{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0",
   "metadata": {},
   "source": [
    "# Segment Anything Model for Geospatial Data \n",
    "\n",
    "[![image](https://jupyterlite.rtfd.io/en/latest/_static/badge.svg)](https://demo.leafmap.org/lab/index.html?path=notebooks/75_segment_anything.ipynb)\n",
    "[![image](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/opengeos/leafmap/blob/master/docs/notebooks/75_segment_anything.ipynb)\n",
    "[![image](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/opengeos/leafmap/HEAD)\n",
    "\n",
    "This notebook shows how to use segment satellite imagery using the Segment Anything Model (SAM) with a few lines of code. \n",
    "\n",
    "Make sure you use GPU runtime for this notebook. For Google Colab, go to `Runtime` -> `Change runtime type` and select `GPU` as the hardware accelerator."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1",
   "metadata": {},
   "source": [
    "## Install dependencies\n",
    "\n",
    "Uncomment and run the following cell to install the required dependencies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# %pip install leafmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# %pip install segment-geospatial localtileserver"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4",
   "metadata": {},
   "source": [
    "## Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import leafmap\n",
    "from samgeo import SamGeo, tms_to_geotiff, get_basemaps"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6",
   "metadata": {},
   "source": [
    "## Create an interactive map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7",
   "metadata": {},
   "outputs": [],
   "source": [
    "m = leafmap.Map(center=[29.676840, -95.369222], zoom=19)\n",
    "m.add_basemap(\"SATELLITE\")\n",
    "m"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8",
   "metadata": {},
   "source": [
    "Pan and zoom the map to select the area of interest. Use the draw tools to draw a polygon or rectangle on the map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9",
   "metadata": {},
   "outputs": [],
   "source": [
    "if m.user_roi_bounds() is not None:\n",
    "    bbox = m.user_roi_bounds()\n",
    "else:\n",
    "    bbox = [-95.3704, 29.6762, -95.368, 29.6775]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10",
   "metadata": {},
   "source": [
    "## Download map tiles\n",
    "\n",
    "Download maps tiles and mosaic them into a single GeoTIFF file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "11",
   "metadata": {},
   "outputs": [],
   "source": [
    "image = \"satellite.tif\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12",
   "metadata": {},
   "source": [
    "Besides the `satellite` basemap, you can use any of the following basemaps returned by the `get_basemaps()` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "13",
   "metadata": {},
   "outputs": [],
   "source": [
    "# get_basemaps().keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14",
   "metadata": {},
   "source": [
    "Specify the basemap as the source."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "15",
   "metadata": {},
   "outputs": [],
   "source": [
    "tms_to_geotiff(output=image, bbox=bbox, zoom=20, source=\"Satellite\", overwrite=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16",
   "metadata": {},
   "source": [
    "You can also use your own image. Uncomment and run the following cell to use your own image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "17",
   "metadata": {},
   "outputs": [],
   "source": [
    "# image = '/path/to/your/own/image.tif'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18",
   "metadata": {},
   "source": [
    "Display the downloaded image on the map."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "19",
   "metadata": {},
   "outputs": [],
   "source": [
    "m.layers[-1].visible = False  # turn off the basemap\n",
    "m.add_raster(image, layer_name=\"Image\")\n",
    "m"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20",
   "metadata": {},
   "source": [
    "![](https://i.imgur.com/KAm84IY.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21",
   "metadata": {},
   "source": [
    "## Initialize SAM class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "22",
   "metadata": {},
   "outputs": [],
   "source": [
    "sam = SamGeo(\n",
    "    model_type=\"vit_h\",\n",
    "    checkpoint=\"sam_vit_h_4b8939.pth\",\n",
    "    sam_kwargs=None,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23",
   "metadata": {},
   "source": [
    "## Segment the image\n",
    "\n",
    "Set `batch=True` to segment the image in batches. This is useful for large images that cannot fit in memory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "24",
   "metadata": {},
   "outputs": [],
   "source": [
    "mask = \"segment.tif\"\n",
    "sam.generate(\n",
    "    image, mask, batch=True, foreground=True, erosion_kernel=(3, 3), mask_multiplier=255\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25",
   "metadata": {},
   "source": [
    "## Polygonize the raster data\n",
    "\n",
    "Save the segmentation results as a GeoPackage file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "26",
   "metadata": {},
   "outputs": [],
   "source": [
    "vector = \"segment.gpkg\"\n",
    "sam.tiff_to_gpkg(mask, vector, simplify_tolerance=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27",
   "metadata": {},
   "source": [
    "You can also save the segmentation results as any vector data format supported by GeoPandas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "28",
   "metadata": {},
   "outputs": [],
   "source": [
    "shapefile = \"segment.shp\"\n",
    "sam.tiff_to_vector(mask, shapefile)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29",
   "metadata": {},
   "source": [
    "## Visualize the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "30",
   "metadata": {},
   "outputs": [],
   "source": [
    "style = {\n",
    "    \"color\": \"#3388ff\",\n",
    "    \"weight\": 2,\n",
    "    \"fillColor\": \"#7c4185\",\n",
    "    \"fillOpacity\": 0.5,\n",
    "}\n",
    "m.add_vector(vector, layer_name=\"Vector\", style=style)\n",
    "m"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31",
   "metadata": {},
   "source": [
    "![](https://i.imgur.com/Ysq3u7E.png)"
   ]
  }
 ],
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