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

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This commit is contained in:
Alexander Alekhin
2018-05-21 16:18:14 +03:00
parent a798127837 433d6c1903
commit f33d180808
12 changed files with 14998 additions and 29 deletions
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21
modules/aruco/doc/aruco.bib Normal file
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@@ -0,0 +1,21 @@
@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},
}

14
modules/aruco/include/opencv2/aruco.hpp
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@@ -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
};
/**

6
modules/aruco/include/opencv2/aruco/dictionary.hpp
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@@ -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
};

14
modules/aruco/samples/detect_markers.cpp
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@@ -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");

15
modules/aruco/src/dictionary.cpp
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@@ -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);
}

14900
modules/aruco/src/predefined_dictionaries_apriltag.hpp Normal file
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5
modules/datasets/CMakeLists.txt
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@@ -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
)

31
modules/face/samples/sampleDetectLandmarks.cpp
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@@ -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( 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);
}
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);
} else {
cout << "Faces not detected." << endl;
}
return 0;
}
}

6
modules/optflow/src/dis_flow.cpp
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@@ -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);

6
modules/surface_matching/src/hash_murmur64.hpp
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@@ -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);

1
modules/text/src/ocr_hmm_decoder.cpp
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@@ -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;

8
modules/ximgproc/src/seeds.cpp
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@@ -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;