{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we need to read frames from the movie\n",
    "# so we install opencv-python - change the next cell type to \"Code\""
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "!pip install opencv-python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'cv2'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-c8ec22b3e787>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mcv2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'cv2'"
     ]
    }
   ],
   "source": [
    "import cv2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from openpiv import pyprocess, piv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the video is the jet PIV from Youtube\n",
    "# https://www.youtube.com/watch?v=EeS1rYMZUxI&ab_channel=USUExperimentalFluidDynamicsLab\n",
    "# all the rights reserved to the authors\n",
    "\n",
    "vidcap = cv2.VideoCapture('../test_movie/videoplayback.mp4')\n",
    "success, image1 = vidcap.read()\n",
    "count = 0\n",
    "U = []\n",
    "V = []\n",
    "\n",
    "while success and count < 10:\n",
    "    # cv2.imwrite(\"frame%d.jpg\" % count, image)     # save frame as JPEG file      \n",
    "    success, image2 = vidcap.read()\n",
    "    # print('Read a new frame: ', success)\n",
    "    if success:\n",
    "        x,y,u,v = piv.simple_piv(image1.sum(axis=2), image2.sum(axis=2),plot=True);\n",
    "        image1 = image2.copy()\n",
    "        count += 1\n",
    "        U.append(u)\n",
    "        V.append(v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "U = np.stack(U)\n",
    "Umean = np.mean(U, axis=0)\n",
    "V = np.stack(V)\n",
    "Vmean = np.mean(V,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig,ax = plt.subplots(figsize=(8,8))\n",
    "ax.imshow(image1,alpha=0.7)\n",
    "ax.quiver(x,y,Umean,Vmean,scale=200,color='r',width=.008)\n",
    "# plt.show()\n",
    "plt.plot(np.mean(Umean,axis=1)*20,y[:,0],color='y',lw=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:openpiv] *",
   "language": "python",
   "name": "conda-env-openpiv-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
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 "nbformat": 4,
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}