/Remote_Sensing_Analysis

Analyzing Remote Sensing Imagery

Primary LanguagePythonMIT LicenseMIT

Remote Sensing Analysis

This package provides tools for processing and analyzing satellite imagery, utilizing advanced machine learning techniques for object detection, image enhancement, and text analytics from images.

Installation

  1. Download Package:

To install the Remote Sensing Analysis package, simply run the following command:

pip install Remote-Sensing-Analysis

  1. Download Model Weights: The package requires specific model weights to function correctly. Download the model weights from the following Google Drive link: Download Model Weights

    After downloading, place the weights under the pretrained folder.

Usage

Parameters

When initializing the ImageProcessor, you can specify the following parameters:

  • model_weights_path: Path to the model weights file, default is "pretrained/YOLOv9_DOTA1_100EPOCHS.pt".
  • confidence_threshold: The confidence threshold for object detection. Objects with a confidence level higher than this threshold are considered. Default is 0.1.
  • output_folder: The directory where results will be saved. Default is "results".
  • known_phrases: A list of phrases against which the descriptions of detected objects will be compared. This helps in identifying specific activities or features in images.

Example Code

Here is how you can use the ImageProcessor in your scripts:

from PIL import Image
from Remote_Sensing_Analysis.ImageProcessor import ImageProcessor

def test_image_processing():
    processor = ImageProcessor(
        model_weights_path="pretrained/YOLOv9_DOTA1_100EPOCHS.pt",
        confidence_threshold=0.1,
        output_folder="results",
        known_phrases=[
            "Rocket positioned on the launch pad for final countdown",
            "Final checks on the launch systems",
            "Lots of Activity in the Image",
            "Rocket being fueled"
        ]
    )
    path = "path_to_your_test_image.jpg"
    im1 = Image.open(path)
    # Using .inference method
    report, percentage = processor.inference(im1)
    # Or using .generate method directly with an image object
    report, percentage = processor.generate(im1)

if __name__ == "__main__":
    test_image_processing()

The report object holds a comprehensive report on the image analysis. The percentage object indicates the likelihood of rocket preparation activities occurring. For additional information and data, please refer to the output_folder directory.

Replace path_to_your_test_image.jpg with the path to the image file you wish to process.