GeoSAM-Image-Encoder

This package is part of the Geo-SAM project and is a standalone Python package that does not depend on QGIS. This package allows you to encode remote sensing images into features that can be recognized by Geo-SAM using a remote server, such as Colab, AWS, Azure or your own HPC.

Installation

You can install GeoSAM-Image-Encoder via pip.

pip install GeoSAM-Image-Encoder
# or
pip install git+https://github.com/Fanchengyan/GeoSAM-Image-Encoder.git

GPU Version

GeoSAM-Image-Encoder supports using GPU to accelerate the encoding process. If your PC has NVIDIA GPUs, you need to download and install the CUDA Toolkit first.

Then install the gpu-version pytorch using the following command (here CUDA 11.7 as an example):

pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu117

Usage

There are two ways to use GeoSAM-Image-Encoder. You can call it in Python or Terminal. We recommend using Python interface directly which will have greater flexibility.

Using Python

After install GeoSAM-Image-Encoder, you can import it using geosam

import geosam
from geosam import ImageEncoder

check if gpu available

geosam.gpu_available()

Run by specify parameters directly

If you want to specify the parameters directly, you can run it like this:

checkpoint_path = '/content/sam_vit_l_0b3195.pth'
image_path = '/content/beiluhe_google_img_201211_clip.tif'
feature_dir = './'

## init ImageEncoder
img_encoder = ImageEncoder(checkpoint_path)
## encode image
img_encoder.encode_image(image_path,feature_dir)

Run by parameters from setting.json file

If you want to using settings.json file which exported from Geo-SAM plugin to provide parameters, you can run it like this:

setting_file = "/content/setting.json"
feature_dir = './'

### parse settings from the setting,json file
settings = geosam.parse_settings_file(setting_file)

### setting file not contains feature_dir, you need add it
settings.update({"feature_dir":feature_dir})

### split settings into init_settings, encode_settings
init_settings, encode_settings = geosam.split_settings(settings)

print(f"settings: {settings}")
print(f"init_settings: {init_settings}")
print(f"encode_settings: {encode_settings}")

Then, you can run image encoding by parameters from setting.json file

img_encoder = ImageEncoder(**init_settings)
img_encoder.encode_image(**encode_settings)

Using Terminal

check the folder of geosam

print(geosam.folder)

add this folder into environment of your machine. Then run in terminal:

image_encoder.py -i /content/beiluhe_google_img_201211_clip.tif -c /content/sam_vit_l_0b3195.pth -f ./

You can overwrite the settings from file by specify the parameter values. For Example

image_encoder.py -s /content/setting.json  -f ./ --stride 256 --value_range "10,255"

check all available parameters:

image_encoder.py -h

This script is for encoding image to SAM features.

=====
Usage
=====
using settings.json:

    image_encoder.py -s <settings.json> -f <feature_dir>
 
 
or directly using parameters:
 
    image_encoder.py -i <image_path> -c <checkpoint_path> -f <feature_dir>
    
All Parameters:
-------------------
-s, --settings:         Path to the settings json file.
-i, --image_path:       Path to the input image.
-c, --checkpoint_path:  Path to the SAM checkpoint.
-f, --feature_dir:      Path to the output feature directory.
--model_type: one of ["vit_h", "vit_l", "vit_b"] or [0, 1, 2] or None, optional
    The type of the SAM model. If None, the model type will be 
    inferred from the checkpoint path. Default: None. 
--bands: list of int, optional .
    The bands to be used for encoding. Should not be more than three bands.
    If None, the first three bands (if available) will be used. Default: None.
--stride: int, optional
    The stride of the sliding window. Default: 512.
--extent: str, optional
    The extent of the image to be encoded. Should be in the format of
    "minx, miny, maxx, maxy, [crs]". If None, the extent of the input
    image will be used. Default: None.
--value_range: tuple of float, optional
    The value range of the input image. If None, the value range will be
    automatically calculated from the input image. Default: None.
--resolution: float, optional
    The resolution of the output feature in the unit of raster crs.
    If None, the resolution of the input image will be used. Default: None.
--batch_size: int, optional
    The batch size for encoding. Default: 1.
--gpu_id: int, optional
    The device id of the GPU to be used. Default: 0.

Colob Example

You can click on the link below to experience GeoSAM-Image-Encoder in Colab:

https://colab.research.google.com/github/coolzhao/Geo-SAM/blob/dev/GeoSAM-Image-Encoder/examples/geosam-image-encoder.ipynb