Added fastsam module (#167)

* Add fastsam module

* Add fastsam functions and notebook

* Add show_anns function

* Update dep

.gitignore

@@ -13,6 +13,7 @@ private/
 **/*.jpg
 **/*.png
 **/*.csv
+**/*.pt
 docs/examples/*.geojson
 
 # C extensions

docs/examples/fast_sam.ipynb

@@ -0,0 +1,267 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Segmenting remote sensing imagery with FastSAM\n",
+    "\n",
+    "[![image](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/opengeos/segment-geospatial/blob/main/docs/examples/fast_sam.ipynb)\n",
+    "[![image](https://img.shields.io/badge/Open-Planetary%20Computer-black?style=flat&logo=microsoft)](https://pccompute.westeurope.cloudapp.azure.com/compute/hub/user-redirect/git-pull?repo=https://github.com/opengeos/segment-geospatial&urlpath=lab/tree/segment-geospatial/docs/examples/fast_sam.ipynb&branch=main)\n",
+    "[![image](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/opengeos/segment-geospatial/blob/main/docs/examples/fast_sam.ipynb)\n",
+    "\n",
+    "FastSAM: https://github.com/CASIA-IVA-Lab/FastSAM\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",
+   "metadata": {},
+   "source": [
+    "## Install dependencies\n",
+    "\n",
+    "Uncomment and run the following cell to install the required dependencies."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# %pip install segment-geospatial segment-anything-fast"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import leafmap\n",
+    "from samgeo import tms_to_geotiff\n",
+    "from samgeo.fast_sam import SamGeo"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create an interactive map"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m = leafmap.Map(center=[-22.17615, -51.253043], zoom=18, height=\"800px\")\n",
+    "m.add_basemap(\"SATELLITE\")\n",
+    "m"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Download a sample image\n",
+    "\n",
+    "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,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bbox = m.user_roi_bounds()\n",
+    "if bbox is None:\n",
+    "    bbox = [-51.2565, -22.1777, -51.2512, -22.175]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "image = \"Image.tif\"\n",
+    "tms_to_geotiff(output=image, bbox=bbox, zoom=19, source=\"Satellite\", overwrite=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "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,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# image = '/path/to/your/own/image.tif'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Display the downloaded image on the map."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m.layers[-1].visible = False\n",
+    "m.add_raster(image, layer_name=\"Image\")\n",
+    "m"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initialize SamGeo class\n",
+    "\n",
+    "The initialization of the SamGeo class might take a few minutes. The initialization downloads the model weights and sets up the model for inference."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from samgeo.fast_sam import SamGeo\n",
+    "sam = SamGeo(model=\"FastSAM-x.pt\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Set the image."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sam.set_image(\"Image.tif\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Segment the image with `everything_prompt`. You can also try `point_prompt`, `box_prompt`, or `text_prompt`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sam.everything_prompt(output=\"mask.tif\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Show the annotated image."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sam.show_anns(\"mask.png\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![](https://i.imgur.com/af4bj7O.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Convert the segmentation results from GeoTIFF to vector."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sam.raster_to_vector(\"mask.tif\", \"mask.geojson\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Show the segmentation results on the map."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m.add_raster(\"mask.tif\", opacity=0.5, layer_name=\"Mask\")\n",
+    "m.add_vector(\"mask.geojson\", layer_name=\"Mask Vector\")\n",
+    "m"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![](https://i.imgur.com/LvEAMSl.png)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "sam",
+   "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": "3.9.16"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

docs/installation.md

@@ -23,7 +23,7 @@ mamba install -c conda-forge segment-geospatial
 Samgeo-geospatial has some optional dependencies that are not included in the default conda environment. To install these dependencies, run the following command:
 
 ```bash
-mamba install -c conda-forge groundingdino-py
+mamba install -c conda-forge groundingdino-py segment-anything-fast
 ```
 
 As of July 9th, 2023 Linux systems have also required that `libgl1` be installed for segment-geospatial to work. The following command will install that dependency

mkdocs.yml

@@ -62,5 +62,6 @@ nav:
     - API Reference:
           - common module: common.md
           - samgeo module: samgeo.md
+          - fast_sam module: fast_sam.md
           - hq_sam module: hq_sam.md
           - text_sam module: text_sam.md

requirements_dev.txt

@@ -1 +1,3 @@
-rio-cogeo
+rio-cogeo
+groundingdino-py
+segment-anything-fast

samgeo/common.py

@@ -1228,7 +1228,7 @@ def draw_tile(source, lat0, lon0, lat1, lon1, zoom, filename, **kwargs):
     return image
 
 
-def raster_to_vector(source, output, simplify_tolerance=None, **kwargs):
+def raster_to_vector(source, output, simplify_tolerance=None, dst_crs=None, **kwargs):
     """Vectorize a raster dataset.
 
     Args:
@@ -1256,6 +1256,10 @@ def raster_to_vector(source, output, simplify_tolerance=None, **kwargs):
     gdf = gpd.GeoDataFrame.from_features(fc)
     if src.crs is not None:
         gdf.set_crs(crs=src.crs, inplace=True)
+
+    if dst_crs is not None:
+        gdf = gdf.to_crs(dst_crs)
+
     gdf.to_file(output, **kwargs)