Skip to content

Commit

Permalink
[GEOS-9068] Allow SLDService to limit classification to a given numbe…
Browse files Browse the repository at this point in the history 
…r of standard deviations from the average
  • Loading branch information
@aaime
aaime committed Dec 19, 2018
1 parent 788b791 commit ceb9d0a
Show file tree
Hide file tree
Showing 5 changed files with 397 additions and 36 deletions.
4 changes: 4 additions & 0 deletions doc/en/user/source/extensions/sldservice/index.rst
View file
Original file line number Diff line number Diff line change
Expand Up @@ -205,6 +205,10 @@ The parameters usable to customize the ColorMap are:
- allows to run the classification on a specific bounding box. Recommended when the overall dataset is too big, and the classification can be performed on a smaller dataset, or to enhance the visualization of a particular subset of data
- same syntax as WMS/WFS, expected axis order is east/north unless the spatial reference system is explicitly provided, ``minx,miny,max,maxy[,srsName]``
-
* - stddevs
- limits the data the classifier is working on to a range of "stddevs" standard deviations around the mean value.
- a positive floating point number (e.g., '1', '2.5', '3').
-

Examples
~~~~~~~~~~
Expand Down
66 changes: 62 additions & 4 deletions ...xtension/sldService/src/main/java/org/geoserver/sldservice/rest/ClassifierController.java
View file
Original file line number Diff line number Diff line change
Expand Up @@ -54,6 +54,8 @@
import org.geotools.data.util.NullProgressListener;
import org.geotools.factory.CommonFactoryFinder;
import org.geotools.feature.FeatureCollection;
import org.geotools.feature.visitor.CalcResult;
import org.geotools.feature.visitor.StandardDeviationVisitor;
import org.geotools.filter.function.RangedClassifier;
import org.geotools.geometry.jts.ReferencedEnvelope;
import org.geotools.image.util.ImageUtilities;
Expand All @@ -68,6 +70,7 @@
import org.geotools.styling.Style;
import org.geotools.styling.StyledLayerDescriptor;
import org.geotools.util.Converters;
import org.geotools.util.NumberRange;
import org.geotools.util.factory.Hints;
import org.geotools.util.logging.Logging;
import org.geotools.xml.styling.SLDTransformer;
Expand All @@ -81,6 +84,7 @@
import org.opengis.feature.type.PropertyDescriptor;
import org.opengis.filter.Filter;
import org.opengis.filter.FilterFactory2;
import org.opengis.filter.PropertyIsBetween;
import org.opengis.filter.spatial.BBOX;
import org.opengis.referencing.FactoryException;
import org.opengis.referencing.operation.TransformException;
Expand Down Expand Up @@ -163,6 +167,7 @@ public Object classify(
@RequestParam(value = "continuous", required = false, defaultValue = "false")
boolean continuous,
@RequestParam(value = "bbox", required = false) ReferencedEnvelope bbox,
@RequestParam(value = "stddevs", required = false) Double stddevs,
final HttpServletResponse response)
throws Exception {
LayerInfo layerInfo = catalog.getLayerByName(layerName);
Expand All @@ -173,6 +178,10 @@ public Object classify(
if (bbox != null && bbox.getCoordinateReferenceSystem() == null) {
bbox = new ReferencedEnvelope(bbox, layerInfo.getResource().getCRS());
}
if (stddevs != null && stddevs <= 0) {
throw new RestException(
"stddevs must be a positive floating point number", HttpStatus.BAD_REQUEST);
}
if (cachingTime > 0) {
response.setHeader(
"cache-control",
Expand Down Expand Up @@ -208,7 +217,8 @@ public Object classify(
pointSize,
(FeatureTypeInfo) obj,
ramp,
bbox);
bbox,
stddevs);
} else if (obj instanceof CoverageInfo) {
rules =
getRasterRules(
Expand All @@ -223,7 +233,8 @@ public Object classify(
(CoverageInfo) obj,
ramp,
continuous,
bbox);
bbox,
stddevs);
} else {
throw new RestException(
"The classifier can only work against vector or raster data, "
Expand Down Expand Up @@ -369,7 +380,8 @@ private List<Rule> getRasterRules(
CoverageInfo coverageInfo,
ColorRamp ramp,
boolean continuous,
ReferencedEnvelope bbox)
ReferencedEnvelope bbox,
Double stddevs)
throws Exception {
int selectedBand = getRequestedBand(property); // one based band name
// read the image to be classified
Expand All @@ -384,6 +396,7 @@ private List<Rule> getRasterRules(
RenderedImage image = imageReader.getImage();

RasterSymbolizerBuilder builder = new RasterSymbolizerBuilder();
builder.setStandardDeviations(stddevs);
ColorMap colorMap;
try {
if (customClasses.isEmpty()) {
Expand Down Expand Up @@ -477,7 +490,8 @@ private List<Rule> getVectorRules(
int pointSize,
FeatureTypeInfo obj,
ColorRamp ramp,
ReferencedEnvelope bbox)
ReferencedEnvelope bbox,
Double stddevs)
throws IOException, TransformException, FactoryException {
if (property == null || property.isEmpty()) {
throw new IllegalArgumentException(
Expand All @@ -502,6 +516,25 @@ private List<Rule> getVectorRules(
query.setHints(getQueryHints(viewParams));
ftCollection =
obj.getFeatureSource(new NullProgressListener(), null).getFeatures(query);

if (stddevs != null) {
NumberRange stdDevRange =
getStandardDeviationsRange(property, ftCollection, stddevs);
PropertyIsBetween between =
FF.between(
FF.property(property),
FF.literal(stdDevRange.getMinimum()),
FF.literal(stdDevRange.getMaximum()));
if (query.getFilter() == Filter.INCLUDE) {
query.setFilter(between);
} else {
query.setFilter(FF.and(query.getFilter(), between));
}

// re-query
ftCollection =
obj.getFeatureSource(new NullProgressListener(), null).getFeatures(query);
}
}

List<Rule> rules = null;
Expand Down Expand Up @@ -586,6 +619,31 @@ else if (geomT == MultiPolygon.class || geomT == Polygon.class) {
return rules;
}

/**
* Returns a range of N standard deviations around the mean for the given attribute and
* collection
*/
private NumberRange getStandardDeviationsRange(
String property, FeatureCollection features, double numStandardDeviations)
throws IOException {
final StandardDeviationVisitor standardDeviationVisitor =
new StandardDeviationVisitor(FF.property(property));
features.accepts(standardDeviationVisitor, null);
final double mean = standardDeviationVisitor.getMean();
final CalcResult result = standardDeviationVisitor.getResult();
if (result.getValue() == null) {
throw new RestException(
"The standard deviation visit did not find any value, the dataset is empty or previous filters removed all values",
HttpStatus.BAD_REQUEST);
}
final double standardDeviation = standardDeviationVisitor.getResult().toDouble();

return new NumberRange(
Double.class,
mean - standardDeviation * numStandardDeviations,
mean + standardDeviation * numStandardDeviations);
}

private ColorRamp getColorRamp(
String customClasses,
String colorRamp,
Expand Down
98 changes: 75 additions & 23 deletions ...vice/src/main/java/org/geoserver/sldservice/utils/classifier/RasterSymbolizerBuilder.java
View file
Original file line number Diff line number Diff line change
Expand Up @@ -6,10 +6,13 @@

import static java.util.Locale.ENGLISH;

import it.geosolutions.jaiext.JAIExt;
import it.geosolutions.jaiext.classbreaks.ClassBreaksDescriptor;
import it.geosolutions.jaiext.classbreaks.ClassBreaksRIF;
import it.geosolutions.jaiext.classbreaks.Classification;
import it.geosolutions.jaiext.classbreaks.ClassificationMethod;
import it.geosolutions.jaiext.stats.Statistics;
import it.geosolutions.jaiext.stats.Statistics.StatsType;
import java.awt.*;
import java.awt.image.DataBuffer;
import java.awt.image.RenderedImage;
Expand All @@ -19,14 +22,17 @@
import java.util.List;
import java.util.Optional;
import javax.media.jai.Histogram;
import javax.media.jai.JAI;
import javax.media.jai.ParameterBlockJAI;
import javax.media.jai.RenderedOp;
import org.geotools.factory.CommonFactoryFinder;
import org.geotools.filter.function.RangedClassifier;
import org.geotools.image.ImageWorker;
import org.geotools.styling.ColorMap;
import org.geotools.styling.ColorMapEntry;
import org.geotools.styling.StyleFactory;
import org.geotools.util.Converters;
import org.geotools.util.NumberRange;
import org.geotools.util.factory.GeoTools;
import org.opengis.filter.FilterFactory2;

Expand Down Expand Up @@ -57,6 +63,7 @@ public class RasterSymbolizerBuilder {
Integer.getInteger("org.geoserver.sldService.maxPixels", 4194304);

private long maxPixels;
private Double standardDeviations;

/**
* Builds the {@link RasterSymbolizerBuilder} with a given pixel reading threshold before
Expand All @@ -83,29 +90,26 @@ public RasterSymbolizerBuilder() {
* exception should be thrown
*/
public ColorMap uniqueIntervalClassification(RenderedImage image, Integer maxIntervals) {
// compute min and max, for the common case avoid doing an extrema a use a pre-defined range
// instead
int low, high;
int dataType = image.getSampleModel().getDataType();
ImageWorker iw = getImageWorker(image);
switch (dataType) {
case DataBuffer.TYPE_BYTE:
low = 0;
high = 255;
break;
// The histogram can be very expensive memory wise as it's backed by a
// AtomicDouble[],
// check how many
case DataBuffer.TYPE_USHORT:
case DataBuffer.TYPE_SHORT:
case DataBuffer.TYPE_INT:
low = (int) iw.getMinimums()[0];
high = (int) iw.getMaximums()[0];
break;
default:
throw new IllegalArgumentException(
"Cannot perform unique value classification over rasters of float type, only integer numbers are supported. Try a classification by intervals or quantiles instead");

// compute min and max, for the common case avoid doing an extrema or use a pre-defined
// range instead
if (dataType == DataBuffer.TYPE_BYTE && standardDeviations == null) {
low = 0;
high = 255;
} else if (dataType == DataBuffer.TYPE_DOUBLE || dataType == DataBuffer.TYPE_FLOAT) {
throw new IllegalArgumentException(
"Cannot perform unique value classification over rasters of float type, only integer numbers are supported. Try a classification by intervals or quantiles instead");
} else {
final NumberRange range = getOperationRange(iw);
low = (int) range.getMinimum();
high = (int) range.getMaximum();
}

// The histogram can be very expensive memory wise as it's backed by a
// AtomicDouble[], check how many are they going to be
if (high - low > MAX_UNIQUE_VALUES) {
throw new IllegalArgumentException(
"Cannot perform unique value classification over rasters with a potential range of values greater than "
Expand Down Expand Up @@ -181,8 +185,9 @@ ImageWorker getImageWorker(RenderedImage image) {
public ColorMap equalIntervalClassification(
RenderedImage image, int intervals, boolean open, boolean continuous) {
ImageWorker iw = getImageWorker(image);
double low = iw.getMinimums()[0];
double high = iw.getMaximums()[0];
final NumberRange range = getOperationRange(iw);
double low = (int) range.getMinimum();
double high = (int) range.getMaximum();

Number[] breaks = new Number[continuous ? intervals : intervals + 1];
double step = (high - low) / (continuous ? (intervals - 1) : intervals);
Expand Down Expand Up @@ -324,9 +329,10 @@ private Number[] getClassificationBreaks(
pb.set(iw.getYPeriod(), 6);
pb.set(noData, 7);
if (numHistogramBins > 0) {
final NumberRange range = getOperationRange(iw);
Double[][] extrema = new Double[2][1];
extrema[0][0] = iw.getMinimums()[0];
extrema[1][0] = iw.getMaximums()[0];
extrema[0][0] = range.getMinimum();
extrema[1][0] = range.getMaximum();
pb.set(extrema, 2);
pb.set(true, 8);
pb.set(numHistogramBins, 9);
Expand Down Expand Up @@ -379,4 +385,50 @@ public ColorMap createCustomColorMap(

return getColorMapFromBreaks(breaks, open, continuous);
}

public void setStandardDeviations(Double standardDeviations) {
this.standardDeviations = standardDeviations;
}

private NumberRange getOperationRange(ImageWorker iw) {
if (standardDeviations == null) {
double min = iw.getMinimums()[0];
double max = iw.getMaximums()[0];
return new NumberRange(Double.class, min, max);
} else {
// Create the parameterBlock
ParameterBlock pb = new ParameterBlock();
pb.setSource(iw.getRenderedImage(), 0);
if (JAIExt.isJAIExtOperation("Stats")) {
StatsType[] stats =
new StatsType[] {StatsType.MEAN, StatsType.DEV_STD, StatsType.EXTREMA};

// Image parameters
pb.set(iw.getXPeriod(), 0); // xPeriod
pb.set(iw.getYPeriod(), 1); // yPeriod
pb.set(iw.getROI(), 2); // ROI
pb.set(iw.getNoData(), 3); // NoData
pb.set(stats, 6); // statistic operation
final RenderedOp statsImage = JAI.create("Stats", pb, iw.getRenderingHints());
// Retrieving the statistics
Statistics[][] results =
(Statistics[][]) statsImage.getProperty(Statistics.STATS_PROPERTY);
double mean = (double) results[0][0].getResult();
double stddev = (double) results[0][1].getResult();
double[] extrema = (double[]) results[0][2].getResult();
double min = extrema[0];
double max = extrema[1];
// return a range centered in the mean with the desired number of standard
// deviations, but make sure it does not exceed the data minimim and maximums
return new NumberRange(
Double.class,
Math.max(mean - stddev * standardDeviations, min),
Math.min(mean + stddev * standardDeviations, max));
} else {
// the op should be unique to jai-ext but best be careful
throw new IllegalArgumentException(
"Stats image operation is not backed by JAIExt, please enable JAIExt");
}
}
}
}

0 comments on commit ceb9d0a

Please sign in to comment.