The code below is a clunky attempt to get a raster numpy array loaded up for basic machine learning from the QGIS Python console. It usually works but has stability issues with the GetRasterBand() and I have asked a separate question. As an improvement I am trying to figure how to get the array values directly from the loaded rasters in QGIS. I.e. you open the raster layers in QGIS, do whatever is needed (e.g. training polygons) and then run the code.
#Part 1. Importing Python modules import numpy as np import gdal from sklearn.cluster import KMeans import os #Part 2. Specify the image file to be classified and the class raster with the training data. MyFolder = 'D:\\RemoteSensing\\Practical_7\\' #Change this to match the folder on your drive. Note that we use \\ instead of \ MyImageRoot = 'S2_mini' #Specify the root image name. Do not nclude the extension, e.g. 'glacier_'. If a color image, just write the file name without extension. Nclasses = 4 #how many classes are in the image MyClassRaster = 'KmeansTEST_4cls' BandList = ['B02'] #Indicate the bands you want to load. B #Part 3 Load the image fileName = os.path.join(MyFolder + MyImageRoot + BandList + '.tif') ImageFile = gdal.Open(fileName) band1 = ImageFile.GetRasterBand(1) array1 = band1.ReadAsArray()
I am trying to get array1 by direct querry to a loaded band in the QGIS layers. I have found QgisProject:
layer = QgsProject.instance().mapLayersByName(BandList)
But I can't find how to access the raster values for the layer and have them as a numpy array for the next processing steps. I'm working with QGIS 3.2 and 3.4.