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commit bc172d651b79f0f9e01a89259ef23636a587cab8
Author: Pieter Kempeneers <kempe...@gmail.com>
Date: Sat Nov 8 23:24:12 2014 +0100
working on documentation, still need to (re-)do the full description
---
doc/createappsdox.sh | 6 ++++++
doc/examples_pkdumpimg.dox | 29 +++++++++++++++++++++++++++--
doc/examples_pkdumpogr.dox | 42 ++++++++++++++++++++++++++++++++++++------
doc/examples_pkextract.dox | 37 ++++++++++++++++++++++++-------------
doc/examples_pkfilter.dox | 17 +++++++++++++----
src/apps/pkdumpimg.cc | 21 ++++++++++++++-------
6 files changed, 120 insertions(+), 32 deletions(-)
diff --git a/doc/createappsdox.sh b/doc/createappsdox.sh
index 4239b2a..c497a7e 100755
--- a/doc/createappsdox.sh
+++ b/doc/createappsdox.sh
@@ -50,6 +50,12 @@ for file in ../src/apps/pk*.cc;do
echo "- \\ref ${THETOOL} ${THESHORTDESCRIPTION}";
done >> ../doc/apps.dox
+#remove depricated utilities and those not ready to publish"
+
+for TOOL in pkeditogr pkenhance pkkalman pkndvi pkreclass; do
+ rm ../doc/${TOOL}.dox ../html/md_doc_${TOOL}.html
../html/${TOOL}_8cc_source.html
+ sed -i "/${TOOL}/d" ../doc/apps.dox
+done
echo "Savannah repository for homepage can only be maintained via cvs"
#mkdir ~/tmp
#cd ~/tmp
diff --git a/doc/examples_pkdumpimg.dox b/doc/examples_pkdumpimg.dox
index dc04cb2..c2fe0c1 100644
--- a/doc/examples_pkdumpimg.dox
+++ b/doc/examples_pkdumpimg.dox
@@ -1,8 +1,33 @@
\section examples_pkdumpimg Examples of pkdumpimg
-Dump the image content of tiny.tif to screen
+We create a 5x5 raster dataset containing all 0 values except for its center
(value=1)
\code
-pkdumpimg -i tiny.tif
+cat input.txt
+
+0 0 0 0 0
+0 0 0 0 0
+0 0 1 0 0
+0 0 0 0 0
+0 0 0 0 0
+\endcode
+
+Use \ref pkascii2img "pkascii2img" to create an raster dataset from input.txt
+
+\code
+pkascii2img -i input.txt -o output.tif -of GTiff -a_srs epsg:3035 -dx 25 -dy
25 -ulx 1000000 -uly 1000000
\endcode
+Dump the image content of output.tif to screen
+
+\code
+pkdumpimg -i output.tif
+\endcode
+
+\code
+0 0 0 0 0
+0 0 0 0 0
+0 0 1 0 0
+0 0 0 0 0
+0 0 0 0 0
+\endcode
diff --git a/doc/examples_pkdumpogr.dox b/doc/examples_pkdumpogr.dox
index cf61c3c..e869e47 100644
--- a/doc/examples_pkdumpogr.dox
+++ b/doc/examples_pkdumpogr.dox
@@ -1,15 +1,45 @@
\section examples_pkdumpogr Examples of pkdumpogr
+
+Dump the attributes label and description for the vector dataset sample.sqlite
to standard output (screen)
+
+\code
+pkdumpogr -i sample.sqlite -n label -n description
+\endcode
+
+\code
+0 2 noforest
+1 2 noforest
+2 2 noforest
+3 2 noforest
+4 2 noforest
+5 2 noforest
+6 2 noforest
+7 2 noforest
+8 2 noforest
+9 2 noforest
+10 1 forest
+11 2 noforest
+\endcode
+
+Similar to previous example, but transpose output (does not work without
explicitly defining options -n for attributes)
+
\code
-pkdumpogr -i input.shp
+pkdumpogr -i sample.sqlite -n label -n description -t
\endcode
-dump entire content of vector file to standard output
\code
-pkdumpogr -i input.shp -o output.txt -n ATTRIBUTE -pos
+0 2 2 2 2 2 2 2 2 2 2 1 2
+1 noforest noforest noforest noforest noforest noforest noforest noforest
noforest noforest forest noforest
\endcode
-dump only ATTRIBUTE of vector file including x and y position and output to
ASCII file
+
+Dump the entire content of vector dataset input.sqlite to standard output
+
+\code
+pkdumpogr -i input.sqlite
+\endcode
+
+Dump only ATTRIBUTE of vector dataset input.sqlite including x and y position
and output to ASCII file output.txt
\code
-pkdumpogr -i input.shp -o output.txt -n ATTRIBUTE1 -n ATTRIBUTE2 -t
+pkdumpogr -i input.sqlite -o output.txt -n ATTRIBUTE -pos
\endcode
-dump the two attributes ATTRIBUTE1 and ATTRIBUTE2 of vector file and transpose
output
diff --git a/doc/examples_pkextract.dox b/doc/examples_pkextract.dox
index 384d3d9..f9c685c 100644
--- a/doc/examples_pkextract.dox
+++ b/doc/examples_pkextract.dox
@@ -1,68 +1,79 @@
\section examples_pkextract Examples of pkextract
\subsection example_pkextract_vector Using vector samples
+
+Extract all points for all layers read in points.sqlite from input.tif. Create
a new point vector dataset named extracted.sqlite, where each point will
contain an attribute for the individual input bands in input.tif. Notice that
the default vector format is Spatialite (.sqlite).
+
\code
pkextract -i input.tif -s points.sqlite -o extracted.sqlite
\endcode
-Extract all points for all layers read in points.sqlite from input.tif. Create
a new point vector dataset named extracted.sqlite, where each point will
contain an attribute for the individual input bands in input.tif. Notice that
the default vector format is Spatialite (.sqlite).
+
+Same example as above, but only extract the points for the layer in
points.sqlite named "valid"
\code
pkextract -i input.tif -s points.sqlite -ln valid -o extracted.sqlite
\endcode
-Same example as above, but only extract the points for the layer in
points.sqlite named "valid"
+
+Extract points and write output in ESRI Shapefile format
\code
pkextract -i input.tif -s points.shp -f "ESRI Shapefile" -o extracted.shp
\endcode
-Extract points and write output in ESRI Shapefile format
+
+Extract the standard deviation for each input band in a 3 by 3 window,
centered around the points in the sample vector dataset points.sqlite. The
output vector dataset will contain polygon features defined by the buffered
points (3x3 window). Use the option -circ to define a circular buffer.
\code
pkextract -i input.tif -s points.sqlite -o extracted.sqlite -r stdev -buf 3
-polygon
\endcode
-Extract the standard deviation for each input band in a 3 by 3 window,
centered around the points in the sample vector dataset points.sqlite. The
output vector dataset will contain polygon features defined by the buffered
points (3x3 window). Use the option -circ to define a circular buffer.
+
+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 dataset
training.sqlite.
\code
pkextract -i input.tif -s polygons.sqlite -o training.sqlite -r point
\endcode
-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 dataset
training.sqlite.
+
+Extract the first band from input.tif at the centroids of the polygons in
vector dataset polygons.sqlite. Assign the extracted point value to a new
attribute of the polygon and write to the vector dataset extracted.sqlite.
\code
pkextract -i input.tif -b 0 -s polygons.sqlite -r centroid -o extracted.sqlite
-polygon
\endcode
-Extract the first band from input.tif at the centroids of the polygons in
vector dataset polygons.sqlite. Assign the extracted point value to a new
attribute of the polygon and write to the vector dataset extracted.sqlite.
+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 dataset extracted.sqlite
\code
pkextract -i input.tif -b 1 -s polygons.sqlite -r mean -o extracted.sqlite
-polygon
\endcode
-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 dataset extracted.sqlite
+
+Extract the majority class in each polygon for the input land cover map. The
land cover map contains five valid classes, labeled 1-5. Other class values
(e.g., labeled as 0) are not taken into account in the voting.
\code
pkextract -i landcover.tif -s polygons.sqlite -r maxvote -o majority.sqlite
-polygon -c 1 -c 2 -c 3 -c 4 -c 5
\endcode
-Extract the majority class in each polygon for the input land cover map. The
land cover map contains five valid classes, labeled 1-5. Other class values
(e.g., labeled as 0) are not taken into account in the voting.
\subsection example_pkextract_random Using random and grid samples
+Extract 100 sample units following a simple random sampling design. For each
sample unit, the median value is extracted from the input raster dataset in a
window of 3 by 3 pixels and written to an attribute of the output vector
dataset. The output vector dataset contains polygon features defined by the
windows centered at the random selected sample units.
+
\code
pkextract -i input.tif -o random.sqlite -rand 100 -median -buf 3 -polygon
\endcode
-Extract 100 sample units following a simple random sampling design. For each
sample unit, the median value is extracted from the input raster dataset in a
window of 3 by 3 pixels and written to an attribute of the output vector
dataset. The output vector dataset contains polygon features defined by the
windows centered at the random selected sample units.
+
+Extract points following a systematic grid with grid cell size of 100 m.
Discard pixels that have a value 0 in the input raster dataset.
\code
pkextract -i input.tif -o systematic.sqlite -grid 100 -srcnodata 0
\endcode
-Extract points following a systematic grid with grid cell size of 100 m.
Discard pixels that have a value 0 in the input raster dataset.
\subsection example_pkextract_raster Using raster samples
+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 dataset extracted.sqlite.
+
\code
pkextract -i input.tif -s sample.tif -o extracted.sqlite -t 10 -c 1 -c 2 -c 3
\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 dataset extracted.sqlite.
+
+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 dataset
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 dataset
extracted.sqlite.
-
diff --git a/doc/examples_pkfilter.dox b/doc/examples_pkfilter.dox
index 7515b23..e054293 100644
--- a/doc/examples_pkfilter.dox
+++ b/doc/examples_pkfilter.dox
@@ -1,21 +1,30 @@
\section examples_pkfilter Examples of pkfilter
+
+\subsection examples_pkfilter_spatial Filtering in spatial domain
+
+Filter input.tif with morphological dilation filter. Use a circular kernel
(instead of rectangular) of size 3x3.
+
\code
pkfilter -i input.tif -o filter.tif -dx 3 -dy 3 -f dilate -c
\endcode
-filter input.tif with morphological dilation filter. Use a circular kernel
(instead of rectangular) of size 3x3.
+
+Similar to previous example, but consider only values of 255 for filtering
operation. Typical usage: dilate cloud values in input image that are flagged
as 255
\code
pkfilter -i input.tif -o filter.tif -dx 3 -dy 3 -class 255 -f dilate -c
\endcode
-Similar to previous example, but consider only values of 255 for filtering
operation. Typical usage: dilate cloud values in input image that are flagged
as 255
+
+\subsection examples_pkfilter_spectral Filtering in spectral/temporal domain
+
+Filtering in spectral/temporal domain. Calculate the median value for each
pixel, calculated on a moving window of width 3 (-dz 3) over all input bands.
The output raster dataset will contain as many bands as the input raster
dataset.
\code
pkfilter -i input.tif -o filter_stdev.tif -dz 3 -f median
\endcode
-Filtering in spectral/temporal domain. Calculate the median value for each
pixel, calculated on a moving window of width 3 (-dz 3) over all input bands.
The output raster dataset will contain as many bands as the input raster
dataset.
+
+"Filtering" in spectral/temporal domain. No moving window (-dz 1). Calculate
the standard deviation for each pixel, calculated on all input bands. The
output raster dataset will contain a single band only.
\code
pkfilter -i input.tif -o filter_stdev.tif -dz 1 -f stdev
\endcode
-"Filtering" in spectral/temporal domain. No moving window (-dz 1). Calculate
the standard deviation for each pixel, calculated on all input bands. The
output raster dataset will contain a single band only.
diff --git a/src/apps/pkdumpimg.cc b/src/apps/pkdumpimg.cc
index 3cae28f..6253e63 100644
--- a/src/apps/pkdumpimg.cc
+++ b/src/apps/pkdumpimg.cc
@@ -48,7 +48,7 @@ int main(int argc, char *argv[])
Optionpk<double> dx_opt("dx", "dx", "Output resolution in x (in meter) (0.0:
keep original resolution)",0.0);
Optionpk<double> dy_opt("dy", "dy", "Output resolution in y (in meter) (0.0:
keep original resolution)",0.0);
Optionpk<string> resample_opt("r", "resampling-method", "Resampling method
(near: nearest neighbour, bilinear: bi-linear interpolation).", "near");
- Optionpk<short> dstnodata_opt("dstnodata", "dstnodata", "nodata value for
ouptut if out of bounds.", 0);
+ Optionpk<short> dstnodata_opt("dstnodata", "dstnodata", "nodata value for
output if out of bounds.", 0);
Optionpk<double> srcnodata_opt("srcnodata", "srcnodata", "set no data
value(s) for input image");
Optionpk<short> verbose_opt("v", "verbose", "verbose (Default: 0)", 0);
@@ -81,7 +81,7 @@ int main(int argc, char *argv[])
}
ofstream outputStream;
- if(!output_opt[0].empty())
+ if(!output_opt.empty())
outputStream.open(output_opt[0].c_str());
RESAMPLE theResample;
@@ -100,14 +100,19 @@ int main(int argc, char *argv[])
exit(1);
}
- ImgWriterGdal imgWriter;
+ // ImgWriterGdal imgWriter;
GDALDataType theType;
+ if(input_opt.empty()){
+ std::cerr << "No input file provided (use option -i). Use --help for help
information" << std::endl;
+ exit(0);
+ }
+
ImgReaderGdal imgReader(input_opt[0]);
for(int inodata=0;inodata<srcnodata_opt.size();++inodata)
imgReader.pushNoDataValue(srcnodata_opt[inodata]);
- ImgWriterGdal virtualWriter;//only for coordinate conversion (no output file
defined)
+ // ImgWriterGdal virtualWriter;//only for coordinate conversion (no output
file defined)
int nband=imgReader.nrOfBand();
//get number of lines
@@ -206,7 +211,7 @@ int main(int argc, char *argv[])
}
double gt[6];
imgReader.getGeoTransform(gt);
- imgWriter.setGeoTransform(gt);
+ // imgWriter.setGeoTransform(gt);
// imgWriter.setProjection(imgReader.getProjection());
double readRow=0;
@@ -231,7 +236,8 @@ int main(int argc, char *argv[])
double x=0;
double y=0;
//convert irow to geo
- imgWriter.image2geo(0,irow,x,y);
+ // imgWriter.image2geo(0,irow,x,y);
+ imgReader.image2geo(0,irow,x,y);
//lookup corresponding row for irow in this file
imgReader.geo2image(x,y,readCol,readRow);
if(readRow<0||readRow>=imgReader.nrOfRow()){
@@ -247,7 +253,8 @@ int main(int argc, char *argv[])
else
imgReader.readData(readBuffer,GDT_Float64,startCol,endCol,readRow,band_opt[iband],theResample);
for(int ib=0;ib<ncropcol;++ib){
- assert(imgWriter.image2geo(ib,irow,x,y));
+ // assert(imgWriter.image2geo(ib,irow,x,y));
+ assert(imgReader.image2geo(ib,irow,x,y));
//lookup corresponding row for irow in this file
imgReader.geo2image(x,y,readCol,readRow);
if(readCol<0||readCol>=imgReader.nrOfCol()){
--
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