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Add SAM2 notebook examples
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‎README.md

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[![image](https://img.shields.io/conda/vn/conda-forge/segment-geospatial.svg)](https://anaconda.org/conda-forge/segment-geospatial)
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[![Docker Pulls](https://badgen.net/docker/pulls/giswqs/segment-geospatial?icon=docker&label=pulls)](https://hub.docker.com/r/giswqs/segment-geospatial)
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[![PyPI Downloads](https://static.pepy.tech/badge/segment-geospatial)](https://pepy.tech/project/segment-geospatial)
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[![Conda Recipe](https://img.shields.io/badge/recipe-segment--geospatial-green.svg)](https://anaconda.org/conda-forge/segment-geospatial)
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[![Conda Recipe](https://img.shields.io/badge/recipe-segment--geospatial-green.svg)](https://github.com/giswqs/segment-geospatial-feedstock)
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[![Conda Downloads](https://anaconda.org/conda-forge/segment-geospatial/badges/downloads.svg)](https://anaconda.org/conda-forge/segment-geospatial)
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[![DOI](https://joss.theoj.org/papers/10.21105/joss.05663/status.svg)](https://doi.org/10.21105/joss.05663)
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‎docs/examples/sam2_automatic.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Automatic Mask Generation with SAM 2\n",
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"\n",
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"[![image](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/opengeos/segment-geospatial/blob/main/docs/examples/sam2_automatic.ipynb)\n",
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"[![image](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/opengeos/segment-geospatial/blob/main/docs/examples/sam2_automatic.ipynb)\n",
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"\n",
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"This notebook shows how to segment objects from an image using the Segment Anything Model 2 (SAM2) with a few lines of code. \n",
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"\n",
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"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. "
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Install dependencies\n",
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"\n",
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"Uncomment and run the following cell to install the required dependencies."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# %pip install -U segment-geospatial"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import leafmap\n",
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"from samgeo import SamGeo2"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Create an interactive map"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"m = leafmap.Map(center=[29.6768, -95.3692], zoom=19)\n",
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"m.add_basemap(\"SATELLITE\")\n",
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"m"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"\n",
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"Pan and zoom the map to select the area of interest. Use the draw tools to draw a polygon or rectangle on the map"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"if m.user_roi_bounds() is not None:\n",
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" bbox = m.user_roi_bounds()\n",
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"else:\n",
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" bbox = [-95.3704, 29.6762, -95.368, 29.6775]"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Download a sample image"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"image = \"satellite.tif\"\n",
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"leafmap.map_tiles_to_geotiff(\n",
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" output=image, bbox=bbox, zoom=20, source=\"Satellite\", overwrite=True\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"You can also use your own image. Uncomment and run the following cell to use your own image."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# image = '/path/to/your/own/image.tif'"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Display the downloaded image on the map."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"m.layers[-1].visible = False\n",
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"m.add_raster(image, layer_name=\"Image\")\n",
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"m"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Initialize SAM class"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"sam2 = SamGeo2(model_id=\"sam2-hiera-large\", automatic=True)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Automatic mask generation\n",
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"\n",
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"Segment the image and save the results to a GeoTIFF file. Set `unique=True` to assign a unique ID to each object. "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"sam2.generate(image)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"sam2.save_masks(output=\"masks.tif\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"sam2.show_masks(cmap=\"binary_r\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"sam2.show_masks(cmap=\"jet\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Show the object annotations (objects with random color) on the map."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"sam2.show_anns(axis=\"off\", alpha=0.7, output=\"annotations.tif\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Compare images with a slider."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"leafmap.image_comparison(\n",
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" \"satellite.tif\",\n",
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" \"annotations.tif\",\n",
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" label1=\"Satellite Image\",\n",
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" label2=\"Image Segmentation\",\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Add image to the map."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"m.add_raster(\"masks.tif\", colormap=\"jet\", layer_name=\"Masks\", nodata=0, opacity=0.7)\n",
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"m"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Convert the object annotations to vector format, such as GeoPackage, Shapefile, or GeoJSON."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"sam2.raster_to_vector(\"masks.tif\", \"masks.gpkg\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"m.add_vector(\"masks.gpkg\", layer_name=\"Objects\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Automatic mask generation options\n",
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"\n",
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"There are several tunable parameters in automatic mask generation that control how densely points are sampled and what the thresholds are for removing low quality or duplicate masks. Additionally, generation can be automatically run on crops of the image to get improved performance on smaller objects, and post-processing can remove stray pixels and holes. Here is an example configuration that samples more masks:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"sam2 = SamGeo2(\n",
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" model_id=\"sam2-hiera-large\",\n",
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" apply_postprocessing=False,\n",
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" points_per_side=32,\n",
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" points_per_batch=64,\n",
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" pred_iou_thresh=0.7,\n",
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" stability_score_thresh=0.92,\n",
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" stability_score_offset=0.7,\n",
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" crop_n_layers=1,\n",
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" box_nms_thresh=0.7,\n",
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" crop_n_points_downscale_factor=2,\n",
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" min_mask_region_area=25.0,\n",
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" use_m2m=True,\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"sam2.generate(image, output=\"masks2.tif\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"sam2.show_masks(cmap=\"jet\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"sam2.show_anns(axis=\"off\", alpha=0.7, output=\"annotations2.tif\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Compare images with a slider."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"leafmap.image_comparison(\n",
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" image,\n",
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" \"annotations2.tif\",\n",
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" label1=\"Image\",\n",
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" label2=\"Image Segmentation\",\n",
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")"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.8"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}

‎docs/index.md

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[![image](https://img.shields.io/conda/vn/conda-forge/segment-geospatial.svg)](https://anaconda.org/conda-forge/segment-geospatial)
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[![Docker Pulls](https://badgen.net/docker/pulls/giswqs/segment-geospatial?icon=docker&label=pulls)](https://hub.docker.com/r/giswqs/segment-geospatial)
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[![PyPI Downloads](https://static.pepy.tech/badge/segment-geospatial)](https://pepy.tech/project/segment-geospatial)
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[![Conda Recipe](https://img.shields.io/badge/recipe-segment--geospatial-green.svg)](https://anaconda.org/conda-forge/segment-geospatial)
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[![Conda Recipe](https://img.shields.io/badge/recipe-segment--geospatial-green.svg)](https://github.com/giswqs/segment-geospatial-feedstock)
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[![Conda Downloads](https://anaconda.org/conda-forge/segment-geospatial/badges/downloads.svg)](https://anaconda.org/conda-forge/segment-geospatial)
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[![DOI](https://joss.theoj.org/papers/10.21105/joss.05663/status.svg)](https://doi.org/10.21105/joss.05663)
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