1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
|
#!/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' )
|
