dataset3_process.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import os\n",
    "import glob\n",
    "import matplotlib  as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import cv2\n",
    "\n",
    "\n",
    "%matplotlib inline\n",
    "# Optionally, tweak styles.\n",
    "mpl.rc('figure',  figsize=(22*1.5, 12*1.5))\n",
    "mpl.rc('image', cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# setup paths\n",
    "non_slip_path = '../dataset3/0'\n",
    "slip_path = '../dataset3/1'\n",
    "\n",
    "target_non_slip_path = '../dataset3/processed/0'\n",
    "target_slip_path = '../dataset3/processed/1'\n",
    "\n",
    "if not os.path.exists(target_non_slip_path):\n",
    "    os.makedirs(target_non_slip_path)\n",
    "if not os.path.exists(target_slip_path):\n",
    "    os.makedirs(target_slip_path)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "frame_names_0 = glob.glob('../dataset3/0/*')\n",
    "frame_names_1 = glob.glob('../dataset3/1/*')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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    }
   ],
   "source": [
    "# readin csv file and transform them into images for visualization\n",
    "\n",
    "\n",
    "Nx, Ny = 30, 30\n",
    "\n",
    "for path in frame_names_0:\n",
    "    frames = pd.read_csv(path)\n",
    "    folder_name = os.path.splitext(os.path.basename(path))[0]\n",
    "    folder_path = os.path.join(target_non_slip_path, folder_name)\n",
    "    \n",
    "    # make folder for each set of frames\n",
    "    if not os.path.exists(folder_path):\n",
    "        os.makedirs(folder_path)\n",
    "    \n",
    "    data = frames.values[:,1:]\n",
    "    print data[:, 1].shape\n",
    "    feature_size = Nx*Ny\n",
    "    for i in range(0, data.shape[1]):\n",
    "        dx_interp = data[:feature_size, i]\n",
    "        dy_interp = data[feature_size:, i]\n",
    "#         print dx_interp.shape, dy_interp.shape\n",
    "        \n",
    "        mag = np.sqrt(dx_interp ** 2 + dy_interp ** 2)\n",
    "\n",
    "        dx_resized = (dx_interp.reshape(Nx, Ny) + 30) * 256 / 60\n",
    "        dy_resized = (dy_interp.reshape(Nx, Ny) + 30) * 256 / 60\n",
    "        mag_resized = (mag.reshape(Nx, Ny) + 30) * 256 / 60\n",
    "        # print '====== ', dx_resized.max(), dx_resized.min()\n",
    "\n",
    "#         # fx, fy = 1, 1\n",
    "#         dx_large = cv2.resize(dx_resized, (0, 0), fx=fx, fy=fy, interpolation=cv2.INTER_LINEAR)\n",
    "#         dy_large = cv2.resize(dy_resized, (0, 0), fx=fx, fy=fy, interpolation=cv2.INTER_LINEAR)\n",
    "#         mag_large = cv2.resize(mag_resized, (0, 0), fx=fx, fy=fy, interpolation=cv2.INTER_LINEAR)\n",
    "\n",
    "        # concated = np.concatenate((dx_large, dy_large, mag_large), axis=1)\n",
    "        # concated = np.array(concated, dtype=np.uint8)\n",
    "        dx_large = np.array(dx_resized, dtype=np.uint8)\n",
    "        dy_large = np.array(dy_resized, dtype=np.uint8)\n",
    "        mag_large = np.array(mag_resized, dtype=np.uint8)\n",
    "\n",
    "        dx_large = cv2.applyColorMap(dx_large, cv2.COLORMAP_JET)\n",
    "        dy_large = cv2.applyColorMap(dy_large, cv2.COLORMAP_JET)\n",
    "        mag_large = cv2.applyColorMap(mag_large, cv2.COLORMAP_JET)\n",
    "        \n",
    "#         print dx_large\n",
    "#         print \n",
    "        cv2.imwrite(os.path.join(folder_path, '{}_dx.jpg'.format(i)), dx_large)\n",
    "        cv2.imwrite(os.path.join(folder_path, '{}_dy.jpg'.format(i)), dy_large)\n",
    "        cv2.imwrite(os.path.join(folder_path, '{}_mag.jpg'.format(i)), mag_large)\n",
    "        \n",
    "        \n",
    "#     break\n",
    "        \n",
    "   \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "for path in frame_names_1:\n",
    "    frames = pd.read_csv(path)\n",
    "    folder_name = os.path.splitext(os.path.basename(path))[0]\n",
    "    folder_path = os.path.join(target_slip_path, folder_name)\n",
    "    \n",
    "    # make folder for each set of frames\n",
    "    if not os.path.exists(folder_path):\n",
    "        os.makedirs(folder_path)\n",
    "    \n",
    "    data = frames.values[:,1:]\n",
    "    print data[:, 1].shape\n",
    "    feature_size = Nx*Ny\n",
    "    for i in range(0, data.shape[1]):\n",
    "        dx_interp = data[:feature_size, i]\n",
    "        dy_interp = data[feature_size:, i]\n",
    "#         print dx_interp.shape, dy_interp.shape\n",
    "        \n",
    "        mag = np.sqrt(dx_interp ** 2 + dy_interp ** 2)\n",
    "\n",
    "        dx_resized = (dx_interp.reshape(Nx, Ny) + 30) * 256 / 60\n",
    "        dy_resized = (dy_interp.reshape(Nx, Ny) + 30) * 256 / 60\n",
    "        mag_resized = (mag.reshape(Nx, Ny) + 30) * 256 / 60\n",
    "        # print '====== ', dx_resized.max(), dx_resized.min()\n",
    "\n",
    "#         # fx, fy = 1, 1\n",
    "#         dx_large = cv2.resize(dx_resized, (0, 0), fx=fx, fy=fy, interpolation=cv2.INTER_LINEAR)\n",
    "#         dy_large = cv2.resize(dy_resized, (0, 0), fx=fx, fy=fy, interpolation=cv2.INTER_LINEAR)\n",
    "#         mag_large = cv2.resize(mag_resized, (0, 0), fx=fx, fy=fy, interpolation=cv2.INTER_LINEAR)\n",
    "\n",
    "        # concated = np.concatenate((dx_large, dy_large, mag_large), axis=1)\n",
    "        # concated = np.array(concated, dtype=np.uint8)\n",
    "        dx_large = np.array(dx_resized, dtype=np.uint8)\n",
    "        dy_large = np.array(dy_resized, dtype=np.uint8)\n",
    "        mag_large = np.array(mag_resized, dtype=np.uint8)\n",
    "\n",
    "        dx_large = cv2.applyColorMap(dx_large, cv2.COLORMAP_JET)\n",
    "        dy_large = cv2.applyColorMap(dy_large, cv2.COLORMAP_JET)\n",
    "        mag_large = cv2.applyColorMap(mag_large, cv2.COLORMAP_JET)\n",
    "        \n",
    "#         print dx_large\n",
    "#         print \n",
    "        cv2.imwrite(os.path.join(folder_path, '{}_dx.jpg'.format(i)), dx_large)\n",
    "        cv2.imwrite(os.path.join(folder_path, '{}_dy.jpg'.format(i)), dy_large)\n",
    "        cv2.imwrite(os.path.join(folder_path, '{}_mag.jpg'.format(i)), mag_large)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# resample dataset to obtain 5 times size dataset with striding=1 and sample length being 10\n",
    "\n",
    "# 0-1-2-3-4-5-6-7-8-9-10-11-12-13--------------20\n",
    "#  --->\n",
    "# 0-----------------9\n",
    "#   1-----------------10\n",
    "#     2------------------11\n",
    "#       3-------------------12\n",
    "#         4--------------------13\n",
    "resample_non_slip_path = '../dataset3/resample/0'\n",
    "resample_slip_path = '../dataset3/resample/1'\n",
    "\n",
    "if not os.path.exists(resample_non_slip_path):\n",
    "        os.makedirs(resample_non_slip_path)\n",
    "if not os.path.exists(resample_slip_path):\n",
    "        os.makedirs(resample_slip_path)\n",
    "        \n",
    "        \n",
    "for path in frame_names_0:\n",
    "    ind = os.path.splitext(os.path.basename(path))[0]\n",
    "    frames = pd.read_csv(path)\n",
    "    data = frames.values[:,1:]\n",
    "    \n",
    "    data_0_9  = data[:, 0:10]\n",
    "    data_1_10 = data[:, 1:11]\n",
    "    data_2_11 = data[:, 2:12]\n",
    "    data_3_12 = data[:, 3:13]\n",
    "    data_4_13 = data[:, 4:14]\n",
    "    \n",
    "    df1 = pd.DataFrame(data_0_9)\n",
    "    df1.to_csv(os.path.join(resample_non_slip_path, ind + '_0_9.csv'), index=False)\n",
    "    df2 = pd.DataFrame(data_1_10)\n",
    "    df2.to_csv(os.path.join(resample_non_slip_path, ind + '_1_10.csv'), index=False)\n",
    "    df3 = pd.DataFrame(data_2_11)\n",
    "    df3.to_csv(os.path.join(resample_non_slip_path, ind + '_2_11.csv'), index=False)\n",
    "    df4 = pd.DataFrame(data_3_12)\n",
    "    df4.to_csv(os.path.join(resample_non_slip_path, ind + '_3_12.csv'), index=False)\n",
    "    df5 = pd.DataFrame(data_4_13)\n",
    "    df5.to_csv(os.path.join(resample_non_slip_path, ind + '_4_13.csv'), index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "for path in frame_names_1:\n",
    "    ind = os.path.splitext(os.path.basename(path))[0]\n",
    "    frames = pd.read_csv(path)\n",
    "    data = frames.values[:,1:]\n",
    "    \n",
    "    data_0_9  = data[:, 0:10]\n",
    "    data_1_10 = data[:, 1:11]\n",
    "    data_2_11 = data[:, 2:12]\n",
    "    data_3_12 = data[:, 3:13]\n",
    "    data_4_13 = data[:, 4:14]\n",
    "    \n",
    "    df1 = pd.DataFrame(data_0_9)\n",
    "    df1.to_csv(os.path.join(resample_slip_path, ind + '_0_9.csv'), index=False)\n",
    "    df2 = pd.DataFrame(data_1_10)\n",
    "    df2.to_csv(os.path.join(resample_slip_path, ind + '_1_10.csv'), index=False)\n",
    "    df3 = pd.DataFrame(data_2_11)\n",
    "    df3.to_csv(os.path.join(resample_slip_path, ind + '_2_11.csv'), index=False)\n",
    "    df4 = pd.DataFrame(data_3_12)\n",
    "    df4.to_csv(os.path.join(resample_slip_path, ind + '_3_12.csv'), index=False)\n",
    "    df5 = pd.DataFrame(data_4_13)\n",
    "    df5.to_csv(os.path.join(resample_slip_path, ind + '_4_13.csv'), index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## resample data  with skipping ---> Halve the size  to increase the dynamic change through the frames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "non-slip case processed=====================\n"
     ]
    }
   ],
   "source": [
    "# resample dataset to obtain 5 times size dataset with striding=1 and sample length being 10\n",
    "\n",
    "# 0-1-2-3-4-5-6-7-8-9-10-11-12-13--------------20\n",
    "#  --->\n",
    "# 0-2-4-6-8-10-12-14-16-18\n",
    "#   1-3-5-7-9-11-13-15-17-19\n",
    "resample_skipping_non_slip_path = '../dataset3/resample_skipping/0'\n",
    "resample_skipping_slip_path = '../dataset3/resample_skipping/1'\n",
    "\n",
    "if not os.path.exists(resample_skipping_non_slip_path):\n",
    "        os.makedirs(resample_skipping_non_slip_path)\n",
    "if not os.path.exists(resample_skipping_slip_path):\n",
    "        os.makedirs(resample_skipping_slip_path)\n",
    "        \n",
    "        \n",
    "for path in frame_names_0:\n",
    "    ind = os.path.splitext(os.path.basename(path))[0]\n",
    "    frames = pd.read_csv(path)\n",
    "    data = frames.values[:,1:]\n",
    "    \n",
    "    data_e  = data[:, [0,2,4,6,8,10,12,14,16,18]]\n",
    "    data_o = data[:, [1,3,5,7,9,11,13,15,17,19]]\n",
    "    \n",
    "    \n",
    "    df1 = pd.DataFrame(data_e)\n",
    "    df1.to_csv(os.path.join(resample_skipping_non_slip_path, ind + '_e.csv'), index=False)\n",
    "    df2 = pd.DataFrame(data_o)\n",
    "    df2.to_csv(os.path.join(resample_skipping_non_slip_path, ind + '_o.csv'), index=False)\n",
    "\n",
    "print 'non-slip case processed====================='\n",
    "\n",
    "for path in frame_names_1:\n",
    "    ind = os.path.splitext(os.path.basename(path))[0]\n",
    "    frames = pd.read_csv(path)\n",
    "    data = frames.values[:,1:]\n",
    "    \n",
    "    data_e  = data[:, [0,2,4,6,8,10,12,14,16,18]]\n",
    "    data_o = data[:, [1,3,5,7,9,11,13,15,17,19]]\n",
    "    \n",
    "    \n",
    "    df1 = pd.DataFrame(data_e)\n",
    "    df1.to_csv(os.path.join(resample_skipping_slip_path, ind + '_e.csv'), index=False)\n",
    "    df2 = pd.DataFrame(data_o)\n",
    "    df2.to_csv(os.path.join(resample_skipping_slip_path, ind + '_o.csv'), index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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