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

cmake/OpenCVFindIPP.cmake

@@ -151,7 +151,7 @@ macro(ipp_detect_version)
         if("${name}" STREQUAL "core")  # https://github.com/opencv/opencv/pull/19681
           if(OPENCV_FORCE_IPP_EXCLUDE_LIBS OR OPENCV_FORCE_IPP_EXCLUDE_LIBS_CORE
               OR (UNIX AND NOT ANDROID AND NOT APPLE
-                  AND (CMAKE_CXX_COMPILER_ID MATCHES "GNU" OR CMAKE_CXX_COMPILER_ID MATCHES "Clang")
+                  AND CMAKE_CXX_COMPILER_ID MATCHES "GNU|Clang|Intel"
               )
               AND NOT OPENCV_SKIP_IPP_EXCLUDE_LIBS_CORE
           )

cmake/OpenCVFindLibsPerf.cmake

@@ -29,7 +29,7 @@ if(WITH_IPP)
     if(OPENCV_FORCE_IPP_EXCLUDE_LIBS
         OR (HAVE_IPP_ICV
             AND UNIX AND NOT ANDROID AND NOT APPLE
-            AND (CMAKE_CXX_COMPILER_ID MATCHES "GNU" OR CMAKE_CXX_COMPILER_ID MATCHES "Clang")
+            AND CMAKE_CXX_COMPILER_ID MATCHES "GNU|Clang|Intel"
         )
         AND NOT OPENCV_SKIP_IPP_EXCLUDE_LIBS
     )

modules/calib3d/include/opencv2/calib3d.hpp

@@ -2690,6 +2690,7 @@ final fundamental matrix. It can be set to something like 1-3, depending on the
 point localization, image resolution, and the image noise.
 @param mask Output array of N elements, every element of which is set to 0 for outliers and to 1
 for the other points. The array is computed only in the RANSAC and LMedS methods.
+@param maxIters The maximum number of robust method iterations.
 
 This function estimates essential matrix based on the five-point algorithm solver in @cite Nister03 .
 @cite SteweniusCFS is also a related. The epipolar geometry is described by the following equation:
@@ -2700,10 +2701,22 @@ where \f$E\f$ is an essential matrix, \f$p_1\f$ and \f$p_2\f$ are corresponding
 second images, respectively. The result of this function may be passed further to
 decomposeEssentialMat or recoverPose to recover the relative pose between cameras.
  */
-CV_EXPORTS_W Mat findEssentialMat( InputArray points1, InputArray points2,
-                                 InputArray cameraMatrix, int method = RANSAC,
-                                 double prob = 0.999, double threshold = 1.0,
-                                 OutputArray mask = noArray() );
+CV_EXPORTS_W
+Mat findEssentialMat(
+    InputArray points1, InputArray points2,
+    InputArray cameraMatrix, int method = RANSAC,
+    double prob = 0.999, double threshold = 1.0,
+    int maxIters = 1000, OutputArray mask = noArray()
+);
+
+/** @overload */
+CV_EXPORTS
+Mat findEssentialMat(
+    InputArray points1, InputArray points2,
+    InputArray cameraMatrix, int method,
+    double prob, double threshold,
+    OutputArray mask
+);  // TODO remove from OpenCV 5.0
 
 /** @overload
 @param points1 Array of N (N \>= 5) 2D points from the first image. The point coordinates should
@@ -2723,6 +2736,7 @@ point localization, image resolution, and the image noise.
 confidence (probability) that the estimated matrix is correct.
 @param mask Output array of N elements, every element of which is set to 0 for outliers and to 1
 for the other points. The array is computed only in the RANSAC and LMedS methods.
+@param maxIters The maximum number of robust method iterations.
 
 This function differs from the one above that it computes camera intrinsic matrix from focal length and
 principal point:
@@ -2734,10 +2748,23 @@ f & 0 & x_{pp}  \\
 0 & 0 & 1
 \end{bmatrix}\f]
  */
-CV_EXPORTS_W Mat findEssentialMat( InputArray points1, InputArray points2,
-                                 double focal = 1.0, Point2d pp = Point2d(0, 0),
-                                 int method = RANSAC, double prob = 0.999,
-                                 double threshold = 1.0, OutputArray mask = noArray() );
+CV_EXPORTS_W
+Mat findEssentialMat(
+    InputArray points1, InputArray points2,
+    double focal = 1.0, Point2d pp = Point2d(0, 0),
+    int method = RANSAC, double prob = 0.999,
+    double threshold = 1.0, int maxIters = 1000,
+    OutputArray mask = noArray()
+);
+
+/** @overload */
+CV_EXPORTS
+Mat findEssentialMat(
+    InputArray points1, InputArray points2,
+    double focal, Point2d pp,
+    int method, double prob,
+    double threshold, OutputArray mask
+);  // TODO remove from OpenCV 5.0
 
 /** @brief Calculates an essential matrix from the corresponding points in two images from potentially two different cameras.
 

modules/calib3d/src/five-point.cpp

@@ -405,7 +405,8 @@ protected:
 
 // Input should be a vector of n 2D points or a Nx2 matrix
 cv::Mat cv::findEssentialMat( InputArray _points1, InputArray _points2, InputArray _cameraMatrix,
-                              int method, double prob, double threshold, OutputArray _mask)
+                              int method, double prob, double threshold,
+                              int maxIters, OutputArray _mask)
 {
     CV_INSTRUMENT_REGION();
 
@@ -448,20 +449,36 @@ cv::Mat cv::findEssentialMat( InputArray _points1, InputArray _points2, InputArr
 
     Mat E;
     if( method == RANSAC )
-        createRANSACPointSetRegistrator(makePtr<EMEstimatorCallback>(), 5, threshold, prob)->run(points1, points2, E, _mask);
+        createRANSACPointSetRegistrator(makePtr<EMEstimatorCallback>(), 5, threshold, prob, maxIters)->run(points1, points2, E, _mask);
     else
-        createLMeDSPointSetRegistrator(makePtr<EMEstimatorCallback>(), 5, prob)->run(points1, points2, E, _mask);
+        createLMeDSPointSetRegistrator(makePtr<EMEstimatorCallback>(), 5, prob, maxIters)->run(points1, points2, E, _mask);
 
     return E;
 }
 
+cv::Mat cv::findEssentialMat( InputArray _points1, InputArray _points2, InputArray _cameraMatrix,
+                              int method, double prob, double threshold,
+                              OutputArray _mask)
+{
+    return cv::findEssentialMat(_points1, _points2, _cameraMatrix, method, prob, threshold, 1000, _mask);
+}
+
+cv::Mat cv::findEssentialMat( InputArray _points1, InputArray _points2, double focal, Point2d pp,
+                              int method, double prob, double threshold, int maxIters, OutputArray _mask)
+{
+    CV_INSTRUMENT_REGION();
+
+    Mat cameraMatrix = (Mat_<double>(3,3) << focal, 0, pp.x, 0, focal, pp.y, 0, 0, 1);
+    return cv::findEssentialMat(_points1, _points2, cameraMatrix, method, prob, threshold, maxIters, _mask);
+}
+
 cv::Mat cv::findEssentialMat( InputArray _points1, InputArray _points2, double focal, Point2d pp,
                               int method, double prob, double threshold, OutputArray _mask)
 {
     CV_INSTRUMENT_REGION();
 
     Mat cameraMatrix = (Mat_<double>(3,3) << focal, 0, pp.x, 0, focal, pp.y, 0, 0, 1);
-    return cv::findEssentialMat(_points1, _points2, cameraMatrix, method, prob, threshold, _mask);
+    return cv::findEssentialMat(_points1, _points2, cameraMatrix, method, prob, threshold, 1000, _mask);
 }
 
 cv::Mat cv::findEssentialMat( InputArray _points1, InputArray _points2,

modules/calib3d/src/fundam.cpp

@@ -888,7 +888,7 @@ cv::Mat cv::findFundamentalMat( InputArray _points1, InputArray _points2,
         if( (method & ~3) == FM_RANSAC && npoints >= 15 )
             result = createRANSACPointSetRegistrator(cb, 7, ransacReprojThreshold, confidence, maxIters)->run(m1, m2, F, _mask);
         else
-            result = createLMeDSPointSetRegistrator(cb, 7, confidence)->run(m1, m2, F, _mask);
+            result = createLMeDSPointSetRegistrator(cb, 7, confidence, maxIters)->run(m1, m2, F, _mask);
     }
 
     if( result <= 0 )

modules/core/perf/opencl/perf_arithm.cpp

@@ -678,7 +678,12 @@ OCL_PERF_TEST_P(SqrtFixture, Sqrt, ::testing::Combine(
 
     OCL_TEST_CYCLE() cv::sqrt(src, dst);
 
-    if (CV_MAT_DEPTH(type) >= CV_32F)
+    // To square root 32 bit floats we use native_sqrt, which has implementation
+    // defined accuracy. We know intel devices have accurate native_sqrt, but
+    // otherwise stick to a relaxed sanity check. For types larger than 32 bits
+    // we can do the accuracy check for all devices as normal.
+    if (CV_MAT_DEPTH(type) > CV_32F || !ocl::useOpenCL() ||
+        ocl::Device::getDefault().isIntel())
         SANITY_CHECK(dst, 1e-5, ERROR_RELATIVE);
     else
         SANITY_CHECK(dst, 1);

modules/features2d/src/blobdetector.cpp

@@ -325,13 +325,19 @@ void SimpleBlobDetectorImpl::detect(InputArray image, std::vector<cv::KeyPoint>&
 
         std::vector < Center > curCenters;
         findBlobs(grayscaleImage, binarizedImage, curCenters);
+        if(params.maxThreshold - params.minThreshold <= params.thresholdStep) {
+            // if the difference between min and max threshold is less than the threshold step
+            // we're only going to enter the loop once, so we need to add curCenters
+            // to ensure we still use minDistBetweenBlobs
+            centers.push_back(curCenters);
+        }
         std::vector < std::vector<Center> > newCenters;
         for (size_t i = 0; i < curCenters.size(); i++)
         {
             bool isNew = true;
             for (size_t j = 0; j < centers.size(); j++)
             {
-                double dist = norm(centers[j][ centers[j].size() / 2 ].location - curCenters[i].location);
+                double dist = norm(centers[j][centers[j].size() / 2 ].location - curCenters[i].location);
                 isNew = dist >= params.minDistBetweenBlobs && dist >= centers[j][ centers[j].size() / 2 ].radius && dist >= curCenters[i].radius;
                 if (!isNew)
                 {

modules/features2d/test/test_blobdetector.cpp

@@ -0,0 +1,21 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+
+#include "test_precomp.hpp"
+
+namespace opencv_test { namespace {
+TEST(Features2d_BlobDetector, bug_6667)
+{
+    cv::Mat image = cv::Mat(cv::Size(100, 100), CV_8UC1, cv::Scalar(255, 255, 255));
+    cv::circle(image, Point(50, 50), 20, cv::Scalar(0), -1);
+    SimpleBlobDetector::Params params;
+    params.minThreshold = 250;
+    params.maxThreshold = 260;
+    std::vector<KeyPoint> keypoints;
+
+    Ptr<SimpleBlobDetector> detector = SimpleBlobDetector::create(params);
+    detector->detect(image, keypoints);
+    ASSERT_NE((int) keypoints.size(), 0);
+}
+}} // namespace

modules/imgproc/perf/opencl/perf_imgproc.cpp

@@ -166,7 +166,17 @@ OCL_PERF_TEST_P(CornerMinEigenValFixture, CornerMinEigenVal,
 
     OCL_TEST_CYCLE() cv::cornerMinEigenVal(src, dst, blockSize, apertureSize, borderType);
 
-    SANITY_CHECK(dst, 1e-6, ERROR_RELATIVE);
+#ifdef HAVE_OPENCL
+    bool strictCheck = !ocl::useOpenCL() || ocl::Device::getDefault().isIntel();
+#else
+    bool strictCheck = true;
+#endif
+
+    // using native_* OpenCL functions on non-intel devices may lose accuracy
+    if (strictCheck)
+        SANITY_CHECK(dst, 1e-6, ERROR_RELATIVE);
+    else
+        SANITY_CHECK(dst, 0.1, ERROR_RELATIVE);
 }
 
 ///////////// CornerHarris ////////////////////////

modules/imgproc/src/imgwarp.cpp

@@ -2190,7 +2190,7 @@ public:
                 int bw = std::min( bw0, dst.cols - x);
                 int bh = std::min( bh0, range.end - y);
 
-                Mat _XY(bh, bw, CV_16SC2, XY), matA;
+                Mat _XY(bh, bw, CV_16SC2, XY);
                 Mat dpart(dst, Rect(x, y, bw, bh));
 
                 for( y1 = 0; y1 < bh; y1++ )
@@ -2979,7 +2979,7 @@ public:
                 int bw = std::min( bw0, width - x);
                 int bh = std::min( bh0, range.end - y); // height
 
-                Mat _XY(bh, bw, CV_16SC2, XY), matA;
+                Mat _XY(bh, bw, CV_16SC2, XY);
                 Mat dpart(dst, Rect(x, y, bw, bh));
 
                 for( y1 = 0; y1 < bh; y1++ )

modules/imgproc/test/ocl/test_imgproc.cpp

@@ -234,10 +234,12 @@ OCL_TEST_P(CornerMinEigenVal, Mat)
         OCL_OFF(cv::cornerMinEigenVal(src_roi, dst_roi, blockSize, apertureSize, borderType));
         OCL_ON(cv::cornerMinEigenVal(usrc_roi, udst_roi, blockSize, apertureSize, borderType));
 
-        if (ocl::Device::getDefault().isIntel())
-          Near(1e-5, true);
+        // The corner kernel uses native_sqrt() which has implementation defined accuracy.
+        // If we're using a CL implementation that isn't intel, test with relaxed accuracy.
+        if (!ocl::useOpenCL() || ocl::Device::getDefault().isIntel())
+            Near(1e-5, true);
         else
-          Near(0.1, true);  // using native_* OpenCL functions may lose accuracy
+            Near(0.1, true);
     }
 }