{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1190\n"
     ]
    }
   ],
   "source": [
    "import cv2\n",
    "vidcap = cv2.VideoCapture(\"concat_mono-fg_realtime.mp4\")\n",
    "success,image = vidcap.read()\n",
    "count = 0\n",
    "while success :\n",
    "    grayimage = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
    "    if count < 926 and count >= 920:\n",
    "        cv2.imwrite(\"betterimages/innerfolder/fram%d.jpg\" % count, grayimage)\n",
    "    success, image = vidcap.read()\n",
    "    count += 1\n",
    "print(count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(512, 640)\n",
      "(512, 640)\n",
      "(512, 640)\n",
      "(512, 640)\n",
      "(512, 640)\n",
      "(512, 640)\n"
     ]
    }
   ],
   "source": [
    "from PIL import Image\n",
    "import numpy as np\n",
    "for i in range(920,926):\n",
    "    im = Image.open(\"betterimages/innerfolder/fram\" + str(i) + \".jpg\")\n",
    "    print(np.array(im).shape)\n",
    "    im.save(\"betterimages/innerfolder/fram\" + str(i) + \".tiff\", 'TIFF')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "591360\n",
      "327680\n"
     ]
    }
   ],
   "source": [
    "print(672*880*1190)\n",
    "print(512*640*1190)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5541125541125541"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "327680/591360"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.421875"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "27/64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(512, 512)\n",
      "(512, 640)\n",
      "(512, 640)\n"
     ]
    }
   ],
   "source": [
    "im = Image.open(\"betterimages/innerfolder/fram\" + str(i) + \".jpg\")\n",
    "def create_gaussian(l=5, sig=1.):\n",
    "    \"\"\"\n",
    "    creates gaussian kernel with side length `l` and a sigma of `sig`\n",
    "    \"\"\"\n",
    "    ax = np.linspace(-(l - 1) / 2., (l - 1) / 2., l)\n",
    "    gauss = np.exp(-0.5 * np.square(ax) / np.square(sig))\n",
    "    kernel = np.outer(gauss, gauss)\n",
    "    return kernel / np.sum(kernel)\n",
    "\n",
    "kernel = create_gaussian(512)\n",
    "print(kernel.shape)\n",
    "im = np.array(im)\n",
    "print(im.shape)\n",
    "print((kernel@im).shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "320.5"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "205120/640"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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