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commit 0df665d75d3397463186c59f6fa07e3ffc0437d6
Author: Pieter Kempeneers <kempe...@gmail.com>
Date: Sun Sep 21 11:46:47 2014 -0700
working on filter in spectral domain
---
ChangeLog | 2 ++
doc/examples_pkextract.dox | 33 +++++++++++++++++++--------------
src/algorithms/Filter.cc | 13 ++++++++++---
3 files changed, 31 insertions(+), 17 deletions(-)
diff --git a/ChangeLog b/ChangeLog
index bbbe5ae..38c5595 100755
--- a/ChangeLog
+++ b/ChangeLog
@@ -308,6 +308,8 @@ version 2.5.3
version 2.5.4
- pkinfo
Support multiple input bands when calculating statistics
+ - pkfilter
+ Support filtering and statistics in spectral domain (see ticket #43252)
Next versions:
- todo for API: ImgReaderGdal (ImgWriterGdal) open in update mode (check
gdal_edit.py: http://searchcode.com/codesearch/view/18938404)
diff --git a/doc/examples_pkextract.dox b/doc/examples_pkextract.dox
index f9a0f51..0ba5b51 100644
--- a/doc/examples_pkextract.dox
+++ b/doc/examples_pkextract.dox
@@ -1,37 +1,42 @@
\section examples_pkextract Examples of pkextract
\code
-pkextract -i input.tif -s points.shp -o extracted.shp
+pkextract -i input.tif -s points.sqlite -o extracted.sqlite
\endcode
-extract the points read in points.shp from input.tif. Create a new point
vector file extracted.shp, where each point will contain an attribute for the
individual input bands in input.tif.
+extract the points read in points.sqlite from input.tif. Create a new point
vector file extracted.sqlite, where each point will contain an attribute for
the individual input bands in input.tif.
\code
-pkextract -i input.tif -s points.shp -m mask.tif -msknodata 255 -o
extracted.shp
+pkextract -i input.tif -s points.shp -f "ESRI Shapefile" -o extracted.shp
\endcode
-extract all bands from input.tif to extracted.shp at pixel locations defined
in points.shp that have not a value 255 in mask.tif
+Same example as above, but vector format is ESRI Shapefile
\code
-pkextract -i input.tif -s polygons.shp -o training.shp -r point
+pkextract -i input.tif -s points.sqlite -m mask.tif -msknodata 255 -o
extracted.sqlite
\endcode
-extract all pixels from input.tif covered by the polygons in locations.shp.
Each polygon can thus result in multiple point features with attributes for
each input band. Write the extracted points to a point vector file training.shp.
+extract all bands from input.tif to extracted.sqlite at pixel locations
defined in points.sqlite that have not a value 255 in mask.tif
\code
-pkextract -i input.tif -b 0 -s polygons.shp -r centroid -o extracted.shp
-polygon
+pkextract -i input.tif -s polygons.sqlite -o training.sqlite -r point
\endcode
-extract the first band from input.tif at the centroids of the polygons in
vector filepolygons.shp. Assign the extracted point value to a new attribute of
the polygon and write to the vector file extracted.shp.
+extract all pixels from input.tif covered by the polygons in locations.sqlite.
Each polygon can thus result in multiple point features with attributes for
each input band. Write the extracted points to a point vector file
training.sqlite.
\code
-pkextract -i input.tif -b 1 -s polygons.shp -r mean -o extracted.shp -polygon
+pkextract -i input.tif -b 0 -s polygons.sqlite -r centroid -o extracted.sqlite
-polygon
\endcode
-extract the mean values for the second band in input.tif covered by each
polygon in polygons.shp. The mean values are written to a copy of the polygons
in output vector file extracted.shp
+extract the first band from input.tif at the centroids of the polygons in
vector filepolygons.sqlite. Assign the extracted point value to a new attribute
of the polygon and write to the vector file extracted.sqlite.
\code
-pkextract -i input.tif -s sample.tif -o extracted.shp -t 10 -c 1 -c 2 -c 3
+pkextract -i input.tif -b 1 -s polygons.sqlite -r mean -o extracted.sqlite
-polygon
\endcode
-Typical use where pixels are extracted based on a land cover map (sample.tif).
Extract all bands for a random sample of 10 percent of the pixels in the land
cover map sample.tif where the land cover classes are either 1,2 or 3 (class
values). Write output to the point vector file extracted.shp.
+extract the mean values for the second band in input.tif covered by each
polygon in polygons.sqlite. The mean values are written to a copy of the
polygons in output vector file extracted.sqlite
\code
-pkextract -i input.tif -s sample.tif -o extracted.shp -t -5000 -c 1
+pkextract -i input.tif -s sample.tif -o extracted.sqlite -t 10 -c 1 -c 2 -c 3
\endcode
-extract all bands for the first 5000 pixels encountered in sample.tif where
pixels have a value equal to 1. Write output to point vector file extracted.shp.
+Typical use where pixels are extracted based on a land cover map (sample.tif).
Extract all bands for a random sample of 10 percent of the pixels in the land
cover map sample.tif where the land cover classes are either 1,2 or 3 (class
values). Write output to the point vector file extracted.sqlite.
+
+\code
+pkextract -i input.tif -s sample.tif -o extracted.sqlite -t -5000 -c 1
+\endcode
+extract all bands for the first 5000 pixels encountered in sample.tif where
pixels have a value equal to 1. Write output to point vector file
extracted.sqlite.
diff --git a/src/algorithms/Filter.cc b/src/algorithms/Filter.cc
index 58080bd..ee48822 100644
--- a/src/algorithms/Filter.cc
+++ b/src/algorithms/Filter.cc
@@ -338,10 +338,14 @@ void filter::Filter::stat(const ImgReaderGdal& input,
ImgWriterGdal& output, con
double progress=0;
pfnProgress(progress,pszMessage,pProgressArg);
for(int y=0;y<input.nrOfRow();++y){
+ if((y+1+down/2)%down)
+ continue;
for(int iband=0;iband<input.nrOfBand();++iband)
input.readData(lineInput[iband],GDT_Float64,y,iband);
vector<double> pixelInput(input.nrOfBand());
for(int x=0;x<input.nrOfCol();++x){
+ if((x+1+down/2)%down)
+ continue;
pixelInput=lineInput.selectCol(x);
switch(getFilterType(method)){
case(filter::median):
@@ -354,11 +358,14 @@ void filter::Filter::stat(const ImgReaderGdal& input,
ImgWriterGdal& output, con
lineOutput[(x-offset+down-1)/down]=stat.mymax(pixelInput);
break;
case(filter::sum):
- lineOutput[(x-offset+down-1)/down]=sqrt(stat.sum(pixelInput));
+ lineOutput[(x-offset+down-1)/down]=stat.sum(pixelInput);
break;
case(filter::var):
lineOutput[(x-offset+down-1)/down]=stat.var(pixelInput);
break;
+ case(filter::stdev):
+ lineOutput[(x-offset+down-1)/down]=sqrt(stat.var(pixelInput));
+ break;
case(filter::mean):
lineOutput[(x-offset+down-1)/down]=stat.mean(pixelInput);
break;
@@ -369,10 +376,10 @@ void filter::Filter::stat(const ImgReaderGdal& input,
ImgWriterGdal& output, con
}
}
try{
- output.writeData(lineOutput,GDT_Float64,y);
+ output.writeData(lineOutput,GDT_Float64,y/down);
}
catch(string errorstring){
- cerr << errorstring << "in line " << y << endl;
+ cerr << errorstring << "in line " << y/down << endl;
}
progress=(1.0+y)/output.nrOfRow();
pfnProgress(progress,pszMessage,pProgressArg);
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