{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "from extract_consecutive_screenshots import get_pickle_name, load_objects, load_snapshot_data, analyze_run\n",
    "from visualization import get_connection, get_gta_image_jpg_dir\n",
    "from os import path\n",
    "import matplotlib.pyplot as plt\n",
    "from PIL import Image\n",
    "from matplotlib import patches\n",
    "import numpy as np\n",
    "# JPEG_DIR = get_gta_image_jpg_dir()\n",
    "JPEG_DIR = 'D:/GTAV_extraction_output/rgb-jpeg'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def display_snapshots(car, indices=100):\n",
    "    if type(indices) is int:\n",
    "        snapshots_range = (0, indices)\n",
    "    elif type(indices) in [tuple, list]:\n",
    "        snapshots_range = indices        \n",
    "    for snapshot in car['snapshots'][snapshots_range[0]:snapshots_range[1]]:\n",
    "        # print(snapshot)\n",
    "        plt.figure(figsize=(18,18))\n",
    "        img_path = path.join(JPEG_DIR, 'info-' + snapshot['image'] + '-0.jpg')\n",
    "        print(img_path)\n",
    "        img = Image.open(img_path)\n",
    "        size = img.size\n",
    "        bbox = np.array(snapshot['bbox'])\n",
    "        print(size)\n",
    "        bbox[:, 0] *= size[0]\n",
    "        bbox[:, 1] *= size[1]\n",
    "        print(bbox)\n",
    "        width, height = bbox[0, :] - bbox[1, :]\n",
    "        rect = patches.Rectangle(bbox[1, :], width, height, linewidth=1, edgecolor='r', facecolor='none')\n",
    "        # Add the patch to the Axes\n",
    "        plt.gca().add_patch(rect)\n",
    "        \n",
    "        plt.imshow(img)\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "run_id = 52\n",
    "analyze_run(run_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "conn = get_connection()\n",
    "run_id = 52\n",
    "cars = load_objects(run_id)\n",
    "len(cars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "car = list(cars.values())[4410]\n",
    "len(car['snapshots'])\n",
    "snaps = car['snapshots']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "for car in list(cars.values())[4410:4411]:\n",
    "    display_snapshots(car, (0, 20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "snapshot = car['snapshots'][0]\n",
    "# print(snapshot)\n",
    "bbox = snapshot['bbox']\n",
    "# print(bbox)\n",
    "pos1 = np.array(car['snapshots'][0]['position'])\n",
    "pos2 = np.array(car['snapshots'][1]['position'])\n",
    "print(pos1, pos2)\n",
    "pos2 - pos1\n",
    "snapshot1 = load_snapshot_data(car['snapshots'][0]['snapshot_id'])\n",
    "snapshot2 = load_snapshot_data(car['snapshots'][1]['snapshot_id'])\n",
    "# print(snapshot1, snapshot2)\n",
    "pos1_homo = np.append(pos1, 1)\n",
    "print(pos1_homo)\n",
    "print(snapshot1['view_matrix'])\n",
    "viewed = np.matmul(pos1_homo, snapshot1['view_matrix'])\n",
    "projected = np.matmul(viewed, snapshot1['proj_matrix'])\n",
    "print('viewed', viewed)\n",
    "print('projected', projected)\n",
    "print(viewed / viewed[3])\n",
    "print(projected / projected[3])\n",
    "print(bbox)\n",
    "\n",
    "print(snapshot1['timestamp'])\n",
    "print(snapshot2['timestamp'])\n",
    "print(snapshot2['timestamp'] - snapshot1['timestamp'])\n",
    "print((snapshot2['timestamp'] - snapshot1['timestamp']).total_seconds())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Testing whether matpltolib displaying works:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "from PIL import Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "'DISPLAY' in os.environ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "img_path = '/datagrid/personal/racinmat/GTA-jpg/info-2017-11-25--01-59-56--393-0.jpg'\n",
    "img = Image.open(img_path)\n",
    "plt.imshow(img)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "exitval = os.system('python -c \"import matplotlib.pyplot as plt; plt.figure()\"')\n",
    "exitval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "arr = [1, 2, 3, 4, 5]\n",
    "arr[4:5]\n",
    "type(cars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "car"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "list(cars.values())[8]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### playing with coordinates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fx = 1\n",
    "fy = 1\n",
    "cx = 1\n",
    "cy = 1\n",
    "\n",
    "projection = np.array([\n",
    "    [1, 0, 0, 0],\n",
    "    [0, 1, 0, 0],\n",
    "    [0, 0, 1, 0],\n",
    "])\n",
    "\n",
    "viewport = np.array([\n",
    "    [fx, 0, cx],\n",
    "    [0, fy, cy],\n",
    "    [0, 0,   1],\n",
    "])\n",
    "\n",
    "# vecs_3d = np.eye(3)\n",
    "vec_3d = np.array([\n",
    "    [1, 2, 1, 1], \n",
    "    [1, 1, 2, 1], \n",
    "    [1, 1, 1, 2]\n",
    "])\n",
    "base_homo = np.vstack((vec_3d, np.ones(vec_3d.shape[1])))\n",
    "tmp = np.matmul(projection, base_homo)\n",
    "in_plane = np.matmul(viewport, tmp)\n",
    "in_plane = in_plane[0:2,:] / in_plane[2,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "in_plane[0:2,:] / in_plane[2,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "proj_matrix = [[0.0033408345178822974,0.00000000000541810525481537,-0.000000000003395690385777847,0.000000006448863859587206],\n",
    "               [0.000000000011212100626821622,0.0033408344344528166,0.00000000000223493727864732,-0.00000001733577048579349],\n",
    "                [0.0000000000006709455750076875,-0.000000000003768028993206621,-0.00046103226552438563,0.92717973872632550],\n",
    "               [0.00000000000000000,0.00000000000000000,0.00000000000000000,1.00000000000000000]]\n",
    "proj_matrix = np.array(proj_matrix)\n",
    "view_matrix = [[0.99702072143554690,-0.07713291049003601,0.00000000000000000,3154.52976655940440000],\n",
    "               [0.06535770744085312,0.84481430053710940,0.53105276823043820,-1790.31525175574030000],\n",
    "               [-0.04096164554357529,-0.52947062253952030,0.84733867645263670,1175.29422811363100000],\n",
    "               [0.00000000000000000,0.00000000000000000,0.00000000000000000,1.00000000000000000]]\n",
    "view_matrix = np.array(view_matrix)\n",
    "bbox = np.array([(0.4197019636631012,0.43263527750968933),\n",
    "                 (0.0730963870882988,0.15234601497650146)])\n",
    "bbox3d = np.array([[-1.10799407958984, -2.97677373886108, -0.731413125991821, 0],\n",
    "                  [1.10799407958984, 2.37375974655151, 1.56551647186279, 0]]).T\n",
    "bbox_pos = np.array([-2712.16064453125, 2290.10668945312, 18.4052906036377, 1]).T\n",
    "bbox3d[:,0] += bbox_pos\n",
    "bbox3d[:,1] += bbox_pos\n",
    "print(bbox3d)\n",
    "# bbox3d_cam = np.matmul(view_matrix, np.matmul(proj_matrix, bbox3d))\n",
    "# bbox3d_cam = np.matmul(view_matrix, bbox3d)\n",
    "bbox3d_cam = np.matmul(np.linalg.inv(view_matrix), bbox3d)\n",
    "# bbox3d_cam = np.matmul(proj_matrix, bbox3d)\n",
    "# bbox3d_cam = np.matmul(proj_matrix, np.matmul(view_matrix, bbox3d))\n",
    "print(bbox3d_cam)\n",
    "bbox2d = bbox3d_cam[0:2,:] / bbox3d_cam[2,:]\n",
    "print(bbox2d)\n",
    "fov = 50\n",
    "# bbox2d * fov\n",
    "bbox2d"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### showing consecutive cars sorted by sequence length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "d = {handle: len(car['snapshots']) for handle, car in cars.items()}\n",
    "list(sorted(d.items(), key=lambda x: -x[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## analyzing one car"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### getting index of car value by its handle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "handle = 504336\n",
    "indices = [key for key, i in enumerate(cars.values()) if i['handle'] == handle]\n",
    "car_index = indices[0]\n",
    "indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_pos_diffs(snapshots):\n",
    "    pos_diffs = []\n",
    "    positions = [snap['position'] for snap in snapshots]\n",
    "    for i in range(1, len(snapshots)):\n",
    "        diff = np.array(positions[i]) - np.array(positions[i - 1])\n",
    "        pos_diffs.append(diff)\n",
    "    return pos_diffs\n",
    "\n",
    "def get_time_diffs(snapshots):\n",
    "    # gets difference in seconds, for normalization\n",
    "    time_diffs = []\n",
    "    times = [snap['timestamp'] for snap in snapshots]\n",
    "    for i in range(1, len(snapshots)):\n",
    "        diff = (times[i] - times[i - 1]).total_seconds()\n",
    "        time_diffs.append(diff)\n",
    "    return time_diffs\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from pprint import pprint\n",
    "# car_index = 4405\n",
    "car = list(cars.values())[car_index]\n",
    "pos_diffs = get_pos_diffs(car['snapshots'])\n",
    "time_diffs = get_time_diffs(car['snapshots'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "car['snapshots']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pprint(list(enumerate(pos_diffs)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "snapshot = snaps[0]\n",
    "snapshot\n",
    "# car"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def show_snapshot(snapshot):\n",
    "    img_path = path.join(JPEG_DIR, 'info-' + snapshot['image'] + '-0.jpg')\n",
    "    # print(img_path)\n",
    "    img = Image.open(img_path)\n",
    "    size = img.size\n",
    "    bbox = np.array(snapshot['bbox'])\n",
    "    # print(size)\n",
    "    bbox[:, 0] *= size[0]\n",
    "    bbox[:, 1] *= size[1]\n",
    "    # print(bbox)\n",
    "    width, height = bbox[0, :] - bbox[1, :]\n",
    "    rect = patches.Rectangle(bbox[1, :], width, height, linewidth=1, edgecolor='r', facecolor='none')\n",
    "    # Add the patch to the Axes\n",
    "    plt.figure()\n",
    "    plt.gca().add_patch(rect)        \n",
    "    plt.imshow(img)\n",
    "    plt.show()\n",
    "    \n",
    "def unit_vector(vector):\n",
    "    return vector / np.linalg.norm(vector)\n",
    "\n",
    "def angle_between(v1, v2):\n",
    "    v1_u = unit_vector(v1)\n",
    "    v2_u = unit_vector(v2)\n",
    "    return np.arccos(np.clip(np.dot(v1_u, v2_u), -1.0, 1.0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "show_snapshot(snaps[0])\n",
    "show_snapshot(snaps[1])\n",
    "show_snapshot(snaps[2])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "direction = snapshot['cam_direction']\n",
    "print(direction)\n",
    "direction[0] ** 2 + direction[1] ** 2 + direction[2] ** 2\n",
    "angle_between(pos_diffs[0], snapshot['cam_direction'])\n",
    "angle_between(pos_diffs[0][0:2], snapshot['cam_direction'][0:2])\n",
    "# angle_between([0, 1], [2, 2])\n",
    "# relative = np.matmul(snapshot['view_matrix'], to_homo(pos_diffs[0]))\n",
    "# relative\n",
    "# make one axe perpendicualr to 2D view, and use it as base for transforming the direction to relative one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# transformation to relative\n",
    "diff = pos_diffs[0]\n",
    "cam = snapshot['cam_direction']\n",
    "base = np.array([\n",
    "    [cam[0], cam[1]],\n",
    "    [cam[1], -cam[0]]\n",
    "])\n",
    "\n",
    "relative = np.matmul(base, diff[0:2])\n",
    "relative"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def transform_to_relative(diff, cam):\n",
    "    base = np.array([\n",
    "        [cam[0], cam[1]],\n",
    "        [cam[1], -cam[0]]\n",
    "    ])\n",
    "    return np.matmul(base, diff[0:2])\n",
    "\n",
    "def normalize_diff(relative_diff, time_diff):\n",
    "    return relative_diff / time_diff\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### calculating and showing all images and their dirs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "car = cars[list(cars.keys())[150]]\n",
    "snaps = car['snapshots']\n",
    "pos_diffs = get_pos_diffs(snaps)\n",
    "time_diffs = get_time_diffs(snaps)\n",
    "\n",
    "for i in range(min(20, len(pos_diffs))):\n",
    "    snap = snaps[i]\n",
    "    print(snap)\n",
    "    diff = pos_diffs[i]\n",
    "    time_diff = time_diffs[i]\n",
    "    show_snapshot(snap)\n",
    "    print('time diff:', time_diff)\n",
    "    diff_relative = transform_to_relative(diff, snap['cam_direction'][0:2])\n",
    "    print('diff_relative:', diff_relative)\n",
    "    diff_normalized = normalize_diff(diff_relative, time_diff)\n",
    "    print('diff_normalized:', diff_normalized)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "diff = pos_diffs[0]\n",
    "cam = snapshot['cam_direction']\n",
    "print('diff:', diff)\n",
    "print('cam:', cam)\n",
    "print('0, 1:', angle_between((diff[0], diff[1]), (cam[0], cam[1])) * (180 / np.pi))\n",
    "print('0, 2:', angle_between((diff[0], diff[2]), (cam[0], cam[2])) * (180 / np.pi))\n",
    "print('1, 2:', angle_between((diff[1], diff[2]), (cam[1], cam[2])) * (180 / np.pi))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib notebook\n",
    "\n",
    "# prepare directions\n",
    "dirs = np.array([s['cam_direction'] for s in snaps])\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "# plot directions\n",
    "ax.scatter(dirs[:,0], dirs[:,1], dirs[:,2], s=2, color='r')\n",
    "\n",
    "# plot sphere, for reference\n",
    "u = np.linspace(0, 2 * np.pi, 100)\n",
    "v = np.linspace(0, np.pi, 100)\n",
    "x = 0.9 * np.outer(np.cos(u), np.sin(v))\n",
    "y = 0.9 * np.outer(np.sin(u), np.sin(v))\n",
    "z = 0.9 * np.outer(np.ones(np.size(u)), np.cos(v))\n",
    "ax.plot_surface(x, y, z, color='b')\n",
    "\n",
    "ax.set_aspect('equal')\n",
    "ax.set_xlim(-1, 1)\n",
    "ax.set_ylim(-1, 1)\n",
    "ax.set_zlim(-1, 1)\n",
    "\n",
    "# plt.show()\n",
    "# rotate the axes and update\n",
    "for angle in range(0, 360):\n",
    "    ax.view_init(30, angle)\n",
    "    plt.draw()\n",
    "#    plt.pause(.001)\n",
    "pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import math\n",
    "in_2d = relative[0:2,:] / relative[2,:]\n",
    "math.atan2(in_2d[1], in_2d[0])\n",
    "dirs[:,0]\n",
    "print(dirs)\n",
    "norms = np.linalg.norm(dirs,axis=1)\n",
    "np.max(norms)\n",
    "sphere = np.hstack((x,y,z))\n",
    "norms = np.linalg.norm(sphere,axis=1)\n",
    "np.min(norms)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def to_homo(vec):\n",
    "    if len(vec.shape) == 1:\n",
    "        # need to transform to column vector from row\n",
    "        vec = vec[np.newaxis].T\n",
    "    return np.vstack((pos_diffs[0][np.newaxis].T, np.ones(1)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### testing coordinates transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def to_homo(vec):\n",
    "    if len(vec.shape) == 1:\n",
    "        # need to transform to column vector from row\n",
    "        vec = vec[np.newaxis].T\n",
    "\n",
    "    return np.vstack((pos_diffs[0][np.newaxis].T, np.ones(1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import time\n",
    "\n",
    "point = snapshot['cam_direction']\n",
    "base = np.array([\n",
    "    [point[0], point[1]],\n",
    "    [point[1], -point[0]]\n",
    "])\n",
    "\n",
    "fig = plt.figure()\n",
    "fig.gca().set_xlim(-1.5, 1.5)\n",
    "fig.gca().set_ylim(-1.5, 1.5)\n",
    "for i in range(0, 100):\n",
    "    j = i*np.pi/200\n",
    "    point = [np.cos(j), np.sin(j)]\n",
    "    fig.gca().scatter(point[0], point[1], s=2, color='r')\n",
    "    # fig.gca().scatter(point[1], -point[0], s=2, color='g')\n",
    "    fig.gca().scatter(-point[1], point[0], s=2, color='g')\n",
    "    plt.draw()\n",
    "    time.sleep(0.1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "snapshot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## inspecting processed features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "def get_pickle_processed_name(run_id):\n",
    "    return os.path.join('runs', 'run_{}_processed_pickle.rick'.format(run_id))\n",
    "\n",
    "def load_processed_objects(run_id):\n",
    "    data_file = get_pickle_processed_name(run_id)\n",
    "    with open(data_file, 'rb') as file:\n",
    "        return pickle.load(file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "run_id = 52\n",
    "cars = load_processed_objects(run_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cars[list(cars.keys())[0]]['snapshots'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "diffs = np.array([snap['diff_normalized'] for key, car in cars.items() for snap in car['snapshots'] if 'diff_normalized' in snap])\n",
    "diffs[:,0]\n",
    "len(diffs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "plt.scatter(diffs[:,0], diffs[:,1], s=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x_range = (-15, 15)\n",
    "y_range = (-15, 15)\n",
    "for "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### playing with depth transformations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2018-03-07--15-19-08--932.tiff\n"
     ]
    }
   ],
   "source": [
    "from configparser import ConfigParser\n",
    "import glob\n",
    "import os\n",
    "from visualization import load_depth\n",
    "from gta_math import ndc_to_real, construct_proj_matrix\n",
    "from PIL import Image\n",
    "\n",
    "def get_base_name(name):\n",
    "    return os.path.basename(os.path.splitext(name)[0])\n",
    "\n",
    "ini_file = \"gta-postprocessing.local.ini\"\n",
    "\n",
    "CONFIG = ConfigParser()\n",
    "CONFIG.read(ini_file)\n",
    "in_directory = CONFIG[\"Images\"][\"Tiff\"]\n",
    "out_directory = CONFIG[\"Images\"][\"MlDataset\"]\n",
    "\n",
    "rgb_format = 'jpg'\n",
    "depth_format = 'png'\n",
    "\n",
    "import visualization\n",
    "visualization.multi_page = False\n",
    "visualization.ini_file = ini_file\n",
    "\n",
    "pattern = '[0-9][0-9][0-9][0-9]-[0-9][0-9]-[0-9][0-9]--[0-9][0-9]-[0-9][0-9]-[0-9][0-9]--[0-9][0-9][0-9].tiff'\n",
    "files = glob.glob(os.path.join(in_directory, pattern))\n",
    "name = files[2000]\n",
    "name = \"2018-03-07--15-19-08--932.tiff\"\n",
    "\n",
    "print(name)\n",
    "\n",
    "conn = visualization.get_connection()\n",
    "cur = conn.cursor()\n",
    "cur.execute(\"\"\"SELECT snapshot_id, imagepath, camera_fov, width, height, cam_near_clip, proj_matrix, view_matrix \\\n",
    "    FROM snapshots \\\n",
    "    WHERE imagepath = '{}' \\\n",
    "    ORDER BY snapshot_id DESC \\\n",
    "    \"\"\".format(name.replace('.tiff', '')))\n",
    "\n",
    "results = [dict(res) for res in cur]\n",
    "#files = [os.path.join(in_directory, i['imagepath']) for i in results]\n",
    "records = [(os.path.join(in_directory, i['imagepath']), \n",
    "            {'H': i['height'], 'W': i['width'], 'fov': i['camera_fov'], 'near_clip': i['cam_near_clip']}) for i in results]\n",
    "res = results[0]\n",
    "\n",
    "infile = os.path.join(in_directory, name)\n",
    "im = Image.open(infile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "depth = load_depth(get_base_name(name))\n",
    "H = depth.shape[0]\n",
    "W = depth.shape[1]\n",
    "fov = 50.0\n",
    "near_clip = 1.5\n",
    "calc_proj_matrix = construct_proj_matrix(H, W, fov, near_clip)\n",
    "proj_matrix = np.array(res['proj_matrix'])\n",
    "calc_new_depth = ndc_to_real(depth, calc_proj_matrix)\n",
    "new_depth = ndc_to_real(depth, proj_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"data:image/png;base64, \" width=\"720\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"data:image/png;base64, \" width=\"720\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"data:image/png;base64, \" width=\"720\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"data:image/png;base64, \" width=\"720\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"data:image/png;base64, \" width=\"720\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"data:image/png;base64, \" width=\"720\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib notebook\n",
    "plt.figure(figsize=(10, 10))\n",
    "plt.imshow(im)\n",
    "\n",
    "plt.figure(figsize=(10, 10))\n",
    "plt.imshow(depth)\n",
    "\n",
    "plt.figure(figsize=(10, 10))\n",
    "plt.imshow(new_depth)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "image 2018-03-07--15-19-08--932.tiff projected\n"
     ]
    }
   ],
   "source": [
    "from gta_math import generate_points, points_to_homo, ndc_to_view, view_to_world\n",
    "import numpy as np\n",
    "\n",
    "def save_csv(vecs_p, name):\n",
    "    a = np.asarray(vecs_p[0:3, :].T)\n",
    "    np.savetxt(\"points-{}.csv\".format(name), a, delimiter=\",\")\n",
    "    \n",
    "width = depth.shape[1]\n",
    "height = depth.shape[0]\n",
    "params = {\n",
    "    'width': width,\n",
    "    'height': height,\n",
    "    'proj_matrix': proj_matrix,\n",
    "    'cam_far_clip': 800\n",
    "}\n",
    "\n",
    "vecs, _ = points_to_homo(params, depth)\n",
    "vecs_p = ndc_to_view(vecs, proj_matrix)\n",
    "calc_vecs_p = ndc_to_view(vecs, calc_proj_matrix)\n",
    "\n",
    "print('image {} projected'.format(name))\n",
    "\n",
    "save_csv(calc_vecs_p, get_base_name(name))\n",
    "save_csv(vecs_p, 'db_'+get_base_name(name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "world_vecs_p = view_to_world(vecs_p, res['view_matrix'])\n",
    "save_csv(world_vecs_p, 'world_'+get_base_name(name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "vecs, _ = points_to_homo(params, depth, tresholding=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "max_depth = params['cam_far_clip']\n",
    "vec = proj_matrix @ np.array([[1], [1], [-max_depth], [1]])\n",
    "# print(vec)\n",
    "vec /= vec[3]\n",
    "threshold = vec[2]\n",
    "lesser = np.count_nonzero(depth <= threshold)\n",
    "print('count lesser than threshold: ', lesser)\n",
    "print('threshold: ', threshold)\n",
    "print('depth min: ', np.min(depth))\n",
    "print('depth max: ', np.max(depth))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'params' is not defined",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-12-05504d8c7e56>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mgta_math\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mpixels_to_ndcs\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mwidth\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mparams\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'width'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0mheight\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mparams\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'height'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0msize\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mheight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mwidth\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'params' is not defined"
     ],
     "output_type": "error"
    }
   ],
   "source": [
    "from gta_math import pixels_to_ndcs\n",
    "width = params['width']\n",
    "height = params['height']\n",
    "size = (height, width)\n",
    "    \n",
    "points = generate_points(width, height)\n",
    "valid_points = np.where(depth > threshold)\n",
    "print(len(np.where(depth[points[:, 0], points[:, 1]] > threshold)))\n",
    "print(len(np.where(depth > threshold)))\n",
    "valid_y, valid_x = valid_points\n",
    "\n",
    "vecs = np.zeros((4, len(valid_y)))\n",
    "\n",
    "ndcs = pixels_to_ndcs(np.array(valid_points), size)\n",
    "\n",
    "print('valid_points shape: ', np.array(valid_points).shape)\n",
    "print('ndcs shape: ', ndcs.shape)\n",
    "print('depth size: ', depth.size)\n",
    "# print('valid_points shape: ', np.shape(valid_points))\n",
    "print('depth shape: ', depth.shape)\n",
    "print('valid 0 min: ', np.min(valid_y))\n",
    "print('valid 0 max: ', np.max(valid_y))\n",
    "print('valid 1 min: ', np.min(valid_x))\n",
    "print('valid 1 max: ', np.max(valid_x))\n",
    "# valid_points = valid_points[0:3, :]\n",
    "print('depth in valid points', depth[valid_y, valid_x])\n",
    "print('depth in valid points shape', depth[valid_y, valid_x].shape)\n",
    "\n",
    "# arr = points\n",
    "# in vector order is [x, y], in ndscs array, order is [y, x]\n",
    "vecs[0, :] = ndcs[1, :]\n",
    "vecs[1, :] = ndcs[0, :]\n",
    "vecs[2, :] = depth[valid_y, valid_x]\n",
    "vecs[3, :] = 1  # last, homogenous coordinate\n",
    "print(vecs[2, :].shape)\n",
    "print(depth[valid_y, valid_x].shape)\n",
    "\n",
    "print('20th vector: ', vecs[:, 20])\n",
    "print('20th ndc: ', ndcs[:, 20])\n",
    "print('20th orig: ', np.array(valid_points)[:, 20])\n",
    "\n",
    "vecs_old = np.zeros((4, len(valid_y)))\n",
    "\n",
    "ndcs_old = pixels_to_ndcs(points, size)\n",
    "\n",
    "i = 0\n",
    "valid_old_points = np.zeros((2, len(valid_y)))\n",
    "for j, (y, x) in enumerate(points):\n",
    "    if depth[(y, x)] <= threshold:\n",
    "        continue\n",
    "    vecs_old[:, i] = np.array([ndcs_old[1, j], ndcs_old[0, j], depth[(y, x)], 1])\n",
    "    valid_old_points[:, i] = np.array([y, x])\n",
    "    if i == 20:\n",
    "        print('20th orig old: ', (y, x))\n",
    "    i += 1\n",
    "\n",
    "print('20th old vector: ', vecs_old[:, 20])\n",
    "print('diff: ', vecs[:, 20] - vecs_old[:, 20])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ndcs.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pixels = np.copy(np.array(valid_points))\n",
    "p_y = pixels[:, 0]\n",
    "p_x = pixels[:, 1]\n",
    "s_y, s_x = size\n",
    "pixels[:, 0] = (-2 / s_y) * p_y + 1\n",
    "pixels[:, 1] = (2 / s_x) * p_x - 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.array(valid_points).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "valid_old_points.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "(np.sort(np.array(valid_points)) == np.sort(valid_old_points)).all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ndcs = pixels_to_ndcs(np.array(valid_points), size)\n",
    "print(ndcs.dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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uNNjM4aVgelTw8ENncHu3GU4rZjvwlRcKnK95/VrFN33N4zz7mdfIBso3fDCI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GhKqWhRZv2XSY84F/r4qsZDDGJj4Wa3zhHPk3YuQE2h5nLpzSy53JLJJMfYkihOVsSpAk4Yx/GK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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2414ac700b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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FPVs5tr87OoTLMf+mLTz62nks+MmW6ht7bD41sKILVnk2E1jcoUXu4+6ThV9+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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2414d550358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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l2JL2By4kSmJiwGuAW4FPAocBdwCnmtkORbfvDwAvBR4FXmVmGUHO1alqEVei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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2414ac978d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from gta_math import depth_from_integer_range\n",
    "from configparser import ConfigParser\n",
    "\n",
    "ini_file = \"gta-postprocessing.local.ini\"\n",
    "\n",
    "CONFIG = ConfigParser()\n",
    "CONFIG.read(ini_file)\n",
    "in_directory = CONFIG[\"Images\"][\"Tiff\"]\n",
    "out_directory = CONFIG[\"Images\"][\"MlDataset\"]\n",
    "out_root_directory = 'D:\\projekty\\GTA-V-extractors\\depthEstimationNN1'\n",
    "\n",
    "rgb_format = 'jpg'\n",
    "depth_format = 'png'\n",
    "\n",
    "import visualization\n",
    "visualization.multi_page = False\n",
    "visualization.ini_file = ini_file\n",
    "\n",
    "name = '2018-03-07--18-26-53--512'\n",
    "\n",
    "rgb_name = os.path.join(out_directory, \"{}.{}\".format(name, rgb_format))\n",
    "depth_name = os.path.join(out_directory, \"{}.{}\".format(name, depth_format))\n",
    "orig_depth_name = os.path.join(in_directory, \"{}-depth.tiff\".format(name))\n",
    "\n",
    "im = Image.open(rgb_name)\n",
    "im_depth = Image.open(depth_name)\n",
    "orig_im_depth = Image.open(orig_depth_name)\n",
    "rgb = np.array(im)\n",
    "depth = np.array(im_depth)\n",
    "\n",
    "orig_depth = load_depth(name)\n",
    "\n",
    "depth = depth_from_integer_range(depth)\n",
    "\n",
    "%matplotlib inline\n",
    "plt.figure()\n",
    "plt.title('orig')\n",
    "plt.imshow(rgb)\n",
    "\n",
    "plt.figure()\n",
    "plt.title('loaded depth')\n",
    "ii = plt.imshow(depth)\n",
    "plt.colorbar(ii)\n",
    "\n",
    "\n",
    "plt.figure()\n",
    "plt.title('orig depth')\n",
    "ii = plt.imshow(orig_depth)\n",
    "plt.colorbar(ii)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "far clip:  800.0\n",
      "image 2018-03-07--18-26-53--512 projected\n",
      "image 2018-03-07--18-26-53--512 saved\n"
     ]
    }
   ],
   "source": [
    "from gta_math import generate_points, points_to_homo, ndc_to_view, view_to_world\n",
    "import numpy as np\n",
    "\n",
    "def save_csv(vecs_p, name):\n",
    "    a = np.asarray(vecs_p[0:3, :].T)\n",
    "    np.savetxt(\"points-{}.csv\".format(name), a, delimiter=\",\")\n",
    "    \n",
    "\n",
    "conn = visualization.get_connection()\n",
    "cur = conn.cursor()\n",
    "cur.execute(\"\"\"SELECT snapshot_id, imagepath, camera_fov, width, height, \\\n",
    "    cam_near_clip, cam_far_clip, \\\n",
    "    proj_matrix, view_matrix \\\n",
    "    FROM snapshots \\\n",
    "    WHERE imagepath = '{}' \\\n",
    "    ORDER BY snapshot_id DESC \\\n",
    "    \"\"\".format(name.replace('.tiff', '')))\n",
    "\n",
    "results = [dict(res) for res in cur]\n",
    "#files = [os.path.join(in_directory, i['imagepath']) for i in results]\n",
    "records = [(os.path.join(in_directory, i['imagepath']), \n",
    "            {'H': i['height'], 'W': i['width'], 'fov': i['camera_fov'], \n",
    "             'near_clip': i['cam_near_clip'],\n",
    "            'far_clip': i['cam_far_clip'],\n",
    "            'proj_matrix': i['proj_matrix']}) for i in results]\n",
    "res = records[0][1]\n",
    "\n",
    "print('far clip: ', res['far_clip'])\n",
    "width = depth.shape[1]\n",
    "height = depth.shape[0]\n",
    "proj_matrix = res['proj_matrix']\n",
    "\n",
    "params = {\n",
    "    'width': width,\n",
    "    'height': height,\n",
    "    'proj_matrix': proj_matrix,\n",
    "    'cam_far_clip': res['far_clip']\n",
    "}\n",
    "params_long = {\n",
    "    'width': width,\n",
    "    'height': height,\n",
    "    'proj_matrix': proj_matrix,\n",
    "    'cam_far_clip': 2000\n",
    "}\n",
    "params_short = {\n",
    "    'width': width,\n",
    "    'height': height,\n",
    "    'proj_matrix': proj_matrix,\n",
    "    'cam_far_clip': 20\n",
    "}\n",
    "\n",
    "calc_proj_matrix = construct_proj_matrix(res['H'], res['W'], res['fov'], res['near_clip'])\n",
    "\n",
    "vecs, _ = points_to_homo(params, depth)\n",
    "orig_vecs, _ = points_to_homo(params, orig_depth)\n",
    "calc_vecs_p = ndc_to_view(orig_vecs, calc_proj_matrix)\n",
    "orig_long_vecs, _ = points_to_homo(params_long, orig_depth)\n",
    "calc_long_vecs_p = ndc_to_view(orig_long_vecs, calc_proj_matrix)\n",
    "orig_short_vecs, _ = points_to_homo(params_short, orig_depth)\n",
    "calc_short_vecs_p = ndc_to_view(orig_short_vecs, calc_proj_matrix)\n",
    "\n",
    "print('image {} projected'.format(name))\n",
    "\n",
    "save_csv(vecs, 'restored-'+get_base_name(name))\n",
    "save_csv(calc_vecs_p, 'orig-'+get_base_name(name))\n",
    "save_csv(calc_long_vecs_p, 'orig-long-'+get_base_name(name))\n",
    "save_csv(calc_short_vecs_p, 'orig-short-'+get_base_name(name))\n",
    "\n",
    "print('image {} saved'.format(name))"
   ]
  },
  {
   "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
}
