Merge branch 'daisy' of https://github.com/cbalint13/opencv_contrib into daisy

modules/adas/tools/fcw_detect/CMakeLists.txt

@@ -13,7 +13,7 @@ endif()
 project(${the_target})
 
 ocv_include_directories("${OpenCV_SOURCE_DIR}/include/opencv")
-ocv_include_modules(${OPENCV_${the_target}_DEPS})
+ocv_include_modules_recurse(${OPENCV_${the_target}_DEPS})
 
 file(GLOB ${the_target}_SOURCES ${CMAKE_CURRENT_SOURCE_DIR}/*.cpp)
 

modules/adas/tools/fcw_train/CMakeLists.txt

@@ -13,7 +13,7 @@ endif()
 project(${the_target})
 
 ocv_include_directories("${OpenCV_SOURCE_DIR}/include/opencv")
-ocv_include_modules(${OPENCV_${the_target}_DEPS})
+ocv_include_modules_recurse(${OPENCV_${the_target}_DEPS})
 
 file(GLOB ${the_target}_SOURCES ${CMAKE_CURRENT_SOURCE_DIR}/*.cpp)
 

modules/matlab/include/opencv2/matlab/bridge.hpp

@@ -85,6 +85,7 @@ typedef cv::Ptr<AlignMTB> Ptr_AlignMTB;
 typedef cv::Ptr<CalibrateDebevec> Ptr_CalibrateDebevec;
 typedef cv::Ptr<CalibrateRobertson> Ptr_CalibrateRobertson;
 typedef cv::Ptr<DenseOpticalFlow> Ptr_DenseOpticalFlow;
+typedef cv::Ptr<DualTVL1OpticalFlow> Ptr_DualTVL1OpticalFlow;
 typedef cv::Ptr<MergeDebevec> Ptr_MergeDebevec;
 typedef cv::Ptr<MergeMertens> Ptr_MergeMertens;
 typedef cv::Ptr<MergeRobertson> Ptr_MergeRobertson;
@@ -468,6 +469,11 @@ public:
   Ptr_DenseOpticalFlow toPtrDenseOpticalFlow() { return Ptr_DenseOpticalFlow(); }
   operator Ptr_DenseOpticalFlow() { return toPtrDenseOpticalFlow(); }
 
+  // ---------------------------   Ptr_DualTVL1OpticalFlow   -------------------
+  Bridge& operator=(const Ptr_DualTVL1OpticalFlow& ) { return *this; }
+  Ptr_DualTVL1OpticalFlow toPtrDualTVL1OpticalFlow() { return Ptr_DualTVL1OpticalFlow(); }
+  operator Ptr_DualTVL1OpticalFlow() { return toPtrDualTVL1OpticalFlow(); }
+
   // ---------------------------   Ptr_MergeDebevec   -----------------------
   Bridge& operator=(const Ptr_MergeDebevec& ) { return *this; }
   Ptr_MergeDebevec toPtrMergeDebevec() { return Ptr_MergeDebevec(); }

modules/optflow/src/motempl.cpp

@@ -212,7 +212,7 @@ void calcMotionGradient( InputArray _mhi, OutputArray _mask,
         float* orient_row = orient.ptr<float>(y);
         uchar* mask_row = mask.ptr<uchar>(y);
 
-        fastAtan2(dY_max_row, dX_min_row, orient_row, size.width, true);
+        hal::fastAtan2(dY_max_row, dX_min_row, orient_row, size.width, true);
 
         // make orientation zero where the gradient is very small
         for( x = 0; x < size.width; x++ )

modules/rgbd/src/depth_to_3d.h

@@ -81,7 +81,7 @@ convertDepthToFloat(const cv::Mat& depth, const cv::Mat& mask, float scale, cv::
         v_mat((int)n_points, 0) = (float)v;
         T depth_i = depth.at<T>(v, u);
 
-        if (cvIsNaN(depth_i) || (depth_i == std::numeric_limits<T>::min()) || (depth_i == std::numeric_limits<T>::max()))
+        if (cvIsNaN((float)depth_i) || (depth_i == std::numeric_limits<T>::min()) || (depth_i == std::numeric_limits<T>::max()))
           z_mat((int)n_points, 0) = std::numeric_limits<float>::quiet_NaN();
         else
           z_mat((int)n_points, 0) = depth_i * scale;
@@ -111,7 +111,7 @@ convertDepthToFloat(const cv::Mat& depth, float scale, const cv::Mat &uv_mat, cv
   {
     T depth_i = depth.at < T > ((int)(*uv_iter)[1], (int)(*uv_iter)[0]);
 
-    if (cvIsNaN(depth_i) || (depth_i == std::numeric_limits < T > ::min())
+    if (cvIsNaN((float)depth_i) || (depth_i == std::numeric_limits < T > ::min())
         || (depth_i == std::numeric_limits < T > ::max()))
       *z_mat_iter = std::numeric_limits<float>::quiet_NaN();
     else

modules/tracking/src/TrackingFunctionPF.hpp

@@ -9,6 +9,7 @@ namespace cv{
         public:
             TrackingFunctionPF(const Mat& chosenRect);
             void update(const Mat& image);
+            int getDims() const { return 4; }
             double calc(const double* x) const;
             void correctParams(double* pt)const;
         private:

modules/xfeatures2d/doc/xfeatures2d.bib

@@ -45,15 +45,6 @@
   organization={Ieee}
 }
 
-@inproceedings{mair2010_agast,
-   title={Adaptive and Generic Corner Detection Based on the Accelerated Segment Test"},
-   author={"Elmar Mair and Gregory D. Hager and Darius Burschka and Michael Suppa and Gerhard Hirzinger"},
-   year={"2010"},
-   month={"September"},
-   booktitle={"European Conference on Computer Vision (ECCV'10)"},
-   url={"http://www6.in.tum.de/Main/ResearchAgast"
-}
-
 @incollection{LUCID,
   title={Locally uniform comparison image descriptor},
   author={Ziegler, Andrew, Eric Christiansen, David Kriegman, and Serge J. Belongie}

modules/xfeatures2d/include/opencv2/xfeatures2d.hpp

@@ -129,53 +129,6 @@ public:
     static Ptr<BriefDescriptorExtractor> create( int bytes = 32 );
 };
 
-/** @overload */
-CV_EXPORTS void AGAST( InputArray image, CV_OUT std::vector<KeyPoint>& keypoints,
-                      int threshold, bool nonmaxSuppression=true );
-
-/** @brief Detects corners using the AGAST algorithm
-
-@param image grayscale image where keypoints (corners) are detected.
-@param keypoints keypoints detected on the image.
-@param threshold threshold on difference between intensity of the central pixel and pixels of a
-circle around this pixel.
-@param nonmaxSuppression if true, non-maximum suppression is applied to detected corners
-(keypoints).
-@param type one of the four neighborhoods as defined in the paper:
-AgastFeatureDetector::AGAST_5_8, AgastFeatureDetector::AGAST_7_12d,
-AgastFeatureDetector::AGAST_7_12s, AgastFeatureDetector::OAST_9_16
-
-Detects corners using the AGAST algorithm by @cite mair2010_agast .
-
- */
-CV_EXPORTS void AGAST( InputArray image, CV_OUT std::vector<KeyPoint>& keypoints,
-                      int threshold, bool nonmaxSuppression, int type );
-
-/** @brief Wrapping class for feature detection using the AGAST method. :
- */
-class CV_EXPORTS_W AgastFeatureDetector : public Feature2D
-{
-public:
-    enum
-    {
-        AGAST_5_8 = 0, AGAST_7_12d = 1, AGAST_7_12s = 2, OAST_9_16 = 3,
-        THRESHOLD = 10000, NONMAX_SUPPRESSION = 10001,
-    };
-
-    CV_WRAP static Ptr<AgastFeatureDetector> create( int threshold=10,
-                                                     bool nonmaxSuppression=true,
-                                                     int type=AgastFeatureDetector::OAST_9_16 );
-
-    CV_WRAP virtual void setThreshold(int threshold) = 0;
-    CV_WRAP virtual int getThreshold() const = 0;
-
-    CV_WRAP virtual void setNonmaxSuppression(bool f) = 0;
-    CV_WRAP virtual bool getNonmaxSuppression() const = 0;
-
-    CV_WRAP virtual void setType(int type) = 0;
-    CV_WRAP virtual int getType() const = 0;
-};
-
 /** @brief Class implementing the locally uniform comparison image descriptor, described in @cite LUCID
 
 An image descriptor that can be computed very fast, while being

modules/xfeatures2d/perf/perf_agast.cpp

-#include "perf_precomp.hpp"
-
-using namespace std;
-using namespace cv;
-using namespace cv::xfeatures2d;
-using namespace perf;
-using std::tr1::make_tuple;
-using std::tr1::get;
-
-CV_ENUM(AgastType, AgastFeatureDetector::AGAST_5_8, AgastFeatureDetector::AGAST_7_12d,
-                   AgastFeatureDetector::AGAST_7_12s, AgastFeatureDetector::OAST_9_16)
-
-typedef std::tr1::tuple<string, AgastType> File_Type_t;
-typedef perf::TestBaseWithParam<File_Type_t> agast;
-
-#define AGAST_IMAGES \
-    "cv/detectors_descriptors_evaluation/images_datasets/leuven/img1.png",\
-    "stitching/a3.png"
-
-PERF_TEST_P(agast, detect, testing::Combine(
-                            testing::Values(AGAST_IMAGES),
-                            AgastType::all()
-                          ))
-{
-    string filename = getDataPath(get<0>(GetParam()));
-    int type = get<1>(GetParam());
-    Mat frame = imread(filename, IMREAD_GRAYSCALE);
-
-    if (frame.empty())
-        FAIL() << "Unable to load source image " << filename;
-
-    declare.in(frame);
-
-    Ptr<FeatureDetector> fd = AgastFeatureDetector::create(20, true, type);
-    ASSERT_FALSE( fd.empty() );
-    vector<KeyPoint> points;
-
-    TEST_CYCLE() fd->detect(frame, points);
-
-    SANITY_CHECK_KEYPOINTS(points);
-}

modules/xfeatures2d/src/agast_score.hpp

-/* This is AGAST and OAST, an optimal and accelerated corner detector
-              based on the accelerated segment tests
-   Below is the original copyright and the references */
-
-/*
-Copyright (C) 2010  Elmar Mair
-All rights reserved.
-
-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 name of the University of Cambridge nor the names of
-     its 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 THE COPYRIGHT OWNER 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.
-*/
-
-/*
-The references are:
- * Adaptive and Generic Corner Detection Based on the Accelerated Segment Test,
-   Elmar Mair and Gregory D. Hager and Darius Burschka
-   and Michael Suppa and Gerhard Hirzinger ECCV 2010
-   URL: http://www6.in.tum.de/Main/ResearchAgast
-*/
-
-
-#ifndef __OPENCV_FEATURES_2D_AGAST_HPP__
-#define __OPENCV_FEATURES_2D_AGAST_HPP__
-
-#ifdef __cplusplus
-
-#include "precomp.hpp"
-namespace cv
-{
-namespace xfeatures2d
-{
-
-void makeAgastOffsets(int pixel[16], int row_stride, int type);
-
-template<int type>
-int agast_cornerScore(const uchar* ptr, const int pixel[], int threshold);
-
-}
-}
-#endif
-#endif

modules/xfeatures2d/src/sift.cpp

@@ -337,9 +337,9 @@ static float calcOrientationHist( const Mat& img, Point pt, int radius,
     len = k;
 
     // compute gradient values, orientations and the weights over the pixel neighborhood
-    exp(W, W, len);
-    fastAtan2(Y, X, Ori, len, true);
-    magnitude(X, Y, Mag, len);
+    hal::exp(W, W, len);
+    hal::fastAtan2(Y, X, Ori, len, true);
+    hal::magnitude(X, Y, Mag, len);
 
     for( k = 0; k < len; k++ )
     {
@@ -620,9 +620,9 @@ static void calcSIFTDescriptor( const Mat& img, Point2f ptf, float ori, float sc
         }
 
     len = k;
-    fastAtan2(Y, X, Ori, len, true);
-    magnitude(X, Y, Mag, len);
-    exp(W, W, len);
+    hal::fastAtan2(Y, X, Ori, len, true);
+    hal::magnitude(X, Y, Mag, len);
+    hal::exp(W, W, len);
 
     for( k = 0; k < len; k++ )
     {

modules/xfeatures2d/test/test_agast.cpp

-/*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) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage 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*/
-
-#include "test_precomp.hpp"
-
-using namespace std;
-using namespace cv;
-using namespace cv::xfeatures2d;
-
-class CV_AgastTest : public cvtest::BaseTest
-{
-public:
-    CV_AgastTest();
-    ~CV_AgastTest();
-protected:
-    void run(int);
-};
-
-CV_AgastTest::CV_AgastTest() {}
-CV_AgastTest::~CV_AgastTest() {}
-
-void CV_AgastTest::run( int )
-{
-  for(int type=0; type <= 2; ++type) {
-    Mat image1 = imread(string(ts->get_data_path()) + "inpaint/orig.png");
-    Mat image2 = imread(string(ts->get_data_path()) + "cameracalibration/chess9.png");
-    string xml = string(ts->get_data_path()) + format("agast/result%d.xml", type);
-
-    if (image1.empty() || image2.empty())
-    {
-        ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
-        return;
-    }
-
-    Mat gray1, gray2;
-    cvtColor(image1, gray1, COLOR_BGR2GRAY);
-    cvtColor(image2, gray2, COLOR_BGR2GRAY);
-
-    vector<KeyPoint> keypoints1;
-    vector<KeyPoint> keypoints2;
-    AGAST(gray1, keypoints1, 30, true, type);
-    AGAST(gray2, keypoints2, (type > 0 ? 30 : 20), true, type);
-
-    for(size_t i = 0; i < keypoints1.size(); ++i)
-    {
-        const KeyPoint& kp = keypoints1[i];
-        cv::circle(image1, kp.pt, cvRound(kp.size/2), Scalar(255, 0, 0));
-    }
-
-    for(size_t i = 0; i < keypoints2.size(); ++i)
-    {
-        const KeyPoint& kp = keypoints2[i];
-        cv::circle(image2, kp.pt, cvRound(kp.size/2), Scalar(255, 0, 0));
-    }
-
-    Mat kps1(1, (int)(keypoints1.size() * sizeof(KeyPoint)), CV_8U, &keypoints1[0]);
-    Mat kps2(1, (int)(keypoints2.size() * sizeof(KeyPoint)), CV_8U, &keypoints2[0]);
-
-    FileStorage fs(xml, FileStorage::READ);
-    if (!fs.isOpened())
-    {
-        fs.open(xml, FileStorage::WRITE);
-        if (!fs.isOpened())
-        {
-            ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_TEST_DATA);
-            return;
-        }
-        fs << "exp_kps1" << kps1;
-        fs << "exp_kps2" << kps2;
-        fs.release();
-        fs.open(xml, FileStorage::READ);
-        if (!fs.isOpened())
-        {
-            ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_TEST_DATA);
-            return;
-        }
-    }
-
-    Mat exp_kps1, exp_kps2;
-    read( fs["exp_kps1"], exp_kps1, Mat() );
-    read( fs["exp_kps2"], exp_kps2, Mat() );
-    fs.release();
-
-     if ( exp_kps1.size != kps1.size || 0 != cvtest::norm(exp_kps1, kps1, NORM_L2) ||
-          exp_kps2.size != kps2.size || 0 != cvtest::norm(exp_kps2, kps2, NORM_L2))
-    {
-        ts->set_failed_test_info(cvtest::TS::FAIL_MISMATCH);
-        return;
-    }
-
-    /*cv::namedWindow("Img1"); cv::imshow("Img1", image1);
-    cv::namedWindow("Img2"); cv::imshow("Img2", image2);
-    cv::waitKey(0);*/
-  }
-
-  ts->set_failed_test_info(cvtest::TS::OK);
-}
-
-TEST(Features2d_AGAST, regression) { CV_AgastTest test; test.safe_run(); }

modules/xphoto/src/gcgraph.hpp

@@ -254,7 +254,7 @@ TWeight GCGraph<TWeight>::maxFlow()
                 minWeight = MIN(minWeight, weight);
                 CV_Assert( minWeight > 0 );
             }
-            weight = abs( TWeight(v->weight) );
+            weight = std::abs( TWeight(v->weight) );
             minWeight = MIN(minWeight, weight);
             CV_Assert( minWeight > 0 );
         }