Merge pull request #11984 from mshabunin:fix-static-1

modules/calib3d/src/calibration.cpp

@@ -2336,10 +2336,13 @@ void cvStereoRectify( const CvMat* _cameraMatrix1, const CvMat* _cameraMatrix2,
     _uu[2] = 1;
     cvCrossProduct(&uu, &t, &ww);
     nt = cvNorm(&t, 0, CV_L2);
+    CV_Assert(fabs(nt) > 0);
     nw = cvNorm(&ww, 0, CV_L2);
+    CV_Assert(fabs(nw) > 0);
     cvConvertScale(&ww, &ww, 1 / nw);
     cvCrossProduct(&t, &ww, &w3);
     nw = cvNorm(&w3, 0, CV_L2);
+    CV_Assert(fabs(nw) > 0);
     cvConvertScale(&w3, &w3, 1 / nw);
     _uu[2] = 0;
 
@@ -3870,12 +3873,14 @@ float cv::rectify3Collinear( InputArray _cameraMatrix1, InputArray _distCoeffs1,
 
     int idx = fabs(t12(0,0)) > fabs(t12(1,0)) ? 0 : 1;
     double c = t12(idx,0), nt = norm(t12, CV_L2);
+    CV_Assert(fabs(nt) > 0);
     Mat_<double> uu = Mat_<double>::zeros(3,1);
     uu(idx, 0) = c > 0 ? 1 : -1;
 
     // calculate global Z rotation
     Mat_<double> ww = t12.cross(uu), wR;
     double nw = norm(ww, CV_L2);
+    CV_Assert(fabs(nw) > 0);
     ww *= acos(fabs(c)/nt)/nw;
     Rodrigues(ww, wR);
 

modules/calib3d/src/dls.cpp

@@ -206,6 +206,7 @@ void dls::run_kernel(const cv::Mat& pp)
 
 void dls::build_coeff_matrix(const cv::Mat& pp, cv::Mat& Mtilde, cv::Mat& D)
 {
+    CV_Assert(!pp.empty());
     cv::Mat eye = cv::Mat::eye(3, 3, CV_64F);
 
     // build coeff matrix

modules/calib3d/src/fisheye.cpp

@@ -126,7 +126,8 @@ void cv::fisheye::projectPoints(InputArray objectPoints, OutputArray imagePoints
     {
         Vec3d Xi = objectPoints.depth() == CV_32F ? (Vec3d)Xf[i] : Xd[i];
         Vec3d Y = aff*Xi;
-
+        if (fabs(Y[2]) < DBL_MIN)
+            Y[2] = 1;
         Vec2d x(Y[0]/Y[2], Y[1]/Y[2]);
 
         double r2 = x.dot(x);
@@ -1186,6 +1187,7 @@ void cv::internal::ComputeExtrinsicRefine(const Mat& imagePoints, const Mat& obj
 {
     CV_Assert(!objectPoints.empty() && objectPoints.type() == CV_64FC3);
     CV_Assert(!imagePoints.empty() && imagePoints.type() == CV_64FC2);
+    CV_Assert(rvec.total() > 2 && tvec.total() > 2);
     Vec6d extrinsics(rvec.at<double>(0), rvec.at<double>(1), rvec.at<double>(2),
                     tvec.at<double>(0), tvec.at<double>(1), tvec.at<double>(2));
     double change = 1;
@@ -1365,9 +1367,13 @@ void cv::internal::InitExtrinsics(const Mat& _imagePoints, const Mat& _objectPoi
     double sc = .5 * (norm(H.col(0)) + norm(H.col(1)));
     H = H / sc;
     Mat u1 = H.col(0).clone();
-    u1  = u1 / norm(u1);
+    double norm_u1 = norm(u1);
+    CV_Assert(fabs(norm_u1) > 0);
+    u1  = u1 / norm_u1;
     Mat u2 = H.col(1).clone() - u1.dot(H.col(1).clone()) * u1;
-    u2 = u2 / norm(u2);
+    double norm_u2 = norm(u2);
+    CV_Assert(fabs(norm_u2) > 0);
+    u2 = u2 / norm_u2;
     Mat u3 = u1.cross(u2);
     Mat RRR;
     hconcat(u1, u2, RRR);

modules/calib3d/src/homography_decomp.cpp

@@ -194,6 +194,7 @@ void HomographyDecompZhang::decompose(std::vector<CameraMotion>& camMotions)
 {
     Mat W, U, Vt;
     SVD::compute(getHnorm(), W, U, Vt);
+    CV_Assert(W.total() > 2 && Vt.total() > 7);
     double lambda1=W.at<double>(0);
     double lambda3=W.at<double>(2);
     double lambda1m3 =  (lambda1-lambda3);

modules/core/include/opencv2/core/mat.inl.hpp

@@ -861,7 +861,9 @@ bool Mat::isSubmatrix() const
 inline
 size_t Mat::elemSize() const
 {
-    return dims > 0 ? step.p[dims - 1] : 0;
+    size_t res = dims > 0 ? step.p[dims - 1] : 0;
+    CV_DbgAssert(res != 0);
+    return res;
 }
 
 inline
@@ -3789,7 +3791,9 @@ bool UMat::isSubmatrix() const
 inline
 size_t UMat::elemSize() const
 {
-    return dims > 0 ? step.p[dims - 1] : 0;
+    size_t res = dims > 0 ? step.p[dims - 1] : 0;
+    CV_DbgAssert(res != 0);
+    return res;
 }
 
 inline

modules/core/src/arithm.cpp

@@ -1233,7 +1233,7 @@ void cv::compare(InputArray _src1, InputArray _src2, OutputArray _dst, int op)
     CV_Assert( op == CMP_LT || op == CMP_LE || op == CMP_EQ ||
                op == CMP_NE || op == CMP_GE || op == CMP_GT );
 
-    if(_src1.empty() && _src2.empty())
+    if(_src1.empty() || _src2.empty())
     {
         _dst.release();
         return;

modules/core/src/batch_distance.cpp

@@ -263,6 +263,7 @@ void cv::batchDistance( InputArray _src1, InputArray _src2,
     if( crosscheck )
     {
         CV_Assert( K == 1 && update == 0 && mask.empty() );
+        CV_Assert(!nidx.empty());
         Mat tdist, tidx;
         batchDistance(src2, src1, tdist, dtype, tidx, normType, K, mask, 0, false);
 

modules/core/src/copy.cpp

@@ -411,6 +411,8 @@ Mat& Mat::operator = (const Scalar& s)
 {
     CV_INSTRUMENT_REGION()
 
+    if (empty()) return *this;
+
     const Mat* arrays[] = { this };
     uchar* dptr;
     NAryMatIterator it(arrays, &dptr, 1);

modules/core/src/rand.cpp

@@ -511,6 +511,8 @@ static RandnScaleFunc randnScaleTab[] =
 void RNG::fill( InputOutputArray _mat, int disttype,
                 InputArray _param1arg, InputArray _param2arg, bool saturateRange )
 {
+    if (_mat.empty())
+        return;
     Mat mat = _mat.getMat(), _param1 = _param1arg.getMat(), _param2 = _param2arg.getMat();
     int depth = mat.depth(), cn = mat.channels();
     AutoBuffer<double> _parambuf;

modules/core/test/test_rand.cpp

@@ -168,11 +168,12 @@ void Core_RandTest::run( int )
         {
             tested_rng = saved_rng;
             int sz = 0, dsz = 0, slice;
-            for( slice = 0; slice < maxSlice; slice++, sz += dsz )
+            for( slice = 0; slice < maxSlice && sz < SZ; slice++, sz += dsz )
             {
-                dsz = slice+1 < maxSlice ? (int)(cvtest::randInt(rng) % (SZ - sz + 1)) : SZ - sz;
+                dsz = slice+1 < maxSlice ? (int)(cvtest::randInt(rng) % (SZ - sz) + 1) : SZ - sz;
                 Mat aslice = arr[k].colRange(sz, sz + dsz);
                 tested_rng.fill(aslice, dist_type, A, B);
+                printf("%d - %d\n", sz, sz + dsz);
             }
         }
 

modules/dnn/src/layers/resize_layer.cpp

@@ -14,7 +14,7 @@ namespace cv { namespace dnn {
 class ResizeLayerImpl : public ResizeLayer
 {
 public:
-    ResizeLayerImpl(const LayerParams& params)
+    ResizeLayerImpl(const LayerParams& params) : scaleWidth(0), scaleHeight(0)
     {
         setParamsFrom(params);
         outWidth = params.get<float>("width", 0);

modules/features2d/src/bagofwords.cpp

@@ -177,6 +177,7 @@ void BOWImgDescriptorExtractor::compute( InputArray keypointDescriptors, OutputA
     CV_INSTRUMENT_REGION()
 
     CV_Assert( !vocabulary.empty() );
+    CV_Assert(!keypointDescriptors.empty());
 
     int clusterCount = descriptorSize(); // = vocabulary.rows
 

modules/features2d/src/blobdetector.cpp

@@ -264,6 +264,8 @@ void SimpleBlobDetectorImpl::findBlobs(InputArray _image, InputArray _binaryImag
             convexHull(Mat(contours[contourIdx]), hull);
             double area = contourArea(Mat(contours[contourIdx]));
             double hullArea = contourArea(Mat(hull));
+            if (fabs(hullArea) < DBL_EPSILON)
+                continue;
             double ratio = area / hullArea;
             if (ratio < params.minConvexity || ratio >= params.maxConvexity)
                 continue;
@@ -309,6 +311,7 @@ void SimpleBlobDetectorImpl::detect(InputArray image, std::vector<cv::KeyPoint>&
     CV_INSTRUMENT_REGION()
 
     keypoints.clear();
+    CV_Assert(params.minRepeatability != 0);
     Mat grayscaleImage;
     if (image.channels() == 3 || image.channels() == 4)
         cvtColor(image, grayscaleImage, COLOR_BGR2GRAY);

modules/features2d/src/brisk.cpp

@@ -506,6 +506,7 @@ BRISK_Impl::smoothedIntensity(const cv::Mat& image, const cv::Mat& integral, con
   // scaling:
   const int scaling = (int)(4194304.0 / area);
   const int scaling2 = int(float(scaling) * area / 1024.0);
+  CV_Assert(scaling2 != 0);
 
   // the integral image is larger:
   const int integralcols = imagecols + 1;
@@ -2238,6 +2239,7 @@ BriskLayer::value(const cv::Mat& mat, float xf, float yf, float scale_in) const
   // scaling:
   const int scaling = (int)(4194304.0f / area);
   const int scaling2 = (int)(float(scaling) * area / 1024.0f);
+  CV_Assert(scaling2 != 0);
 
   // calculate borders
   const float x_1 = xf - sigma_half;

modules/imgproc/src/deriv.cpp

@@ -546,10 +546,10 @@ static bool ocl_Laplacian5(InputArray _src, OutputArray _dst,
     size_t lmsz = dev.localMemSize();
     size_t src_step = _src.step(), src_offset = _src.offset();
     const size_t tileSizeYmax = wgs / tileSizeX;
+    CV_Assert(src_step != 0 && esz != 0);
 
     // workaround for NVIDIA: 3 channel vector type takes 4*elem_size in local memory
     int loc_mem_cn = dev.vendorID() == ocl::Device::VENDOR_NVIDIA && cn == 3 ? 4 : cn;
-
     if (((src_offset % src_step) % esz == 0) &&
         (
          (borderType == BORDER_CONSTANT || borderType == BORDER_REPLICATE) ||

modules/imgproc/src/filter.cpp

@@ -4284,10 +4284,14 @@ static bool ocl_sepFilter2D_SinglePass(InputArray _src, OutputArray _dst,
     size_t src_step = _src.step(), src_offset = _src.offset();
     bool doubleSupport = ocl::Device::getDefault().doubleFPConfig() > 0;
 
-    if ((src_offset % src_step) % esz != 0 || (!doubleSupport && (sdepth == CV_64F || ddepth == CV_64F)) ||
-            !(borderType == BORDER_CONSTANT || borderType == BORDER_REPLICATE ||
-              borderType == BORDER_REFLECT || borderType == BORDER_WRAP ||
-              borderType == BORDER_REFLECT_101))
+    if (esz == 0
+        || (src_offset % src_step) % esz != 0
+        || (!doubleSupport && (sdepth == CV_64F || ddepth == CV_64F))
+        || !(borderType == BORDER_CONSTANT
+             || borderType == BORDER_REPLICATE
+             || borderType == BORDER_REFLECT
+             || borderType == BORDER_WRAP
+             || borderType == BORDER_REFLECT_101))
         return false;
 
     size_t lt2[2] = { optimizedSepFilterLocalWidth, optimizedSepFilterLocalHeight };

modules/imgproc/src/grabcut.cpp

@@ -174,6 +174,7 @@ void GMM::addSample( int ci, const Vec3d color )
 
 void GMM::endLearning()
 {
+    CV_Assert(totalSampleCount > 0);
     const double variance = 0.01;
     for( int ci = 0; ci < componentsCount; ci++ )
     {

modules/imgproc/src/imgwarp.cpp

@@ -3286,6 +3286,7 @@ void cv::warpPolar(InputArray _src, OutputArray _dst, Size dsize,
 
     if (!(flags & CV_WARP_INVERSE_MAP))
     {
+        CV_Assert(!dsize.empty());
         double Kangle = CV_2PI / dsize.height;
         int phi, rho;
 
@@ -3332,6 +3333,7 @@ void cv::warpPolar(InputArray _src, OutputArray _dst, Size dsize,
         Mat src = _dst.getMat();
         Size ssize = _dst.size();
         ssize.height -= 2 * ANGLE_BORDER;
+        CV_Assert(!ssize.empty());
         const double Kangle = CV_2PI / ssize.height;
         double Kmag;
         if (semiLog)

modules/imgproc/src/linefit.cpp

@@ -47,6 +47,7 @@ static const double eps = 1e-6;
 
 static void fitLine2D_wods( const Point2f* points, int count, float *weights, float *line )
 {
+    CV_Assert(count > 0);
     double x = 0, y = 0, x2 = 0, y2 = 0, xy = 0, w = 0;
     double dx2, dy2, dxy;
     int i;
@@ -98,6 +99,7 @@ static void fitLine2D_wods( const Point2f* points, int count, float *weights, fl
 
 static void fitLine3D_wods( const Point3f * points, int count, float *weights, float *line )
 {
+    CV_Assert(count > 0);
     int i;
     float w0 = 0;
     float x0 = 0, y0 = 0, z0 = 0;

modules/imgproc/src/lsd.cpp

@@ -772,6 +772,7 @@ bool LineSegmentDetectorImpl::refine(std::vector<RegionPoint>& reg, double reg_a
             ++n;
         }
     }
+    CV_Assert(n > 0);
     double mean_angle = sum / double(n);
     // 2 * standard deviation
     double tau = 2.0 * sqrt((s_sum - 2.0 * mean_angle * sum) / double(n) + mean_angle * mean_angle);

modules/imgproc/src/moments.cpp

@@ -495,6 +495,13 @@ static bool ocl_moments( InputArray _src, Moments& m, bool binary)
     const int TILE_SIZE = 32;
     const int K = 10;
 
+    Size sz = _src.getSz();
+    int xtiles = divUp(sz.width, TILE_SIZE);
+    int ytiles = divUp(sz.height, TILE_SIZE);
+    int ntiles = xtiles*ytiles;
+    if (ntiles == 0)
+        return false;
+
     ocl::Kernel k = ocl::Kernel("moments", ocl::imgproc::moments_oclsrc,
         format("-D TILE_SIZE=%d%s",
         TILE_SIZE,
@@ -504,10 +511,6 @@ static bool ocl_moments( InputArray _src, Moments& m, bool binary)
         return false;
 
     UMat src = _src.getUMat();
-    Size sz = src.size();
-    int xtiles = (sz.width + TILE_SIZE-1)/TILE_SIZE;
-    int ytiles = (sz.height + TILE_SIZE-1)/TILE_SIZE;
-    int ntiles = xtiles*ytiles;
     UMat umbuf(1, ntiles*K, CV_32S);
 
     size_t globalsize[] = {(size_t)xtiles, std::max((size_t)TILE_SIZE, (size_t)sz.height)};

modules/imgproc/src/smooth.cpp

@@ -1709,6 +1709,7 @@ void cv::sqrBoxFilter( InputArray _src, OutputArray _dst, int ddepth,
 
 cv::Mat cv::getGaussianKernel( int n, double sigma, int ktype )
 {
+    CV_Assert(n > 0);
     const int SMALL_GAUSSIAN_SIZE = 7;
     static const float small_gaussian_tab[][SMALL_GAUSSIAN_SIZE] =
     {
@@ -1747,6 +1748,7 @@ cv::Mat cv::getGaussianKernel( int n, double sigma, int ktype )
         }
     }
 
+    CV_DbgAssert(fabs(sum) > 0);
     sum = 1./sum;
     for( i = 0; i < n; i++ )
     {
@@ -5334,6 +5336,7 @@ public:
                         wsum += w;
                     }
                     // overflow is not possible here => there is no need to use cv::saturate_cast
+                    CV_DbgAssert(fabs(wsum) > 0);
                     dptr[j] = (uchar)cvRound(sum/wsum);
                 }
             }
@@ -5419,6 +5422,7 @@ public:
                         sum_b += b*w; sum_g += g*w; sum_r += r*w;
                         wsum += w;
                     }
+                    CV_DbgAssert(fabs(wsum) > 0);
                     wsum = 1.f/wsum;
                     b0 = cvRound(sum_b*wsum);
                     g0 = cvRound(sum_g*wsum);
@@ -5678,6 +5682,7 @@ public:
                         sum += val*w;
                         wsum += w;
                     }
+                    CV_DbgAssert(fabs(wsum) > 0);
                     dptr[j] = (float)(sum/wsum);
                 }
             }
@@ -5768,6 +5773,7 @@ public:
                         sum_b += b*w; sum_g += g*w; sum_r += r*w;
                         wsum += w;
                     }
+                    CV_DbgAssert(fabs(wsum) > 0);
                     wsum = 1.f/wsum;
                     b0 = sum_b*wsum;
                     g0 = sum_g*wsum;

modules/imgproc/test/test_grabcut.cpp

@@ -89,7 +89,6 @@ void CV_GrabcutTest::run( int /* start_from */)
     Mat exp_bgdModel, exp_fgdModel;
 
     Mat mask;
-    mask = Scalar(0);
     Mat bgdModel, fgdModel;
     grabCut( img, mask, rect, bgdModel, fgdModel, 0, GC_INIT_WITH_RECT );
     bgdModel.copyTo(exp_bgdModel);

modules/ml/src/em.cpp

@@ -616,6 +616,7 @@ public:
             expDiffSum += v; // sum_j(exp(L_ij - L_iq))
         }
 
+        CV_Assert(expDiffSum > 0);
         if(probs)
             L.convertTo(*probs, ptype, 1./expDiffSum);
 

modules/ml/src/rtrees.cpp

@@ -170,6 +170,7 @@ public:
                 double val = std::abs(w->ord_responses[w->sidx[i]]);
                 max_response = std::max(max_response, val);
             }
+            CV_Assert(fabs(max_response) > 0);
         }
 
         if( rparams.calcVarImportance )

modules/ml/src/tree.cpp

@@ -630,7 +630,7 @@ void DTreesImpl::calcValue( int nidx, const vector<int>& _sidx )
                 w->cv_Tn[nidx*cv_n + j] = INT_MAX;
             }
         }
-
+        CV_Assert(fabs(sumw) > 0);
         node->node_risk = sum2 - (sum/sumw)*sum;
         node->node_risk /= sumw;
         node->value = sum/sumw;

modules/objdetect/src/haar.cpp

@@ -599,7 +599,7 @@ cvSetImagesForHaarClassifierCascade( CvHaarClassifierCascade* _cascade,
                     else
                         sum0 += hidfeature->rect[k].weight * tr.width * tr.height;
                 }
-
+                CV_Assert(area0 > 0);
                 hidfeature->rect[0].weight = (float)(-sum0/area0);
             } /* l */
         } /* j */

modules/objdetect/src/qrcode.cpp

@@ -111,6 +111,7 @@ vector<Vec3d> QRDecode::searchVerticalLines()
 
                 for (size_t i = 0; i < test_lines.size(); i++) { length += test_lines[i]; }
 
+                CV_Assert(length > 0);
                 for (size_t i = 0; i < test_lines.size(); i++)
                 {
                     if (i == 2) { weight += fabs((test_lines[i] / length) - 3.0/7.0); }
@@ -182,6 +183,7 @@ vector<Point2f> QRDecode::separateHorizontalLines(vector<Vec3d> list_lines)
 
             for (size_t i = 0; i < test_lines.size(); i++) { length += test_lines[i]; }
 
+            CV_Assert(length > 0);
             for (size_t i = 0; i < test_lines.size(); i++)
             {
                 if (i % 3 == 0) { weight += fabs((test_lines[i] / length) - 3.0/14.0); }

modules/photo/src/tonemap.cpp

@@ -140,6 +140,7 @@ public:
 
         double max;
         minMaxLoc(gray_img, NULL, &max);
+        CV_Assert(max > 0);
 
         Mat map;
         log(gray_img + 1.0f, map);
@@ -429,12 +430,15 @@ public:
         for(int i = 0; i < max_iterations; i++)
         {
             calculateProduct(p, product);
-            float alpha = rr / static_cast<float>(p.dot(product));
+            double dprod = p.dot(product);
+            CV_Assert(fabs(dprod) > 0);
+            float alpha = rr / static_cast<float>(dprod);
 
             r -= alpha * product;
             x += alpha * p;
 
             float new_rr = static_cast<float>(r.dot(r));
+            CV_Assert(fabs(rr) > 0);
             p = r + (new_rr / rr) * p;
             rr = new_rr;
 

modules/shape/src/sc_dis.cpp

@@ -743,6 +743,7 @@ void SCDMatcher::hungarian(cv::Mat &costMatrix, std::vector<cv::DMatch> &outMatc
 
     // calculate symmetric shape context cost
     cv::Mat trueCostMatrix(costMatrix, cv::Rect(0,0,sizeScd1, sizeScd2));
+    CV_Assert(!trueCostMatrix.empty());
     float leftcost = 0;
     for (int nrow=0; nrow<trueCostMatrix.rows; nrow++)
     {

modules/videostab/src/inpainting.cpp

@@ -447,7 +447,7 @@ public:
             }
         }
 
-        float wSumInv = 1.f / wSum;
+        float wSumInv = (std::fabs(wSum) > 0) ? (1.f / wSum) : 0; // if wSum is 0, c1-c3 will be 0 too
         frame(y,x) = Point3_<uchar>(
                 static_cast<uchar>(c1*wSumInv),
                 static_cast<uchar>(c2*wSumInv),