#!/usr/bin/env python #****************************************************************************** # # Project: GDAL # Purpose: Example doing range based classification # Author: Frank Warmerdam, warmerdam@pobox.com # #****************************************************************************** # Copyright (c) 2008, Frank Warmerdam <warmerdam@pobox.com> # # Permission is hereby granted, free of charge, to any person obtaining a # copy of this software and associated documentation files (the "Software"), # to deal in the Software without restriction, including without limitation # the rights to use, copy, modify, merge, publish, distribute, sublicense, # and/or sell copies of the Software, and to permit persons to whom the # Software is furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included # in all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS # OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL # THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING # FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER # DEALINGS IN THE SOFTWARE. #****************************************************************************** import gdal import gdalnumeric try: import numpy except: import Numeric as numpy class_defs = [(1, 10, 20), (2, 20, 30), (3, 128, 255)] src_ds = gdal.Open('utm.tif') xsize = src_ds.RasterXSize ysize = src_ds.RasterYSize src_image = gdalnumeric.LoadFile( 'utm.tif' ) dst_image = numpy.zeros((ysize,xsize)) for class_info in class_defs: class_id = class_info[0] class_start = class_info[1] class_end = class_info[2] class_value = numpy.ones((ysize,xsize)) * class_id mask = numpy.bitwise_and( numpy.greater_equal(src_image,class_start), numpy.less_equal(src_image,class_end)) dst_image = numpy.choose( mask, (dst_image,class_value) ) gdalnumeric.SaveArray( dst_image, 'classes.tif' )