{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Points cloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5f5b06f9ff234a1bb22020d7b8456c5a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np \n",
    "import k3d\n",
    "\n",
    "plot = k3d.plot(camera_auto_fit=False)\n",
    "plot.display()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from k3d.helpers import download\n",
    "from pyunpack import Archive\n",
    "\n",
    "filename = download('http://www.semantic3d.net/data/point-clouds/testing1/' +\n",
    "                    'stgallencathedral_station1_intensity_rgb.7z')\n",
    "Archive(filename).extractall('./')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "\n",
    "data = None\n",
    "\n",
    "with open(filename.replace('.7z', '.txt'), mode='r') as csv_file:\n",
    "    csv_reader = csv.reader(csv_file, delimiter=' ')    \n",
    "    data = np.array(list(csv_reader), dtype=np.float32)\n",
    "\n",
    "# compute color in hex format\n",
    "data[:, 4] = np.sum(data[:, 4:7].astype(np.uint32) * np.array([1, 256, 256 ** 2]), axis=1)    \n",
    "data = data[:, 0:5]\n",
    "data = data[::10] # every 10th points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((3117977, 5), 3.117977)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape, data.shape[0] / 1e6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot += k3d.points(data[:, 0:3], \n",
    "                   data[:, 4].astype(np.uint32), point_size=0.15, shader=\"flat\", \n",
    "                   compression_level=9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.8"
  },
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    "state": {
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       "outputs": [
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           "model_id": "853f81d55ea64dc792335c15983b91ba",
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          "text/plain": "Plot(antialias=3, axes=['x', 'y', 'z'], axes_helper=1.0, background_color=16777215, camera=[2, -3, 0.2, 0.0, 0…"
         },
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       "camera": [
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       "camera_auto_fit": false,
       "camera_fov": 60,
       "camera_mode": "trackball",
       "camera_no_pan": false,
       "camera_no_rotate": false,
       "camera_no_zoom": false,
       "clipping_planes": [],
       "colorbar_object_id": -1,
       "colorbar_scientific": false,
       "fps": 25,
       "fps_meter": false,
       "grid": [
        -1,
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       "lighting": 1.5,
       "manipulate_mode": "translate",
       "menu_visibility": true,
       "mode": "view",
       "name": null,
       "object_ids": [],
       "rendering_steps": 1,
       "screenshot": "",
       "screenshot_scale": 2,
       "snapshot": "",
       "snapshot_include_js": true,
       "time": 0,
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