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

cmake/OpenCVUtils.cmake

@@ -402,8 +402,8 @@ endmacro()
 
 set(OCV_COMPILER_FAIL_REGEX
     "argument .* is not valid"                  # GCC 9+ (including support of unicode quotes)
-    "command line option .* is valid for .* but not for C\\+\\+" # GNU
-    "command line option .* is valid for .* but not for C" # GNU
+    "command[- ]line option .* is valid for .* but not for C\\+\\+" # GNU
+    "command[- ]line option .* is valid for .* but not for C" # GNU
     "unrecognized .*option"                     # GNU
     "unknown .*option"                          # Clang
     "ignoring unknown option"                   # MSVC

doc/py_tutorials/py_imgproc/py_thresholding/py_thresholding.markdown

@@ -188,7 +188,7 @@ blur = cv.GaussianBlur(img,(5,5),0)
 
 # find normalized_histogram, and its cumulative distribution function
 hist = cv.calcHist([blur],[0],None,[256],[0,256])
-hist_norm = hist.ravel()/hist.max()
+hist_norm = hist.ravel()/hist.sum()
 Q = hist_norm.cumsum()
 
 bins = np.arange(256)
@@ -199,6 +199,8 @@ thresh = -1
 for i in xrange(1,256):
     p1,p2 = np.hsplit(hist_norm,[i]) # probabilities
     q1,q2 = Q[i],Q[255]-Q[i] # cum sum of classes
+    if q1 < 1.e-6 or q2 < 1.e-6:
+        continue
     b1,b2 = np.hsplit(bins,[i]) # weights
 
     # finding means and variances

modules/calib3d/include/opencv2/calib3d.hpp

@@ -2388,7 +2388,7 @@ CV_EXPORTS_W void filterSpeckles( InputOutputArray img, double newVal,
 //! computes valid disparity ROI from the valid ROIs of the rectified images (that are returned by cv::stereoRectify())
 CV_EXPORTS_W Rect getValidDisparityROI( Rect roi1, Rect roi2,
                                         int minDisparity, int numberOfDisparities,
-                                        int SADWindowSize );
+                                        int blockSize );
 
 //! validates disparity using the left-right check. The matrix "cost" should be computed by the stereo correspondence algorithm
 CV_EXPORTS_W void validateDisparity( InputOutputArray disparity, InputArray cost,
@@ -2813,8 +2813,8 @@ public:
     the smoother the disparity is. P1 is the penalty on the disparity change by plus or minus 1
     between neighbor pixels. P2 is the penalty on the disparity change by more than 1 between neighbor
     pixels. The algorithm requires P2 \> P1 . See stereo_match.cpp sample where some reasonably good
-    P1 and P2 values are shown (like 8\*number_of_image_channels\*SADWindowSize\*SADWindowSize and
-    32\*number_of_image_channels\*SADWindowSize\*SADWindowSize , respectively).
+    P1 and P2 values are shown (like 8\*number_of_image_channels\*blockSize\*blockSize and
+    32\*number_of_image_channels\*blockSize\*blockSize , respectively).
     @param disp12MaxDiff Maximum allowed difference (in integer pixel units) in the left-right
     disparity check. Set it to a non-positive value to disable the check.
     @param preFilterCap Truncation value for the prefiltered image pixels. The algorithm first

modules/calib3d/src/ap3p.cpp

@@ -46,7 +46,7 @@ void solveQuartic(const double *factors, double *realRoots) {
     complex<double> sqrt_2m = sqrt(static_cast<complex<double> >(-2 * p4 / 3 + t));
     double B_4A = -a3 / (4 * a4);
     double complex1 = 4 * p4 / 3 + t;
-#if defined(__clang__) && defined(__arm__) && (__clang_major__ == 3 || __clang_minor__ == 4) && !defined(__ANDROID__)
+#if defined(__clang__) && defined(__arm__) && (__clang_major__ == 3 || __clang_major__ == 4) && !defined(__ANDROID__)
     // details: https://github.com/opencv/opencv/issues/11135
     // details: https://github.com/opencv/opencv/issues/11056
     complex<double> complex2 = 2 * q4;

modules/dnn/src/layers/resize_layer.cpp

@@ -25,7 +25,9 @@ namespace cv { namespace dnn {
 class ResizeLayerImpl : public ResizeLayer
 {
 public:
-    ResizeLayerImpl(const LayerParams& params) : zoomFactorWidth(0), zoomFactorHeight(0), scaleWidth(0), scaleHeight(0)
+    ResizeLayerImpl(const LayerParams& params) : zoomFactorWidth(params.get<int>("zoom_factor_x", params.get<int>("zoom_factor", 0))),
+                                                 zoomFactorHeight(params.get<int>("zoom_factor_y", params.get<int>("zoom_factor", 0))),
+                                                 scaleWidth(0), scaleHeight(0)
     {
         setParamsFrom(params);
         outWidth = params.get<float>("width", 0);
@@ -33,13 +35,10 @@ public:
         if (params.has("zoom_factor"))
         {
             CV_Assert(!params.has("zoom_factor_x") && !params.has("zoom_factor_y"));
-            zoomFactorWidth = zoomFactorHeight = params.get<int>("zoom_factor");
         }
         else if (params.has("zoom_factor_x") || params.has("zoom_factor_y"))
         {
             CV_Assert(params.has("zoom_factor_x") && params.has("zoom_factor_y"));
-            zoomFactorWidth = params.get<int>("zoom_factor_x");
-            zoomFactorHeight = params.get<int>("zoom_factor_y");
         }
         interpolation = params.get<String>("interpolation");
         CV_Assert(interpolation == "nearest" || interpolation == "bilinear");
@@ -54,8 +53,8 @@ public:
     {
         CV_Assert_N(inputs.size() == 1, inputs[0].size() == 4);
         outputs.resize(1, inputs[0]);
-        outputs[0][2] = outHeight > 0 ? outHeight : (outputs[0][2] * zoomFactorHeight);
-        outputs[0][3] = outWidth > 0 ? outWidth : (outputs[0][3] * zoomFactorWidth);
+        outputs[0][2] = zoomFactorHeight > 0 ? (outputs[0][2] * zoomFactorHeight) : outHeight;
+        outputs[0][3] = zoomFactorWidth > 0 ? (outputs[0][3] * zoomFactorWidth) : outWidth;
         // We can work in-place (do nothing) if input shape == output shape.
         return (outputs[0][2] == inputs[0][2]) && (outputs[0][3] == inputs[0][3]);
     }
@@ -82,11 +81,8 @@ public:
         inputs_arr.getMatVector(inputs);
         outputs_arr.getMatVector(outputs);
 
-        if (!outWidth && !outHeight)
-        {
-            outHeight = outputs[0].size[2];
-            outWidth = outputs[0].size[3];
-        }
+        outHeight = outputs[0].size[2];
+        outWidth = outputs[0].size[3];
         if (alignCorners && outHeight > 1)
             scaleHeight = static_cast<float>(inputs[0].size[2] - 1) / (outHeight - 1);
         else
@@ -214,7 +210,7 @@ public:
             ieLayer.setType("Interp");
             ieLayer.getParameters()["pad_beg"] = 0;
             ieLayer.getParameters()["pad_end"] = 0;
-            ieLayer.getParameters()["align_corners"] = false;
+            ieLayer.getParameters()["align_corners"] = alignCorners;
         }
         else
             CV_Error(Error::StsNotImplemented, "Unsupported interpolation: " + interpolation);
@@ -238,7 +234,7 @@ public:
         attrs.pads_begin.push_back(0);
         attrs.pads_end.push_back(0);
         attrs.axes = ngraph::AxisSet{2, 3};
-        attrs.align_corners = false;
+        attrs.align_corners = alignCorners;
 
         if (interpolation == "nearest") {
             attrs.mode = "nearest";
@@ -257,7 +253,8 @@ public:
 #endif  // HAVE_DNN_NGRAPH
 
 protected:
-    int outWidth, outHeight, zoomFactorWidth, zoomFactorHeight;
+    int outWidth, outHeight;
+    const int zoomFactorWidth, zoomFactorHeight;
     String interpolation;
     float scaleWidth, scaleHeight;
     bool alignCorners;
@@ -281,79 +278,18 @@ public:
     {
         CV_Assert_N(inputs.size() == 1, inputs[0].size() == 4);
         outputs.resize(1, inputs[0]);
-        outputs[0][2] = outHeight > 0 ? outHeight : (1 + zoomFactorHeight * (outputs[0][2] - 1));
-        outputs[0][3] = outWidth > 0 ? outWidth : (1 + zoomFactorWidth * (outputs[0][3] - 1));
+        outputs[0][2] = zoomFactorHeight > 0 ? (1 + zoomFactorHeight * (outputs[0][2] - 1)) : outHeight;
+        outputs[0][3] = zoomFactorWidth > 0 ? (1 + zoomFactorWidth * (outputs[0][3] - 1)) : outWidth;
         // We can work in-place (do nothing) if input shape == output shape.
         return (outputs[0][2] == inputs[0][2]) && (outputs[0][3] == inputs[0][3]);
     }
-
-    virtual bool supportBackend(int backendId) CV_OVERRIDE
-    {
-#ifdef HAVE_INF_ENGINE
-        if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019
-            || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
-            return true;
-#endif
-        return backendId == DNN_BACKEND_OPENCV ||
-               backendId == DNN_BACKEND_CUDA;
-    }
-
-    virtual void finalize(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr) CV_OVERRIDE
-    {
-        std::vector<Mat> inputs, outputs;
-        inputs_arr.getMatVector(inputs);
-        outputs_arr.getMatVector(outputs);
-
-        if (!outWidth && !outHeight)
-        {
-            outHeight = outputs[0].size[2];
-            outWidth = outputs[0].size[3];
-        }
-        int inpHeight = inputs[0].size[2];
-        int inpWidth = inputs[0].size[3];
-        scaleHeight = (outHeight > 1) ? (static_cast<float>(inpHeight - 1) / (outHeight - 1)) : 0.f;
-        scaleWidth = (outWidth > 1) ? (static_cast<float>(inpWidth - 1) / (outWidth - 1)) : 0.f;
-    }
-
-#ifdef HAVE_INF_ENGINE
-    virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
-    {
-        InferenceEngine::Builder::Layer ieLayer(name);
-        ieLayer.setName(name);
-        ieLayer.setType("Interp");
-        ieLayer.getParameters()["pad_beg"] = 0;
-        ieLayer.getParameters()["pad_end"] = 0;
-        ieLayer.getParameters()["width"] = outWidth;
-        ieLayer.getParameters()["height"] = outHeight;
-        ieLayer.setInputPorts(std::vector<InferenceEngine::Port>(1));
-        ieLayer.setOutputPorts(std::vector<InferenceEngine::Port>(1));
-        return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
-    }
-#endif  // HAVE_INF_ENGINE
-
-#ifdef HAVE_DNN_NGRAPH
-    virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,
-                                        const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
-    {
-        auto& ieInpNode = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
-        ngraph::op::InterpolateAttrs attrs;
-        attrs.pads_begin.push_back(0);
-        attrs.pads_end.push_back(0);
-        attrs.axes = ngraph::AxisSet{2, 3};
-        attrs.mode = "linear";
-        std::vector<int64_t> shape = {outHeight, outWidth};
-        auto out_shape = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{2}, shape.data());
-        auto interp = std::make_shared<ngraph::op::Interpolate>(ieInpNode, out_shape, attrs);
-        return Ptr<BackendNode>(new InfEngineNgraphNode(interp));
-    }
-#endif  // HAVE_DNN_NGRAPH
-
 };
 
 Ptr<Layer> InterpLayer::create(const LayerParams& params)
 {
     LayerParams lp(params);
     lp.set("interpolation", "bilinear");
+    lp.set("align_corners", true);
     return Ptr<Layer>(new InterpLayerImpl(lp));
 }
 

modules/dnn/test/test_layers.cpp

@@ -1760,4 +1760,35 @@ INSTANTIATE_TEST_CASE_P(/**/, Layer_Test_Eltwise_unequal, Combine(
     dnnBackendsAndTargets()
 ));
 
+typedef testing::TestWithParam<tuple<Backend, Target> > Layer_Test_Resize;
+TEST_P(Layer_Test_Resize, change_input)
+{
+    int backendId = get<0>(GetParam());
+    int targetId = get<1>(GetParam());
+
+    Net net;
+    LayerParams lp;
+    lp.type = "Resize";
+    lp.name = "testLayer";
+    lp.set("zoom_factor", 2);
+    lp.set("interpolation", "nearest");
+    net.addLayerToPrev(lp.name, lp.type, lp);
+
+    for (int i = 0; i < 2; ++i)
+    {
+        Mat inp(4 + i, 5 + i, CV_8UC3), ref;
+        randu(inp, 0, 255);
+        resize(inp, ref, Size(0, 0), 2, 2, INTER_NEAREST);
+        ref = blobFromImage(ref);
+
+        net.setInput(blobFromImage(inp));
+        net.setPreferableBackend(backendId);
+        net.setPreferableTarget(targetId);
+        Mat out = net.forward();
+        normAssert(out, ref);
+    }
+}
+
+INSTANTIATE_TEST_CASE_P(/**/, Layer_Test_Resize, dnnBackendsAndTargets());
+
 }} // namespace

modules/highgui/src/window_QT.cpp

@@ -54,9 +54,12 @@
 #include <unistd.h>
 #endif
 
+// Get GL_PERSPECTIVE_CORRECTION_HINT definition, not available in GLES 2 or
+// OpenGL 3 core profile or later
 #ifdef HAVE_QT_OPENGL
-    #if defined Q_WS_X11 /* Qt4 */ || defined Q_OS_LINUX /* Qt5 */
-        #include <GL/glx.h>
+    #if defined Q_WS_X11 /* Qt4 */ || \
+        (!defined(QT_OPENGL_ES_2) && defined Q_OS_LINUX) /* Qt5 with desktop OpenGL */
+        #include <GL/gl.h>
     #endif
 #endif
 
@@ -3225,7 +3228,9 @@ void OpenGlViewPort::updateGl()
 
 void OpenGlViewPort::initializeGL()
 {
+#ifdef GL_PERSPECTIVE_CORRECTION_HINT
     glHint(GL_PERSPECTIVE_CORRECTION_HINT, GL_NICEST);
+#endif
 }
 
 void OpenGlViewPort::resizeGL(int w, int h)

modules/imgproc/include/opencv2/imgproc.hpp

@@ -1446,7 +1446,7 @@ equal to sigmaX, if both sigmas are zeros, they are computed from ksize.width an
 respectively (see #getGaussianKernel for details); to fully control the result regardless of
 possible future modifications of all this semantics, it is recommended to specify all of ksize,
 sigmaX, and sigmaY.
-@param borderType pixel extrapolation method, see #BorderTypes
+@param borderType pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
 
 @sa  sepFilter2D, filter2D, blur, boxFilter, bilateralFilter, medianBlur
  */
@@ -1507,7 +1507,7 @@ algorithms, and so on). If you need to compute pixel sums over variable-size win
 @param anchor anchor point; default value Point(-1,-1) means that the anchor is at the kernel
 center.
 @param normalize flag, specifying whether the kernel is normalized by its area or not.
-@param borderType border mode used to extrapolate pixels outside of the image, see #BorderTypes
+@param borderType border mode used to extrapolate pixels outside of the image, see #BorderTypes. #BORDER_WRAP is not supported.
 @sa  blur, bilateralFilter, GaussianBlur, medianBlur, integral
  */
 CV_EXPORTS_W void boxFilter( InputArray src, OutputArray dst, int ddepth,
@@ -1530,7 +1530,7 @@ variance and standard deviation around the neighborhood of a pixel.
 @param anchor kernel anchor point. The default value of Point(-1, -1) denotes that the anchor is at the kernel
 center.
 @param normalize flag, specifying whether the kernel is to be normalized by it's area or not.
-@param borderType border mode used to extrapolate pixels outside of the image, see #BorderTypes
+@param borderType border mode used to extrapolate pixels outside of the image, see #BorderTypes. #BORDER_WRAP is not supported.
 @sa boxFilter
 */
 CV_EXPORTS_W void sqrBoxFilter( InputArray src, OutputArray dst, int ddepth,
@@ -1553,7 +1553,7 @@ the depth should be CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
 @param ksize blurring kernel size.
 @param anchor anchor point; default value Point(-1,-1) means that the anchor is at the kernel
 center.
-@param borderType border mode used to extrapolate pixels outside of the image, see #BorderTypes
+@param borderType border mode used to extrapolate pixels outside of the image, see #BorderTypes. #BORDER_WRAP is not supported.
 @sa  boxFilter, bilateralFilter, GaussianBlur, medianBlur
  */
 CV_EXPORTS_W void blur( InputArray src, OutputArray dst,
@@ -1587,7 +1587,7 @@ separate color planes using split and process them individually.
 the kernel; the anchor should lie within the kernel; default value (-1,-1) means that the anchor
 is at the kernel center.
 @param delta optional value added to the filtered pixels before storing them in dst.
-@param borderType pixel extrapolation method, see #BorderTypes
+@param borderType pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
 @sa  sepFilter2D, dft, matchTemplate
  */
 CV_EXPORTS_W void filter2D( InputArray src, OutputArray dst, int ddepth,
@@ -1608,7 +1608,7 @@ kernel kernelY. The final result shifted by delta is stored in dst .
 @param anchor Anchor position within the kernel. The default value \f$(-1,-1)\f$ means that the anchor
 is at the kernel center.
 @param delta Value added to the filtered results before storing them.
-@param borderType Pixel extrapolation method, see #BorderTypes
+@param borderType Pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
 @sa  filter2D, Sobel, GaussianBlur, boxFilter, blur
  */
 CV_EXPORTS_W void sepFilter2D( InputArray src, OutputArray dst, int ddepth,
@@ -1661,7 +1661,7 @@ The second case corresponds to a kernel of:
 @param scale optional scale factor for the computed derivative values; by default, no scaling is
 applied (see #getDerivKernels for details).
 @param delta optional delta value that is added to the results prior to storing them in dst.
-@param borderType pixel extrapolation method, see #BorderTypes
+@param borderType pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
 @sa  Scharr, Laplacian, sepFilter2D, filter2D, GaussianBlur, cartToPolar
  */
 CV_EXPORTS_W void Sobel( InputArray src, OutputArray dst, int ddepth,
@@ -1682,7 +1682,8 @@ Sobel( src, dy, CV_16SC1, 0, 1, 3 );
 @param dx output image with first-order derivative in x.
 @param dy output image with first-order derivative in y.
 @param ksize size of Sobel kernel. It must be 3.
-@param borderType pixel extrapolation method, see #BorderTypes
+@param borderType pixel extrapolation method, see #BorderTypes.
+                  Only #BORDER_DEFAULT=#BORDER_REFLECT_101 and #BORDER_REPLICATE are supported.
 
 @sa Sobel
  */
@@ -1710,7 +1711,7 @@ is equivalent to
 @param scale optional scale factor for the computed derivative values; by default, no scaling is
 applied (see #getDerivKernels for details).
 @param delta optional delta value that is added to the results prior to storing them in dst.
-@param borderType pixel extrapolation method, see #BorderTypes
+@param borderType pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
 @sa  cartToPolar
  */
 CV_EXPORTS_W void Scharr( InputArray src, OutputArray dst, int ddepth,
@@ -1741,7 +1742,7 @@ details. The size must be positive and odd.
 @param scale Optional scale factor for the computed Laplacian values. By default, no scaling is
 applied. See #getDerivKernels for details.
 @param delta Optional delta value that is added to the results prior to storing them in dst .
-@param borderType Pixel extrapolation method, see #BorderTypes
+@param borderType Pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
 @sa  Sobel, Scharr
  */
 CV_EXPORTS_W void Laplacian( InputArray src, OutputArray dst, int ddepth,
@@ -1810,7 +1811,7 @@ of the formulae in the cornerEigenValsAndVecs description.
 src .
 @param blockSize Neighborhood size (see the details on #cornerEigenValsAndVecs ).
 @param ksize Aperture parameter for the Sobel operator.
-@param borderType Pixel extrapolation method. See #BorderTypes.
+@param borderType Pixel extrapolation method. See #BorderTypes. #BORDER_WRAP is not supported.
  */
 CV_EXPORTS_W void cornerMinEigenVal( InputArray src, OutputArray dst,
                                      int blockSize, int ksize = 3,
@@ -1833,7 +1834,7 @@ size as src .
 @param blockSize Neighborhood size (see the details on #cornerEigenValsAndVecs ).
 @param ksize Aperture parameter for the Sobel operator.
 @param k Harris detector free parameter. See the formula above.
-@param borderType Pixel extrapolation method. See #BorderTypes.
+@param borderType Pixel extrapolation method. See #BorderTypes. #BORDER_WRAP is not supported.
  */
 CV_EXPORTS_W void cornerHarris( InputArray src, OutputArray dst, int blockSize,
                                 int ksize, double k,
@@ -1861,7 +1862,7 @@ The output of the function can be used for robust edge or corner detection.
 @param dst Image to store the results. It has the same size as src and the type CV_32FC(6) .
 @param blockSize Neighborhood size (see details below).
 @param ksize Aperture parameter for the Sobel operator.
-@param borderType Pixel extrapolation method. See #BorderTypes.
+@param borderType Pixel extrapolation method. See #BorderTypes. #BORDER_WRAP is not supported.
 
 @sa  cornerMinEigenVal, cornerHarris, preCornerDetect
  */
@@ -1890,7 +1891,7 @@ The corners can be found as local maximums of the functions, as shown below:
 @param src Source single-channel 8-bit of floating-point image.
 @param dst Output image that has the type CV_32F and the same size as src .
 @param ksize %Aperture size of the Sobel .
-@param borderType Pixel extrapolation method. See #BorderTypes.
+@param borderType Pixel extrapolation method. See #BorderTypes. #BORDER_WRAP is not supported.
  */
 CV_EXPORTS_W void preCornerDetect( InputArray src, OutputArray dst, int ksize,
                                    int borderType = BORDER_DEFAULT );
@@ -2154,7 +2155,7 @@ structuring element is used. Kernel can be created using #getStructuringElement.
 @param anchor position of the anchor within the element; default value (-1, -1) means that the
 anchor is at the element center.
 @param iterations number of times erosion is applied.
-@param borderType pixel extrapolation method, see #BorderTypes
+@param borderType pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
 @param borderValue border value in case of a constant border
 @sa  dilate, morphologyEx, getStructuringElement
  */
@@ -2186,7 +2187,7 @@ structuring element is used. Kernel can be created using #getStructuringElement
 @param anchor position of the anchor within the element; default value (-1, -1) means that the
 anchor is at the element center.
 @param iterations number of times dilation is applied.
-@param borderType pixel extrapolation method, see #BorderTypes
+@param borderType pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not suported.
 @param borderValue border value in case of a constant border
 @sa  erode, morphologyEx, getStructuringElement
  */
@@ -2211,7 +2212,7 @@ CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
 @param anchor Anchor position with the kernel. Negative values mean that the anchor is at the
 kernel center.
 @param iterations Number of times erosion and dilation are applied.
-@param borderType Pixel extrapolation method, see #BorderTypes
+@param borderType Pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
 @param borderValue Border value in case of a constant border. The default value has a special
 meaning.
 @sa  dilate, erode, getStructuringElement

modules/imgproc/src/resize.cpp

@@ -1674,93 +1674,9 @@ struct HResizeLinearVecU8_X4
                 }
             }
         }
-        else if(cn < 9)
-        {
-            const int step = 8;
-            const int len0 = xmax & -step;
-            for( ; k <= (count - 2); k+=2 )
-            {
-                const uchar *S0 = src[k];
-                int *D0 = dst[k];
-                const uchar *S1 = src[k+1];
-                int *D1 = dst[k+1];
-
-                for( dx = 0; dx < len0; dx += cn )
-                {
-                    v_int16x8 a0 = v_load(alpha+dx*2);
-                    v_int16x8 a1 = v_load(alpha+dx*2 + 8);
-                    v_uint16x8 s0, s1;
-                    v_zip(v_load_expand(S0+xofs[dx]), v_load_expand(S0+xofs[dx]+cn), s0, s1);
-                    v_store(&D0[dx], v_dotprod(v_reinterpret_as_s16(s0), a0));
-                    v_store(&D0[dx+4], v_dotprod(v_reinterpret_as_s16(s1), a1));
-                    v_zip(v_load_expand(S1+xofs[dx]), v_load_expand(S1+xofs[dx]+cn), s0, s1);
-                    v_store(&D1[dx], v_dotprod(v_reinterpret_as_s16(s0), a0));
-                    v_store(&D1[dx+4], v_dotprod(v_reinterpret_as_s16(s1), a1));
-                }
-            }
-            for( ; k < count; k++ )
-            {
-                const uchar *S = src[k];
-                int *D = dst[k];
-                for( dx = 0; dx < len0; dx += cn )
-                {
-                    v_int16x8 a0 = v_load(alpha+dx*2);
-                    v_int16x8 a1 = v_load(alpha+dx*2 + 8);
-                    v_uint16x8 s0, s1;
-                    v_zip(v_load_expand(S+xofs[dx]), v_load_expand(S+xofs[dx]+cn), s0, s1);
-                    v_store(&D[dx], v_dotprod(v_reinterpret_as_s16(s0), a0));
-                    v_store(&D[dx+4], v_dotprod(v_reinterpret_as_s16(s1), a1));
-                }
-            }
-        }
         else
         {
-            const int step = 16;
-            const int len0 = (xmax - cn) & -step;
-            for( ; k <= (count - 2); k+=2 )
-            {
-                const uchar *S0 = src[k];
-                int *D0 = dst[k];
-                const uchar *S1 = src[k+1];
-                int *D1 = dst[k+1];
-
-                for( dx = 0; dx < len0; dx += step )
-                {
-                    v_int16x8 a0 = v_load(alpha+dx*2);
-                    v_int16x8 a1 = v_load(alpha+dx*2 + 8);
-                    v_int16x8 a2 = v_load(alpha+dx*2 + 16);
-                    v_int16x8 a3 = v_load(alpha+dx*2 + 24);
-                    v_uint8x16 s01, s23;
-                    v_zip(v_lut(S0, xofs+dx), v_lut(S0+cn, xofs+dx), s01, s23);
-                    v_store(&D0[dx], v_dotprod(v_reinterpret_as_s16(v_expand_low(s01)), a0));
-                    v_store(&D0[dx+4], v_dotprod(v_reinterpret_as_s16(v_expand_high(s01)), a1));
-                    v_store(&D0[dx+8], v_dotprod(v_reinterpret_as_s16(v_expand_low(s23)), a2));
-                    v_store(&D0[dx+12], v_dotprod(v_reinterpret_as_s16(v_expand_high(s23)), a3));
-                    v_zip(v_lut(S1, xofs+dx), v_lut(S1+cn, xofs+dx), s01, s23);
-                    v_store(&D1[dx], v_dotprod(v_reinterpret_as_s16(v_expand_low(s01)), a0));
-                    v_store(&D1[dx+4], v_dotprod(v_reinterpret_as_s16(v_expand_high(s01)), a1));
-                    v_store(&D1[dx+8], v_dotprod(v_reinterpret_as_s16(v_expand_low(s23)), a2));
-                    v_store(&D1[dx+12], v_dotprod(v_reinterpret_as_s16(v_expand_high(s23)), a3));
-                }
-            }
-            for( ; k < count; k++ )
-            {
-                const uchar *S = src[k];
-                int *D = dst[k];
-                for( dx = 0; dx < len0; dx += step )
-                {
-                    v_int16x8 a0 = v_load(alpha+dx*2);
-                    v_int16x8 a1 = v_load(alpha+dx*2 + 8);
-                    v_int16x8 a2 = v_load(alpha+dx*2 + 16);
-                    v_int16x8 a3 = v_load(alpha+dx*2 + 24);
-                    v_uint8x16 s01, s23;
-                    v_zip(v_lut(S, xofs+dx), v_lut(S+cn, xofs+dx), s01, s23);
-                    v_store(&D[dx], v_dotprod(v_reinterpret_as_s16(v_expand_low(s01)), a0));
-                    v_store(&D[dx+4], v_dotprod(v_reinterpret_as_s16(v_expand_high(s01)), a1));
-                    v_store(&D[dx+8], v_dotprod(v_reinterpret_as_s16(v_expand_low(s23)), a2));
-                    v_store(&D[dx+12], v_dotprod(v_reinterpret_as_s16(v_expand_high(s23)), a3));
-                }
-            }
+            return 0;  // images with channels >4 are out of optimization scope
         }
         return dx;
     }

modules/imgproc/test/test_drawing.cpp

@@ -583,4 +583,14 @@ TEST(Drawing, line)
     ASSERT_THROW(line(mat, Point(1,1),Point(99,99),Scalar(255),0), cv::Exception);
 }
 
+TEST(Drawing, regression_16308)
+{
+    Mat_<uchar> img(Size(100, 100), (uchar)0);
+    circle(img, Point(50, 50), 50, 255, 1, LINE_AA);
+    EXPECT_NE(0, (int)img.at<uchar>(0, 50));
+    EXPECT_NE(0, (int)img.at<uchar>(50, 0));
+    EXPECT_NE(0, (int)img.at<uchar>(50, 99));
+    EXPECT_NE(0, (int)img.at<uchar>(99, 50));
+}
+
 }} // namespace

modules/objdetect/src/qrcode.cpp

@@ -2289,7 +2289,8 @@ bool QRCodeDetector::decodeMulti(
     CV_Assert((points.size().width % 4) == 0);
     vector< vector< Point2f > > src_points ;
     Mat qr_points = points.getMat();
-    for (int i = 0; i < points.size().width ; i += 4)
+    qr_points = qr_points.reshape(2, 1);
+    for (int i = 0; i < qr_points.size().width ; i += 4)
     {
         vector<Point2f> tempMat = qr_points.colRange(i, i + 4);
         if (contourArea(tempMat) > 0.0)

modules/objdetect/test/test_qrcode.cpp

@@ -481,6 +481,26 @@ INSTANTIATE_TEST_CASE_P(/**/, Objdetect_QRCode_Close, testing::ValuesIn(qrcode_i
 INSTANTIATE_TEST_CASE_P(/**/, Objdetect_QRCode_Monitor, testing::ValuesIn(qrcode_images_monitor));
 INSTANTIATE_TEST_CASE_P(/**/, Objdetect_QRCode_Multi, testing::ValuesIn(qrcode_images_multiple));
 
+TEST(Objdetect_QRCode_decodeMulti, decode_regression_16491)
+{
+#ifdef HAVE_QUIRC
+    Mat zero_image = Mat::zeros(256, 256, CV_8UC1);
+    Point corners_[] = {Point(16, 16), Point(128, 16), Point(128, 128), Point(16, 128),
+                        Point(16, 16), Point(128, 16), Point(128, 128), Point(16, 128)};
+    std::vector<Point> vec_corners;
+    int array_size = 8;
+    vec_corners.assign(corners_, corners_ + array_size);
+    std::vector<cv::String> decoded_info;
+    std::vector<Mat> straight_barcode;
+    QRCodeDetector vec_qrcode;
+    EXPECT_NO_THROW(vec_qrcode.decodeMulti(zero_image, vec_corners, decoded_info, straight_barcode));
+
+    Mat mat_corners(2, 4, CV_32SC2, (void*)&vec_corners[0]);
+    QRCodeDetector mat_qrcode;
+    EXPECT_NO_THROW(mat_qrcode.decodeMulti(zero_image, mat_corners, decoded_info, straight_barcode));
+#endif
+}
+
 TEST(Objdetect_QRCode_basic, not_found_qrcode)
 {
     std::vector<Point> corners;