fix test and float conversions

Author: berak

modules/ptcloud/doc/ptcloud.bib

@@ -15,10 +15,19 @@ @inproceedings{NI03
   year={2003}
 }
 
-@article{RA13
+@article{USAC13
   title={USAC: A Universal Framework for Random Sample Consensus},
-  author={Raguram, Rahul and Chum,Ondrej and Pollefeys, Marc and Matas,Jiri and Frahm, Jan-Michael},
+  author={Raguram,Rahul and Chum,Ondrej and Pollefeys,Marc and Matas,Jiri and Frahm,Jan-Michael},
   journal={IEEE Transactions on Software Engineering},
   year={2013},
   organization={IEEE}
+}
+
+@article{SPRT05
+  title={Randomized RANSAC with Sequential Probability Ratio Test},
+  author={Matas,Jirı and Chum,Ondrej},
+  booktitle={International Conference on Computer Vision (ICCV)},
+  year={2005},
+  volume={2},
+  pages={1727–1732}
 }

modules/ptcloud/include/opencv2/ptcloud/sac_segmentation.hpp

@@ -94,7 +94,7 @@ class CV_EXPORTS_W SACModelFitting {
     */
     CV_WRAP virtual void set_method_type(int method_type) = 0;
 
-    /** @brief Use Wald's Sequential Probabilistic Ratio Test with ransac fitting
+    /** @brief Use Wald's Sequential Probability Ratio Test with ransac fitting
 
     This will result in less iterations and less evaluated data points, and thus be
     much faster, but it might miss some inliers

modules/ptcloud/misc/python/test/test_sac.py

@@ -14,7 +14,7 @@
 
 class sac_test(NewOpenCVTests):
 
-    def test_plane():
+    def test_plane(self):
         N = 64;
         plane = np.zeros((N,N,3),np.float32)
         for i in range(0,N):

modules/ptcloud/perf/perf_main.cpp

@@ -1,44 +1,6 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2010-2012, Multicoreware, Inc., all rights reserved.
-// Copyright (C) 2010-2012, Advanced Micro Devices, Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors as is and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
 
 #include "perf_precomp.hpp"
 

modules/ptcloud/samples/sac_demo.cpp

@@ -9,26 +9,26 @@ using namespace cv::ptcloud;
 
 
 int main(int argc,char **argv) {
-	CommandLineParser parser (argc,argv,
-		"{help         h |        | print this help}"
-		"{model_type   m | 1      | 1:plane 2:sphere 3:cylinder 4:cluster 0:all}"
-		"{use_sprt     u | false  | use sprt evaluation/termination with non-preemptive ransac}"
-		"{ply          P |        | load a .ply file}"
-		"{threshold    t | 0.0015 | rejection threshold .001 for planes, 0.1 for spheres, 2.5 for cylinders}"
-		"{iters        i | 10000  | ransac iterations}"
-		"{cloud        c | 15     | or'ed synthetic model types to generate 0:none 1:planes 2:sphere 4:cylinder 8:random 16:noise distortion 32:from ply}"
-		"{min_inliers  M | 60     | rejection inlier count}"
-		"{min_distance d | 6      | distance for clustering (partition)}"
-		"{sac_method   s | 0      | SacMethodType (0:RANSAC or 1:MSAC)}"
-		"{preemptive   p | 0      | number of hypotheses for preemptive evaluation. set to 0 to disable}"
-		"{napsac       n | 0      | radius for napsac sampling. set to 0 to disable}"
-		"{max_sphere   S | 50     | (sphere only) reject larger spheres}"
-		"{normal_weight  w | 0.5  | (cylinder only) interpolate between point and normal(dot) distance. setting it to 0 will not use (or generate) normals}"
-		"");
-	if (parser.has("help")) {
+    CommandLineParser parser (argc,argv,
+        "{help         h |        | print this help}"
+        "{model_type   m | 1      | 1:plane 2:sphere 3:cylinder 4:cluster 0:all}"
+        "{use_sprt     u | false  | use sprt evaluation/termination with non-preemptive ransac}"
+        "{ply          P |        | load a .ply file}"
+        "{threshold    t | 0.0015 | rejection threshold .001 for planes, 0.1 for spheres, 2.5 for cylinders}"
+        "{iters        i | 10000  | ransac iterations}"
+        "{cloud        c | 15     | or'ed synthetic model types to generate 0:none 1:planes 2:sphere 4:cylinder 8:random 16:noise distortion 32:from ply}"
+        "{min_inliers  M | 60     | rejection inlier count}"
+        "{min_distance d | 6      | distance for clustering (partition)}"
+        "{sac_method   s | 0      | SacMethodType (0:RANSAC or 1:MSAC)}"
+        "{preemptive   p | 0      | number of hypotheses for preemptive evaluation. set to 0 to disable}"
+        "{napsac       n | 0      | radius for napsac sampling. set to 0 to disable}"
+        "{max_sphere   S | 50     | (sphere only) reject larger spheres}"
+        "{normal_weight  w | 0.5  | (cylinder only) interpolate between point and normal(dot) distance. setting it to 0 will not use (or generate) normals}"
+        "");
+    if (parser.has("help")) {
         parser.printMessage();
         return 0;
-	}
+    }
     int typ = parser.get<int>("model_type");
     int iters = parser.get<int>("iters");
     int elems = parser.get<int>("cloud");
@@ -43,75 +43,75 @@ int main(int argc,char **argv) {
     bool sprt = parser.get<bool>("use_sprt");
     string ply = parser.get<string>("ply");
 
-	Mat_<Point3f> pts;
-	if (! ply.empty()) {
-	    pts = cv::ppf_match_3d::loadPLYSimple(ply.c_str(), false);
-	}
-	if (elems & 1) {
-		generatePlane(pts, vector<double>{0.1,.1,.8, .76}, 64);
-		generatePlane(pts, vector<double>{-.2,-.7,.3, .16}, 64);
-	}
-	if (elems & 2) {
-		generateSphere(pts, vector<double>{12,15,-12, 5}, 128);
-		generateSphere(pts, vector<double>{-12,15,-12, 5}, 128);
-	}
-	if (elems & 4) {
-		generateCylinder(pts, vector<double>{-12,-15,-12, 0,0,1, 5}, 256);
-		generateCylinder(pts, vector<double>{20,5,12, 0,1,0, 5}, 256);
-	}
-	if (elems & 8) {
-		generateRandom(pts, vector<double>{-12,31,-5, 5}, 32);
-		generateRandom(pts, vector<double>{12,-21,15, 5}, 32);
-		generateRandom(pts, vector<double>{1,-2,1, 25}, 64);
-	}
-	if (elems & 16) {
-		Mat fuzz(pts.size(), pts.type());
-		float F = 0.001;
-		randu(fuzz,Scalar(-F,-F,-F), Scalar(F,F,F));
-		pts += fuzz;
-	}
-	cout << pts.size() << " points." << endl;
-	cv::ppf_match_3d::writePLY(pts, "cloud.ply");
+    Mat_<Point3f> pts;
+    if (! ply.empty()) {
+        pts = cv::ppf_match_3d::loadPLYSimple(ply.c_str(), false);
+    }
+    if (elems & 1) {
+        generatePlane(pts, vector<double>{0.1,.1,.8, .76}, 64);
+        generatePlane(pts, vector<double>{-.2,-.7,.3, .16}, 64);
+    }
+    if (elems & 2) {
+        generateSphere(pts, vector<double>{12,15,-12, 5}, 128);
+        generateSphere(pts, vector<double>{-12,15,-12, 5}, 128);
+    }
+    if (elems & 4) {
+        generateCylinder(pts, vector<double>{-12,-15,-12, 0,0,1, 5}, 256);
+        generateCylinder(pts, vector<double>{20,5,12, 0,1,0, 5}, 256);
+    }
+    if (elems & 8) {
+        generateRandom(pts, vector<double>{-12,31,-5, 5}, 32);
+        generateRandom(pts, vector<double>{12,-21,15, 5}, 32);
+        generateRandom(pts, vector<double>{1,-2,1, 25}, 64);
+    }
+    if (elems & 16) {
+        Mat fuzz(pts.size(), pts.type());
+        float F = 0.001f;
+        randu(fuzz,Scalar(-F,-F,-F), Scalar(F,F,F));
+        pts += fuzz;
+    }
+    cout << pts.size() << " points." << endl;
+    cv::ppf_match_3d::writePLY(pts, "cloud.ply");
 
-	auto segment = [napsac,normal_weight,max_sphere,preemptive,sprt](const Mat &cloud, std::vector<SACModel> &models, int model_type, float threshold, int max_iters, int min_inlier, int method_type) -> Mat {
-	    Ptr<SACModelFitting> fitting = SACModelFitting::create(cloud, model_type, method_type, threshold, max_iters);
-	    fitting->set_normal_distance_weight(normal_weight);
-	    fitting->set_max_napsac_radius(napsac);
-	    fitting->set_max_sphere_radius(max_sphere);
-	    fitting->set_preemptive_count(preemptive);
-	    fitting->set_min_inliers(min_inlier);
-	    fitting->set_use_sprt(sprt);
+    auto segment = [napsac,normal_weight,max_sphere,preemptive,sprt](const Mat &cloud, std::vector<SACModel> &models, int model_type, float threshold, int max_iters, int min_inlier, int method_type) -> Mat {
+        Ptr<SACModelFitting> fitting = SACModelFitting::create(cloud, model_type, method_type, threshold, max_iters);
+        fitting->set_normal_distance_weight(normal_weight);
+        fitting->set_max_napsac_radius(napsac);
+        fitting->set_max_sphere_radius(max_sphere);
+        fitting->set_preemptive_count(preemptive);
+        fitting->set_min_inliers(min_inlier);
+        fitting->set_use_sprt(sprt);
 
-	    Mat new_cloud;
-	    fitting->segment(models, new_cloud);
+        Mat new_cloud;
+        fitting->segment(models, new_cloud);
 
-	    return new_cloud;
-	};
+        return new_cloud;
+    };
 
     std::vector<SACModel> models;
-	if (typ==4) { // cluster only
+    if (typ==4) { // cluster only
         cv::ptcloud::cluster(pts, min_distance, min_inliers, models, pts);
-	} else
+    } else
     if (typ==0) { // end to end
         pts = segment(pts, models, 1, thresh, iters, min_inliers, method);
         cout << pts.total() << " points left." << endl;
-		pts = segment(pts, models, 2, 0.145, iters, min_inliers, method);
-		cout << pts.total() << " points left." << endl;
-		pts = segment(pts, models, 3, 5.0, iters, min_inliers, method);
-		cout << pts.total() << " points left." << endl;
-		cv::ptcloud::cluster(pts, 7, 20, models, pts);
+        pts = segment(pts, models, 2, 0.145f, iters, min_inliers, method);
+        cout << pts.total() << " points left." << endl;
+        pts = segment(pts, models, 3, 5.0f, iters, min_inliers, method);
+        cout << pts.total() << " points left." << endl;
+        cv::ptcloud::cluster(pts, 7, 20, models, pts);
     } else // single model type
-		pts = segment(pts, models, typ, thresh, iters, min_inliers, method);
+        pts = segment(pts, models, typ, thresh, iters, min_inliers, method);
 
     string names[] = {"", "plane","sphere","cylinder","blob"};
     for (size_t i=0; i<models.size(); i++) {
-	    SACModel &model = models.at(i);
-	    cout << model.type << " " << model.points.size() << " " << model.score.second << "\t";
-	    cout << Mat(model.coefficients).t() << endl;
-		cv::ppf_match_3d::writePLY(Mat(model.points), format("cloud_%s_%d.ply",names[model.type].c_str(), i+1).c_str());
-	}
+        SACModel &model = models.at(i);
+        cout << model.type << " " << model.points.size() << " " << model.score.second << "\t";
+        cout << Mat(model.coefficients).t() << endl;
+        cv::ppf_match_3d::writePLY(Mat(model.points), format("cloud_%s_%d.ply",names[model.type].c_str(), int(i+1)).c_str());
+    }
 
     cout << pts.total() << " points left." << endl;
-	cv::ppf_match_3d::writePLY(pts, "cloud_left.ply");
-	return 0;
+    cv::ppf_match_3d::writePLY(pts, "cloud_left.ply");
+    return 0;
 }

modules/ptcloud/samples/viz/sample_cylinder_fitting.cpp

@@ -1,5 +1,5 @@
+#include "opencv2/opencv_modules.hpp"
 #ifdef HAVE_OPENCV_VIZ
-
 #include <opencv2/viz.hpp>
 #include <opencv2/highgui.hpp>
 #include <opencv2/viz/widgets.hpp>
@@ -16,7 +16,6 @@ using namespace cv;
 using namespace std;
 
 int main() {
-    // Mat cloud = cv::ppf_match_3d::loadPLYSimple("./data/semi-cylinder-with-normals-usingOpenCV2.ply", true);
     Mat cloud = cv::ppf_match_3d::loadPLYSimple("./data/cylinder-big.ply", false);
 
     Mat ptset;

modules/ptcloud/samples/viz/sample_plane_fitting.cpp

@@ -1,3 +1,4 @@
+#include "opencv2/opencv_modules.hpp"
 #ifdef HAVE_OPENCV_VIZ
 #include <opencv2/viz.hpp>
 #include <opencv2/highgui.hpp>

modules/ptcloud/samples/viz/sample_sphere_fitting.cpp

@@ -1,3 +1,4 @@
+#include "opencv2/opencv_modules.hpp"
 #ifdef HAVE_OPENCV_VIZ
 #include <opencv2/viz.hpp>
 #include <opencv2/highgui.hpp>