------------------------\n",
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      "------------------------\n",
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      "------------------------\n",
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      "\n",
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      "------------------------\n",
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      "------------------------\n",
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      "\n",
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      "------------------------\n",
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      "\n",
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      "------------------------\n",
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      "------------------------\n",
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      "------------------------\n",
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      "------------------------\n",
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      "\n",
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      "\n",
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      "------------------------\n",
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      "\n",
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      "\n",
      " [[ 103.35328674   93.69116211]]]\n",
      "------------------------\n",
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      "\n",
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      "\n",
      " [[  76.05479431   58.06652069]]\n",
      "\n",
      " [[ 103.73310089   93.76132965]]]\n",
      "------------------------\n",
      "points:  [[[  67.99190521   63.06072235]]\n",
      "\n",
      " [[  89.67442322   63.41330719]]\n",
      "\n",
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      "\n",
      " [[ 104.0344162    93.99537659]]]\n",
      "------------------------\n"
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    {
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      " [[ 104.42602539   94.23742676]]]\n",
      "------------------------\n",
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      "\n",
      " [[  89.4318924    63.36818695]]\n",
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      "------------------------\n",
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      "\n",
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      "\n",
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      "------------------------\n",
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      "\n",
      " [[  89.15399933   63.31776428]]\n",
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      "------------------------\n",
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      "\n",
      " [[  89.01560974   63.34160614]]\n",
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      "------------------------\n",
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      "\n",
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      "\n",
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      "------------------------\n",
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      "\n",
      " [[  88.22900391   63.18519592]]\n",
      "\n",
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      "\n",
      " [[ 105.43073273   95.54417419]]]\n",
      "------------------------\n",
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      "\n",
      " [[  87.82047272   63.11304855]]\n",
      "\n",
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      "\n",
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      "\n",
      " [[ 105.5657959    95.80832672]]]\n",
      "------------------------\n",
      "points:  [[[  67.98618317   63.11382675]]\n",
      "\n",
      " [[  87.5667038    63.13897324]]\n",
      "\n",
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      "\n",
      " [[ 105.73387146   95.90981293]]]\n",
      "------------------------\n",
      "points:  [[[  67.96894836   63.12115479]]\n",
      "\n",
      " [[  87.43423462   63.25928879]]\n",
      "\n",
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      "\n",
      " [[  76.02869415   58.1081543 ]]\n",
      "\n",
      " [[ 106.08805847   96.38864136]]]\n",
      "------------------------\n",
      "points:  [[[  67.96575928   63.13729095]]\n",
      "\n",
      " [[  87.3142395    63.35737991]]\n",
      "\n",
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      "\n",
      " [[  76.02893066   58.11555099]]\n",
      "\n",
      " [[ 106.177948     96.82321167]]]\n",
      "------------------------\n",
      "points:  [[[  67.97261047   63.13729095]]\n",
      "\n",
      " [[  87.10795593   63.383255  ]]\n",
      "\n",
      " [[ 140.31587219   81.11040497]]\n",
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      "\n",
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      "\n",
      " [[ 106.364151     97.2437973 ]]]\n",
      "------------------------\n",
      "points:  [[[  67.95427704   63.13689041]]\n",
      "\n",
      " [[  86.79481506   63.32761765]]\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      " [[  75.98724365   58.11299515]]\n",
      "\n",
      " [[ 106.3760376    97.52492523]]]\n",
      "------------------------\n",
      "points:  [[[  67.9680481    63.11493683]]\n",
      "\n",
      " [[  86.49177551   63.30309677]]\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      " [[  76.01431274   58.09950638]]\n",
      "\n",
      " [[ 106.3397522    97.66345215]]]\n",
      "------------------------\n",
      "points:  [[[  67.98504639   63.07910538]]\n",
      "\n",
      " [[  86.15176392   63.31306076]]\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      " [[  76.048172     58.0708046 ]]\n",
      "\n",
      " [[ 106.28347778   97.62527466]]]\n",
      "------------------------\n",
      "points:  [[[  68.00848389   63.03154373]]\n",
      "\n",
      " [[  85.64971161   63.35741425]]\n",
      "\n",
      " [[ 144.32015991   79.76430511]]\n",
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      "\n",
      " [[  76.07391357   58.04163361]]\n",
      "\n",
      " [[ 106.19749451   97.77870941]]]\n",
      "------------------------\n",
      "points:  [[[  68.01239014   63.01909637]]\n",
      "\n",
      " [[  85.1710968    63.37085724]]\n",
      "\n",
      " [[ 145.53060913   79.23093414]]\n",
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      "\n",
      " [[  76.08830261   58.04941559]]\n",
      "\n",
      " [[ 106.02690125   98.17697144]]]\n",
      "------------------------\n",
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      "\n",
      " [[  84.67778778   63.29452515]]\n",
      "\n",
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      "\n",
      " [[ 105.8543396    98.6342926 ]]]\n",
      "------------------------\n"
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    {
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      "------------------------\n",
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      "\n",
      " [[  84.22670746   63.24742889]]\n",
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      "------------------------\n",
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      "\n",
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      "\n",
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      "\n",
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      "------------------------\n",
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      "\n",
      " [[  84.0140686    63.45013046]]\n",
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      "------------------------\n",
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      "\n",
      " [[  83.88822937   63.45386124]]\n",
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      "------------------------\n",
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      "\n",
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      "\n",
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      "------------------------\n",
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      "\n",
      " [[  83.52236938   63.43484116]]\n",
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      "\n",
      " [[ 103.52531433  100.6478653 ]]]\n",
      "------------------------\n",
      "points:  [[[  68.06396484   62.95770264]]\n",
      "\n",
      " [[  83.32818604   63.4343071 ]]\n",
      "\n",
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      "\n",
      " [[ 103.04662323  100.67240906]]]\n",
      "------------------------\n",
      "points:  [[[  68.05258942   62.96435165]]\n",
      "\n",
      " [[  83.13282776   63.45460129]]\n",
      "\n",
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      "\n",
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      "\n",
      " [[ 102.49919128  100.47267151]]]\n",
      "------------------------\n",
      "points:  [[[  68.04081726   62.97959518]]\n",
      "\n",
      " [[  82.87744904   63.4732933 ]]\n",
      "\n",
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      "\n",
      " [[  76.14691925   58.01603317]]\n",
      "\n",
      " [[ 101.82455444  100.64296722]]]\n",
      "------------------------\n",
      "points:  [[[  68.05671692   63.00333786]]\n",
      "\n",
      " [[  82.62182617   63.43344498]]\n",
      "\n",
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      "\n",
      " [[  76.13845062   58.03150558]]\n",
      "\n",
      " [[ 100.93724823  101.1704483 ]]]\n",
      "------------------------\n",
      "points:  [[[  68.04393005   63.00717926]]\n",
      "\n",
      " [[  82.46360779   63.40768814]]\n",
      "\n",
      " [[ 156.63529968   74.1411972 ]]\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      " [[  76.11064148   58.04041672]]\n",
      "\n",
      " [[  99.81446838  101.75644684]]]\n",
      "------------------------\n",
      "points:  [[[  68.03990173   63.00617981]]\n",
      "\n",
      " [[  82.3524704    63.4052124 ]]\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      " [[  76.09846497   58.03898239]]\n",
      "\n",
      " [[  98.58864594  102.24398804]]]\n",
      "------------------------\n",
      "points:  [[[  68.0382843    62.991539  ]]\n",
      "\n",
      " [[  82.31084442   63.40706635]]\n",
      "\n",
      " [[ 157.38499451   72.87036133]]\n",
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      "\n",
      " [[  76.11488342   58.01051331]]\n",
      "\n",
      " [[  97.46762848  102.36052704]]]\n",
      "------------------------\n",
      "points:  [[[  68.00704956   62.99180603]]\n",
      "\n",
      " [[  82.22119141   63.42802048]]\n",
      "\n",
      " [[ 157.56228638   72.45526123]]\n",
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      "\n",
      " [[  76.08239746   58.01280594]]\n",
      "\n",
      " [[  96.51980591  102.24398041]]]\n",
      "------------------------\n",
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      "\n",
      " [[  82.09677124   63.43273544]]\n",
      "\n",
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      "\n",
      " [[  76.04403687   58.02526474]]\n",
      "\n",
      " [[  95.6917572   102.50307465]]]\n",
      "------------------------\n",
      "points:  [[[  67.99661255   63.02173233]]\n",
      "\n",
      " [[  81.96353912   63.41214371]]\n",
      "\n",
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      "\n",
      " [[  76.05213165   58.02696609]]\n",
      "\n",
      " [[  94.66635895  103.20800781]]]\n",
      "------------------------\n",
      "points:  [[[  67.98429871   63.0340271 ]]\n",
      "\n",
      " [[  81.85772705   63.40911865]]\n",
      "\n",
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      "\n",
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      "------------------------\n"
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      "------------------------\n",
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      "\n",
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      "------------------------\n",
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      "\n",
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      "------------------------\n",
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      "\n",
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      "------------------------\n",
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      "\n",
      " [[  81.59494019   63.4016304 ]]\n",
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      "------------------------\n",
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      "\n",
      " [[  81.57418823   63.4286232 ]]\n",
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      "------------------------\n",
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      "\n",
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      "\n",
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      "------------------------\n",
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      "\n",
      " [[  81.56864929   63.43283844]]\n",
      "\n",
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      "\n",
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      "------------------------\n",
      "points:  [[[  68.05133057   62.88974762]]\n",
      "\n",
      " [[  81.5958252    63.4343605 ]]\n",
      "\n",
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      "\n",
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      "\n",
      " [[  83.17636871  106.32276917]]]\n",
      "------------------------\n",
      "points:  [[[  68.05728149   62.89026642]]\n",
      "\n",
      " [[  81.57505035   63.43425751]]\n",
      "\n",
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      "\n",
      " [[  76.15480804   57.95375061]]\n",
      "\n",
      " [[  82.03497314  106.89885712]]]\n",
      "------------------------\n",
      "points:  [[[  68.06690979   62.90076065]]\n",
      "\n",
      " [[  81.57479858   63.43080902]]\n",
      "\n",
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      "\n",
      " [[  76.1500473    57.95628738]]\n",
      "\n",
      " [[  80.88287354  107.73146057]]]\n",
      "------------------------\n",
      "points:  [[[  68.0506897    62.92022705]]\n",
      "\n",
      " [[  81.56570435   63.43718719]]\n",
      "\n",
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      "\n",
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      "------------------------\n",
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      "\n",
      " [[  81.57621002   63.43913651]]\n",
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      "------------------------\n",
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      "\n",
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      "------------------------\n",
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      "\n",
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      "\n",
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      "------------------------\n",
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      "\n",
      " [[  81.58983612   63.42095947]]\n",
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      "------------------------\n",
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      "\n",
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      "------------------------\n"
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      "------------------------\n",
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      "\n",
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      "------------------------\n",
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      "\n",
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      "------------------------\n",
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      "\n",
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      "------------------------\n",
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      "------------------------\n",
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      "------------------------\n",
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      "\n",
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      "------------------------\n",
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      "\n",
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      "\n",
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      "------------------------\n",
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      "\n",
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      "\n",
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      "------------------------\n",
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      "\n",
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      "------------------------\n",
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      "------------------------\n",
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      "------------------------\n",
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      "------------------------\n",
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      "------------------------\n",
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      "------------------------\n",
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      "------------------------\n"
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      "------------------------\n",
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      "------------------------\n",
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      "------------------------\n",
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      "------------------------\n",
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      "------------------------\n",
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      "------------------------\n",
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      "------------------------\n",
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      "\n",
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      "------------------------\n",
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      "\n",
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      "------------------------\n",
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      "------------------------\n",
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      "\n",
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      "------------------------\n",
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      "------------------------\n",
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      "------------------------\n",
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      "------------------------\n",
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      "------------------------\n"
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      "------------------------\n",
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      "------------------------\n",
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      "------------------------\n",
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      "------------------------\n",
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      "------------------------\n",
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      "------------------------\n",
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      "------------------------\n",
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      "\n",
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      "------------------------\n",
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      "\n",
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      "------------------------\n",
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      "------------------------\n",
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      "------------------------\n",
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      "------------------------\n",
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      "------------------------\n",
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      "------------------------\n",
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      "------------------------\n",
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      "------------------------\n",
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      "------------------------\n",
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      "------------------------\n",
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      "------------------------\n",
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      "------------------------\n",
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      "------------------------\n"
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      "------------------------\n",
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      "------------------------\n",
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      "------------------------\n",
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      "------------------------\n",
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      "------------------------\n",
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      "\n",
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      "------------------------\n",
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      "\n",
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      "\n",
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      "------------------------\n",
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      "\n",
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      "\n",
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      "------------------------\n",
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      "\n",
      " [[  80.70835114   63.36087036]]\n",
      "\n",
      " [[ 115.11698914   95.53466034]]\n",
      "\n",
      " [[ 221.56124878   54.82584763]]\n",
      "\n",
      " [[ 188.09216309   48.30863953]]\n",
      "\n",
      " [[ 207.69851685   45.2457428 ]]\n",
      "\n",
      " [[  89.76837921   66.81241608]]\n",
      "\n",
      " [[  81.73667908   65.36631775]]\n",
      "\n",
      " [[  61.91733551   57.03275299]]\n",
      "\n",
      " [[ 231.88267517    6.12898684]]\n",
      "\n",
      " [[ 133.80004883   33.03034973]]\n",
      "\n",
      " [[ 120.02436829   34.60815048]]\n",
      "\n",
      " [[ 196.8679657    58.38565063]]\n",
      "\n",
      " [[  75.66802216   58.00111389]]\n",
      "\n",
      " [[  70.40818787  106.91616058]]]\n",
      "------------------------\n",
      "points:  [[[  67.94406128   63.00037384]]\n",
      "\n",
      " [[  80.69350433   63.36318588]]\n",
      "\n",
      " [[ 114.0254364    96.20302582]]\n",
      "\n",
      " [[ 221.70243835   54.78091812]]\n",
      "\n",
      " [[ 187.89448547   48.56069946]]\n",
      "\n",
      " [[ 207.99362183   45.00824738]]\n",
      "\n",
      " [[  89.85909271   66.83377075]]\n",
      "\n",
      " [[  81.80901337   65.3788681 ]]\n",
      "\n",
      " [[  61.91570282   57.0350914 ]]\n",
      "\n",
      " [[ 232.00848389    6.17238283]]\n",
      "\n",
      " [[ 133.82794189   33.06133652]]\n",
      "\n",
      " [[ 120.02039337   34.63031769]]\n",
      "\n",
      " [[ 197.08074951   58.2838974 ]]\n",
      "\n",
      " [[  75.66789246   58.00298691]]\n",
      "\n",
      " [[  69.08883667  107.35984039]]]\n",
      "------------------------\n",
      "points:  [[[  67.94258881   63.02163696]]\n",
      "\n",
      " [[  80.71904755   63.36360168]]\n",
      "\n",
      " [[ 113.06057739   96.59621429]]\n",
      "\n",
      " [[ 221.89181519   54.76518631]]\n",
      "\n",
      " [[ 187.82394409   48.64506912]]\n",
      "\n",
      " [[ 208.36187744   44.77412033]]\n",
      "\n",
      " [[  89.96414185   66.83633423]]\n",
      "\n",
      " [[  81.93856812   65.37438965]]\n",
      "\n",
      " [[  61.92337799   57.0480957 ]]\n",
      "\n",
      " [[ 232.0113678     6.05052376]]\n",
      "\n",
      " [[ 133.82073975   33.03031921]]\n",
      "\n",
      " [[ 120.01641083   34.60772324]]\n",
      "\n",
      " [[ 197.56639099   57.94599915]]\n",
      "\n",
      " [[  75.6654129    58.0267067 ]]\n",
      "\n",
      " [[  67.75067902  107.80178833]]]\n",
      "------------------------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "points:  [[[  67.93740845   63.01368332]]\n",
      "\n",
      " [[  80.70755005   63.37857437]]\n",
      "\n",
      " [[ 112.27980042   96.74752045]]\n",
      "\n",
      " [[ 222.01005554   54.77111816]]\n",
      "\n",
      " [[ 187.61894226   48.63604355]]\n",
      "\n",
      " [[ 208.54673767   44.72698975]]\n",
      "\n",
      " [[  90.03768921   66.84586334]]\n",
      "\n",
      " [[  81.9969101    65.38694763]]\n",
      "\n",
      " [[  61.9130249    57.0401535 ]]\n",
      "\n",
      " [[ 232.07325745    5.98511648]]\n",
      "\n",
      " [[ 133.82409668   33.02713776]]\n",
      "\n",
      " [[ 120.02798462   34.60349655]]\n",
      "\n",
      " [[ 198.10389709   57.77966309]]\n",
      "\n",
      " [[  75.65081024   58.02481842]]\n",
      "\n",
      " [[  66.60243225  108.34803009]]]\n",
      "------------------------\n",
      "points:  [[[  67.93881989   63.00210571]]\n",
      "\n",
      " [[  80.74310303   63.38113403]]\n",
      "\n",
      " [[ 111.50090027   97.09465027]]\n",
      "\n",
      " [[ 222.04086304   54.74756622]]\n",
      "\n",
      " [[ 187.34805298   48.9447937 ]]\n",
      "\n",
      " [[ 208.72663879   44.69393158]]\n",
      "\n",
      " [[  90.11126709   66.84719086]]\n",
      "\n",
      " [[  82.14537048   65.41537476]]\n",
      "\n",
      " [[  61.9088974    57.03411484]]\n",
      "\n",
      " [[ 232.06155396    5.93835115]]\n",
      "\n",
      " [[ 133.82003784   33.04136658]]\n",
      "\n",
      " [[ 120.02103424   34.60897064]]\n",
      "\n",
      " [[ 198.71325684   57.55515289]]\n",
      "\n",
      " [[  75.66706848   58.01966858]]\n",
      "\n",
      " [[  64.29246521  109.60874939]]]\n",
      "------------------------\n",
      "points:  [[[  67.95104218   62.98101425]]\n",
      "\n",
      " [[  80.76200104   63.3835144 ]]\n",
      "\n",
      " [[ 110.73475647   97.71815491]]\n",
      "\n",
      " [[ 222.05805969   54.7097702 ]]\n",
      "\n",
      " [[ 187.11436462   49.46266556]]\n",
      "\n",
      " [[ 208.90608215   44.61357117]]\n",
      "\n",
      " [[  90.19848633   66.87106323]]\n",
      "\n",
      " [[  82.31494141   65.45779419]]\n",
      "\n",
      " [[  61.90797043   57.03411102]]\n",
      "\n",
      " [[ 232.12806702    5.8800993 ]]\n",
      "\n",
      " [[ 133.82417297   33.04691315]]\n",
      "\n",
      " [[ 120.02651215   34.61056137]]\n",
      "\n",
      " [[ 199.18309021   57.39664078]]\n",
      "\n",
      " [[  75.69638824   58.00061798]]\n",
      "\n",
      " [[  62.62952805  110.38439941]]]\n",
      "------------------------\n",
      "points:  [[[  67.98017883   62.96278   ]]\n",
      "\n",
      " [[  80.80856323   63.38808441]]\n",
      "\n",
      " [[ 109.82803345   98.33675385]]\n",
      "\n",
      " [[ 222.12661743   54.72128677]]\n",
      "\n",
      " [[ 186.89749146   49.828022  ]]\n",
      "\n",
      " [[ 209.20213318   44.52499771]]\n",
      "\n",
      " [[  90.2552948    66.87776184]]\n",
      "\n",
      " [[  82.42938232   65.48369598]]\n",
      "\n",
      " [[  61.90369415   57.03134537]]\n",
      "\n",
      " [[ 232.24472046    5.95175791]]\n",
      "\n",
      " [[ 133.82215881   33.05311584]]\n",
      "\n",
      " [[ 120.01224518   34.6153183 ]]\n",
      "\n",
      " [[ 199.31178284   57.23786926]]\n",
      "\n",
      " [[  75.76812744   57.97697449]]\n",
      "\n",
      " [[  61.87580109  110.89736176]]]\n",
      "------------------------\n",
      "points:  [[[  67.98582458   62.95069504]]\n",
      "\n",
      " [[  80.82227325   63.38726044]]\n",
      "\n",
      " [[ 108.96495056   98.63951111]]\n",
      "\n",
      " [[ 222.13150024   54.69841766]]\n",
      "\n",
      " [[ 186.54255676   49.88876343]]\n",
      "\n",
      " [[ 209.44334412   44.3263588 ]]\n",
      "\n",
      " [[  90.28022003   66.860672  ]]\n",
      "\n",
      " [[  82.51608276   65.49847412]]\n",
      "\n",
      " [[  61.89909363   57.02593613]]\n",
      "\n",
      " [[ 232.32504272    6.0142808 ]]\n",
      "\n",
      " [[ 133.81143188   33.04206467]]\n",
      "\n",
      " [[ 120.01969147   34.61724091]]\n",
      "\n",
      " [[ 199.50706482   57.0982933 ]]\n",
      "\n",
      " [[  75.78510284   57.96480942]]\n",
      "\n",
      " [[  61.04158401  111.13864136]]]\n",
      "------------------------\n",
      "points:  [[[  67.96059418   62.93258667]]\n",
      "\n",
      " [[  80.79837799   63.37598038]]\n",
      "\n",
      " [[ 108.1178894    98.74414062]]\n",
      "\n",
      " [[ 222.13096619   54.65266037]]\n",
      "\n",
      " [[ 186.24337769   49.96259308]]\n",
      "\n",
      " [[ 209.59254456   44.10557175]]\n",
      "\n",
      " [[  90.37848663   66.84443665]]\n",
      "\n",
      " [[  82.73534393   65.53005981]]\n",
      "\n",
      " [[  61.88761902   56.99926758]]\n",
      "\n",
      " [[ 232.44503784    6.21497631]]\n",
      "\n",
      " [[ 133.8081665    33.03878403]]\n",
      "\n",
      " [[ 120.01872253   34.61151886]]\n",
      "\n",
      " [[ 199.81063843   57.0096283 ]]\n",
      "\n",
      " [[  75.75225067   57.94800949]]\n",
      "\n",
      " [[  60.45195007  111.23322296]]]\n",
      "------------------------\n",
      "points:  [[[  67.9708252    62.93202972]]\n",
      "\n",
      " [[  80.76963043   63.36771011]]\n",
      "\n",
      " [[ 107.14276123   99.13549805]]\n",
      "\n",
      " [[ 222.14863586   54.62287521]]\n",
      "\n",
      " [[ 186.02275085   50.17942429]]\n",
      "\n",
      " [[ 209.65548706   44.1065979 ]]\n",
      "\n",
      " [[  90.4823761    66.87123871]]\n",
      "\n",
      " [[  82.75241089   65.53419495]]\n",
      "\n",
      " [[  61.91526413   57.00474167]]\n",
      "\n",
      " [[ 232.49176025    6.13723469]]\n",
      "\n",
      " [[ 133.81472778   33.04397202]]\n",
      "\n",
      " [[ 120.01130676   34.60991669]]\n",
      "\n",
      " [[ 200.50953674   56.93281555]]\n",
      "\n",
      " [[  75.72928619   57.95930862]]\n",
      "\n",
      " [[  60.19161987  111.39179993]]]\n",
      "------------------------\n",
      "points:  [[[  67.9650116    62.94098282]]\n",
      "\n",
      " [[  80.7516861    63.37319565]]\n",
      "\n",
      " [[ 106.15931702   99.70787048]]\n",
      "\n",
      " [[ 222.22538757   54.57100677]]\n",
      "\n",
      " [[ 185.83323669   50.56952667]]\n",
      "\n",
      " [[ 209.77632141   44.0583725 ]]\n",
      "\n",
      " [[  90.59581757   66.89987183]]\n",
      "\n",
      " [[  82.72006226   65.53437805]]\n",
      "\n",
      " [[  61.91806793   57.01378632]]\n",
      "\n",
      " [[ 232.52638245    6.11178398]]\n",
      "\n",
      " [[ 133.81011963   33.05702209]]\n",
      "\n",
      " [[ 120.01050568   34.60841751]]\n",
      "\n",
      " [[ 201.14059448   56.89071274]]\n",
      "\n",
      " [[  75.72002411   57.96575165]]\n",
      "\n",
      " [[  59.12906265  112.06076813]]]\n",
      "------------------------\n",
      "points:  [[[  67.94973755   62.94323349]]\n",
      "\n",
      " [[  80.70909119   63.35907745]]\n",
      "\n",
      " [[ 105.06841278  100.38568878]]\n",
      "\n",
      " [[ 222.27156067   54.56253052]]\n",
      "\n",
      " [[ 185.66252136   50.85244751]]\n",
      "\n",
      " [[ 209.9602356    43.94742584]]\n",
      "\n",
      " [[  90.67823029   66.89762878]]\n",
      "\n",
      " [[  82.73934174   65.53372192]]\n",
      "\n",
      " [[  61.91716766   57.01596832]]\n",
      "\n",
      " [[ 232.51875305    6.10579205]]\n",
      "\n",
      " [[ 133.79449463   33.0447731 ]]\n",
      "\n",
      " [[ 120.01639557   34.6002655 ]]\n",
      "\n",
      " [[ 201.359375     56.65827942]]\n",
      "\n",
      " [[  75.67844391   57.95103836]]\n",
      "\n",
      " [[  57.56013107  112.45578766]]]\n",
      "------------------------\n",
      "points:  [[[  67.95243073   62.94491959]]\n",
      "\n",
      " [[  80.72533417   63.36817932]]\n",
      "\n",
      " [[ 104.07591248  100.8147049 ]]\n",
      "\n",
      " [[ 222.32305908   54.54129791]]\n",
      "\n",
      " [[ 185.69874573   50.92853165]]\n",
      "\n",
      " [[ 210.23765564   43.89009857]]\n",
      "\n",
      " [[  90.79256439   66.88531494]]\n",
      "\n",
      " [[  82.73421478   65.53787231]]\n",
      "\n",
      " [[  61.92808914   57.00827026]]\n",
      "\n",
      " [[ 232.56411743    6.1217041 ]]\n",
      "\n",
      " [[ 133.80856323   33.04705811]]\n",
      "\n",
      " [[ 120.01236725   34.60529327]]\n",
      "\n",
      " [[ 201.70724487   56.35761261]]\n",
      "\n",
      " [[  75.67170715   57.95793915]]\n",
      "\n",
      " [[  56.17691803  112.96363831]]]\n",
      "------------------------\n"
     ]
    }
   ],
   "source": [
    "\n",
    "frameset_path = r'C:\\Users\\Legion\\Desktop\\Internship_IIT\\UCSD_Anomaly_Dataset.v1p2\\UCSDped1\\Train\\Train001'\n",
    "\n",
    "list_frames = os.listdir(frameset_path)\n",
    "list_frames.remove('._.DS_Store')\n",
    "list_frames.remove('.DS_Store')\n",
    "\n",
    "ref_frame = cv2.imread(os.path.join(frameset_path,list_frames[0]),0)\n",
    "\n",
    "\n",
    "#cap = cv2.VideoCapture('slow.flv')\n",
    "# params for ShiTomasi corner detection\n",
    "feature_params = dict( maxCorners = 100,\n",
    "                       qualityLevel = 0.3,\n",
    "                       minDistance = 7,\n",
    "                       blockSize = 7 )\n",
    "\n",
    "\n",
    "# Parameters for lucas kanade optical flow\n",
    "lk_params = dict( winSize  = (15,15),\n",
    "                  maxLevel = 2,\n",
    "                  criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.03))\n",
    "\n",
    "# Create some random colors\n",
    "color = np.random.randint(0,255,(100,3))\n",
    "\n",
    "#old_gray = cv2.cvtColor(ref_frame, cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "p0 = cv2.goodFeaturesToTrack(ref_frame, mask = None, **feature_params)\n",
    "\n",
    "color_ref_frame = cv2.cvtColor(ref_frame, cv2.COLOR_GRAY2BGR)\n",
    "\n",
    "# Create a mask image for drawing purposes\n",
    "mask = np.zeros_like(color_ref_frame)\n",
    "\n",
    "\n",
    "for frame_name in list_frames[1:]:\n",
    "    \n",
    "    frame_path = os.path.join(frameset_path, frame_name)\n",
    "    \n",
    "    frame = cv2.imread(frame_path, 0)\n",
    "    \n",
    "    color_frame = cv2.cvtColor(frame, cv2.COLOR_GRAY2BGR)\n",
    "    \n",
    "    # calculate optical flow\n",
    "    p1, st, err = cv2.calcOpticalFlowPyrLK(ref_frame, frame, p0, None, **lk_params)\n",
    "    \n",
    "    # Select good points\n",
    "    good_new = p1[st==1]\n",
    "    good_old = p0[st==1]\n",
    "    \n",
    "    # draw the tracks\n",
    "    for i,(new,old) in enumerate(zip(good_new,good_old)):\n",
    "        \n",
    "        a,b = new.ravel()\n",
    "        c,d = old.ravel()\n",
    "        mask = cv2.line(mask, (a,b),(c,d), (0,255,0), 2)\n",
    "        color_frame = cv2.circle(color_frame,(a,b),3,color[i].tolist(),-1)\n",
    "    \n",
    "    img = cv2.add(color_frame,mask)\n",
    "    print(\"points: \",p1)\n",
    "    print('------------------------')\n",
    "    cv2.imshow('frame',color_frame)\n",
    "    cv2.imshow('mask',img)\n",
    "    k = cv2.waitKey(30) & 0xff\n",
    "    if k == 27:\n",
    "        break\n",
    "    # Now update the previous frame and previous points\n",
    "    ref_frame = frame.copy()\n",
    "    p0 = good_new.reshape(-1,1,2)\n",
    "cv2.destroyAllWindows()\n",
    "\n",
    "    \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on built-in function imshow:\n",
      "\n",
      "imshow(...)\n",
      "    imshow(winname, mat) -> None\n",
      "    .   @brief Displays an image in the specified window.\n",
      "    .   \n",
      "    .   The function imshow displays an image in the specified window. If the window was created with the\n",
      "    .   cv::WINDOW_AUTOSIZE flag, the image is shown with its original size, however it is still limited by the screen resolution.\n",
      "    .   Otherwise, the image is scaled to fit the window. The function may scale the image, depending on its depth:\n",
      "    .   \n",
      "    .   -   If the image is 8-bit unsigned, it is displayed as is.\n",
      "    .   -   If the image is 16-bit unsigned or 32-bit integer, the pixels are divided by 256. That is, the\n",
      "    .   value range [0,255\\*256] is mapped to [0,255].\n",
      "    .   -   If the image is 32-bit or 64-bit floating-point, the pixel values are multiplied by 255. That is, the\n",
      "    .   value range [0,1] is mapped to [0,255].\n",
      "    .   \n",
      "    .   If window was created with OpenGL support, cv::imshow also support ogl::Buffer , ogl::Texture2D and\n",
      "    .   cuda::GpuMat as input.\n",
      "    .   \n",
      "    .   If the window was not created before this function, it is assumed creating a window with cv::WINDOW_AUTOSIZE.\n",
      "    .   \n",
      "    .   If you need to show an image that is bigger than the screen resolution, you will need to call namedWindow(\"\", WINDOW_NORMAL) before the imshow.\n",
      "    .   \n",
      "    .   @note This function should be followed by cv::waitKey function which displays the image for specified\n",
      "    .   milliseconds. Otherwise, it won't display the image. For example, **waitKey(0)** will display the window\n",
      "    .   infinitely until any keypress (it is suitable for image display). **waitKey(25)** will display a frame\n",
      "    .   for 25 ms, after which display will be automatically closed. (If you put it in a loop to read\n",
      "    .   videos, it will display the video frame-by-frame)\n",
      "    .   \n",
      "    .   @note\n",
      "    .   \n",
      "    .   [__Windows Backend Only__] Pressing Ctrl+C will copy the image to the clipboard.\n",
      "    .   \n",
      "    .   [__Windows Backend Only__] Pressing Ctrl+S will show a dialog to save the image.\n",
      "    .   \n",
      "    .   @param winname Name of the window.\n",
      "    .   @param mat Image to be shown.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(cv2.imshow)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(512, 512, 3)\n"
     ]
    }
   ],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "im =cv2.imread(r'C:\\Users\\Legion\\Documents\\standard_test_images\\peppers_color.tif')\n",
    "print(im.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "color = np.random.randint(0,255,(100,3))\n",
    "im = cv2.line(im,(0,0),(100,100),color[0].tolist(),2)\n",
    "cv2.imshow('im',im)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(512, 512, 3)\n"
     ]
    }
   ],
   "source": [
    "k= np.zeros((im.shape[0],im.shape[1],im.shape[2]))\n",
    "print(k.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "\n",
    "video_path = r'C:\\Users\\Legion\\Desktop\\Internship_IIT\\Meet_Crowd.mpg'\n",
    "\n",
    "cap = cv2.VideoCapture(video_path)\n",
    "\n",
    "feature_params = dict( maxCorners = 100,\n",
    "                     qualityLevel = 0.3,\n",
    "                     minDistance = 7,\n",
    "                     blockSize = 7)\n",
    "\n",
    "lk_params = dict( winSize = (15, 15),\n",
    "                maxLevel = 2,\n",
    "                criteria = (cv2.TERM_CRITERIA_EPS| cv2.TERM_CRITERIA_COUNT, 10,0.03))\n",
    "\n",
    "\n",
    "color =np.random.randint(0, 255, (100, 3))\n",
    "\n",
    "ret, old = cap.read()\n",
    "old_grey = cv2.cvtColor(old, cv2.COLOR_BGR2GRAY)\n",
    "p0 = cv2.goodFeaturesToTrack(old_grey, mask = None, **feature_params)\n",
    "\n",
    "mask = np.zeros_like(old)\n",
    "\n",
    "while(1):\n",
    "    \n",
    "    ret, frame = cap.read()\n",
    "    \n",
    "    if ret == False:\n",
    "        break\n",
    "    \n",
    "    frame_grey = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n",
    "    \n",
    "    p1, status, err = cv2.calcOpticalFlowPyrLK(old_grey, frame_grey, p0, None, **lk_params)\n",
    "    \n",
    "    good_new = p1[status == 1]\n",
    "    good_old = p0[status == 1]\n",
    "    \n",
    "    for idx,(new,old) in enumerate(zip(good_new, good_old)):\n",
    "        \n",
    "        a, b = new.ravel()\n",
    "        c, d = old.ravel()\n",
    "        \n",
    "        \n",
    "        mask = cv2.line(mask, (a, b), (c, d), color[idx].tolist(), 2)\n",
    "        frame = cv2.circle(frame, (a, b), 5, color[idx].tolist(), -1)\n",
    "        \n",
    "    img =cv2.add(frame, mask)\n",
    "    cv2.imshow('frame', img)\n",
    "    k = cv2.waitKey(1) & 0xff\n",
    "    if k == ord('q'):\n",
    "        break\n",
    "        \n",
    "    old_grey = frame_grey.copy()\n",
    "    p0 = good_new.reshape(-1,1,2)\n",
    "    \n",
    "cv2.destroyAllWindows()\n",
    "cap.release()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([248, 113,  69])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "color[0].\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter the frame directory:C:\\Users\\Legion\\Desktop\\Internship_IIT\\UCSD_Anomaly_Dataset.v1p2\\UCSDped2\\Test\\Test001\n",
      "(240, 360)\n",
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     ]
    }
   ],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "\n",
    "frameset_path = input(\"Enter the frame directory:\")\n",
    "\n",
    "list_frames_name = os.listdir(frameset_path)\n",
    "\n",
    "if '._.DS_Store' in list_frames_name:\n",
    "    list_frames_name.remove('._.DS_Store')\n",
    "    \n",
    "if '.DS_Store' in list_frames_name:\n",
    "    list_frames_name.remove('.DS_Store')\n",
    "    \n",
    "\n",
    "    \n",
    "ref_frame_path = os.path.join(frameset_path,list_frames_name[0])\n",
    "ref_frame = cv2.imread(ref_frame_path,0)\n",
    "#ref_frame = cv2.resize(ref_frame,(240,240))\n",
    "\n",
    "prvs = ref_frame\n",
    "\n",
    "hsv = np.zeros((ref_frame.shape[0],ref_frame.shape[1],3))\n",
    "hsv[...,1] = 255\n",
    "\n",
    "for frame_name in list_frames_name[1:]:\n",
    "    frame_path = os.path.join(frameset_path,frame_name)\n",
    "    next_img = cv2.imread(frame_path,0)\n",
    "\n",
    "    flow = cv2.calcOpticalFlowFarneback(prvs,next_img, None, 0.5, 3, 15, 3, 5, 1.2, 0)\n",
    "    #print(flow)\n",
    "    #print()\n",
    "    \n",
    "    mag, ang = cv2.cartToPolar(flow[...,0], flow[...,1])\n",
    "    print(ang.shape)\n",
    "    print(mag.shape)\n",
    "    hsv[...,0] = ang*360/np.pi\n",
    "    hsv[...,2] = cv2.normalize(mag,None,0,255,cv2.NORM_MINMAX)\n",
    "    rgb = cv2.cvtColor(np.uint8(hsv),cv2.COLOR_HSV2BGR)\n",
    "    \n",
    "    #cv2.imshow('frame',flow)\n",
    "    cv2.imshow('frame2',rgb)\n",
    "    k = cv2.waitKey(30) & 0xff\n",
    "    if k == 27:\n",
    "        break\n",
    "    elif k == ord('s'):\n",
    "        cv2.imwrite('opticalfb.png',frame2)\n",
    "        cv2.imwrite('opticalhsv.png',rgb)\n",
    "    \n",
    "    prvs = next_img\n",
    "\n",
    "\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(288, 384, 2)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flow.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "arrays used as indices must be of integer (or boolean) type",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-8-f02441699f51>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     59\u001b[0m         \u001b[0mprevgray\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgray\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     60\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 61\u001b[1;33m         \u001b[0mcv2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'flow'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdraw_flow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgray\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mflow\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     62\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mshow_hsv\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     63\u001b[0m             \u001b[0mcv2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'flow HSV'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdraw_hsv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mflow\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-8-f02441699f51>\u001b[0m in \u001b[0;36mdraw_flow\u001b[1;34m(img, flow, step)\u001b[0m\n\u001b[0;32m     13\u001b[0m     \u001b[0mh\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mw\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mimg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     14\u001b[0m     \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmgrid\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mstep\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mh\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mstep\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstep\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mw\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mstep\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 15\u001b[1;33m     \u001b[0mfx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfy\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mflow\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mT\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     16\u001b[0m     \u001b[0mlines\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvstack\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mfx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mfy\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mT\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     17\u001b[0m     \u001b[0mlines\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mint32\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlines\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m0.5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mIndexError\u001b[0m: arrays used as indices must be of integer (or boolean) type"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import cv2\n",
    "#import video\n",
    "\n",
    "help_message = '''\n",
    "USAGE: opt_flow.py [<video_source>]\n",
    "Keys:\n",
    " 1 - toggle HSV flow visualization\n",
    " 2 - toggle glitch\n",
    "'''\n",
    "\n",
    "def draw_flow(img, flow, step=16):\n",
    "    h, w = img.shape[:2]\n",
    "    y, x = np.mgrid[step/2:h:step, step/2:w:step].reshape(2,-1)\n",
    "    fx, fy = flow[y,x].T\n",
    "    lines = np.vstack([x, y, x+fx, y+fy]).T.reshape(-1, 2, 2)\n",
    "    lines = np.int32(lines + 0.5)\n",
    "    vis = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)\n",
    "    cv2.polylines(vis, lines, 0, (0, 255, 0))\n",
    "    for (x1, y1), (x2, y2) in lines:\n",
    "        cv2.circle(vis, (x1, y1), 1, (0, 255, 0), -1)\n",
    "    return vis\n",
    "\n",
    "def draw_hsv(flow):\n",
    "    h, w = flow.shape[:2]\n",
    "    fx, fy = flow[:,:,0], flow[:,:,1]\n",
    "    ang = np.arctan2(fy, fx) + np.pi\n",
    "    v = np.sqrt(fx*fx+fy*fy)\n",
    "    hsv = np.zeros((h, w, 3), np.uint8)\n",
    "    hsv[...,0] = ang*(180/np.pi/2)\n",
    "    hsv[...,1] = 255\n",
    "    hsv[...,2] = np.minimum(v*4, 255)\n",
    "    bgr = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)\n",
    "    return bgr\n",
    "\n",
    "def warp_flow(img, flow):\n",
    "    h, w = flow.shape[:2]\n",
    "    flow = -flow\n",
    "    flow[:,:,0] += np.arange(w)\n",
    "    flow[:,:,1] += np.arange(h)[:,np.newaxis]\n",
    "    res = cv2.remap(img, flow, None, cv2.INTER_LINEAR)\n",
    "    return res\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    \n",
    "    cap = cv2.VideoCapture(video_path)\n",
    "    _,prev = cap.read()\n",
    "    #ret, prev = cam.read()\n",
    "    prevgray = cv2.cvtColor(prev, cv2.COLOR_BGR2GRAY)\n",
    "    show_hsv = False\n",
    "    show_glitch = False\n",
    "    cur_glitch = prev.copy()\n",
    "\n",
    "    while True:\n",
    "        ret, img = cap.read()\n",
    "        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
    "        flow = cv2.calcOpticalFlowFarneback(prevgray, gray,None, 0.5, 3, 15, 3, 5, 1.2, 0)\n",
    "        #flow = cv2.calcOpticalFlowFarneback(prvs,next, None, 0.5, 3, 15, 3, 5, 1.2, 0)\n",
    "        prevgray = gray\n",
    "        \n",
    "        cv2.imshow('flow', draw_flow(gray, flow))\n",
    "        if show_hsv:\n",
    "            cv2.imshow('flow HSV', draw_hsv(flow))\n",
    "        if show_glitch:\n",
    "            cur_glitch = warp_flow(cur_glitch, flow)\n",
    "            cv2.imshow('glitch', cur_glitch)\n",
    "\n",
    "        ch = 0xFF & cv2.waitKey(5)\n",
    "        if ch == 27:\n",
    "            break\n",
    "        if ch == ord('1'):\n",
    "            show_hsv = not show_hsv\n",
    "            print('HSV flow visualization is', ['off', 'on'][show_hsv])\n",
    "        if ch == ord('2'):\n",
    "            show_glitch = not show_glitch\n",
    "            if show_glitch:\n",
    "                cur_glitch = img.copy()\n",
    "            print('glitch is', ['off', 'on'][show_glitch])\n",
    "    cv2.destroyAllWindows() \t\t\t\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "f = cv2.imread(r'C:\\Users\\Legion\\Documents\\standard_test_images\\peppers_color.tif')\n",
    "\n",
    "cv2.imshow('frame', f)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(512, 512, 3)\n"
     ]
    }
   ],
   "source": [
    "print(f.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(512, 512, 3)\n"
     ]
    }
   ],
   "source": [
    "g= cv2.cvtColor(f,cv2.COLOR_BGR2GRAY)\n",
    "h=cv2.cvtColor(g,cv2.COLOR_GRAY2BGR)\n",
    "\n",
    "k=np.zeros_like(h)\n",
    "print(k.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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