video_analysis_surface_plot.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import numpy as np\n",
    "from pylab import *\n",
    "# %matplotlib notebook\n",
    "\n",
    "def surface_plot(X,Y,Z,**kwargs):\n",
    "    \"\"\" WRITE DOCUMENTATION\n",
    "    \"\"\"\n",
    "    ion()\n",
    "    xlabel, ylabel, zlabel, title = kwargs.get('xlabel',\"\"), kwargs.get('ylabel',\"\"), kwargs.get('zlabel',\"\"), kwargs.get('title',\"\")\n",
    "    fig = plt.figure()\n",
    "    fig.patch.set_facecolor('white')\n",
    "    ax = fig.add_subplot(111, projection='3d')\n",
    "    ax.scatter(X,Y,Z, c='r',marker='o')\n",
    "#     ax.plot_surface(X,Y,Z)\n",
    "\n",
    "    ax.set_xlabel(xlabel)\n",
    "    ax.set_ylabel(ylabel)\n",
    "    ax.set_zlabel(zlabel)\n",
    "    ax.set_title(title)\n",
    "    plt.show()\n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def parse_stats_data(line):\n",
    "    split_info = line.strip('\\n').split(',')\n",
    "    original_resolution = split_info[0].split('x')\n",
    "    compressed_resolution = split_info[1].split('x')\n",
    "    int_original_resolution = int(original_resolution[0]) *int(original_resolution[1])\n",
    "    int_compressed_resolution = int(compressed_resolution[0]) *int(compressed_resolution[1])\n",
    "    data_list = []\n",
    "    for i, d in enumerate(split_info[2:]):\n",
    "        if d == 'nan':\n",
    "            data_list.append(np.nan)\n",
    "        else:\n",
    "            data_list.append(float(d))\n",
    "    data_list.insert(0,int_compressed_resolution)\n",
    "    data_list.insert(0,int_original_resolution)\n",
    "    \n",
    "    return original_resolution, compressed_resolution, data_list\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cur_dir = os.getcwd()\n",
    "\n",
    "# replace the name of the stats file to the one that you want to analyse\n",
    "stats_path = os.path.join(cur_dir, 'video_analysis\\original_videos\\log\\stats_10311226.txt')\n",
    "\n",
    "title_list = []\n",
    "data_list = []\n",
    "with open(stats_path, 'r') as f:\n",
    "    i = 0\n",
    "    ori_res_list = []\n",
    "    comp_res_list = []\n",
    "    for line in f.readlines():\n",
    "        if (i==0):\n",
    "            title_list = line.split(',')\n",
    "            i += 1\n",
    "            continue\n",
    "        else:\n",
    "            original_resolution, compressed_resolution, data = parse_stats_data(line)\n",
    "            ori_res_list.append(original_resolution) \n",
    "            comp_res_list.append(compressed_resolution)\n",
    "            data_list.append(data)\n",
    "\n",
    "# np_data = np.asarray(data_list)\n",
    "np_data=np.array([np.array(xi) for xi in data_list])\n",
    "# print(np_data)\n",
    "\n",
    "# 4: psnr\n",
    "# 8: ssim\n",
    "# 12: lpips\n",
    "# 16: tof\n",
    "# 20: tlp100\n",
    "\n",
    "metric_index = 16\n",
    "for i in range(len(data_list)//8):\n",
    "    x = np.array([np.array(xi[1]) for xi in data_list[8*i:8*(i+1)]])\n",
    "    y = np.array([np.array(xi[2]) for xi in data_list[8*i:8*(i+1)]])\n",
    "    z = np.array([np.array(xi[metric_index]) for xi in data_list[8*i:8*(i+1)]])\n",
    "    # print(x)\n",
    "    # print(y)\n",
    "    # print(z)\n",
    "    # surface_plot(x,y,z)\n",
    "\n",
    "    arg_list = np.argsort(y)\n",
    "\n",
    "    x_sort = x[arg_list]\n",
    "    y_sort = y[arg_list]\n",
    "    z_sort = z[arg_list]\n",
    "    \n",
    "    from scipy.interpolate import interp1d\n",
    "\n",
    "    xnew = np.linspace(y_sort.min(),y_sort.max(),300) #300 represents number of points to make between T.min and T.max\n",
    "\n",
    "    f = interp1d(y_sort, z_sort, kind='cubic')\n",
    "\n",
    "    # plt.plot(y_sort, z_sort)\n",
    "    plt.figure()\n",
    "    plt.plot(y_sort, z_sort, 'o', xnew, f(xnew), '-') #, xnew, f2(xnew), '--')\n",
    "    plt.title(title_list[metric_index])\n",
    "    plt.show()\n",
    "    \n",
    "#     x_grid = np.linspace(min(x), max(x) + 1, 1000*len(x))\n",
    "#     y_grid = np.linspace(min(y), max(y) + 1, 1000*len(y))\n",
    "#     B1, B2 = np.meshgrid(x_grid, y_grid, indexing='xy')\n",
    "#     Z = np.zeros((x.size, z.size))\n",
    "\n",
    "#     import scipy as sp\n",
    "#     import scipy.interpolate\n",
    "#     spline = sp.interpolate.Rbf(x,y,z,function='thin_plate',smooth=5, episilon=5)\n",
    "\n",
    "#     Z = spline(B1,B2)\n",
    "#     fig = plt.figure(figsize=(10,6))\n",
    "#     ax = axes3d.Axes3D(fig)\n",
    "#     ax.plot_wireframe(B1, B2, Z)\n",
    "#     ax.plot_surface(B1, B2, Z,alpha=0.2)\n",
    "#     ax.scatter3D(x,y,z, c='r')\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}