Feature_Processing.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Feature Processing.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "HWxLNOSJojy_"
      },
      "source": [
        "!unzip '/content/drive/MyDrive/30 FPS JSON.zip'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zVJoy3oZooU7"
      },
      "source": [
        "!rm -rf \"/content/csv_folders\"\n",
        "import json \n",
        "import csv \n",
        "import os\n",
        "import glob\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "!pip install pandas\n",
        "from pandas.errors import EmptyDataError\n",
        "!find . -name \".DS_Store\" -delete\n",
        "\n",
        "directories = [\"/content/csv_folders/Left\",\"/content/csv_folders/Right\"]\n",
        "for dirs in directories:\n",
        "\tif not os.path.exists(dirs):\n",
        "\t    os.makedirs(dirs)\n",
        "     \n",
        "counter = 0\n",
        "for folder in os.listdir('/content/30 FPS JSON Skeleton/Left'):\n",
        "  for i in os.listdir('/content/30 FPS JSON Skeleton/Left/'+folder):\n",
        "    \n",
        "      # Opening JSON file and loading the data \n",
        "      # into the variable data \n",
        "      with open('/content/30 FPS JSON Skeleton/Left/'+folder+'/'+i) as json_file: \n",
        "          data = json.load(json_file) \n",
        "      if not os.path.exists('/content/csv_folders/Left/'+folder):\n",
        "        os.makedirs('/content/csv_folders/Left/'+folder)\n",
        "      employee_data = data['people'] \n",
        "      new_csv = i.replace(\".json\",\".csv\")\n",
        "      new_csv = \"/content/csv_folders/Left/\"+folder+'/'+new_csv\n",
        "      # now we will open a file for writing \n",
        "    \n",
        "      data_file = open(new_csv, 'w') \n",
        "        \n",
        "      # create the csv writer object \n",
        "      csv_writer = csv.writer(data_file) \n",
        "        \n",
        "      # Counter variable used for writing  \n",
        "      # headers to the CSV file \n",
        "      count = 0\n",
        "      \n",
        "\n",
        "      for emp in employee_data: \n",
        "          if count == 0: \n",
        "        \n",
        "              # Writing headers of CSV file \n",
        "              header = emp.keys() \n",
        "              csv_writer.writerow(header) \n",
        "              count += 1\n",
        "        \n",
        "          # Writing data of CSV file \n",
        "          csv_writer.writerow(emp.values()) \n",
        "        \n",
        "      data_file.close() \n",
        "\n",
        "      try:\n",
        "          f = pd.read_csv(new_csv)\n",
        "          keep_col = ['pose_keypoints_2d']\n",
        "          new_f = f[keep_col]\n",
        "          new_f.to_csv(new_csv, index=False)\n",
        "      except EmptyDataError:\n",
        "          f = pd.DataFrame()\n",
        "\n",
        "    \n",
        "      counter = counter+1\n",
        "      print(counter)\n",
        "\n",
        "\n",
        "for folder in os.listdir('/content/30 FPS JSON Skeleton/Right'):\n",
        "  for i in os.listdir('/content/30 FPS JSON Skeleton/Right/'+folder):\n",
        "\n",
        "      # Opening JSON file and loading the data \n",
        "      # into the variable data \n",
        "      with open('/content/30 FPS JSON Skeleton/Right/'+folder+'/'+i) as json_file: \n",
        "          data = json.load(json_file) \n",
        "      if not os.path.exists('/content/csv_folders/Right/'+folder):\n",
        "        os.makedirs('/content/csv_folders/Right/'+folder)\n",
        "      employee_data = data['people'] \n",
        "      new_csv = i.replace(\".json\",\".csv\")\n",
        "      new_csv = \"/content/csv_folders/Right/\"+folder+'/'+new_csv\n",
        "      # now we will open a file for writing \n",
        "    \n",
        "      data_file = open(new_csv, 'w') \n",
        "        \n",
        "      # create the csv writer object \n",
        "      csv_writer = csv.writer(data_file) \n",
        "        \n",
        "      # Counter variable used for writing  \n",
        "      # headers to the CSV file \n",
        "      count = 0\n",
        "      \n",
        "\n",
        "\n",
        "      for emp in employee_data: \n",
        "          if count == 0: \n",
        "        \n",
        "              # Writing headers of CSV file \n",
        "              header = emp.keys() \n",
        "              csv_writer.writerow(header) \n",
        "              count += 1\n",
        "        \n",
        "          # Writing data of CSV file \n",
        "          csv_writer.writerow(emp.values()) \n",
        "        \n",
        "      data_file.close() \n",
        "\n",
        "      try:\n",
        "          f = pd.read_csv(new_csv)\n",
        "          keep_col = ['pose_keypoints_2d']\n",
        "          new_f = f[keep_col]\n",
        "          new_f.to_csv(new_csv, index=False)\n",
        "      except EmptyDataError:\n",
        "          f = pd.DataFrame()\n",
        "\n",
        "    \n",
        "      counter = counter+1\n",
        "      print(counter)\n",
        "    "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "otJ8QxBExYuw"
      },
      "source": [
        "!rm -rf \"/content/list_folders\"\n",
        "import csv\n",
        "import pickle\n",
        "directories = [\"/content/list_folders/Left\",\"/content/list_folders/Right\"]\n",
        "for dirs in directories:\n",
        "\tif not os.path.exists(dirs):\n",
        "\t    os.makedirs(dirs)\n",
        "counter=0 \n",
        "for folder in os.listdir('/content/csv_folders/Left'):\n",
        "  final_list=[]\n",
        "  for count, item in enumerate(sorted(os.listdir('/content/csv_folders/Left/'+folder)), start=0):\n",
        "\n",
        "    path=item.replace('.csv','.txt')\n",
        "    \n",
        "    with open('/content/csv_folders/Left/'+folder+'/'+item, newline='') as f:\n",
        "        reader = csv.reader(f)\n",
        "        data = list(reader)\n",
        "\n",
        "    \n",
        "    if (len(data)==3):\n",
        "      data.remove(data[0])\n",
        "      flat_list = []\n",
        "      flat_list2 = []\n",
        "      flat_list3 = []\n",
        "      for sublist in data:\n",
        "          for item in sublist:\n",
        "            if type(item) == str:\n",
        "              item.split(',')\n",
        "            flat_list.append(item)\n",
        "   \n",
        "\n",
        "      if len(flat_list) == 2:\n",
        "        res = flat_list[0].strip('][').split(', ') \n",
        "        res2 = flat_list[1].strip('][').split(', ') \n",
        "      flat_list2 = res+res2\n",
        "      for item in flat_list2:\n",
        "        flat_list3.append(float(item))\n",
        "      k=3\n",
        "      del flat_list3[k-1::k]\n",
        "      arr=np.array(flat_list3)\n",
        "      \n",
        "      if len(final_list) != 60: #2 seconds at 30 FPS\n",
        "        final_list.append(flat_list3)\n",
        "      \n",
        "      counter= counter+1\n",
        "\n",
        "      \n",
        "  with open('/content/list_folders/Left/'+folder+'.txt', 'wb') as fp:\n",
        "    pickle.dump(final_list, fp)\n",
        " \n",
        " \n",
        "\n",
        "\n",
        "for folder in os.listdir('/content/csv_folders/Right'):\n",
        "  final_list=[]\n",
        "  for count, item in enumerate(sorted(os.listdir('/content/csv_folders/Right/'+folder)), start=0):\n",
        "    path=item.replace('.csv','.txt')\n",
        "    \n",
        "    with open('/content/csv_folders/Right/'+folder+'/'+item, newline='') as f:\n",
        "        reader = csv.reader(f)\n",
        "        data = list(reader)\n",
        "\n",
        "    \n",
        "    if (len(data)==3):\n",
        "      data.remove(data[0])\n",
        " \n",
        "      flat_list = []\n",
        "      flat_list2 = []\n",
        "      flat_list3 = []\n",
        "      for sublist in data:\n",
        "          for item in sublist:\n",
        "            if type(item) == str:\n",
        "              item.split(',')\n",
        "            flat_list.append(item)\n",
        "\n",
        "\n",
        "      if len(flat_list) == 2:\n",
        "        res = flat_list[0].strip('][').split(', ') \n",
        "        res2 = flat_list[1].strip('][').split(', ') \n",
        "\n",
        "      flat_list2 = res+res2\n",
        "\n",
        "      for item in flat_list2:\n",
        "        flat_list3.append(float(item))\n",
        "      k=3\n",
        "      del flat_list3[k-1::k]\n",
        "      arr=np.array(flat_list3)\n",
        "\n",
        "      \n",
        "      if len(final_list) != 60:\n",
        "        final_list.append(flat_list3)\n",
        "      \n",
        "      counter= counter+1\n",
        "\n",
        "      \n",
        "  with open('/content/list_folders/Right/'+folder+'.txt', 'wb') as fp:\n",
        "    pickle.dump(final_list, fp)\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "id": "Gf0Xf1Suov0E",
        "outputId": "35c50ddf-fb75-42d4-89fb-460e52ece9ef"
      },
      "source": [
        "#for testing purposes\n",
        "with open('/content/list_folders/Left/10-L10-4-flipped.txt', 'rb') as f:\n",
        "    data = pickle.load(f)\n",
        "    print(data)\n",
        "    print(data[18][51])\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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260.689, 285.163, 260.706, 285.121, 253.842, 427.003, 144.269, 447.511, 159.96, 457.343, 157.001, 464.156, 180.505, 456.34, 196.141, 434.84, 161.924, 434.781, 192.241, 421.132, 211.789, 448.514, 203.974, 456.31, 203.962, 449.466, 237.187, 469.067, 262.667, 441.618, 204.937, 413.306, 234.297, 410.408, 270.534, 0.0, 0.0, 427.035, 138.443, 448.505, 139.419, 433.837, 142.335, 395.679, 271.462, 398.63, 276.317, 413.29, 277.277, 457.357, 269.475, 459.261, 268.489, 473.962, 265.602], [276.35, 137.462, 269.512, 150.158, 256.778, 152.087, 246.995, 175.571, 253.819, 160.895, 280.288, 147.229, 283.19, 167.773, 293.961, 179.502, 276.314, 191.236, 267.55, 192.219, 280.249, 222.569, 285.1, 251.91, 284.146, 189.307, 295.921, 218.642, 294.928, 246.013, 270.52, 136.417, 276.371, 135.466, 264.625, 135.477, 0.0, 0.0, 299.815, 253.878, 301.786, 251.888, 292.947, 247.021, 292.019, 257.784, 286.103, 259.716, 284.161, 253.849, 438.733, 142.337, 457.296, 158.931, 466.082, 153.153, 472.942, 176.552, 462.207, 190.254, 447.54, 160.897, 448.488, 191.227, 433.834, 207.898, 457.337, 200.079, 465.101, 200.081, 457.347, 236.225, 472.915, 265.608, 450.483, 201.021, 427.984, 233.323, 413.292, 269.524, 0.0, 0.0, 441.62, 137.424, 457.356, 136.477, 448.502, 137.492, 399.596, 268.545, 402.516, 270.495, 415.273, 275.367, 458.324, 269.455, 461.226, 267.562, 473.999, 269.517], [270.496, 130.638, 262.634, 149.196, 250.904, 152.085, 239.222, 174.637, 246.009, 161.908, 275.374, 146.252, 277.295, 165.81, 287.113, 175.639, 269.502, 190.278, 261.7, 191.264, 277.358, 221.554, 285.109, 253.831, 277.309, 188.319, 284.175, 215.752, 261.696, 239.169, 268.498, 129.597, 270.499, 128.673, 259.734, 132.57, 0.0, 0.0, 267.556, 250.911, 268.512, 247.044, 254.854, 239.177, 292.939, 258.752, 286.149, 260.7, 284.154, 255.797, 449.487, 142.341, 466.103, 158.935, 473.93, 153.129, 480.781, 175.556, 471.01, 184.379, 458.328, 160.917, 458.345, 190.248, 444.608, 206.916, 466.141, 200.048, 472.96, 200.025, 464.183, 237.187, 473.008, 268.536, 461.228, 200.082, 439.722, 235.262, 423.095, 270.51, 449.461, 137.475, 450.511, 137.441, 0.0, 0.0, 459.267, 137.482, 408.387, 270.474, 410.386, 273.413, 425.044, 277.3, 458.274, 269.475, 460.248, 268.501, 475.931, 271.453]]\n",
            "138.432\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1BIXvegS0zl8"
      },
      "source": [
        "!zip -r '/content/30 FPS LIST.zip' /content/list_folders\n",
        "from google.colab import files\n",
        "files.download(\"/content/30 FPS LIST.zip\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B339UmQHkkeM"
      },
      "source": [
        "import os\n",
        "if not os.path.exists('/content/content/list_folders'):\n",
        "  !unzip \"/content/drive/MyDrive/30 FPS LIST.zip\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iC9NTZVvuP52"
      },
      "source": [
        "def rotatex(origin, point, angle):\n",
        "    \"\"\"\n",
        "    Rotate a point counterclockwise by a given angle around a given origin.\n",
        "\n",
        "    The angle should be given in radians.\n",
        "    \"\"\"\n",
        "    ox, oy = origin\n",
        "    px, py = point\n",
        "\n",
        "    qx = ox + math.cos(angle) * (px - ox) - math.sin(angle) * (py - oy)\n",
        "    qy = oy + math.sin(angle) * (px - ox) + math.cos(angle) * (py - oy)\n",
        "    return qx\n",
        "def rotatey(origin, point, angle):\n",
        "    \"\"\"\n",
        "    Rotate a point counterclockwise by a given angle around a given origin.\n",
        "\n",
        "    The angle should be given in radians.\n",
        "    \"\"\"\n",
        "    ox, oy = origin\n",
        "    px, py = point\n",
        "\n",
        "    qx = ox + math.cos(angle) * (px - ox) - math.sin(angle) * (py - oy)\n",
        "    qy = oy + math.sin(angle) * (px - ox) + math.cos(angle) * (py - oy)\n",
        "    return qy\n",
        "outIndex = 0\n",
        "from numpy import percentile\n",
        "def outlier (data):\n",
        "  outIndex = 0\n",
        "  outList = []\n",
        "  # calculate interquartile range\n",
        "  q25, q75 = percentile(data, 25), percentile(data, 75)\n",
        "  iqr = q75 - q25\n",
        "  cut_off = iqr * 1.5\n",
        "  lower, upper = q25 - cut_off, q75 + cut_off\n",
        "  # identify outliers\n",
        "  for x in data:\n",
        "    if x < lower or x > upper:\n",
        "      outList.append(outIndex)\n",
        "    outIndex = outIndex + 2\n",
        "  return outList\n",
        "def outlier2 (data):\n",
        "  outIndex = 0\n",
        "  outList = []\n",
        "  # calculate interquartile range\n",
        "  q25, q75 = percentile(data, 25), percentile(data, 75)\n",
        "  iqr = q75 - q25\n",
        "  cut_off = iqr * 1.5\n",
        "  lower, upper = q25 - cut_off, q75 + cut_off\n",
        "  # identify outliers\n",
        "  for x in data:\n",
        "    if x < lower or x > upper:\n",
        "      outList.append(outIndex)\n",
        "    outIndex = outIndex + 1\n",
        "  return outList\n",
        "import os\n",
        "counter = 0\n",
        "for file in os.walk('/content/drive/My Drive/lights/Right'):\n",
        "  counter += 1\n",
        "for file in os.walk('/content/drive/My Drive/lights/Left'):\n",
        "  counter += 1"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NgAXesq70W4f"
      },
      "source": [
        "import pickle\n",
        "import numpy as np\n",
        "from numpy import percentile\n",
        "import os\n",
        "from sklearn import preprocessing\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn import preprocessing\n",
        "import math \n",
        "from random import randint\n",
        "from math import sqrt, cos, sin\n",
        "import statistics\n",
        "import warnings\n",
        "warnings.simplefilter('ignore', np.RankWarning)\n",
        "X=[]\n",
        "Y=[]\n",
        "nameList = []\n",
        "left=0\n",
        "right=0\n",
        "lessthan60 = 0\n",
        "for root, dirs, files in os.walk('/content/content/list_folders'):\n",
        "  for name in files:\n",
        "      if name.endswith((\".txt\")):\n",
        "\n",
        "          with open (root+'/'+name, 'rb') as fp:\n",
        "            itemlist = pickle.load(fp)\n",
        "            if len(itemlist) > 20:\n",
        "              arr2 = np.array(itemlist)\n",
        "              counter = 10\n",
        "              onlyFrames = []\n",
        "              yList = []\n",
        "              yMin1 = []\n",
        "              yMin2 = []\n",
        "              frameBefore = itemlist[0]\n",
        "              for frame in itemlist:\n",
        "                q25, q75 = percentile(frame[1::2], 25), percentile(frame[1::2], 75)\n",
        "                onlyFrames.append(q75-q25)\n",
        "              for frame in itemlist:\n",
        "                yCounter = 1\n",
        "                xCounter = 0\n",
        "                for x in range (0,int(len(frame)/2),1):\n",
        "                  ydistance = 180-frame[yCounter]\n",
        "                  frame[yCounter] = frame[yCounter] + ydistance*2\n",
        "                  xCounter = xCounter +2\n",
        "                  yCounter = yCounter+2\n",
        "                if frame [2] > frame[52]:\n",
        "                  temp=[]\n",
        "                  for item in range(49,-1,-1):\n",
        "                    temp.append(frame.pop(item)) \n",
        "                  temp.reverse()\n",
        "                  frame.extend(temp)\n",
        "\n",
        "                if frame[8] < frame[14]:    #### right arm always first\n",
        "                  arm1=frame[4:10]\n",
        "                  arm2=frame[10:16]\n",
        "                  frame[4:10]=arm2\n",
        "                  frame[10:16]=arm1\n",
        "\n",
        "                if frame[58] > frame[64]:    #### left arm always first\n",
        "                  arm1=frame[54:60]\n",
        "                  arm2=frame[60:66]\n",
        "                  frame[54:60]=arm2\n",
        "                  frame[60:66]=arm1\n",
        "\n",
        "                if frame[22] < frame[28]:\n",
        "                  leg1=frame[18:24]\n",
        "                  leg2=frame[24:30]\n",
        "                  frame[18:24]=leg2\n",
        "                  frame[24:30]=leg1\n",
        "                  foot1=frame[44:50]\n",
        "                  foot2=frame[38:44]\n",
        "                  frame[44:50]=foot2\n",
        "                  frame[38:44]=foot1\n",
        "                if frame[72] > frame[78]:\n",
        "                  leg1=frame[68:74]\n",
        "                  leg2=frame[74:80]\n",
        "                  frame[68:74]=leg2\n",
        "                  frame[74:80]=leg1\n",
        "                  foot1=frame[94:100]\n",
        "                  foot2=frame[88:94]\n",
        "                  frame[94:100]=foot2\n",
        "                  frame[88:94]=foot1\n",
        "                indexes=[30,31,80,81,32,33,82,83,34,35,84,85,36,37,86,87,40,41,90,91,42,43,92,93,46,47,96,97,48,49,98,99, 0,1,50,51, \n",
        "                                16,17,66,67, 2,3,52,53, 38,39,44,45,88,89,94,95,]\n",
        "\n",
        "                for index in sorted(indexes, reverse=True):\n",
        "                    del frame[index]\n",
        "                outList = outlier(frame[:24:2])  \n",
        "                outList = outList + [x+24 for x in (outlier(frame[24::2]))] \n",
        "                outList = outList + [y+1 for y in (outlier(frame[1:24:2]))] \n",
        "                outList = outList + [y+24+1 for y in (outlier(frame[25::2]))] \n",
        "                outList2=[]\n",
        "                for index in outList:\n",
        "                  if (index % 2) == 0:\n",
        "                    outList2.append(index+1)\n",
        "                  else:\n",
        "                    outList2.append(index-1)\n",
        "                outList = outList + outList2\n",
        "                outList = list(set(outList))   \n",
        "                for index in outList:\n",
        "                  frame[index] = 0\n",
        "                yCounter = 1\n",
        "                xCounter = 0\n",
        "                Distance = 0\n",
        "                y1 = statistics.median(frame[1:24:2])\n",
        "                y2 = statistics.median(frame[25::2])\n",
        "                y1before = statistics.median(frameBefore[1:24:2])\n",
        "                y2before = statistics.median(frameBefore[25::2])\n",
        "                if abs(y1-y1before) > 90 or abs(y2-y2before) > 90:\n",
        "                  frame[::] = [0]*48#48                                            ########\n",
        "                else:     #else to prevent removing all frames after an outlier \n",
        "                  frameBefore = frame\n",
        "                if frame != [0]*48:#48:                                           ########\n",
        "                  yMin1.append(statistics.median([x for x in frame[1:24:2] if x != 0]))\n",
        "                  yMin2.append(statistics.median([x for x in frame[25::2] if x != 0]))\n",
        "                yList.extend([i for i in frame[1::2] if i != 0])\n",
        "              hfactor = 1\n",
        "              if statistics.median(yMin1) > statistics.median(yMin2):\n",
        "                  hfactor = statistics.median(yMin1)/(statistics.median(yMin2))\n",
        "              if statistics.median(yMin1) < statistics.median(yMin2):\n",
        "                  hfactor = statistics.median(yMin2)/(statistics.median(yMin1))\n",
        "              factor = 100/statistics.median(onlyFrames)\n",
        "              y_translation = 200-(statistics.median(yList))\n",
        "              for frame in itemlist:               \n",
        "                frame[1::2] = [y-180 if y != 0 else y for y in frame[1::2]]\n",
        "                frame[::2] = [x-320 if x != 0 else x for x in frame[::2]]\n",
        "                frame[::] = [z*factor for z in frame[::]]\n",
        "                frame[1::2] = [y+180 if y != 0 else y for y in frame[1::2]]\n",
        "                frame[::2] = [x+320 if x != 0 else x for x in frame[::2]]\n",
        "                frame[1::2] = [y+y_translation if y != 0  else y for y in frame[1::2]]\n",
        "                x_translation = 0\n",
        "                if min(frame[::2]) < 0: \n",
        "                  x_translation = 0 - min(frame[::2])\n",
        "                if max(frame[::2]) > 840: \n",
        "                  x_translation = 840-max(frame[::2])\n",
        "                frame[::2] = [x+x_translation if x != 0 else x for x in frame [::2]]\n",
        "\n",
        "              yList = []\n",
        "              Lheight=[]\n",
        "              Rheight = []\n",
        "              slope=[]\n",
        "              intercept = []\n",
        "\n",
        "              for frame in itemlist:\n",
        "                if (frame != [0]*48):\n",
        "                  x5, y5 = [],[]\n",
        "                  indices=[]\n",
        "                  x5=([frame[16],frame[22],frame[40],frame[46]])\n",
        "                  y5=([frame[17],frame[23],frame[41],frame[47]])\n",
        "                  if (x5 != [0]*4 and y5 != [0]*4):\n",
        "                    indices = [i for i, x in enumerate(x5) if x == 0]\n",
        "                    indices += [i for i, x in enumerate(y5) if x == 0]\n",
        "                    indices = list(set(indices))\n",
        "                    for index in sorted(indices, reverse=True):\n",
        "                      del x5[index]\n",
        "                      del y5[index]\n",
        "                    \n",
        "                    m, b = np.polyfit(np.array(x5), np.array(y5), 1)    #slope should be constant, b should be dynamic\n",
        "                    slope.append(m)\n",
        "                    intercept.append(b)\n",
        "                  else:\n",
        "                    slope.append(m)\n",
        "                    intercept.append(b)\n",
        "                else:\n",
        "                  slope.append(m)\n",
        "                  intercept.append(b)\n",
        "\n",
        "              for index in outlier2(slope):\n",
        "                slope[index] = slope[index-1]\n",
        "                intercept[index] = intercept[index-1]\n",
        "              index = 0\n",
        "\n",
        "              if (abs(np.arctan(statistics.median(slope)))> .1):\n",
        "                for frame in itemlist:\n",
        "                  m = slope[index]\n",
        "                  b = (intercept[index])\n",
        "                  f = -(np.arctan(m))\n",
        "\n",
        "                  frame[1::2] = [200+y-(m*frame[frame.index(y)-1]+b) if y != 0 else y for y in frame[1::2]]\n",
        "\n",
        "                  yList.extend([i for i in frame[1::2] if i != 0])\n",
        "                  index+=1\n",
        "\n",
        "                yListLeft, yListRight = [],[]\n",
        "                LeftList, RightList =  [],[]\n",
        "                  \n",
        "\n",
        "                LFrame = [x for x in itemlist[0][1:24:2] if x != 0]\n",
        "                RFrame = [x for x in itemlist[0][25::2] if x != 0]\n",
        "                if (max(LFrame) - min(LFrame)) != 0 and 100/(max(RFrame) - min(RFrame)) != 0:\n",
        "                      Lfactor = (100/(max(LFrame) - min(LFrame)))\n",
        "                      Rfactor = (100/(max(RFrame) - min(RFrame)))\n",
        "                for frame in itemlist:\n",
        "                  if frame != [0]*48:\n",
        "\n",
        "                    LFrame,RFrame = [],[]\n",
        "                    LFrame = [x for x in frame[1:24:2] if x != 0]\n",
        "                    RFrame = [x for x in frame[25::2] if x != 0]\n",
        "                    if (max(LFrame) - min(LFrame)) != 0 and 100/(max(RFrame) - min(RFrame)) != 0:\n",
        "                      Lfactor = statistics.mean([Lfactor, 100/(max(LFrame) - min(LFrame))])\n",
        "                      Rfactor = statistics.mean([Rfactor,100/(max(RFrame) - min(RFrame))])\n",
        "                    frame[1::2] = [y-180 if y != 0 else y for y in frame[1::2]]\n",
        "                    frame[::2] = [x-320 if x != 0 else x for x in frame[::2]]\n",
        "\n",
        "                    frame[:24:] = [z*Lfactor for z in frame[:24:]]\n",
        "                    frame[25::] = [z*Rfactor for z in frame[25::]]\n",
        "                    frame[1::2] = [y+180 if y != 0 else y for y in frame[1::2]]\n",
        "                    frame[::2] = [x+320 if x != 0 else x for x in frame[::2]]\n",
        "                    yListLeft.extend([i for i in frame[1:24:2] if i != 0])\n",
        "                    yListRight.extend([i for i in frame[25::2] if i != 0])\n",
        "\n",
        "                leftShift,rightShift = 0,0\n",
        "                try:\n",
        "                  leftShift = 200-(statistics.median(yListLeft))\n",
        "                  rightShift = 200-(statistics.median(yListRight))\n",
        "                except:\n",
        "                  leftShift,rightShift = 0,0\n",
        "                for frame in itemlist:\n",
        "                  frame[1:24:2] = [z+leftShift if z != 0 else z for z in frame[1:24:2]]\n",
        "                  frame[25::2] = [z+rightShift if z != 0 else z for z in frame[25::2]]\n",
        "\n",
        "              \n",
        "              for frame in itemlist:\n",
        "                frame[::] = [x/840 for x in frame[::]]  \n",
        "\n",
        "              arr2=np.array(itemlist)\n",
        "\n",
        "\n",
        "              if name [3] == 'L' or name[2] == 'L':\n",
        "                with open ('/content/drive/My Drive/lights/Left/'+name, 'rb') as fp:\n",
        "                  lights = pickle.load(fp) \n",
        "                  dif = len(lights)-len(itemlist)\n",
        "                  del lights[:dif]\n",
        "                  try:\n",
        "                    arr2 = np.concatenate((arr2,lights), axis = 1)\n",
        "                  except:\n",
        "                    print(name)\n",
        "              else:\n",
        "                with open ('/content/drive/My Drive/lights/Right/'+name, 'rb') as fp:\n",
        "                  lights = pickle.load(fp) \n",
        "                  dif = len(lights)-len(itemlist)\n",
        "                  del lights[:dif]\n",
        "                  try:\n",
        "                    arr2 = np.concatenate((arr2,lights), axis = 1)\n",
        "                  except:\n",
        "                    print(name)\n",
        "\n",
        "              array_sum = np.sum(arr2)\n",
        "              if np.isnan(array_sum):\n",
        "                print(name)\n",
        "              elif arr2.shape == (60,48):\n",
        "\n",
        "                    listitem=arr2\n",
        "\n",
        "                    nameList.append(name)\n",
        "                    X.append(listitem)\n",
        "                    if name [3] == 'L' or name[2] == 'L':\n",
        "                      Y.append(1)\n",
        "                      left=left+1\n",
        "                    else:\n",
        "                      Y.append(0)       \n",
        "                      right=right+1\n",
        "              else:\n",
        "\n",
        "                  lessthan60 +=1\n",
        "                    \n",
        "            else:\n",
        "              print(name)\n",
        "\n",
        "print(right)\n",
        "print(left)\n",
        "print(lessthan60)\n"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}