{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import os\n",
    "from configparser import ConfigParser\n",
    "from visualization import load_depth\n",
    "import visualization\n",
    "from PIL import Image\n",
    "import pickle\n",
    "import json\n",
    "import time\n",
    "from shutil import copyfile\n",
    "import moviepy\n",
    "from moviepy.editor import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ini_file = \"gta-postprocessing.ini\"\n",
    "visualization.multi_page = False\n",
    "visualization.ini_file = ini_file\n",
    "visualization.use_cache = False\n",
    "\n",
    "conn = visualization.get_connection()\n",
    "cur = conn.cursor()\n",
    "\n",
    "CONFIG = ConfigParser()\n",
    "CONFIG.read(ini_file)\n",
    "in_directory = r'D:\\generic-dataset-ota'\n",
    "out_directory = r'D:\\showing-pointclouds'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 3817 snapshots\n"
     ]
    }
   ],
   "source": [
    "#run_id = 19\n",
    "run_id = 3677\n",
    "\n",
    "cur.execute(\"\"\"SELECT imagepath, \\\n",
    "      ARRAY[st_x(camera_relative_rotation), st_y(camera_relative_rotation), st_z(camera_relative_rotation)] as camera_relative_rotation\n",
    "      FROM snapshots \\\n",
    "      WHERE run_id = {} AND camera_fov != 0 \\\n",
    "      ORDER BY timestamp ASC \\\n",
    "    \"\"\".format(run_id))\n",
    "# camera fov is sanity check for malformed images\n",
    "results = []\n",
    "for row in cur:\n",
    "    res = dict(row)\n",
    "    #res['camera_relative_rotation'] = np.array(res['camera_relative_rotation'])\n",
    "    results.append(res)\n",
    "\n",
    "print('There are {} snapshots'.format(len(results)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def split_results_by_relative_cam_rot(results):\n",
    "    cam_rotations = [\n",
    "        [0, 0, 0],\n",
    "        [0, 0, 90],\n",
    "        [0, 0, 180],\n",
    "        [0, 0, 270],\n",
    "    ]\n",
    "    res_groups = {}\n",
    "    for cam_rot in cam_rotations:\n",
    "        res_groups[str(cam_rot)] = [i for i in results if i['camera_relative_rotation'] == cam_rot]\n",
    "    return res_groups\n",
    "\n",
    "\n",
    "def result_group_to_video(results, suffix):\n",
    "    img_sequence = [os.path.join(in_directory, i['imagepath']+suffix) for i in results]\n",
    "    clip = ImageSequenceClip(img_sequence, fps=10)\n",
    "    return clip\n",
    "\n",
    "def result_depth_group_to_video(results, suffix):\n",
    "    img_sequence = [os.path.join(in_directory, i['imagepath']+suffix) for i in results]\n",
    "    img_sequence = [np.array(Image.open(i).convert('RGB')) for i in img_sequence]\n",
    "    clip = ImageSequenceClip(img_sequence, fps=10)\n",
    "    return clip"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### preparing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "result_groups = split_results_by_relative_cam_rot(results)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 0, 0]\n",
      "959\n",
      "[0, 0, 90]\n",
      "953\n",
      "[0, 0, 180]\n",
      "954\n",
      "[0, 0, 270]\n",
      "951\n"
     ]
    }
   ],
   "source": [
    "for cam_rot, res in result_groups.items():\n",
    "    print(cam_rot)\n",
    "    print(len(res))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### dumping data to videos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[MoviePy] >>>> Building video D:\\showing-pointclouds\\camera-[0, 0, 0].mp4\n",
      "[MoviePy] Writing video D:\\showing-pointclouds\\camera-[0, 0, 0].mp4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████| 959/959 [01:48<00:00,  8.83it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[MoviePy] Done.\n",
      "[MoviePy] >>>> Video ready: D:\\showing-pointclouds\\camera-[0, 0, 0].mp4 \n",
      "\n",
      "[MoviePy] >>>> Building video D:\\showing-pointclouds\\-[0, 0, 0]-depth.mp4\n",
      "[MoviePy] Writing video D:\\showing-pointclouds\\-[0, 0, 0]-depth.mp4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████| 959/959 [00:11<00:00, 86.61it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[MoviePy] Done.\n",
      "[MoviePy] >>>> Video ready: D:\\showing-pointclouds\\-[0, 0, 0]-depth.mp4 \n",
      "\n",
      "[MoviePy] >>>> Building video D:\\showing-pointclouds\\camera-[0, 0, 90].mp4\n",
      "[MoviePy] Writing video D:\\showing-pointclouds\\camera-[0, 0, 90].mp4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████| 953/953 [01:53<00:00,  8.37it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[MoviePy] Done.\n",
      "[MoviePy] >>>> Video ready: D:\\showing-pointclouds\\camera-[0, 0, 90].mp4 \n",
      "\n",
      "[MoviePy] >>>> Building video D:\\showing-pointclouds\\-[0, 0, 90]-depth.mp4\n",
      "[MoviePy] Writing video D:\\showing-pointclouds\\-[0, 0, 90]-depth.mp4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████| 953/953 [00:12<00:00, 78.08it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[MoviePy] Done.\n",
      "[MoviePy] >>>> Video ready: D:\\showing-pointclouds\\-[0, 0, 90]-depth.mp4 \n",
      "\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-7-bab7b406713d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mcam_rot\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mres\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mresult_groups\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m     \u001b[0mclip\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mresult_group_to_video\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mres\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'.png'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m     \u001b[0mdepth_clip\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mresult_depth_group_to_video\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mres\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'-depth.png'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m     \u001b[0mvideo_name\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mout_directory\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"camera-{}.mp4\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcam_rot\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[0mclip\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrite_videofile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvideo_name\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maudio\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcodec\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'mpeg4'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-4-5c64489e50f9>\u001b[0m in \u001b[0;36mresult_depth_group_to_video\u001b[1;34m(results, suffix)\u001b[0m\n\u001b[0;32m     19\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mresult_depth_group_to_video\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msuffix\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     20\u001b[0m     \u001b[0mimg_sequence\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0min_directory\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'imagepath'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0msuffix\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mresults\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 21\u001b[1;33m     \u001b[0mimg_sequence\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mImage\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconvert\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'RGB'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mimg_sequence\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     22\u001b[0m     \u001b[0mclip\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mImageSequenceClip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimg_sequence\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfps\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     23\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mclip\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-4-5c64489e50f9>\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m     19\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mresult_depth_group_to_video\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msuffix\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     20\u001b[0m     \u001b[0mimg_sequence\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0min_directory\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'imagepath'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0msuffix\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mresults\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 21\u001b[1;33m     \u001b[0mimg_sequence\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mImage\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconvert\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'RGB'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mimg_sequence\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     22\u001b[0m     \u001b[0mclip\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mImageSequenceClip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimg_sequence\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfps\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     23\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mclip\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\PIL\\Image.py\u001b[0m in \u001b[0;36mconvert\u001b[1;34m(self, mode, matrix, dither, palette, colors)\u001b[0m\n\u001b[0;32m    858\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    859\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 860\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    861\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    862\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mmatrix\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\PIL\\ImageFile.py\u001b[0m in \u001b[0;36mload\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    232\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    233\u001b[0m                         \u001b[0mb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mb\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 234\u001b[1;33m                         \u001b[0mn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merr_code\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdecoder\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    235\u001b[0m                         \u001b[1;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[1;33m<\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    236\u001b[0m                             \u001b[1;32mbreak\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "for cam_rot, res in result_groups.items():\n",
    "    clip = result_group_to_video(res, '.png')\n",
    "    depth_clip = result_depth_group_to_video(res, '-depth.png')\n",
    "    video_name = os.path.join(out_directory, \"camera-{}.mp4\".format(cam_rot))\n",
    "    clip.write_videofile(video_name, audio=False, codec='mpeg4')\n",
    "    video_name = os.path.join(out_directory, \"camera-{}-depth.mp4\".format(cam_rot))\n",
    "    depth_clip.write_videofile(video_name, audio=False, codec='mpeg4')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "name = os.path.join(in_directory, result_groups['[0, 0, 0]'][0]['imagepath']+'-depth.png')\n",
    "depth_arr = np.array(Image.open(name).convert('RGB'))\n",
    "depth_arr_2 = np.array(Image.open(name).convert('L').convert('RGB'))\n",
    "depth_arr_3 = np.array(Image.open(name).convert('L'))\n",
    "arr_single = np.array(Image.open(name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "255"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "depth_arr.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "46909"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_single.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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QdLe7nyPp7uy5JF0s6ZzsZ5ekTzVcNyJD+ALiZ2ZR/owV9ThetQOYmZ0s6U2S9kiSu7/g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\n4wMWswAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x24b80ab2240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig = plt.figure(figsize=(10, 10))\n",
    "plt.imshow(arr_single, cmap='gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ukqTJkydr8uTJmb1v9MvYV0X8LfrlGw1m8c7ZQ4YMyWue9EHMV6DiQUraESB7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\nBgAAkLH/BwRPVKG8Er25AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x24b8095f9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 10))\n",
    "plt.imshow(depth_arr_3, cmap='gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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33KyG35fLLrss/jmY4UsigAEAtHsojOnTp2v69OmZvW/4ZeyrIv4RfvmGwSzZ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\nMAAAgIwRwAAAADJGAAMAAMjY/w8P6b2CLiZUXwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x24b81102860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 10))\n",
    "plt.imshow(depth_arr_2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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33KyG35fLLrss/jmY4UsigAEAtHsojOnTp2v69OmZvW/4ZeyrIv4RfvmGwSzZ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\nMAAAgIwRwAAAADJGAAMAAMjY/w8P6b2CLiZUXwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x24b80112ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 10))\n",
    "plt.imshow(depth_arr)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9qrY/8cQTz/kXBAA43H03LoCoVWa9hvlanuPZg+5ru3tTd2/asGHDPnYJADCfA4m5x3ad\n2hw/Hx/zHUlOWVrv5CSP7GN+8irztTwHAMC6ciAxd2uSXVekbk5yy9L88nHF6dlJnhqnSG9Pcm5V\nHTcufDg3ye1j2der6uxxFevlu+3ruTwHAMC6cvT+rFRVH0ryuiQnVNWOLK5KfXeSm6rqiiRfTvLG\nsfptSS5MspLkG0nelCTdvbOq3pnkzrHeO7p710UVb8niitkXJfnYuOW5PgcAwHqzXzHX3ZftYdE5\nq6zbSa7cw362Jtm6ynx7kletMv/qc30OAID1xDdAAABMTMwBAExMzAEATEzMAQBMTMwBAExMzAEA\nTEzMAQBMTMwBAExMzAEATEzMAQBMTMwBAExMzAEATEzMAQBMTMwBAExMzAEATEzMAQBMTMwBAExM\nzAEATEzMAQBMTMwBAExMzAEATEzMAQBMTMwBAExMzAEATEzMAQBMTMwBAExMzAEATEzMAQBMTMwB\nAExMzAEATEzMAQBMTMwBAExMzAEATEzMAQBMTMwBAExMzAEATEzMAQBMTMwBAExMzAEATEzMAQBM\nTMwBAExMzAEATEzMAQBMTMwBAExMzAEATEzMAQBMTMwBAExMzAEATEzMAQBMTMwBAExMzAEATEzM\nAQBMTMwBAExMzAEATEzMAQBMTMwBAExMzAEATEzMAQBMTMwBAExMzAEATEzMAQBMTMwBAExszTFX\nVf+squ5euv11Vf1iVf1KVf3V0vzCpW3eXlUrVXV/VZ23ND9/zFaq6qql+elV9ZmqeqCqPlxVx4z5\nC8fjlbH8tLX+HgAAM1tzzHX3/d19ZnefmeQ1Sb6R5KNj8Xt3Levu25Kkqs5IcmmSVyY5P8lvVtVR\nVXVUkvcnuSDJGUkuG+smyXvGvjYmeTLJFWN+RZInu/sHkrx3rAcAsO4crNOs5yT5Ynf/5V7WuSjJ\njd39ze7+UpKVJGeN20p3P9jd30pyY5KLqqqSvD7JzWP765NcvLSv68f9m5OcM9YHAFhXDlbMXZrk\nQ0uP31pV91TV1qo6bsxOSvLw0jo7xmxP85cm+Vp3P73b/Fn7GsufGus/S1VtqartVbX9iSeeOJDf\nDwDgsHTAMTc+x/ZTSX5vjK5J8v1JzkzyaJJf37XqKpv3GuZ729ezB93Xdvem7t60YcOGPf4OAACz\nOhjvzF2Q5E+6+7Ek6e7HuvuZ7v52kt/K4jRqsnhn7ZSl7U5O8she5l9JcmxVHb3b/Fn7GstfkmTn\nQfhdAACmcjBi7rIsnWKtqhOXlv10knvH/VuTXDquRD09ycYkn01yZ5KN48rVY7I4ZXtrd3eSTyS5\nZGy/OcktS/vaPO5fkuTjY30AgHXl6H2vsmdV9Y+T/OskP780/rWqOjOL054P7VrW3fdV1U1J/izJ\n00mu7O5nxn7emuT2JEcl2drd9419vS3JjVX1q0k+l+S6Mb8uyW9X1UoW78hdeiC/BwDArA4o5rr7\nG9ntwoPu/tm9rP+uJO9aZX5bkttWmT+Y75ymXZ7/XZI3ruElAwAcUXwDBADAxMQcAMDExBwAwMTE\nHADAxMQcAMDExBwAwMTEHADAxMQcAMDExBwAwMTEHADAxMQcAMDExBwAwMTEHADAxMQcAMDExBwA\nwMTEHADAxMQcAMDExBwAwMTEHADAxMQcAMDExBwAwMTEHADAxMQcAMDExBwAwMTEHADAxMQcAMDE\nxBwAwMTEHADAxMQcAMDExBwAwMTEHADAxMQcAMDExBwAwMTEHADAxMQcAMDExBwAwMTEHADAxMQc\nAMDExBwAwMTEHADAxMQcAMDExBwAwMTEHADAxMQcAMDExBwAwMTEHADAxMQcAMDExBwAwMTEHADA\nxMQcAMDExBwAwMTEHADAxMQcAMDExBwAwMTEHADAxMQcAMDExBwAwMTEHADAxMQcAMDExBwAwMTE\nHADAxMQcAMDEDjjmquqhqvp8Vd1dVdvH7Piq2lZVD4yfx415VdX7qmqlqu6pqlcv7WfzWP+Bqtq8\nNH/N2P/K2Lb29hwAAOvJwXpn7l9195ndvWk8virJHd29Mckd43GSXJBk47htSXJNsgizJFcneW2S\ns5JcvRRn14x1d213/j6eAwBg3fhunWa9KMn14/71SS5emt/QC59OcmxVnZjkvCTbuntndz+ZZFuS\n88eyF3f3p7q7k9yw275Wew4AgHXjYMRcJ/mjqrqrqraM2cu7+9EkGT9fNuYnJXl4adsdY7a3+Y5V\n5nt7DgCAdePog7CPH+3uR6rqZUm2VdWf72XdWmXWa5jvlxGXW5Lk1FNP3d/NAACmccDvzHX3I+Pn\n40k+msVn3h4bp0gzfj4+Vt+R5JSlzU9O8sg+5ievMs9enmP5tV3b3Zu6e9OGDRsO5NcEADgsHVDM\nVdU/qarv23U/yblJ7k1ya5JdV6RuTnLLuH9rksvHVa1nJ3lqnCK9Pcm5VXXcuPDh3CS3j2Vfr6qz\nx1Wsl++2r9WeAwBg3TjQ06wvT/LR8a+FHJ3kd7v7f1fVnUluqqorknw5yRvH+rcluTDJSpJvJHlT\nknT3zqp6Z5I7x3rv6O6d4/5bknwwyYuSfGzckuTde3gOAIB144BirrsfTPLPV5l/Nck5q8w7yZV7\n2NfWJFtXmW9P8qr9fQ4AgPXEN0AAAExMzAEATEzMAQBMTMwBAExMzAEATEzMAQBMTMwBAExMzAEA\nTEzMAQBMTMwBAExMzAEATEzMAQBMTMwBAExMzAEATEzMAQBMTMwBAExMzAEATEzMAQBMTMwBAExM\nzAEATEzMAQBMTMwBAExMzAEATEzMAQBMTMwBAExMzAEATEzMAQBMTMwBAExMzAEATEzMAQBMTMwB\nAExMzAEATEzMAQBMTMwBAExMzAEATEzMAQBMTMwBAExMzAEATEzMAQBMTMwBAExMzAEATEzMAQBM\nTMwBAExMzAEATEzMAQBMTMwBAExMzAEATEzMAQBMTMwBAExMzAEATEzMAQBMTMwBAExMzAEATEzM\nAQBMTMwBAExMzAEATEzMAQBMTMwBAExMzAEATEzMAQBMTMwBAExMzAEATGzNMVdVp1TVJ6rqC1V1\nX1X9wpj/SlX9VVXdPW4XLm3z9qpaqar7q+q8pfn5Y7ZSVVctzU+vqs9U1QNV9eGqOmbMXzger4zl\np6319wAAmNmBvDP3dJL/2N0/lOTsJFdW1Rlj2Xu7+8xxuy1JxrJLk7wyyflJfrOqjqqqo5K8P8kF\nSc5IctnSft4z9rUxyZNJrhjzK5I82d0/kOS9Yz0AgHVnzTHX3Y9295+M+19P8oUkJ+1lk4uS3Njd\n3+zuLyVZSXLWuK1094Pd/a0kNya5qKoqyeuT3Dy2vz7JxUv7un7cvznJOWN9AIB15aB8Zm6c5vyR\nJJ8Zo7dW1T1VtbWqjhuzk5I8vLTZjjHb0/ylSb7W3U/vNn/Wvsbyp8b6AADrygHHXFV9b5KPJPnF\n7v7rJNck+f4kZyZ5NMmv71p1lc17DfO97Wv317alqrZX1fYnnnhir78HAMCMDijmquoFWYTc73T3\n7ydJdz/W3c9097eT/FYWp1GTxTtrpyxtfnKSR/Yy/0qSY6vq6N3mz9rXWP6SJDt3f33dfW13b+ru\nTRs2bDiQXxUA4LB0IFezVpLrknyhu39jaX7i0mo/neTecf/WJJeOK1FPT7IxyWeT3Jlk47hy9Zgs\nLpK4tbs7ySeSXDK235zklqV9bR73L0ny8bE+AMC6cvS+V9mjH03ys0k+X1V3j9l/zuJq1DOzOO35\nUJKfT5Luvq+qbkryZ1lcCXtldz+TJFX11iS3Jzkqydbuvm/s721JbqyqX03yuSziMePnb1fVShbv\nyF16AL8HAMC01hxz3f1/s/pn127byzbvSvKuVea3rbZddz+Y75ymXZ7/XZI3PpfXCwBwJPINEAAA\nExNzAAATE3MAABMTcwAAExNzAAATE3MAABMTcwAAExNzAAATE3MAABMTcwAAExNzAAATE3MAABMT\ncwAAExNzAAATE3MAABMTcwAAExNzAAATE3MAABMTcwAAExNzAAATE3MAABMTcwAAExNzAAATE3MA\nABMTcwAAExNzAAATE3MAABMTcwAAExNzAAATE3MAABMTcwAAExNzAAATE3MAABMTcwAAExNzAAAT\nE3MAABMTcwAAExNzAAATE3MAABMTcwAAExNzAAATE3MAABMTcwAAExNzAAATE3MAABMTcwAAExNz\nAAATE3MAABMTcwAAEzv6UL8AAGDPTrvqDw/1SzgoHnr3Gw71SzhieWcOAGBiYg4AYGJOswJwxDlS\nTk0eSY6kY3K4nTL2zhwAwMTEHADAxJxmZVXeDgeAOXhnDgBgYmIOAGBiYg4AYGJiDgBgYmIOAGBi\nYg4AYGJTx1xVnV9V91fVSlVddahfDwDA823amKuqo5K8P8kFSc5IcllVnXFoXxUAwPNr2phLclaS\nle5+sLu/leTGJBcd4tcEAPC8mvkbIE5K8vDS4x1JXnuIXgvA9I6kb36B9WTmmKtVZv2sFaq2JNky\nHv5NVd3/XX9VyQlJvvI8PA/7qd6z18WO11wcr7k4XnNxvPbTPv67crD80/1dceaY25HklKXHJyd5\nZHmF7r42ybXP54uqqu3dven5fE7WzvGai+M1F8drLo7XvGb+zNydSTZW1elVdUySS5PceohfEwDA\n82rad+a6++mqemuS25MclWRrd993iF8WAMDzatqYS5Luvi3JbYf6dezmeT2tywFzvObieM3F8ZqL\n4zWp6u59rwUAwGFp5s/MAQCse2LuIPHVYodOVW2tqser6t6l2fFVta2qHhg/jxvzqqr3jeN0T1W9\nemmbzWP9B6pq89L8NVX1+bHN+6pqtX8Wh/1UVadU1Seq6gtVdV9V/cKYO2aHoar6nqr6bFX96The\n/3XMT6+qz4w/+w+PC9FSVS8cj1fG8tOW9vX2Mb+/qs5bmvv78yCrqqOq6nNV9QfjseN1JOtutwO8\nZXEBxheTvCLJMUn+NMkZh/p1rZdbkh9P8uok9y7Nfi3JVeP+VUneM+5fmORjWfw7hWcn+cyYH5/k\nwfHzuHH/uLHss0n+xdjmY0kuONS/88y3JCcmefW4/31J/iKLr+RzzA7D2/gz/N5x/wVJPjOOw01J\nLh3zDyR5y7j/H5J8YNy/NMmHx/0zxt+NL0xy+vg78yh/f37XjtsvJfndJH8wHjteR/DNO3MHh68W\nO4S6+5NJdu42vijJ9eP+9UkuXprf0AufTnJsVZ2Y5Lwk27p7Z3c/mWRbkvPHshd396d68TfcDUv7\nYg26+9Hu/pNx/+tJvpDFN7o4Zoeh8ef+N+PhC8atk7w+yc1jvvvx2nUcb05yznhn9KIkN3b3N7v7\nS0lWsvi709+fB1lVnZzkDUn+53hccbyOaGLu4Fjtq8VOOkSvhYWXd/ejySIekrxszPd0rPY237HK\nnINgnNL5kSze7XHMDlPjlN3dSR7PIpq/mORr3f30WGX5z/jvj8tY/lSSl+a5H0fW7r8n+U9Jvj0e\nvzSO1xFNzB0c+/xqMQ4bezpWz3XOAaqq703ykSS/2N1/vbdVV5k5Zs+j7n6mu8/M4pt2zkryQ6ut\nNn46XodQVf1kkse7+67l8SqrOl5HEDF3cOzzq8V43j02Trdl/Hx8zPd0rPY2P3mVOQegql6QRcj9\nTnf//hg7Zoe57v5akv+TxWfmjq2qXf9W6fKf8d8fl7H8JVl8DOK5HkfW5keT/FRVPZTFKdDXZ/FO\nneN1BBNzB4evFjv83Jpk19WNm5PcsjS/fFwheXaSp8YpvduTnFtVx42rKM9NcvtY9vWqOnt8juTy\npX2xBuPP8bokX+ju31ha5JgdhqpqQ1UdO+6/KMlPZPE5x08kuWSstvvx2nUcL0ny8fHZxVuTXDqu\nnjw9ycYsLlTx9+dB1N1v7+6Tu/u0LP4sP97dPxPH68h2qK/AOFJuWVxx9xdZfJbklw/161lPtyQf\nSvJokv+Xxf81XpHFZz7uSPLA+Hn8WLeSvH8cp88n2bS0n5/L4kO+K0netDTflOTesc3/yPjHtt3W\nfLz+ZRanZe5Jcve4XeiYHZ63JD+c5HPjeN2b5L+M+Suy+I/7SpLfS/LCMf+e8XhlLH/F0r5+eRyT\n+7N0hbG/P79rx+51+c7VrI7XEXzzDRAAABNzmhUAYGJiDgBgYmIOAGBiYg4AYGJiDgBgYmIOAGBi\nYg4AYGJiDgBgYv8fdRHcGFLsDGcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x24b80178438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 10))\n",
    "plt.hist(arr_single.flatten())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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cAMBQQg4AYCghBwAwlJADABhKyAEADCXkAACGEnIAAEMJOQCAoYQcAMBQQg4AYCghBwAw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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x24b801786d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 10))\n",
    "plt.hist(depth_arr.flatten())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x24b801438d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 10))\n",
    "plt.hist(depth_arr_2.flatten())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rFxx8Nsn9y2PdkOSdSd5RVfuy8nm2G5ddbkzykmX8HUmuWh7ngSS3ZSUCP5rkyu7+3vIZ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\nAMBQQg4AYKj/D5hb/6iW//WCAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x24b80a55080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 10))\n",
    "plt.hist(depth_arr_3.flatten())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4wQYAMJxgAwAYTrABAAwn2AAAhhNsAADDCTYAgOEEGwDAcIINAGA4wQYAMJxgAwAYTrAB\nAAwn2AAAhhNsAADDCTYAgOEEGwDAcIINAGA4wQYAMJxgAwAYTrABAAwn2AAAhhNsAADDCTYAgOEE\nGwDAcIINAGA4wQYAMJxgAwAYTrABAAwn2AAAhhNsAADDCTYAgOEEGwDAcIINAGA4wQYAMJxgAwAY\nTrABAAwn2AAAhhNsAADDCTYAgOEEGwDAcIINAGA4wQYAMJxgAwAYTrABAAwn2AAAhhNsAADDCTYA\ngOEEGwDAcIINAGA4wQYAMJxgAwAYTrABAAwn2AAAhhNsAADDCTYAgOEEGwDAcIINAGA4wQYAMJxg\nAwAYTrABAAwn2AAAhhNsAADDCTYAgOEEGwDAcIINAGA4wQYAMJxgAwAYTrABAAwn2AAAhhNsAADD\nCTYAgOEEGwDAcIINAGA4wQYAMNy2wVZVt1fVC1X1jXVj76yqB6rqqeX3Gct4VdXNVXWoqr5eVR9c\n95qrl/lPVdXV68Z/uqoeXV5zc1XVVusAANhtdvIJ2xeS7D9q7PokD3b3viQPLs+T5NIk+5afa5Pc\nkqzFV5IbkvxMkguT3LAuwG5Z5r72uv3brAMAYFfZNti6+0+TvHjU8IEkdyyP70hy2brxO3vNV5Kc\nXlXvTXJJkge6+8XufinJA0n2L8t+rLv/rLs7yZ1HvddG6wAA2FXe6HfY3tPdzyfJ8vvdy/hZSZ5d\nN+/wMrbV+OENxrdaBwDArvJmX3RQG4z1Gxg/tpVWXVtVB6vq4JEjR4715QAAo73RYPv2cjozy+8X\nlvHDSc5ZN+/sJM9tM372BuNbreN1uvvW7r6guy/Ys2fPG/wnAQDM9EaD7d4kr13peXWSe9aNX7Vc\nLXpRkpeX05n3J7m4qs5YLja4OMn9y7LvVdVFy9WhVx31XhutAwBgVzl1uwlV9dtJPpzkzKo6nLWr\nPW9McndVXZPkW0kuX6bfl+RjSQ4l+X6STyRJd79YVb+R5KFl3q9392sXMvxi1q5EfUeSP15+ssU6\nAAB2lW2Drbuv3GTRRzeY20mu2+R9bk9y+wbjB5P85Abjf7nROgAAdht3OgAAGE6wAQAMJ9gAAIYT\nbAAAwwk2AIDhBBsAwHCCDQBgOMEGADCcYAMAGE6wAQAMJ9gAAIYTbAAAwwk2AIDhBBsAwHCCDQBg\nOMEGADCcYAMAGE6wAQAMJ9gAAIYTbAAAwwk2AIDhBBsAwHCCDQBgOMEGADCcYAMAGE6wAQAMJ9gA\nAIYTbAAAwwk2AIDhBBsAwHCCDQBgOMEGADCcYAMAGE6wAQAMJ9gAAIYTbAAAwwk2AIDhBBsAwHCC\nDQBgOMEGADCcYAMAGE6wAQAMJ9gAAIYTbAAAwwk2AIDhBBsAwHCCDQBgOMEGADCcYAMAGE6wAQAM\nJ9gAAIYTbAAAwwk2AIDhBBsAwHCCDQBgOMEGADCcYAMAGE6wAQAMJ9gAAIYTbAAAwwk2AIDhBBsA\nwHCCDQBgOMEGADCcYAMAGE6wAQAMJ9gAAIYTbAAAwwk2AIDhBBsAwHCCDQBgOMEGADCcYAMAGE6w\nAQAMJ9gAAIYTbAAAwwk2AIDhBBsAwHCCDQBgOMEGADDcqaveAFZr7/V/tOpNeNM8c+PHV70JAPCW\n8AkbAMBwgg0AYDjBBgAwnO+wwTC+VwjA0XzCBgAwnGADABhOsAEADCfYAACGE2wAAMMJNgCA4QQb\nAMBwgg0AYDjBBgAwnGADABjOrakAOCG5jRu7iU/YAACGE2wAAMM5JQqwy5xMpxJhtxBsnDT8jxAA\nJyunRAEAhvMJG8AO+AQXWCXBBrxlRA7Am8MpUQCA4QQbAMBw44OtqvZX1ZNVdaiqrl/19gAAHG+j\nv8NWVack+VySf5XkcJKHqure7n58tVsGAGzkZPnu6rTbhY0OtiQXJjnU3U8nSVXdleRAEsEGwEnj\nZIkc3jrTT4meleTZdc8PL2MAALvG9E/YaoOxft2kqmuTXLs8/euqevIt3arkzCTfeYvXwc7ZH3PY\nF7PYH3PYF7Nsuz/qs8dpS5J/upNJ04PtcJJz1j0/O8lzR0/q7luT3Hq8NqqqDnb3BcdrfWzN/pjD\nvpjF/pjDvpjlRNwf00+JPpRkX1WdW1WnJbkiyb0r3iYAgONq9Cds3f1KVX0yyf1JTklye3c/tuLN\nAgA4rkYHW5J0931J7lv1dhzluJ1+ZUfsjznsi1nsjznsi1lOuP1R3a/7Dj8AAINM/w4bAMCuJ9iO\nkVtlrVZVPVNVj1bVI1V1cBl7Z1U9UFVPLb/PWPV2nqyq6vaqeqGqvrFubMO/f625eTlWvl5VH1zd\nlp98NtkX/76q/u9yfDxSVR9bt+zTy754sqouWc1Wn5yq6pyq+lJVPVFVj1XVp5Zxx8YKbLE/Tujj\nQ7Adg3W3yro0yXlJrqyq81a7VbvSv+ju89ddkn19kge7e1+SB5fnvDW+kGT/UWOb/f0vTbJv+bk2\nyS3HaRt3iy/k9fsiSW5ajo/zl+8AZ/nv1BVJ3r+85vPLf894c7yS5Fe6+31JLkpy3fI3d2ysxmb7\nIzmBjw/Bdmz+7lZZ3f3DJK/dKovVOpDkjuXxHUkuW+G2nNS6+0+TvHjU8GZ//wNJ7uw1X0lyelW9\n9/hs6clvk32xmQNJ7uruH3T3N5Mcytp/z3gTdPfz3f3ny+PvJXkia3flcWyswBb7YzMnxPEh2I6N\nW2WtXif5H1X18HKHiyR5T3c/n6wdqEnevbKt2502+/s7Xlbjk8tpttvXfT3AvjhOqmpvkg8k+Woc\nGyt31P5ITuDjQ7Admx3dKou31Ie6+4NZO6VwXVX981VvEJtyvBx/tyT5Z0nOT/J8kv+0jNsXx0FV\n/WiS30vyS939V1tN3WDM/niTbbA/TujjQ7Admx3dKou3Tnc/t/x+IckfZO1j62+/djph+f3C6rZw\nV9rs7+94Oc66+9vd/Wp3/22S/5K/P61jX7zFquptWYuD3+ru31+GHRsrstH+ONGPD8F2bNwqa4Wq\n6h9X1T957XGSi5N8I2v74Opl2tVJ7lnNFu5am/39701y1XJF3EVJXn7t9BBvjaO+B/WzWTs+krV9\ncUVVvb2qzs3al92/dry372RVVZXktiRPdPdvrlvk2FiBzfbHiX58jL/TwSRulbVy70nyB2vHYk5N\n8t+7+0+q6qEkd1fVNUm+leTyFW7jSa2qfjvJh5OcWVWHk9yQ5MZs/Pe/L8nHsvYF3u8n+cRx3+CT\n2Cb74sNVdX7WTuc8k+QXkqS7H6uqu5M8nrUr6K7r7ldXsd0nqQ8l+fkkj1bVI8vYr8WxsSqb7Y8r\nT+Tjw50OAACGc0oUAGA4wQYAMJxgAwAYTrABAAwn2AAAhhNsAADDCTYAgOEEGwDAcP8P3RdZeDLD\n0D8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x24b809d8eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "arr_single_cropped = arr_single[arr_single < 256]\n",
    "fig = plt.figure(figsize=(10, 10))\n",
    "plt.hist(arr_single_cropped.flatten())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
