notebooks/Visualize_poles.ipynb

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      "source": [
        "# Visualize extracted poles\n",
        "\n",
        "In order to verify the results and spot potential issues, we can visualize individual objects. We take the output of `Extract_poles.ipynb` as input. The visualization renders the object from two directions (X and Y axis), along with a 3D projection."
      ],
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    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import pandas as pd\n",
        "import geopandas as gpd\n",
        "import numpy as np\n",
        "import laspy\n",
        "\n",
        "import set_path  # add project src to path\n",
        "from upc_analysis import visualization\n",
        "\n",
        "import config as cf  # use config or config_azure"
      ],
      "outputs": [],
      "execution_count": null,
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    {
      "cell_type": "markdown",
      "source": [
        "### Load poles data"
      ],
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    },
    {
      "cell_type": "code",
      "source": [
        "# Get extracted poles (after filtering)\n",
        "poles_df = pd.read_csv(cf.output_file_filter)\n",
        "poles_df.sort_values('tilecode', inplace=True)\n",
        "print('# poles: ', len(poles_df))"
      ],
      "outputs": [],
      "execution_count": null,
      "metadata": {
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          "logged": 1691588704543
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        "jupyter": {
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    },
    {
      "cell_type": "markdown",
      "source": [
        "### Loop over all poles"
      ],
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    },
    {
      "cell_type": "code",
      "source": [
        "# Define and create out folder for images\n",
        "if not os.path.isdir(cf.img_out_folder):\n",
        "    os.makedirs(cf.img_out_folder + 'object_all_axes/')\n",
        "    os.makedirs(cf.img_out_folder + 'object_per_axis/x/')\n",
        "    os.makedirs(cf.img_out_folder + 'object_per_axis/y/')"
      ],
      "outputs": [],
      "execution_count": null,
      "metadata": {
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    {
      "cell_type": "code",
      "source": [
        "# Create 3 images for each of the extracted poles\n",
        "visualization.create_images_for_poles(poles_df, cf.dataset_folder, cf.pred_folder,\n",
        "                                      cf.img_out_folder, cf.prefix, cf.prefix_pred)"
      ],
      "outputs": [],
      "execution_count": null,
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    },
    {
      "cell_type": "markdown",
      "source": [
        "### Check locations"
      ],
      "metadata": {
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    },
    {
      "cell_type": "code",
      "source": [
        "gdf = gpd.GeoDataFrame(poles_df, geometry=gpd.points_from_xy(poles_df['rd_x'], poles_df['rd_y']))\n",
        "gdf.plot()"
      ],
      "outputs": [],
      "execution_count": null,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
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