Merge pull request #1046 from pierotofy/pcres

Added --pc-sample

Former-commit-id: 7f882493c6

modules/odm_filterpoints/src/main.cpp

@@ -2,6 +2,7 @@
 #include <algorithm>
 #include <pdal/filters/OutlierFilter.hpp>
 #include <pdal/filters/RangeFilter.hpp>
+#include <pdal/filters/SampleFilter.hpp>
 #include "CmdLineParser.h"
 #include "Logger.h"
 #include "FloatPlyReader.hpp"
@@ -15,12 +16,13 @@ cmdLineParameter< char* >
 cmdLineParameter< float >
     StandardDeviation( "sd" ) ,
     MeanK ( "meank" ) ,
-    Confidence ( "confidence" );
+    Confidence ( "confidence" ) ,
+    Sample ( "sample" );
 cmdLineReadable
 	Verbose( "verbose" );
 
 cmdLineReadable* params[] = {
-    &InputFile , &OutputFile , &StandardDeviation, &MeanK, &Confidence, &Verbose ,
+    &InputFile , &OutputFile , &StandardDeviation, &MeanK, &Confidence, &Sample, &Verbose ,
     NULL
 };
 
@@ -66,33 +68,41 @@ int main(int argc, char **argv) {
 
     pdal::PointTable table;
     pdal::FloatPlyReader plyReader;
+    pdal::Stage *currentStage = &plyReader;
     plyReader.setOptions(inPlyOpts);
 
     pdal::RangeFilter confidenceFilter;
     if (Confidence.set){
+        logWriter("Filtering confidence\n");
+
         pdal::Options confidenceFilterOpts;
         float confidenceValue = std::min(1.0f, std::max(Confidence.value, 0.0f));
         std::ostringstream confidenceLimit;
         confidenceLimit << "confidence[" << confidenceValue << ":1]";
         confidenceFilterOpts.add("limits", confidenceLimit.str());
 
-        confidenceFilter.setInput(plyReader);
+        confidenceFilter.setInput(*currentStage);
+        currentStage = &confidenceFilter;
         confidenceFilter.setOptions(confidenceFilterOpts);
     }
 
+    pdal::SampleFilter sampleFilter;
+    if (Sample.set && Sample.value > 0.0f){
+        logWriter("Radius sampling\n");
+        pdal::Options sampleFilterOpts;
+        sampleFilterOpts.add("radius", Sample.value);
+        sampleFilter.setOptions(sampleFilterOpts);
+        sampleFilter.setInput(*currentStage);
+        currentStage = &sampleFilter;
+    }
+
     pdal::Options outlierOpts;
     outlierOpts.add("method", "statistical");
     outlierOpts.add("mean_k", MeanK.value);
     outlierOpts.add("multiplier", StandardDeviation.value);
 
     pdal::OutlierFilter outlierFilter;
-    if (Confidence.set){
-        logWriter("Filtering confidence\n");
-        outlierFilter.setInput(confidenceFilter);
-    }else{
-        outlierFilter.setInput(plyReader);
-
-    }
+    outlierFilter.setInput(*currentStage);
     outlierFilter.setOptions(outlierOpts);
 
     pdal::Options rangeOpts;

opendm/config.py

@@ -320,6 +320,14 @@ def config():
                         help='Filters the point cloud by removing points that deviate more than N standard deviations from the local mean. Set to 0 to disable filtering.'
                              '\nDefault: '
                              '%(default)s')
+    
+    parser.add_argument('--pc-sample',
+                        metavar='<positive float>',
+                        type=float,
+                        default=0,
+                        help='Filters the point cloud by keeping only a single point around a radius N (in meters). This can be useful to limit the output resolution of the point cloud. Set to 0 to disable sampling.'
+                             '\nDefault: '
+                             '%(default)s')
 
     parser.add_argument('--smrf-scalar',
                         metavar='<positive float>',

opendm/point_cloud.py

@@ -4,18 +4,23 @@ from opendm import log
 from opendm import context
 from opendm.system import run
 
-def filter(input_point_cloud, output_point_cloud, standard_deviation=2.5, meank=16, confidence=None, verbose=False):
+def filter(input_point_cloud, output_point_cloud, standard_deviation=2.5, meank=16, confidence=None, sample_radius=0, verbose=False):
     """
     Filters a point cloud
     """
-    if standard_deviation <= 0 or meank <= 0:
+    if (standard_deviation <= 0 or meank <= 0) and sample_radius <= 0:
         log.ODM_INFO("Skipping point cloud filtering")
         return
 
-    log.ODM_INFO("Filtering point cloud (statistical, meanK {}, standard deviation {})".format(meank, standard_deviation))
+    if standard_deviation > 0 and meank > 0:
+        log.ODM_INFO("Filtering point cloud (statistical, meanK {}, standard deviation {})".format(meank, standard_deviation))
+    
     if confidence:
         log.ODM_INFO("Keeping only points with > %s confidence" % confidence)
 
+    if sample_radius > 0:
+        log.ODM_INFO("Sampling points around a %sm radius" % sample_radius)
+
     if not os.path.exists(input_point_cloud):
         log.ODM_ERROR("{} does not exist, cannot filter point cloud. The program will now exit.".format(input_point_cloud))
         sys.exit(1)
@@ -34,12 +39,15 @@ def filter(input_point_cloud, output_point_cloud, standard_deviation=2.5, meank=
       'meank': meank,
       'verbose': '-verbose' if verbose else '',
       'confidence': '-confidence %s' % confidence if confidence else '',
+      'sample': max(0, sample_radius)
     }
 
     system.run('{bin} -inputFile {inputFile} '
          '-outputFile {outputFile} '
          '-sd {sd} '
-         '-meank {meank} {confidence} {verbose} '.format(**filterArgs))
+         '-meank {meank} '
+         '-sample {sample} '
+         '{confidence} {verbose} '.format(**filterArgs))
 
     # Remove input file, swap temp file
     if not os.path.exists(output_point_cloud):

stages/odm_filterpoints.py

@@ -23,7 +23,11 @@ class ODMFilterPoints(types.ODM_Stage):
             else:
                 inputPointCloud = tree.mve_model
 
-            point_cloud.filter(inputPointCloud, tree.filtered_point_cloud, standard_deviation=args.pc_filter, confidence=None, verbose=args.verbose)
+            point_cloud.filter(inputPointCloud, tree.filtered_point_cloud, 
+                                standard_deviation=args.pc_filter, 
+                                confidence=None, 
+                                sample_radius=args.pc_sample,
+                                verbose=args.verbose)
         else:
             log.ODM_WARNING('Found a valid point cloud file in: %s' %
                             tree.filtered_point_cloud)