Merge remote-tracking branch 'upstream/3.4' into merge-3.4

modules/aruco/doc/aruco.bib

+@article{Aruco2014,
+ title = "Automatic generation and detection of highly reliable fiducial markers under occlusion ",
+ journal = "Pattern Recognition ",
+ volume = "47",
+ number = "6",
+ pages = "2280 - 2292",
+ year = "2014",
+ issn = "0031-3203",
+ doi = "http://dx.doi.org/10.1016/j.patcog.2014.01.005",
+ url = "http://www.sciencedirect.com/science/article/pii/S0031320314000235",
+ author = "S. Garrido-Jurado and R. Mu\~noz-Salinas and F.J. Madrid-Cuevas and M.J. Mar\'in-Jim\'enez"
+}
+
+@inproceedings{wang2016iros,
+    AUTHOR     = {John Wang and Edwin Olson},
+    TITLE      = {{AprilTag} 2: Efficient and robust fiducial detection},
+    BOOKTITLE  = {Proceedings of the {IEEE/RSJ} International Conference on Intelligent
+                 Robots and Systems {(IROS)}},
+    YEAR       = {2016},
+    MONTH      = {October},
+}

modules/aruco/include/opencv2/aruco.hpp

@@ -49,14 +49,16 @@ the use of this software, even if advised of the possibility of such damage.
  * These markers are useful for easy, fast and robust camera pose estimation.ç
  *
  * The main functionalities are:
- * - Detection of markers in a image
+ * - Detection of markers in an image
  * - Pose estimation from a single marker or from a board/set of markers
  * - Detection of ChArUco board for high subpixel accuracy
  * - Camera calibration from both, ArUco boards and ChArUco boards.
  * - Detection of ChArUco diamond markers
  * The samples directory includes easy examples of how to use the module.
  *
- * The implementation is based on the ArUco Library by R. Muñoz-Salinas and S. Garrido-Jurado.
+ * The implementation is based on the ArUco Library by R. Muñoz-Salinas and S. Garrido-Jurado @cite Aruco2014.
+ *
+ * Markers can also be detected based on the AprilTag 2 @cite wang2016iros fiducial detection method.
  *
  * @sa S. Garrido-Jurado, R. Muñoz-Salinas, F. J. Madrid-Cuevas, and M. J. Marín-Jiménez. 2014.
  * "Automatic generation and detection of highly reliable fiducial markers under occlusion".
@@ -77,10 +79,10 @@ namespace aruco {
 //! @{
 
 enum CornerRefineMethod{
-	CORNER_REFINE_NONE,     // default corners
-	CORNER_REFINE_SUBPIX,   // refine the corners using subpix
-	CORNER_REFINE_CONTOUR,  // refine the corners using the contour-points
-	CORNER_REFINE_APRILTAG, // detect corners using the AprilTag2 approach
+    CORNER_REFINE_NONE,     ///< Tag and corners detection based on the ArUco approach
+    CORNER_REFINE_SUBPIX,   ///< ArUco approach and refine the corners locations using corner subpixel accuracy
+    CORNER_REFINE_CONTOUR,  ///< ArUco approach and refine the corners locations using the contour-points line fitting
+    CORNER_REFINE_APRILTAG, ///< Tag and corners detection based on the AprilTag 2 approach @cite wang2016iros
 };
 
 /**

modules/aruco/include/opencv2/aruco/dictionary.hpp

@@ -155,7 +155,11 @@ enum CV_EXPORTS_W_SIMPLE PREDEFINED_DICTIONARY_NAME {
     DICT_7X7_100,
     DICT_7X7_250,
     DICT_7X7_1000,
-    DICT_ARUCO_ORIGINAL
+    DICT_ARUCO_ORIGINAL,
+    DICT_APRILTAG_16h5,     ///< 4x4 bits, minimum hamming distance between any two codes = 5, 30 codes
+    DICT_APRILTAG_25h9,     ///< 5x5 bits, minimum hamming distance between any two codes = 9, 35 codes
+    DICT_APRILTAG_36h10,    ///< 6x6 bits, minimum hamming distance between any two codes = 10, 2320 codes
+    DICT_APRILTAG_36h11     ///< 6x6 bits, minimum hamming distance between any two codes = 11, 587 codes
 };
 
 

modules/aruco/samples/detect_markers.cpp

@@ -50,13 +50,16 @@ const char* keys  =
         "{d        |       | dictionary: DICT_4X4_50=0, DICT_4X4_100=1, DICT_4X4_250=2,"
         "DICT_4X4_1000=3, DICT_5X5_50=4, DICT_5X5_100=5, DICT_5X5_250=6, DICT_5X5_1000=7, "
         "DICT_6X6_50=8, DICT_6X6_100=9, DICT_6X6_250=10, DICT_6X6_1000=11, DICT_7X7_50=12,"
-        "DICT_7X7_100=13, DICT_7X7_250=14, DICT_7X7_1000=15, DICT_ARUCO_ORIGINAL = 16}"
+        "DICT_7X7_100=13, DICT_7X7_250=14, DICT_7X7_1000=15, DICT_ARUCO_ORIGINAL = 16,"
+        "DICT_APRILTAG_16h5=17, DICT_APRILTAG_25h9=18, DICT_APRILTAG_36h10=19, DICT_APRILTAG_36h11=20}"
         "{v        |       | Input from video file, if ommited, input comes from camera }"
         "{ci       | 0     | Camera id if input doesnt come from video (-v) }"
         "{c        |       | Camera intrinsic parameters. Needed for camera pose }"
         "{l        | 0.1   | Marker side lenght (in meters). Needed for correct scale in camera pose }"
         "{dp       |       | File of marker detector parameters }"
-        "{r        |       | show rejected candidates too }";
+        "{r        |       | show rejected candidates too }"
+        "{refine   |       | Corner refinement: CORNER_REFINE_NONE=0, CORNER_REFINE_SUBPIX=1,"
+        "CORNER_REFINE_CONTOUR=2, CORNER_REFINE_APRILTAG=3}";
 }
 
 /**
@@ -127,7 +130,12 @@ int main(int argc, char *argv[]) {
             return 0;
         }
     }
-    detectorParams->cornerRefinementMethod = aruco::CORNER_REFINE_SUBPIX; // do corner refinement in markers
+
+    if (parser.has("refine")) {
+        //override cornerRefinementMethod read from config file
+        detectorParams->cornerRefinementMethod = parser.get<int>("refine");
+    }
+    std::cout << "Corner refinement method (0: None, 1: Subpixel, 2:contour, 3: AprilTag 2): " << detectorParams->cornerRefinementMethod << std::endl;
 
     int camId = parser.get<int>("ci");
 

modules/aruco/src/dictionary.cpp

@@ -41,6 +41,7 @@ the use of this software, even if advised of the possibility of such damage.
 #include <opencv2/core.hpp>
 #include <opencv2/imgproc.hpp>
 #include "predefined_dictionaries.hpp"
+#include "predefined_dictionaries_apriltag.hpp"
 #include "opencv2/core/hal/hal.hpp"
 
 namespace cv {
@@ -291,6 +292,11 @@ const Dictionary DICT_7X7_100_DATA = Dictionary(Mat(100, (7*7 + 7)/8 ,CV_8UC4, (
 const Dictionary DICT_7X7_250_DATA = Dictionary(Mat(250, (7*7 + 7)/8 ,CV_8UC4, (uchar*)DICT_7X7_1000_BYTES), 7, 8);
 const Dictionary DICT_7X7_1000_DATA = Dictionary(Mat(1000, (7*7 + 7)/8 ,CV_8UC4, (uchar*)DICT_7X7_1000_BYTES), 7, 6);
 
+const Dictionary DICT_APRILTAG_16h5_DATA = Dictionary(Mat(30, (4*4 + 7)/8, CV_8UC4, (uchar*)DICT_APRILTAG_16h5_BYTES), 4, 0);
+const Dictionary DICT_APRILTAG_25h9_DATA = Dictionary(Mat(35, (5*5 + 7)/8, CV_8UC4, (uchar*)DICT_APRILTAG_25h9_BYTES), 5, 0);
+const Dictionary DICT_APRILTAG_36h10_DATA = Dictionary(Mat(2320, (6*6 + 7)/8, CV_8UC4, (uchar*)DICT_APRILTAG_36h10_BYTES), 6, 0);
+const Dictionary DICT_APRILTAG_36h11_DATA = Dictionary(Mat(587, (6*6 + 7)/8, CV_8UC4, (uchar*)DICT_APRILTAG_36h11_BYTES), 6, 0);
+
 
 Ptr<Dictionary> getPredefinedDictionary(PREDEFINED_DICTIONARY_NAME name) {
     switch(name) {
@@ -334,6 +340,15 @@ Ptr<Dictionary> getPredefinedDictionary(PREDEFINED_DICTIONARY_NAME name) {
     case DICT_7X7_1000:
         return makePtr<Dictionary>(DICT_7X7_1000_DATA);
 
+    case DICT_APRILTAG_16h5:
+        return makePtr<Dictionary>(DICT_APRILTAG_16h5_DATA);
+    case DICT_APRILTAG_25h9:
+        return makePtr<Dictionary>(DICT_APRILTAG_25h9_DATA);
+    case DICT_APRILTAG_36h10:
+        return makePtr<Dictionary>(DICT_APRILTAG_36h10_DATA);
+    case DICT_APRILTAG_36h11:
+        return makePtr<Dictionary>(DICT_APRILTAG_36h11_DATA);
+
     }
     return makePtr<Dictionary>(DICT_4X4_50_DATA);
 }

modules/datasets/CMakeLists.txt

@@ -9,4 +9,7 @@ endif()
 
 ocv_define_module(datasets opencv_core opencv_imgcodecs opencv_ml opencv_flann OPTIONAL opencv_text WRAP python)
 
-ocv_warnings_disable(CMAKE_CXX_FLAGS /wd4267) # flann, Win64
+ocv_warnings_disable(CMAKE_CXX_FLAGS
+    /wd4267                                      # flann, Win64
+    -Wimplicit-fallthrough                       # tinyxml2.cpp
+)

modules/face/samples/sampleDetectLandmarks.cpp

@@ -75,19 +75,26 @@ int main(int argc,char** argv){
     resize(img,img,Size(460,460), 0, 0, INTER_LINEAR_EXACT);
     facemark->getFaces(img,faces);
     vector< vector<Point2f> > shapes;
-    if(facemark->fit(img,faces,shapes))
-    {
-        for( size_t i = 0; i < faces.size(); i++ )
+
+    // Check if faces detected or not
+    // Helps in proper exception handling when writing images to the directories.
+    if(faces.size() != 0) {
+        if(facemark->fit(img,faces,shapes))
         {
-            cv::rectangle(img,faces[i],Scalar( 255, 0, 0 ));
-        }
-        for(unsigned long i=0;i<faces.size();i++){
-            for(unsigned long k=0;k<shapes[i].size();k++)
-                cv::circle(img,shapes[i][k],5,cv::Scalar(0,0,255),FILLED);
+            for( size_t i = 0; i < faces.size(); i++ )
+            {
+                cv::rectangle(img,faces[i],Scalar( 255, 0, 0 ));
+            }
+            for(unsigned long i=0;i<faces.size();i++){
+                for(unsigned long k=0;k<shapes[i].size();k++)
+                    cv::circle(img,shapes[i][k],5,cv::Scalar(0,0,255),FILLED);
+            }
+            namedWindow("Detected_shape");
+            imshow("Detected_shape",img);
+            waitKey(0);
         }
-        namedWindow("Detected_shape");
-        imshow("Detected_shape",img);
-        waitKey(0);
+    } else {
+        cout << "Faces not detected." << endl;
     }
     return 0;
-}
\ No newline at end of file
+}

modules/optflow/src/dis_flow.cpp

@@ -1316,7 +1316,8 @@ bool DISOpticalFlowImpl::ocl_calc(InputArray I0, InputArray I1, InputOutputArray
     else
         flow.create(I1Mat.size(), CV_32FC2);
     UMat &u_flowMat = flow.getUMatRef();
-    coarsest_scale = (int)(log((2 * I0Mat.cols) / (4.0 * patch_size)) / log(2.0) + 0.5) - 1;
+    coarsest_scale = min((int)(log(max(I0Mat.cols, I0Mat.rows) / (4.0 * patch_size)) / log(2.0) + 0.5), /* Original code serach for maximal movement of width/4 */
+                         (int)(log(min(I0Mat.cols, I0Mat.rows) / patch_size) / log(2.0)));              /* Deepest pyramid level greater or equal than patch*/
 
     ocl_prepareBuffers(I0Mat, I1Mat, u_flowMat, use_input_flow);
     u_Ux[coarsest_scale].setTo(0.0f);
@@ -1382,7 +1383,8 @@ void DISOpticalFlowImpl::calc(InputArray I0, InputArray I1, InputOutputArray flo
     else
         flow.create(I1Mat.size(), CV_32FC2);
     Mat flowMat = flow.getMat();
-    coarsest_scale = (int)(log((2 * I0Mat.cols) / (4.0 * patch_size)) / log(2.0) + 0.5) - 1;
+    coarsest_scale = min((int)(log(max(I0Mat.cols, I0Mat.rows) / (4.0 * patch_size)) / log(2.0) + 0.5), /* Original code serach for maximal movement of width/4 */
+                         (int)(log(min(I0Mat.cols, I0Mat.rows) / patch_size) / log(2.0)));              /* Deepest pyramid level greater or equal than patch*/
     int num_stripes = getNumThreads();
 
     prepareBuffers(I0Mat, I1Mat, flowMat, use_input_flow);

modules/surface_matching/src/hash_murmur64.hpp

@@ -125,16 +125,22 @@ FORCE_INLINE void hashMurmurx64 ( const void * key, const int len, const uint se
   {
   case 7:
     k2 ^= tail[6] << 16;
+    /* fallthrough */
   case 6:
     k2 ^= tail[5] << 8;
+    /* fallthrough */
   case 5:
     k2 ^= tail[4] << 0;
+    /* fallthrough */
   case 4:
     k1 ^= tail[3] << 24;
+    /* fallthrough */
   case 3:
     k1 ^= tail[2] << 16;
+    /* fallthrough */
   case 2:
     k1 ^= tail[1] << 8;
+    /* fallthrough */
   case 1:
     k1 ^= tail[0] << 0;
     bmix32(h1,h2,k1,k2,c1,c2);

modules/text/src/ocr_hmm_decoder.cpp

@@ -935,6 +935,7 @@ Ptr<OCRHMMDecoder::ClassifierCallback> loadOCRHMMClassifier(const String& _filen
             break;
         case OCR_CNN_CLASSIFIER:
             pt = loadOCRHMMClassifierCNN(_filename);
+            break;
         default:
             CV_Error(Error::StsBadArg, "Specified HMM classifier is not supported!");
             break;

modules/ximgproc/src/seeds.cpp

@@ -960,16 +960,16 @@ bool SuperpixelSEEDSImpl::probability(int image_idx, int label1, int label2,
         switch( seeds_prior )
         {
         case 5: p *= p;
-            //no break
+            /* fallthrough */
         case 4: p *= p;
-            //no break
+            /* fallthrough */
         case 3: p *= p;
-            //no break
+            /* fallthrough */
         case 2:
             p *= p;
             P_label1 *= T[seeds_top_level][label2];
             P_label2 *= T[seeds_top_level][label1];
-            //no break
+            /* fallthrough */
         case 1:
             P_label1 *= p;
             break;