Merge pull request #382 from zhou-chao:l0smooth

modules/ximgproc/doc/ximgproc.bib

@@ -89,6 +89,18 @@
   organization={ACM}
 }
 
+
+@inproceedings{xu2011image,
+  title={Image smoothing via L 0 gradient minimization},
+  author={Xu, Li and Lu, Cewu and Xu, Yi and Jia, Jiaya},
+  booktitle={ACM Transactions on Graphics (TOG)},
+  volume={30},
+  number={6},
+  pages={174},
+  year={2011},
+  organization={ACM}
+}
+
 @inproceedings{Revaud2015,
   title={EpicFlow: Edge-Preserving Interpolation of Correspondences for Optical Flow},
   author={Revaud, Jerome and Weinzaepfel, Philippe and Harchaoui, Zaid and Schmid, Cordelia},

modules/ximgproc/include/opencv2/ximgproc/edge_filter.hpp

@@ -378,6 +378,19 @@ it should be 0.25. Setting it to 1.0 may lead to streaking artifacts.
 */
 CV_EXPORTS_W void fastGlobalSmootherFilter(InputArray guide, InputArray src, OutputArray dst, double lambda, double sigma_color, double lambda_attenuation=0.25, int num_iter=3);
 
+/** @brief Global image smoothing via L0 gradient minimization.
+
+@param src source image for filtering with unsigned 8-bit or signed 16-bit or floating-point depth.
+
+@param dst destination image.
+
+@param lambda parameter defining the smooth term weight.
+
+@param kappa parameter defining the increasing factor of the weight of the gradient data term.
+
+For more details about L0 Smoother, see the original paper @cite xu2011image.
+*/
+CV_EXPORTS_W void l0Smooth(InputArray src, OutputArray dst, double lambda = 0.02, double kappa = 2.0);
 //! @}
 }
 }

modules/ximgproc/perf/perf_l0_smooth.cpp

+/*
+ *  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
+ *  (3 - clause BSD License)
+ *
+ *  Redistribution and use in source and binary forms, with or without modification,
+ *  are permitted provided that the following conditions are met :
+ *
+ *  *Redistributions of source code must retain the above copyright notice,
+ *  this list of conditions and the following disclaimer.
+ *
+ *  * Redistributions 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.
+ *
+ *  * Neither the names of the copyright holders nor the names of the contributors
+ *  may 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 copyright holders 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.
+ */
+
+#include "perf_precomp.hpp"
+
+namespace cvtest
+{
+
+using std::tr1::tuple;
+using std::tr1::get;
+using namespace perf;
+using namespace testing;
+using namespace cv;
+using namespace cv::ximgproc;
+
+typedef tuple<Size, MatType, int> L0SmoothTestParam;
+typedef TestBaseWithParam<L0SmoothTestParam> L0SmoothTest;
+
+PERF_TEST_P(L0SmoothTest, perf,
+    Combine(
+    SZ_TYPICAL,
+    Values(CV_8U, CV_16U, CV_32F, CV_64F),
+    Values(1, 3))
+)
+{
+    L0SmoothTestParam params = GetParam();
+    Size sz         = get<0>(params);
+    int depth       = get<1>(params);
+    int srcCn        = get<2>(params);
+
+    Mat src(sz, CV_MAKE_TYPE(depth, srcCn));
+    Mat dst(sz, src.type());
+
+    cv::setNumThreads(cv::getNumberOfCPUs());
+    declare.in(src, WARMUP_RNG).out(dst).tbb_threads(cv::getNumberOfCPUs());
+
+    RNG rnd(sz.height + depth + srcCn);
+    double lambda = rnd.uniform(0.01, 0.05);
+    double kappa = rnd.uniform(1.0, 3.0);
+
+    TEST_CYCLE_N(1)
+    {
+        l0Smooth(src, dst, lambda, kappa);
+    }
+
+    SANITY_CHECK_NOTHING();
+}
+}

modules/ximgproc/test/test_l0_smooth.cpp

+/*
+ *  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
+ *  (3 - clause BSD License)
+ *
+ *  Redistribution and use in source and binary forms, with or without modification,
+ *  are permitted provided that the following conditions are met :
+ *
+ *  *Redistributions of source code must retain the above copyright notice,
+ *  this list of conditions and the following disclaimer.
+ *
+ *  * Redistributions 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.
+ *
+ *  * Neither the names of the copyright holders nor the names of the contributors
+ *  may 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 copyright holders 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.
+ */
+
+#include "test_precomp.hpp"
+
+namespace cvtest
+{
+
+using namespace std;
+using namespace std::tr1;
+using namespace testing;
+using namespace perf;
+using namespace cv;
+using namespace cv::ximgproc;
+
+CV_ENUM(SrcTypes, CV_8UC1, CV_8UC3, CV_16UC1, CV_16UC3);
+typedef tuple<Size, SrcTypes> L0SmoothParams;
+typedef TestWithParam<L0SmoothParams> L0SmoothTest;
+
+TEST(L0SmoothTest, SplatSurfaceAccuracy)
+{
+    RNG rnd(0);
+
+    for (int i = 0; i < 3; i++)
+    {
+        Size sz(rnd.uniform(512, 1024), rnd.uniform(512, 1024));
+
+        Scalar surfaceValue;
+        int srcCn = 3;
+        rnd.fill(surfaceValue, RNG::UNIFORM, 0, 255);
+        Mat src(sz, CV_MAKE_TYPE(CV_8U, srcCn), surfaceValue);
+
+        double lambda = rnd.uniform(0.01, 0.05);
+        double kappa  = rnd.uniform(1.5, 5.0);
+
+        Mat res;
+        l0Smooth(src, res, lambda, kappa);
+
+        // When filtering a constant image we should get the same image:
+        double normL1 = cvtest::norm(src, res, NORM_L1)/src.total()/src.channels();
+        EXPECT_LE(normL1, 1.0/64);
+    }
+}
+
+TEST_P(L0SmoothTest, MultiThreadReproducibility)
+{
+    if (cv::getNumberOfCPUs() == 1)
+        return;
+
+    double MAX_DIF = 10.0;
+    double MAX_MEAN_DIF = 1.0 / 8.0;
+    int loopsCount = 2;
+    RNG rng(0);
+
+    L0SmoothParams params = GetParam();
+    Size size     = get<0>(params);
+    int srcType   = get<1>(params);
+
+    Mat src(size,srcType);
+    if(src.depth()==CV_8U)
+        randu(src, 0, 255);
+    else if(src.depth()==CV_16U)
+        randu(src, 0, 65535);
+    else
+        randu(src, -100000.0f, 100000.0f);
+
+
+    for (int iter = 0; iter <= loopsCount; iter++)
+    {
+        double lambda = rng.uniform(0.01, 0.05);
+        double kappa  = rng.uniform(1.5, 5.0);
+
+        cv::setNumThreads(cv::getNumberOfCPUs());
+        Mat resMultiThread;
+        l0Smooth(src, resMultiThread, lambda, kappa);
+
+        cv::setNumThreads(1);
+        Mat resSingleThread;
+        l0Smooth(src, resSingleThread, lambda, kappa);
+
+        EXPECT_LE(cv::norm(resSingleThread, resMultiThread, NORM_INF), MAX_DIF);
+        EXPECT_LE(cv::norm(resSingleThread, resMultiThread, NORM_L1), MAX_MEAN_DIF*src.total()*src.channels());
+    }
+}
+INSTANTIATE_TEST_CASE_P(FullSet, L0SmoothTest,Combine(Values(szODD, szQVGA), SrcTypes::all()));
+
+}