Metadata-Version: 2.1
Name: SatIOpsT
Version: 0.0.1
Summary: Satellite Image Operations Toolbox
Home-page: https://github.com/SubhadipDatta/SatIOpsT
Author: Subhadip Datta, Soumyadeep Dutta
Author-email: subhadipdatta007@gmail.com
License: MIT
Description: # SatIOpsT: Satellite Image Oparations Toolbox
        This package helps you to run various oparations on satellite images.Currently Under development.
        
        ### 1. Installation Process:
        You need anaconda or miniconda to run this package. First install anaconda or miniconda in your PC, From anaconda navigator or anaconda terminal run:
        
            conda install gdal rasterio geopandas pandas numpy
            # After finish this installation install this package.
            pip install SatIOpsT
        
        ### 2. Usage:
        **Read and Write image:**
        
            from satiopst import imgReadWrite
            img,meta=imgReadWrite.imgRead(r"C:\Users\subha\Documents\MechineLearning\s2\S2.tif","r")
            imgReadWrite.imgWrite(img,r"C:\Users\subha\Documents\MechineLearning\s2\S21.tif",meta)
            
        **Image to Pandas dataframe and pandas dataframe to image conversion:**
        
            from satiopst.imageFrame import imagetoframe, frametoimage
            iframe= imagetoframe(img)
            img2=frametoimage(iframe, meta)
            
        **Extract Pixel values according to class using polygon shapefile:**
        
            from satiopst.extracttraindata import extractbypolygon as ext
            traind=ext(r"C:\Users\subha\Documents\MechineLearning\s2\S2forClassification.tif",
                   r"C:\Users\subha\Documents\MechineLearning\s2\trysinglemarged.shp","r","ClassID")
                   
        ** Crop, mask and Layer Stack of satellite images:**
        
            from satiopst.utils import icrop, imask, layerStack
            crop=icrop(r"C:\Users\subha\Documents\MechineLearning\s2\S2forClassification.tif",
                       r"C:\Users\subha\Documents\MechineLearning\s2\trysinglemarged.shp")
            
            mask=imask(r"C:\Users\subha\Documents\MechineLearning\s2\S2forClassification.tif",
                       r"C:\Users\subha\Documents\MechineLearning\s2\trysinglemarged.shp",nodata=0)
            
            imglist=[r"C:\Users\subha\Documents\MechineLearning\L2A_T45QXE_A015074_20200125T044114\IMG_DATA\R20m\b1.jp2",
                     r"C:\Users\subha\Documents\MechineLearning\L2A_T45QXE_A015074_20200125T044114\IMG_DATA\R20m\b2.jp2",
                     r"C:\Users\subha\Documents\MechineLearning\L2A_T45QXE_A015074_20200125T044114\IMG_DATA\R20m\b3.jp2"]
            stacki,meta=layerStack(imglist)
            
        ### Contact:
        https://github.com/SubhadipDatta/SatIOpsT
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
