Merge 3.4

modules/face/include/opencv2/face.hpp

@@ -162,7 +162,7 @@ class CV_EXPORTS_W FaceRecognizer : public Algorithm
     @param src The training images, that means the faces you want to learn. The data has to be
     given as a vector\<Mat\>.
     @param labels The labels corresponding to the images have to be given either as a vector\<int\>
-    or a
+    or a Mat of type CV_32SC1.
 
     The following source code snippet shows you how to learn a Fisherfaces model on a given set of
     images. The images are read with imread and pushed into a std::vector\<Mat\>. The labels of each
@@ -175,6 +175,8 @@ class CV_EXPORTS_W FaceRecognizer : public Algorithm
     // holds images and labels
     vector<Mat> images;
     vector<int> labels;
+    // using Mat of type CV_32SC1
+    // Mat labels(number_of_samples, 1, CV_32SC1);
     // images for first person
     images.push_back(imread("person0/0.jpg", IMREAD_GRAYSCALE)); labels.push_back(0);
     images.push_back(imread("person0/1.jpg", IMREAD_GRAYSCALE)); labels.push_back(0);
@@ -211,7 +213,7 @@ class CV_EXPORTS_W FaceRecognizer : public Algorithm
     @param src The training images, that means the faces you want to learn. The data has to be given
     as a vector\<Mat\>.
     @param labels The labels corresponding to the images have to be given either as a vector\<int\> or
-    a
+    a Mat of type CV_32SC1.
 
     This method updates a (probably trained) FaceRecognizer, but only if the algorithm supports it. The
     Local Binary Patterns Histograms (LBPH) recognizer (see createLBPHFaceRecognizer) can be updated.

modules/sfm/src/libmv_light/libmv/correspondence/CMakeLists.txt

@@ -8,6 +8,10 @@ FILE(GLOB CORRESPONDENCE_HDRS *.h)
 
 ADD_LIBRARY(correspondence STATIC ${CORRESPONDENCE_SRC} ${CORRESPONDENCE_HDRS})
 
-TARGET_LINK_LIBRARIES(correspondence multiview)
+TARGET_LINK_LIBRARIES(correspondence LINK_PRIVATE multiview)
+IF(TARGET Eigen3::Eigen)
+  TARGET_LINK_LIBRARIES(correspondence LINK_PUBLIC Eigen3::Eigen)
+ENDIF()
 
-LIBMV_INSTALL_LIB(correspondence)
+
+LIBMV_INSTALL_LIB(correspondence)

modules/sfm/src/libmv_light/libmv/multiview/CMakeLists.txt

@@ -17,6 +17,9 @@ SET(MULTIVIEW_SRC conditioning.cc
 FILE(GLOB MULTIVIEW_HDRS *.h)
 
 ADD_LIBRARY(multiview STATIC ${MULTIVIEW_SRC} ${MULTIVIEW_HDRS})
-TARGET_LINK_LIBRARIES(multiview ${GLOG_LIBRARY} numeric)
+TARGET_LINK_LIBRARIES(multiview LINK_PRIVATE ${GLOG_LIBRARY} numeric)
+IF(TARGET Eigen3::Eigen)
+  TARGET_LINK_LIBRARIES(multiview LINK_PUBLIC Eigen3::Eigen)
+ENDIF()
 
 LIBMV_INSTALL_LIB(multiview)

modules/sfm/src/libmv_light/libmv/numeric/CMakeLists.txt

@@ -7,6 +7,8 @@ FILE(GLOB NUMERIC_HDRS *.h)
 
 ADD_LIBRARY(numeric STATIC ${NUMERIC_SRC} ${NUMERIC_HDRS})
 
-TARGET_LINK_LIBRARIES(numeric)
+IF(TARGET Eigen3::Eigen)
+  TARGET_LINK_LIBRARIES(numeric LINK_PUBLIC Eigen3::Eigen)
+ENDIF()
 
-LIBMV_INSTALL_LIB(numeric)
+LIBMV_INSTALL_LIB(numeric)

modules/sfm/src/libmv_light/libmv/simple_pipeline/CMakeLists.txt

@@ -17,6 +17,6 @@ FILE(GLOB SIMPLE_PIPELINE_HDRS *.h)
 
 ADD_LIBRARY(simple_pipeline STATIC ${SIMPLE_PIPELINE_SRC} ${SIMPLE_PIPELINE_HDRS})
 
-TARGET_LINK_LIBRARIES(simple_pipeline multiview ${CERES_LIBRARIES})
+TARGET_LINK_LIBRARIES(simple_pipeline LINK_PRIVATE multiview ${CERES_LIBRARIES})
 
-LIBMV_INSTALL_LIB(simple_pipeline)
+LIBMV_INSTALL_LIB(simple_pipeline)

modules/ximgproc/samples/fld_lines.cpp

@@ -27,10 +27,6 @@ int main(int argc, char** argv)
         return -1;
     }
 
-    // Create LSD detector
-    Ptr<LineSegmentDetector> lsd = createLineSegmentDetector();
-    vector<Vec4f> lines_lsd;
-
     // Create FLD detector
     // Param               Default value   Description
     // length_threshold    10            - Segments shorter than this will be discarded
@@ -57,29 +53,18 @@ int main(int argc, char** argv)
     vector<Vec4f> lines_fld;
 
     // Because of some CPU's power strategy, it seems that the first running of
-    // an algorithm takes much longer. So here we run both of the algorithmes 10
-    // times to see each algorithm's processing time with sufficiently warmed-up
+    // an algorithm takes much longer. So here we run the algorithm 10 times
+    // to see the algorithm's processing time with sufficiently warmed-up
     // CPU performance.
     for(int run_count = 0; run_count < 10; run_count++) {
-        lines_lsd.clear();
-        int64 start_lsd = getTickCount();
-        lsd->detect(image, lines_lsd);
-        // Detect the lines with LSD
         double freq = getTickFrequency();
-        double duration_ms_lsd = double(getTickCount() - start_lsd) * 1000 / freq;
-        std::cout << "Elapsed time for LSD: " << duration_ms_lsd << " ms." << std::endl;
-
         lines_fld.clear();
         int64 start = getTickCount();
         // Detect the lines with FLD
         fld->detect(image, lines_fld);
         double duration_ms = double(getTickCount() - start) * 1000 / freq;
-        std::cout << "Ealpsed time for FLD " << duration_ms << " ms." << std::endl;
+        std::cout << "Elapsed time for FLD " << duration_ms << " ms." << std::endl;
     }
-    // Show found lines with LSD
-    Mat line_image_lsd(image);
-    lsd->drawSegments(line_image_lsd, lines_lsd);
-    imshow("LSD result", line_image_lsd);
 
     // Show found lines with FLD
     Mat line_image_fld(image);