1_distance_based_sampling.ipynb

{
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   "source": [
    "---\n",
    "\n",
    "title: Distance based sampling (Jaccard Distance)\n",
    "\n",
    "author: Tiago Ribeiro\n",
    "\n",
    "date: 04/05/2023\n",
    "\n",
    "updated: 22/05/2024 (minor updates)\n",
    "\n",
    "---\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Author: \u001B[1mTiago F. R. Ribeiro\u001B[0m\n",
      "\n",
      "Github username: \u001B[1mTiago1Ribeiro\u001B[0m\n",
      "\n",
      "Last updated: 2023-04-04 12:39:32\n",
      "\n",
      "Python implementation: CPython\n",
      "Python version       : 3.10.9\n",
      "IPython version      : 8.11.0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from watermark import watermark\n",
    "print(watermark(author=\"\\033[1m\" +\"Tiago F. R. Ribeiro\" + \"\\033[0m\", \n",
    "                github_username=\"\\033[1m\" +\"Tiago1Ribeiro\"+ \"\\033[0m\", \n",
    "                current_date=True, current_time=True, python=True, updated=True,\n",
    "                iversions=True,  globals_= globals()))"
   ]
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": ""
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-22T17:57:28.807665Z",
     "start_time": "2024-05-22T17:57:27.517985Z"
    }
   },
   "source": [
    "import os\n",
    "from shapely.wkt import loads\n",
    "from scipy.spatial.distance import jaccard\n",
    "import numpy as np\n",
    "from rasterio.features import rasterize"
   ],
   "outputs": [],
   "execution_count": 1
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data Sources"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-22T17:57:31.272162Z",
     "start_time": "2024-05-22T17:57:31.118429Z"
    }
   },
   "source": [
    "DATA_DIR = \"D:\\BurnedAreaUAV_files\\BurnedAreaUAV_dataset\\BurnedAreaUAV_dataset\" # Replace with your data directory\n",
    "\n",
    "SEG_POL_FILE = os.path.join(DATA_DIR, \"Results\",  \"WKT\", \"segs_unet_base.wkt\")\n",
    "SEGS_TRAIN_FILE = os.path.join(DATA_DIR, \"WKT\", \"train_valid.wkt\")\n",
    "SAMPLED_SEGS = os.path.join(DATA_DIR, \"Interpolation\", \"reference_masks\", \"sampled_masks.txt\")\n",
    "SAMPLED_SEGS_WKT = os.path.join(DATA_DIR, \"Interpolation\", \"reference_masks\", \"sampled_masks.wkt\")\n",
    "\n",
    "# SEGS_POL_FILE = \"E://BurnedAreaUAV_files//Results//WKT_files//segs_unet_base.wkt\"\n",
    "# SEGS_TRAIN_FILE = \"E://BurnedAreaUAV_files//BurnedAreaUAV_dataset//WKT_files//train_valid.wkt\"\n",
    "# SAMPLED_SEGS = \"E://BurnedAreaUAV_files//Interpolation//reference_masks//sampled_masks.txt\"\n",
    "# SAMPLED_SEGS_WKT = \"E://BurnedAreaUAV_files//Interpolation//reference_masks//sampled_masks.wkt\"\n",
    "\n",
    "with open(SEGS_TRAIN_FILE, 'r') as wkt_file:\n",
    "    # list of polygons\n",
    "    multipolygons = wkt_file.readlines()\n",
    "# convert multipolygons to shapely polygons\n",
    "sh_polygons = list()\n",
    "for multipolygon in multipolygons:\n",
    "    sh_polygon = loads(multipolygon)\n",
    "    sh_polygons.append(sh_polygon)"
   ],
   "outputs": [],
   "execution_count": 2
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Functions"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### calculate_distances"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-22T17:57:33.511157Z",
     "start_time": "2024-05-22T17:57:33.496654Z"
    }
   },
   "source": [
    "def calculate_distances(polygons, out_shape = (720, 1280)):\n",
    "    \"\"\"\n",
    "    Calculates the Jaccard distance between binary segmentation masks of the \n",
    "    first and subsequent polygons\n",
    "\n",
    "    Args:\n",
    "        polygons (list): A list of polygons represented as a list of coordinate \n",
    "        tuples.\n",
    "        out_shape (tuple): The shape of the output rasterized mask. \n",
    "        Default is (720, 1280).\n",
    "\n",
    "    Returns:\n",
    "        dict: A dictionary with two keys - \"Jaccard distance\". The values\n",
    "        for each key are lists containing the distance values between the \n",
    "        first and subsequent polygons\n",
    "\n",
    "    \"\"\"\n",
    "    distances = {\"Jaccard distance\":[]}\n",
    "    mask_t0 = rasterize([polygons[0]], out_shape)\n",
    "    # coord_t0 = np.argwhere(mask_t0)\n",
    "\n",
    "    # Convert each polygon to a binary mask and calculate distances\n",
    "    for i in range(1, len(polygons)):\n",
    "        mask_tn = rasterize([polygons[i]], out_shape)\n",
    "        jaccard_distance = jaccard(mask_t0.flatten(), mask_tn.flatten())\n",
    "        distances[\"Jaccard distance\"].append(jaccard_distance)\n",
    "        print(f\"{i}/{len(polygons)-1}: Jaccard distance: {jaccard_distance:.4f} \", end=\"\\r\")\n",
    "    return distances"
   ],
   "outputs": [],
   "execution_count": 3
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-22T17:57:39.930857Z",
     "start_time": "2024-05-22T17:57:37.156550Z"
    }
   },
   "source": [
    "distances = calculate_distances(sh_polygons)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "225/225: Jaccard distance: 0.6958 \r"
     ]
    }
   ],
   "execution_count": 4
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### plot_polygons_as_images"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-22T17:57:42.376098Z",
     "start_time": "2024-05-22T17:57:42.356433Z"
    }
   },
   "source": [
    "def plot_polygons_as_images(distances, polygons, image_size=(720, 1280)):\n",
    "    # counts number of polygons\n",
    "    num_polygons = len(distances['index'])\n",
    "    # print('num_polygons:', num_polygons)\n",
    "    # calculate number of rows and columns in the grid\n",
    "    num_cols = 3\n",
    "    num_rows = int(np.ceil(num_polygons / num_cols))\n",
    "    # print('num. of graphs:', num_rows*num_cols)\n",
    "    \n",
    "    # create a new figure with the desired size\n",
    "    _, axes = plt.subplots(num_rows, num_cols, figsize=(25, num_rows*4))\n",
    "    axes = axes.flatten()  \n",
    "    \n",
    "    # loop over the polygons and plot them as images on the grid\n",
    "    i = 0\n",
    "    for idx in distances['index']:\n",
    "        ax = axes[i]\n",
    "        img = rasterio.features.rasterize([polygons[idx]], out_shape=image_size)\n",
    "        ax.imshow(img)\n",
    "        ax.set_title(f\"Frame #{idx}\")\n",
    "        i += 1\n",
    "    \n",
    "    # adjust the layout to make sure all images are visible\n",
    "    fig.tight_layout()\n",
    "    \n",
    "    # show the plot\n",
    "    plt.show()"
   ],
   "outputs": [],
   "execution_count": 5
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### sample_polygons"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-22T17:57:44.532151Z",
     "start_time": "2024-05-22T17:57:44.506365Z"
    }
   },
   "source": [
    "def sample_polygons_v2(polygons, threshold=0.15, out_shape = (720, 1280)):\n",
    "    \"\"\"\n",
    "    Generate a set of samples from a list of polygons based on their similarity.\n",
    "\n",
    "    Args:\n",
    "        polygons (list): A list of polygons represented as lists of (x, y) tuples.\n",
    "        threshold (float): The Jaccard distance threshold for creating a new sample.\n",
    "            Defaults to 0.15.\n",
    "        out_shape (tuple): The output shape of the rasterized polygons.\n",
    "            Defaults to (720, 1280).\n",
    "\n",
    "    Returns:\n",
    "        dict: A dictionary with two keys: \"index\" and \"Jaccard distance\".\n",
    "            The \"index\" value is the index of the polygon in the input list \n",
    "            for each sample.\n",
    "\n",
    "    \"\"\"\n",
    "    # instantiate dictionary to store index and distance values\n",
    "    samples = {\"index\": [0], \"Jaccard distance\":[0.0]}\n",
    "    idx = 0\n",
    "    while idx < len(polygons)-1:\n",
    "        mask_n = rasterize([polygons[idx]], out_shape)\n",
    "        jaccard_distance = 0.0\n",
    "        while jaccard_distance < threshold and idx < len(polygons)-1:\n",
    "            idx += 1\n",
    "            mask_m = rasterize([polygons[idx]], out_shape)\n",
    "            jaccard_distance = jaccard(mask_n.flatten(), mask_m.flatten())\n",
    "            \n",
    "        # append index and distance to dictionary\n",
    "        samples[\"index\"].append(idx)\n",
    "        samples[\"Jaccard distance\"].append(jaccard_distance)\n",
    "        print(f\"index: {idx}, Jaccard distance: {jaccard_distance:.4f}\")\n",
    "    print(f\"Number of resulting samples: {len(samples['index'])}\")\n",
    "\n",
    "    return samples"
   ],
   "outputs": [],
   "execution_count": 6
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-22T17:57:50.267859Z",
     "start_time": "2024-05-22T17:57:47.529076Z"
    }
   },
   "source": [
    "threshold = 0.15\n",
    "samples = sample_polygons_v2(sh_polygons, threshold=threshold)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "index: 19, Jaccard distance: 0.1590\n",
      "index: 28, Jaccard distance: 0.1528\n",
      "index: 48, Jaccard distance: 0.1830\n",
      "index: 56, Jaccard distance: 0.1570\n",
      "index: 74, Jaccard distance: 0.1745\n",
      "index: 82, Jaccard distance: 0.1522\n",
      "index: 87, Jaccard distance: 0.1598\n",
      "index: 106, Jaccard distance: 0.1684\n",
      "index: 128, Jaccard distance: 0.1717\n",
      "index: 173, Jaccard distance: 0.1501\n",
      "index: 195, Jaccard distance: 0.1529\n",
      "index: 225, Jaccard distance: 0.1120\n",
      "Number of resulting samples: 13\n"
     ]
    }
   ],
   "execution_count": 7
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-22T17:58:20.823491Z",
     "start_time": "2024-05-22T17:58:19.849186Z"
    }
   },
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# create two subplots side by side\n",
    "fig, axs = plt.subplots(2, 1, figsize=(22, 15))\n",
    "\n",
    "# plot the distances for the first subplot\n",
    "axs[0].grid(color='grey', linestyle='-.', linewidth=0.5, alpha=0.6)\n",
    "axs[0].set_xticks(range(0, len(distances[\"Jaccard distance\"]), 10))\n",
    "axs[0].plot(samples[\"index\"], samples[\"Jaccard distance\"], marker='o', markersize=3,\n",
    "            markerfacecolor='black', linewidth=0.4)\n",
    "axs[0].set_xlabel(\"Polygon index\")\n",
    "axs[0].set_ylabel(\"Jaccard distance\")\n",
    "axs[0].set_title(\"Jaccard distances from the selected samples, threshold=0.15\")\n",
    "# add a threshold line with a label\n",
    "axs[0].axhline(y=threshold, color='r', linestyle='--', linewidth=0.5)\n",
    "axs[0].text(0.5, threshold+0.002, \"Threshold\", color='r', fontsize=12)\n",
    "# add a text annotation to the first and last sample\n",
    "axs[0].annotate(\"First sample\", xy=(0, 0), xytext=(0, 0.0), fontsize=10)\n",
    "axs[0].annotate(\"Last sample\", xy=(samples[\"index\"][-1], 0), \n",
    "                xytext=(samples[\"index\"][-1], samples[\"Jaccard distance\"][-1]), \n",
    "                fontsize=10)\n",
    "\n",
    "# plot the distances for the second subplot\n",
    "axs[1].grid(color='grey', linestyle='-.', linewidth=0.5, alpha=0.6)\n",
    "# xticks \n",
    "axs[1].set_xticks(range(0, len(distances[\"Jaccard distance\"]), 10))\n",
    "\n",
    "axs[1].plot(range(len(distances[\"Jaccard distance\"])), distances[\"Jaccard distance\"],\n",
    "            c='black', linewidth=0.4) \n",
    "\n",
    "# size = [40 if i in samples[\"index\"]  else 3 for i in range(len(distances[\"Jaccard distance\"])+1)]             \n",
    "axs[1].scatter(range(len(distances[\"Jaccard distance\"])), \n",
    "               distances[\"Jaccard distance\"],\n",
    "               s=[40 if i in samples[\"index\"]  else 3 for i in range(len(distances[\"Jaccard distance\"])+1)],\n",
    "               c=[\"red\" if i in samples[\"index\"] else 'black' for i in range(len(distances[\"Jaccard distance\"])+1)],\n",
    "               marker='o', facecolor='black', linewidth=0.4)\n",
    "# plot line plot to connect the dots\n",
    "axs[1].set_xlabel(\"Polygon index\")\n",
    "axs[1].set_ylabel(\"Jaccard distance\")\n",
    "axs[1].set_title(\"Jaccard distances in relation to the first polygon\")\n",
    "# add a legend for the highlighted samples\n",
    "axs[1].legend([\"Jaccard distance\", \"Selected samples\"])\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'plt' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mNameError\u001B[0m                                 Traceback (most recent call last)",
      "Cell \u001B[1;32mIn[8], line 2\u001B[0m\n\u001B[0;32m      1\u001B[0m \u001B[38;5;66;03m# create two subplots side by side\u001B[39;00m\n\u001B[1;32m----> 2\u001B[0m fig, axs \u001B[38;5;241m=\u001B[39m \u001B[43mplt\u001B[49m\u001B[38;5;241m.\u001B[39msubplots(\u001B[38;5;241m2\u001B[39m, \u001B[38;5;241m1\u001B[39m, figsize\u001B[38;5;241m=\u001B[39m(\u001B[38;5;241m22\u001B[39m, \u001B[38;5;241m15\u001B[39m))\n\u001B[0;32m      4\u001B[0m \u001B[38;5;66;03m# plot the distances for the first subplot\u001B[39;00m\n\u001B[0;32m      5\u001B[0m axs[\u001B[38;5;241m0\u001B[39m]\u001B[38;5;241m.\u001B[39mgrid(color\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mgrey\u001B[39m\u001B[38;5;124m'\u001B[39m, linestyle\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124m-.\u001B[39m\u001B[38;5;124m'\u001B[39m, linewidth\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m0.5\u001B[39m, alpha\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m0.6\u001B[39m)\n",
      "\u001B[1;31mNameError\u001B[0m: name 'plt' is not defined"
     ]
    }
   ],
   "execution_count": 8
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
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wFemerefJtPbmTWmr2qvFj3uvK6jQp9AfFsn6a8ZD8img4TP8sJwhgMbthJAgbTz3Tc06U7r3lquk7760OyQcIQrB/MkwtOOmvhp4w2z1DJ+poZE56vTDjeoyeiXFYADqlDO+qbIHd5JVyfcUJQPydWG76i13EOPK0Li4X/4a3VYXeJGAxuGtvCTF/bxRLFoEwJ/6hjq18Zw3NfsMU1dMv1GdXy6StWR1hZHvhsslw+VS8Zk9tKfVwVPDsJ2mor5ZItPtrpWXCEZIiAzDkKdvN+V2ClFRc0MXDf5NklTgC9GMyccraM/+8yb+kiVry95lUizLkuU+9LIqABqO2A1e+SyzHg/iAFBbfJap8FdjFTp1nirO5QMAlTvwmSHeGaF3Wv96iNb1+/niwGtp5YrUB21n7N+Z8ot07t4/5vgK9dzOvjItQx7LqW8/PlGhO/fmU83S82Ws+EOSZPlMWZ7Dz05WJYYhIzj4sMUodZW/GUHBMpz77xd5Y9UYISEqGni0ChMrvgNImLdbWrOx0uPMUk+lM+2h7mwb3l49g32S6ur9DYCG6tRQKfbWLdJ3dkeCI0UhmB84QkNlhIUp47Ku+uzup5UStG/qSKdWD3hFvW+9TS2frNqU5M7oaMlZ8R/qnAu7qChp/0O0q1Bq8dF6mbt2++9hHUCDYRUVa2eqRxvPedOPvZJEAP7ks0w9vbS/2mZVrzATAKrqpFCHNg56Q7POkkZOv17xc1xq9tUaZV7cWaWxhnRCnkZ3maEBET8dkMNUlO0r1MePdtFzU89T8zk+Rc5YI9/uvMOe3xERISO44mj3vLM7K6/d3i/lLUM6e9h8dQnL0InhM9QzuJKR9XcsKPfxm3+GaktpU0lSjjdKEz89U86/vtNv9UOeHH/+VSRWXCyzpPwSLjVxsOvYxyotlVlYeMTnQf3jCA+XEVKDWWp9Pvny8/0fEBQxc7XG7+ym++NX2x0KgDr21M6uipi9jiIwADiMeGeEHkvY/13Tv8csKvvz7BJTy0qSJUlfZB6jbT+2kSS5iqSWH/0hy733XZMvL39/YY9hyBkTLR1QiObt1kbbTwkv++yOszT2vKlyav+sZZXxWC698NW5Cs6r3dmLEvtt1cUt0ss+7/JG6v3PzpKzRGoxs1Cu1ZvJ4/7ijI5W0cmdtf1kl2658DsNi3pGrSpZPnBGsUOr3c0r7eOpuecoYm2wZEmtv9ohZeb4LR9H1RQlWdVYhh0ADu0/bb7U93YHgSNmWFbgzQubn5+vmJgY9R7+mHJ77501JmqT1HzqlrI25q5cvz/EOZs0kREZIUlyd0jQpgF7vxBOPCZL93f4RkcH76x0feVvikL19O1XKXLx9nLbrfBQbbg8Qb6Q/X8Fl58zS6kR6yv00SckV02cBzxYWx7NLA7XmPTLZG6MUORmQy2+3ixrz54qvZQB0PA5u3XSrV99qYHhjHYC6qOXdifrm9O6yLdjh20xeC2PZuhL5eXlKTo62rY4ANTcvtwod227vcufHEKur0jz3E10auieGn9BuNlboGmFHfTEd4PlKD30l/dnnrlYF8UtrLD9YHmTP6S7S7XDFyVJenbT2dqwYO9LjtBdhlpP3lzpMZnnJis/5eB9nn3WIl3QZNFB93+V20vTpvcqty1kt6E2E/6QNzOrmldQc0ZQsJyJzSosA27GRemPS2LLzRTrLJXaTc6RUVAsc+euRj2DtbNpnIzwv3Jtp0MbR7SSJ2pvjt77lDW6OnF2tftcUtxab37bT4YlpXxWIFdGLrm6nxjH99Atkz7VBRGN979ZoLHKM4t1wrvjlPLQAlle/8+pTG4EBL7q5EYoz2P59EtxqMy/ZkW7+dcr5MramzP6wk09MeAjxTr3P3+1cOVVPognACwtLdF2b4w+3XmcZvzSs1HmRs5mzeTu0VqbzgnWbed+q3MjV6j9IQaHVcfsElN7zDA9tXGgtqa3kCS1nlaq0LVZsoqL5cvZ6ZfzYD9ns2Y6Z8ZajWmyye5QADQQq3Lz1b1bNrlRgAvoQrCxv52v/7ZfJWnvlwGbvPu/8L7jj4u1fn2SWn9tKHLJdnm3bK1y/65WLWVFR0iGofVXxMkTt/fLhf69lmtUwi+SpCjDe8hR83+31VugXWb5CdicstQlKMQvSxrsu/5nM8/WjCVdlDjLqbhFu2QUlcj7Z+UvPAA0fBufTNXCK55VpCMwE3OgoXJbHh374m1qNb5qM4bWFl52AIGPlx1V47Y8WuOpfA6RNi5LMQ7/Lkvttjx6J6+tnp98odp+skPm+k1HPJOzERQsR4c2ZYVeuUfHKfP0/dcUGleiyce9KYdRPsUPkqmuweHltvksU6s9bvlk6JbVl2vbpqZq+7kUtmm3X2Ktz5yxMTJTWmndyCiZYT4NP2GBRjSZW7a/a1CQX5dDX+spVInl1H8y+uvXpZ2VNNOpuLQMeTfyJX11OUJDFfRDrL7qyLhUoLHKM4t1/Hvj1O6hdL//W0VuBAQ+ciPURGW5kSRFrgtS8rc5Ze2MklJ5N/xpU5RHwDDk7JAiBbm07ex45Xfz6KYTZ2h49KJqveM8Epu9BdptuvTR7uM1cV5fGSVOdXq/QI7dhfKtr3y5SVTC4ZSzQ1vJdUC+ahja8FCwlp00wa95LIDGjUKwhiGgC8HWrkpU+9ioQ7bN9RXps4J2Gr9ooFpPcCn458UVR40Zhoxjuqm0WZg2j/Tp9l4/aUjk3gKzBGe4Xwq16lqeWawi06c5JS30r8UXSqsj1fbrAmnRqloZNWcHV7u28iZU/svHUeKVuWSVFHj/eQN+ZYSE6Kz0HN0V94fdoQA4QO/04Uq4PEPmnj22xsHLDiDw8bKjfnNbHm33ujV40fWKfz5crhmL9y+xchCOqCiZR+2domxHrwgVnVEgSWoSVaQPj3pXQX+1C3c4/VrAluMr1B7T0vBl16pkRrxa/FYg5x8Zts5c6S+OiAiZPdpr3WXh6nvcGv03+etam5XucPLMYv1W0kTj0oer2Udhil6SzcuPw3C1SVZh9yS5R+/SjJ5TFGIEHf4gAA1Wnlms498fp3YP+rcYjNwICHzkRvAnt+XRLt/+lTZWemI0euHlZa+cPKUutXnPqaD8vf8WOddskS83145Qy9n33mxP23DtGlIop9PUW73eUxtXsWIcwfVm+cAMb4HWeKJ188IRMk1DzT4KU8SWIhmL1jTogUmH4kxMkC8lSdtOj5T32IrfGQcHe/XBMe+omaP8O95AfY8NoP6iEKxhCOhCsOo+0Gf7CnXVuuEq8pR/0HE6TL3ZaaKaO+vPQ5C/+SxTO81iXbVuuDbuaKrm74bI8FkyLCkkbU2VXkQ7u3VScfJf/7MbhrZc6VWL+N3l2mzNbKI2k8r/nYSv3eG30c6u5Fba07uF8q7eozu6TNOwyO2Vttvk9ermtZfLtCouV5OdF6nkl1xyzDz48i4N2YF/j5sus9Sq+a6yfTvyI5U4IVSGt/yvBWepKdevSxtMEWGj4nDqtMUFui9+jd2RADhAh0k3qf2dcw/fsJbxsgMIfLzsCBwZ3gJdve5SFXsPXcTSOipXr7b+Xg45FGQ4bRnV67F88lg+PZTdR58uPVZtJhsKS1srX35+ncdSU4bLJe8pPfXnuSE67sQ1erPNtwozguvVF+Ruy6NPCpL0n9VnK3JCjKJ/31at2cwbCle7tirqGF9uW26nYEWflyFJuiT5d10T/UeD/b4GQPWVzQz2oP+WiSQ3AgIfuRHqWpG5v2DpnxmnaOmuFtq6NkFtpvoUtjFXvrW1OzjbGd9Uxb1TZLkMZV9TrPioQl3X5lcNi9wup2EE1AAKt+XRHrNUV/8xTBt3xin+nQg5PKbC0jc22OUkHRERcp/UVXkpQYoYkqmTE/7Q/c1+t+17AADYh0KwhqFRFYJhP59llv35nqze2lYcK0n6/dcuarZw738Su9s7dPR5q8rajUhM08Cw/eujH+wL9AP7lqSJexL0/c4eWr4jSZGTY2SYlmJ+Wivf7t1Vm7HLMOQ941hlnBiiUZd/rZtiNh3xl/cbPQU6P/1GxUyJUszP6xrsg6TvjGNVlBgszxW71LHJ3pH8VyXO0dlhxZIq/zv8+9+fJBVYbo3beraKffsTh1LTqQ0TOyps196/w5hFfxvJHni/Whqk0oHH693Xn1Vrm2Y8AFA5CsEA+Au5EeqCzzJ1e0YfzX7jOCXMy5O5eKXdIVXOMOQ5u7e2nxykk/ov0zOtpvl92c/a4rNMvZGXrFm5nbRoeyvFfRwhSYr6Y4+shX/d70DIsYwDBmMZDpWe3UulMa6yTdv7e5XatfwLsYFNl2lEVHa5bfWpYA9A/ZRnFmvYmuFyP99cYV8tOOLfkeRGQOAjN0J94bNMfVEYq0939Nb63fFyvd9U4Vmlcs74a3KCQ/2bZVSc3ECSDFeQigceI2+YQ9vP9im123p1jszS/fHLJTW85+d976nG7+ymlXuaS5LSlnRUixl/m4giyy3HrMUVOzjYPT7I/XXGRCu/XxdZjor7Y5bvkm/VuqoHv+9Ux3bTnvYVV7XyhBuKu2qzjorJ0JNJCyj6AlDvUAjWMFAIhjrns0z9L7eDcjyRmjy/j+LSXQdtW9LU0LkXp+mmpr+qfS2tV/7Uzo6a9PbZavneavl27jr8AQHA2ayZNl3fUZ/f+G91Coqok3N+WhCt9MK2kqRvNh0l17excpZK8V+slFlcIsvtPnQHqBW7r0rVvCdfsTsMAAcoMEt0ypPjlPDiHLtD4WUH0ACQG6GuzSh26Jpp16n5DIeiP19k+3O+ERKi0lO6KzM1ROcPmaMxTX9TqwY0CGJ2ialv8o7R6j2J+nNyBxk+KTLDp/BpS2VZVu3ef8OQERwso5KXJSWndVd+m/IzDHjDDfW9fJGaBhWWbRvTdI5ty3ACaByyfYU6adKd6vRaxt4VCWr4VTe5ERD4yI1Qn/3hKdBbu05UvrCBJfYAAQAASURBVDdMs6b0VtCeiv9emcGGjrp8pVqHVXx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      "text/plain": [
       "<Figure size 2500x2000 with 15 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import rasterio\n",
    "plot_polygons_as_images(distances=samples, polygons=sh_polygons)"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "# save the sampled polygons and their indices to a csv file\n",
    "for i in samples[\"index\"]:\n",
    "    with open(SAMPLED_SEGS, \"a\") as f:\n",
    "        f.write(str(i) +','+  sh_polygons[i].wkt + \"\\n\")"
   ],
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save the sampled polygons to a WKV file\n",
    "with open(SAMPLED_SEGS_WKT, \"w\") as f:\n",
    "    for i in samples[\"index\"]:\n",
    "        f.write(sh_polygons[i].wkt + \"\\n\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "interpol",
   "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": "3.10.9"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}