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.

``` BASH 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):

BASH 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

python import geosam from geosam import ImageEncoder

check if gpu available

python geosam.gpu_available()

Run by specify parameters directly

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

```python checkpointpath = '/content/samvitl0b3195.pth' imagepath = '/content/beiluhegoogleimg201211clip.tif' featuredir = './'

init ImageEncoder

imgencoder = ImageEncoder(checkpointpath)

encode image

imgencoder.encodeimage(imagepath,featuredir) ```

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:

```python settingfile = "/content/setting.json" featuredir = './'

parse settings from the setting,json file

settings = geosam.parsesettingsfile(setting_file)

setting file not contains feature_dir, you need add it

settings.update({"featuredir":featuredir})

split settings into initsettings, encodesettings

initsettings, encodesettings = geosam.split_settings(settings)

print(f"settings: {settings}") print(f"initsettings: {initsettings}") print(f"encodesettings: {encodesettings}") ```

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

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

Using Terminal

check the folder of geosam

python print(geosam.folder)

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

bash 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

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

check all available parameters:

bash 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, --imagepath: Path to the input image. -c, --checkpointpath: Path to the SAM checkpoint. -f, --featuredir: Path to the output feature directory. --modeltype: one of ["vith", "vitl", "vitb"] 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. --valuerange: 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. --batchsize: int, optional The batch size for encoding. Default: 1. --gpuid: 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