+The first time use will be longer as the model needs to be downloaded. Subsequent runs will be faster.
+Additionally, Cuda is required for GPU acceleration. If you do not have a GPU, you can use the CPU by setting the environment variable `CUDA_VISIBLE_DEVICES` to `-1`.
+
+
REFERENCES
+
+
Wu, Q., & Osco, L. (2023). samgeo: A Python package for segmenting geospatial data with the Segment Anything Model (SAM). Journal of Open Source Software, 8(89), 5663. https://doi.org/10.21105/joss.05663
+
Osco, L. P., Wu, Q., de Lemos, E. L., Gonçalves, W. N., Ramos, A. P. M., Li, J., & Junior, J. M. (2023). The Segment Anything Model (SAM) for remote sensing applications: From zero to one shot. International Journal of Applied Earth Observation and Geoinformation, 124, 103540. https://doi.org/10.1016/j.jag.2023.103540
+Corey T. White (NCSU GeoForAll Lab & OpenPlains Inc.)
diff --git a/src/imagery/i.sam2/i.sam2.py b/src/imagery/i.sam2/i.sam2.py
new file mode 100644
index 0000000000..03a2e34461
--- /dev/null
+++ b/src/imagery/i.sam2/i.sam2.py
@@ -0,0 +1,263 @@
+#!/usr/bin/env python3
+
+############################################################################
+#
+# MODULE: i.sam2
+# AUTHOR: Corey T. White, OpenPlains Inc.
+# PURPOSE: Uses the SAMGeo model for segmentation in GRASS GIS.
+# COPYRIGHT: (C) 2023-2025 Corey White
+# This program is free software under the GNU General
+# Public License (>=v2). Read the file COPYING that
+# comes with GRASS for details.
+#
+#############################################################################
+
+# %module
+# % description: Integrates SAMGeo model with text prompt for segmentation in GRASS GIS.
+# % keyword: imagery
+# % keyword: segmentation
+# % keyword: object recognition
+# % keyword: deep learning
+# %end
+
+# %option G_OPT_I_GROUP
+# % key: group
+# % description: Name of input imagery group
+# % required: yes
+# %end
+
+# %option
+# % key: output
+# % type: string
+# % description: Name of output segmented raster map
+# % gisprompt: new,cell,raster
+# % required: yes
+# %end
+
+# %option
+# % key: model_path
+# % type: string
+# % description: Path to the SAMGeo model file (optional if using default model)
+# % required: no
+# %end
+
+# %option
+# % key: text_prompt
+# % type: string
+# % description: Text prompt to guide segmentation
+# % required: yes
+# %end
+
+# %option
+# % key: text_threshold
+# % type: double
+# % answer: 0.24
+# % description: Text threshold for text segmentation
+# % required: no
+# % multiple: no
+# %end
+
+# %option
+# % key: box_threshold
+# % type: double
+# % answer: 0.24
+# % description: Box threshold for text segmentation
+# % required: no
+# % multiple: no
+# %end
+
+import os
+import sys
+import grass.script as gs
+import torch
+import numpy as np
+from PIL import Image
+from grass.script import array as garray
+
+DEFAULT_MODEL_PATH = "samgeo_default_model.pth"
+
+
+def get_device():
+ """
+ Determines the available device for computation (CUDA or CPU).
+
+ This function checks if a CUDA-enabled GPU is available and returns "cuda" if it is,
+ otherwise it returns "cpu". If CUDA is available, it also clears the CUDA cache.
+
+ Returns:
+ str: "cuda" if a CUDA-enabled GPU is available, otherwise "cpu".
+ """
+ device = "cuda" if torch.cuda.is_available() else "cpu"
+ gs.message(_(f"Running computation on {device}..."))
+ if device == "cuda":
+ torch.cuda.empty_cache()
+ return device
+
+
+def read_raster_group(group):
+ """
+ Reads a group of raster maps and returns them as a list of numpy arrays.
+
+ Parameters:
+ group (str): The name of the raster group to read.
+
+ Returns:
+ list: A list of numpy arrays, each representing a raster map in the group.
+ """
+ gs.message(_("Reading imagery group..."))
+ rasters = gs.read_command("i.group", group=group, flags="lg")
+ # raster_list = [raster.strip() for raster in rasters.splitlines()]
+ raster_list = map(str.split, rasters.splitlines())
+ return [garray.array(raster, dtype=np.uint8) for raster in raster_list]
+
+
+def normalize_rgb_array(rgb_array):
+ """
+ Normalize an RGB array to the range [0, 255].
+
+ This function takes an RGB array and normalizes its values to the range [0, 255].
+ If the input array is not of type np.uint8, it scales the values to fit within
+ this range and converts the array to np.uint8.
+
+ Parameters:
+ rgb_array (numpy.ndarray): The input RGB array to be normalized.
+
+ Returns:
+ numpy.ndarray: The normalized RGB array with values in the range [0, 255] and type np.uint8.
+ """
+ if rgb_array.dtype != np.uint8:
+ gs.message(_("Converting RGB array to uint8..."))
+ min_val = rgb_array.min()
+ max_val = rgb_array.max()
+ scale = 255 / (max_val - min_val)
+ rgb_array = ((rgb_array - min_val) * scale).astype(np.uint8)
+ # rgb_array = ((rgb_array - rgb_array.min()) / (rgb_array.max() - rgb_array.min()) * 255).astype(np.uint8)
+ return rgb_array
+
+
+def run_langsam_segmentation(
+ np_image, text_prompt, box_threshold, text_threshold, device
+):
+ """
+ Runs LangSAM segmentation on the given image using the specified text prompt and thresholds.
+
+ Parameters:
+ np_image (numpy.ndarray): The input image as a NumPy array.
+ text_prompt (str): The text prompt to guide the segmentation.
+ box_threshold (float): The threshold for box predictions.
+ text_threshold (float): The threshold for text predictions.
+ device (str): The device to run the segmentation on (e.g., 'cpu' or 'cuda').
+
+ Returns:
+ list: A list of masks generated by the segmentation.
+ """
+ from samgeo.text_sam import LangSAM
+ from torch.amp.autocast_mode import autocast
+
+ gs.message(_("Running LangSAM segmentation..."))
+ sam = LangSAM(model_type="sam2-hiera-large")
+ with autocast(device_type=device):
+ masks, boxes, phrases, logits = sam.predict(
+ image=np_image,
+ text_prompt=text_prompt,
+ box_threshold=box_threshold,
+ text_threshold=text_threshold,
+ return_results=True,
+ )
+ return masks
+
+
+def run_samgeo_segmentation(temp_input_path, temp_output_path, model_path, device):
+ """
+ Run SAMGeo segmentation on an input image and save the output.
+
+ Parameters:
+ temp_input_path (str): The file path to the input image.
+ temp_output_path (str): The file path to save the segmented output image.
+ model_path (str): The file path to the SAMGeo model.
+ device (str): The device to run the model on (e.g., 'cpu', 'cuda').
+
+ Returns:
+ None
+ """
+ from samgeo import SamGeo
+
+ gs.message(_("Running SAMGeo segmentation..."))
+ sam = SamGeo(model_type="vit_h", model_path=model_path, device=device)
+ sam.generate(input_image=temp_input_path, output=temp_output_path)
+
+
+def write_raster(input_np_array, output_raster):
+ """
+ Writes a segmented raster into GRASS GIS.
+
+ Parameters:
+ input_np_array (list of numpy.ndarray): A list of numpy arrays representing the input raster bands.
+ output_raster (str): The name of the output raster map to be created in GRASS GIS.
+
+ Raises:
+ ValueError: If the input array is empty or if the masks do not have the same shape.
+
+ This function merges multiple raster bands into a single raster, where each band is assigned a unique value.
+ The merged raster is then written to a GRASS GIS raster map.
+ """
+
+ gs.message(_("Importing the segmented raster into GRASS GIS..."))
+
+ if len(input_np_array) == 0:
+ gs.fatal("No masks found.")
+
+ merged_raster = np.zeros_like(input_np_array[0], dtype=np.int32)
+ for idx, band in enumerate(input_np_array):
+ if band.shape != input_np_array[0].shape:
+ gs.fatal(_("All masks must have the same shape."))
+ unique_value = idx + 1
+ mask = band != 0
+ merged_raster[mask] = unique_value
+
+ mask_raster = garray.array()
+ mask_raster[...] = merged_raster
+ mask_raster.write(mapname=output_raster)
+
+
+def main():
+ group = options["group"]
+ output_raster = options["output"]
+ model_path = options.get("model_path") or os.path.join(
+ gs.gisenv()["GISDBASE"], DEFAULT_MODEL_PATH
+ )
+ text_prompt = options.get("text_prompt")
+ text_threshold = float(options.get("text_threshold"))
+ box_threshold = float(options.get("box_threshold"))
+
+ tmp_dir = gs.tempdir()
+ temp_input_path = os.path.join(tmp_dir, "input.tif")
+ temp_output_path = tmp_dir
+
+ input_image_np = read_raster_group(group)
+ rgb_array = normalize_rgb_array(np.stack(input_image_np, axis=-1))
+ np_image = Image.fromarray(rgb_array[:, :, :3])
+
+ device = get_device()
+
+ try:
+ if text_prompt:
+ masks = run_langsam_segmentation(
+ np_image, text_prompt, box_threshold, text_threshold, device
+ )
+ else:
+ run_samgeo_segmentation(
+ temp_input_path, temp_output_path, model_path, device
+ )
+ except Exception as e:
+ gs.fatal(_(f"Error while running SAMGeo: {e}"))
+ return 1
+
+ gs.message(_("Segmentation complete."))
+ write_raster(masks, output_raster)
+ return 0
+
+
+if __name__ == "__main__":
+ options, flags = gs.parser()
+ sys.exit(main())
diff --git a/src/imagery/i.sam2/i_sam2_trees.jpg b/src/imagery/i.sam2/i_sam2_trees.jpg
new file mode 100644
index 0000000000..dcab417416
Binary files /dev/null and b/src/imagery/i.sam2/i_sam2_trees.jpg differ
diff --git a/src/imagery/i.sam2/requirements.txt b/src/imagery/i.sam2/requirements.txt
new file mode 100644
index 0000000000..b6b74919a9
--- /dev/null
+++ b/src/imagery/i.sam2/requirements.txt
@@ -0,0 +1,4 @@
+Pillow>=10.2.0
+numpy>=1.26.1
+torch>=2.5.1
+segment-geospatial>=0.12.3
diff --git a/src/imagery/i.sam2/testsuite/test_i_sam2.py b/src/imagery/i.sam2/testsuite/test_i_sam2.py
new file mode 100644
index 0000000000..1da3e0a5de
--- /dev/null
+++ b/src/imagery/i.sam2/testsuite/test_i_sam2.py
@@ -0,0 +1,67 @@
+# import os
+# import sys
+# import pytest
+# import numpy as np
+# from unittest.mock import patch, MagicMock
+# from grass.script import array as garray
+# from PIL import Image
+# from grass.gunittest.case import TestCase
+# from grass.gunittest.main import test
+
+
+# @pytest.fixture(scope="module")
+# def mock_torch():
+# with patch('torch.cuda.is_available') as mock_is_available:
+# mock_is_available.return_value = False
+# yield mock_is_available
+
+
+# @pytest.fixture(scope="module")
+# def mock_run_langsam_segmentation():
+# with patch('i.sam2.run_langsam_segmentation') as mock_run_langsam:
+# # Define the mock return value
+# mock_run_langsam.return_value = [np.random.randint(0, 2, (100, 100), dtype=np.uint8) for _ in range(3)]
+# yield mock_run_langsam
+
+
+# class TestISam2(TestCase):
+
+# RED_BAND = "lsat7_2002_30"
+# GREEN_BAND = "lsat7_2002_20"
+# BLUE_BAND = "lsat7_2002_10"
+
+# def _create_imagery_group(cls):
+# cls.runModule("i.group", group="test_group", input=','.join([cls.RED_BAND, cls.GREEN_BAND, cls.BLUE_BAND]))
+
+# @classmethod
+# def setUpClass(cls):
+# """Ensures expected computational region"""
+# # to not override mapset's region (which might be used by other tests)
+# cls.use_temp_region()
+# cls.runModule("g.region", raster="elev_lid792_1m", res=30)
+# cls._create_imagery_group(cls)
+
+# @classmethod
+# def tearDown(self):
+# """
+# Remove the outputs created from the centroids module
+# This is executed after each test run.
+# """
+# self.runModule("g.remove", flags="f", type="raster", name="test_output")
+
+# @pytest.mark.usefixtures("mock_torch", "mock_run_langsam")
+# def test_main_with_text_prompt(self):
+# options = {
+# "group": "test_group",
+# "output": "test_output",
+# "model_path": None,
+# "text_prompt": "Waterbodies",
+# "text_threshold": "0.24",
+# "box_threshold": "0.24"
+# }
+
+# self.assertModule("i.sam2", **options)
+
+
+# if __name__ == "__main__":
+# test()
+
+