More issues found by static analysis

apps/createsamples/utility.cpp

@@ -1044,12 +1044,10 @@ void cvCreateTrainingSamples( const char* filename,
         output = fopen( filename, "wb" );
         if( output != NULL )
         {
-            int hasbg;
             int i;
             int inverse;
 
-            hasbg = 0;
-            hasbg = (bgfilename != NULL && icvInitBackgroundReaders( bgfilename,
+            const int hasbg = (bgfilename != NULL && icvInitBackgroundReaders( bgfilename,
                      Size( winwidth,winheight ) ) );
 
             Mat sample( winheight, winwidth, CV_8UC1 );

modules/calib3d/src/circlesgrid.cpp

@@ -224,7 +224,7 @@ void CirclesGridClusterFinder::findOutsideCorners(const std::vector<cv::Point2f>
   CV_Assert(!corners.empty());
   outsideCorners.clear();
   //find two pairs of the most nearest corners
-  int i, j, n = (int)corners.size();
+  const size_t n = corners.size();
 
 #ifdef DEBUG_CIRCLES
   Mat cornersImage(1024, 1248, CV_8UC1, Scalar(0));
@@ -232,22 +232,22 @@ void CirclesGridClusterFinder::findOutsideCorners(const std::vector<cv::Point2f>
   imshow("corners", cornersImage);
 #endif
 
-  std::vector<Point2f> tangentVectors(corners.size());
-  for(size_t k=0; k<corners.size(); k++)
+  std::vector<Point2f> tangentVectors(n);
+  for(size_t k=0; k < n; k++)
   {
-    Point2f diff = corners[(k + 1) % corners.size()] - corners[k];
+    Point2f diff = corners[(k + 1) % n] - corners[k];
     tangentVectors[k] = diff * (1.0f / norm(diff));
   }
 
   //compute angles between all sides
-  Mat cosAngles(n, n, CV_32FC1, 0.0f);
-  for(i = 0; i < n; i++)
+  Mat cosAngles((int)n, (int)n, CV_32FC1, 0.0f);
+  for(size_t i = 0; i < n; i++)
   {
-    for(j = i + 1; j < n; j++)
+    for(size_t j = i + 1; j < n; j++)
     {
       float val = fabs(tangentVectors[i].dot(tangentVectors[j]));
-      cosAngles.at<float>(i, j) = val;
-      cosAngles.at<float>(j, i) = val;
+      cosAngles.at<float>((int)i, (int)j) = val;
+      cosAngles.at<float>((int)j, (int)i) = val;
     }
   }
 
@@ -276,10 +276,10 @@ void CirclesGridClusterFinder::findOutsideCorners(const std::vector<cv::Point2f>
   const int bigDiff = 4;
   if(maxIdx - minIdx == bigDiff)
   {
-    minIdx += n;
+    minIdx += (int)n;
     std::swap(maxIdx, minIdx);
   }
-  if(maxIdx - minIdx != n - bigDiff)
+  if(maxIdx - minIdx != (int)n - bigDiff)
   {
     return;
   }

modules/calib3d/src/dls.cpp

@@ -206,7 +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_Assert(!pp.empty() && N > 0);
     cv::Mat eye = cv::Mat::eye(3, 3, CV_64F);
 
     // build coeff matrix

modules/calib3d/test/test_chesscorners.cpp

@@ -334,19 +334,19 @@ bool validateData(const ChessBoardGenerator& cbg, const Size& imgSz,
 
             tmp = cv::norm(cur - mat(i + 1, j + 1)); // TODO cvtest
             if (tmp < minNeibDist)
-                tmp = minNeibDist;
+                minNeibDist = tmp;
 
             tmp = cv::norm(cur - mat(i - 1, j + 1)); // TODO cvtest
             if (tmp < minNeibDist)
-                tmp = minNeibDist;
+                minNeibDist = tmp;
 
             tmp = cv::norm(cur - mat(i + 1, j - 1)); // TODO cvtest
             if (tmp < minNeibDist)
-                tmp = minNeibDist;
+                minNeibDist = tmp;
 
             tmp = cv::norm(cur - mat(i - 1, j - 1)); // TODO cvtest
             if (tmp < minNeibDist)
-                tmp = minNeibDist;
+                minNeibDist = tmp;
         }
 
     const double threshold = 0.25;

modules/core/src/persistence_json.cpp

@@ -123,7 +123,6 @@ static char* icvJSONParseKey( CvFileStorage* fs, char* ptr, CvFileNode* map, CvF
         CV_PARSE_ERROR( "Key must start with \'\"\'" );
 
     char * beg = ptr + 1;
-    char * end = beg;
 
     do {
         ++ptr;
@@ -133,7 +132,7 @@ static char* icvJSONParseKey( CvFileStorage* fs, char* ptr, CvFileNode* map, CvF
     if( *ptr != '"' )
         CV_PARSE_ERROR( "Key must end with \'\"\'" );
 
-    end = ptr;
+    const char * end = ptr;
     ptr++;
     ptr = icvJSONSkipSpaces( fs, ptr );
     if ( ptr == 0 || fs->dummy_eof )
@@ -576,12 +575,12 @@ void icvJSONParse( CvFileStorage* fs )
     if ( *ptr == '{' )
     {
         CvFileNode* root_node = (CvFileNode*)cvSeqPush( fs->roots, 0 );
-        ptr = icvJSONParseMap( fs, ptr, root_node );
+        icvJSONParseMap( fs, ptr, root_node );
     }
     else if ( *ptr == '[' )
     {
         CvFileNode* root_node = (CvFileNode*)cvSeqPush( fs->roots, 0 );
-        ptr = icvJSONParseSeq( fs, ptr, root_node );
+        icvJSONParseSeq( fs, ptr, root_node );
     }
     else
     {
@@ -668,7 +667,7 @@ void icvJSONWrite( CvFileStorage* fs, const char* key, const char* data )
             *ptr++ = '\n';
             *ptr++ = '\0';
             ::icvPuts( fs, fs->buffer_start );
-            ptr = fs->buffer = fs->buffer_start;
+            fs->buffer = fs->buffer_start;
         }
         ptr = icvFSFlush(fs);
     }

modules/core/test/test_mat.cpp

@@ -1014,8 +1014,8 @@ protected:
         Size mSize(rng.uniform(minMSize, maxMSize), rng.uniform(minMSize, maxMSize));
         size_t mvSize = rng.uniform(1, maxMvSize);
 
-        int res = cvtest::TS::OK, curRes = res;
-        curRes = run_case(CV_8U, mvSize, mSize, rng);
+        int res = cvtest::TS::OK;
+        int curRes = run_case(CV_8U, mvSize, mSize, rng);
         res = curRes != cvtest::TS::OK ? curRes : res;
 
         curRes = run_case(CV_8S, mvSize, mSize, rng);

modules/core/test/test_rand.cpp

@@ -375,9 +375,11 @@ TEST(Core_Rand, Regression_Stack_Corruption)
     int bufsz = 128; //enough for 14 doubles
     AutoBuffer<uchar> buffer(bufsz);
     size_t offset = 0;
-    cv::Mat_<cv::Point2d> x(2, 3, (cv::Point2d*)(buffer.data()+offset)); offset += x.total()*x.elemSize();
-    double& param1 = *(double*)(buffer.data()+offset); offset += sizeof(double);
-    double& param2 = *(double*)(buffer.data()+offset); offset += sizeof(double);
+    cv::Mat_<cv::Point2d> x(2, 3, (cv::Point2d*)(buffer.data()+offset));
+    offset += x.total()*x.elemSize();
+    double& param1 = *(double*)(buffer.data()+offset);
+    offset += sizeof(double);
+    double& param2 = *(double*)(buffer.data()+offset);
     param1 = -9; param2 = 2;
 
     cv::theRNG().fill(x, cv::RNG::NORMAL, param1, param2);

modules/dnn/src/layers/convolution_layer.cpp

@@ -560,7 +560,7 @@ public:
             int ngroups = ngroups_, batchSize = input_->size[0]*ngroups;
             int outW = output_->size[3], outH = output_->size[2], outCn = output_->size[1]/ngroups;
             int width = input_->size[3], height = input_->size[2], inpCn = input_->size[1]/ngroups;
-            int nstripes = nstripes_;
+            const int nstripes = nstripes_;
             int kernel_w = kernel_.width, kernel_h = kernel_.height;
             int pad_w = pad_.width, pad_h = pad_.height;
             int stride_w = stride_.width, stride_h = stride_.height;
@@ -587,7 +587,6 @@ public:
                 int samplesPerStripe = std::max((batchSize + nstripes - 1)/nstripes, 1);
                 r.start *= samplesPerStripe;
                 r.end *= samplesPerStripe;
-                nstripes *= samplesPerStripe;
                 stripeSize = outPlaneSize;
             }
 

modules/dnn/src/layers/eltwise_layer.cpp

@@ -187,7 +187,7 @@ public:
             int c, j, k, n = nsrcs;
             const float* coeffsptr = coeffs && !coeffs->empty() ? &coeffs->at(0) : 0;
             float* dstptr0 = dst->ptr<float>();
-            int blockSize0 = 1 << 12, blockSize = blockSize0;
+            int blockSize0 = 1 << 12, blockSize;
 
             for( size_t ofs = stripeStart; ofs < stripeEnd; ofs += blockSize )
             {

modules/dnn/src/layers/normalize_bbox_layer.cpp

@@ -190,6 +190,7 @@ public:
 
         size_t num = total(shape(inp0.size), 0, startAxis);
         size_t numPlanes = total(shape(inp0.size), startAxis, endAxis + 1);
+        CV_Assert(num * numPlanes != 0);
         size_t planeSize = inp0.total() / (num * numPlanes);
         for (size_t n = 0; n < num; ++n)
         {

modules/dnn/src/layers/recurrent_layers.cpp

@@ -189,18 +189,16 @@ public:
         else
             outTailShape_.assign(1, _numOut);
 
-        int _numTimeStamps, _numSamples;
+        int _numSamples;
         if (useTimestampDim)
         {
             CV_Assert(inp0.size() >= 2 && total(inp0, 2) == _numInp);
-            _numTimeStamps = inp0[0];
             _numSamples = inp0[1];
-            outResShape.push_back(_numTimeStamps);
+            outResShape.push_back(inp0[0]);
         }
         else
         {
             CV_Assert(inp0.size() >= 2 && total(inp0, 1) == _numInp);
-            _numTimeStamps = 1;
             _numSamples = inp0[0];
         }
 

modules/features2d/src/brisk.cpp

@@ -1236,7 +1236,6 @@ BriskScaleSpace::isMax2D(const int layer, const int x_layer, const int y_layer)
   {
     // in this case, we have to analyze the situation more carefully:
     // the values are gaussian blurred and then we really decide
-    data = scores.ptr() + y_layer * scorescols + x_layer;
     int smoothedcenter = 4 * center + 2 * (s_10 + s10 + s0_1 + s01) + s_1_1 + s1_1 + s_11 + s11;
     for (unsigned int i = 0; i < deltasize; i += 2)
     {
@@ -1312,8 +1311,7 @@ BriskScaleSpace::refine3D(const int layer, const int x_layer, const int y_layer,
       int s_2_2 = l.getAgastScore_5_8(x_layer + 1, y_layer + 1, 1);
       max_below = std::max(s_2_2, max_below);
 
-      max_below_float = subpixel2D(s_0_0, s_0_1, s_0_2, s_1_0, s_1_1, s_1_2, s_2_0, s_2_1, s_2_2, delta_x_below,
-                                   delta_y_below);
+      subpixel2D(s_0_0, s_0_1, s_0_2, s_1_0, s_1_1, s_1_2, s_2_0, s_2_1, s_2_2, delta_x_below, delta_y_below);
       max_below_float = (float)max_below;
     }
     else

modules/features2d/src/kaze/KAZEFeatures.cpp

@@ -373,8 +373,6 @@ void KAZEFeatures::Determinant_Hessian(std::vector<KeyPoint>& kpts)
                     is_out = true;
                 }
 
-                is_out = false;
-
                 if (is_out == false) {
                     if (is_repeated == false) {
                         kpts.push_back(kpts_par_[i][j]);

modules/imgcodecs/src/exif.cpp

@@ -175,7 +175,6 @@ std::map<int, ExifEntry_t > ExifReader::getExif()
                     CV_THROW (ExifParsingError());
                 }
                 m_stream.read( reinterpret_cast<char*>(&m_data[0]), exifSize - offsetToTiffHeader );
-                count = m_stream.gcount();
                 exifFound = true;
                 break;
 

modules/imgcodecs/src/grfmt_bmp.cpp

@@ -265,7 +265,7 @@ bool  BmpDecoder::readData( Mat& img )
                 for(;;)
                 {
                     int code = m_strm.getWord();
-                    int len = code & 255;
+                    const int len = code & 255;
                     code >>= 8;
                     if( len != 0 ) // encoded mode
                     {
@@ -304,16 +304,13 @@ bool  BmpDecoder::readData( Mat& img )
                     else
                     {
                         int x_shift3 = (int)(line_end - data);
-                        int y_shift = m_height - y;
 
                         if( code == 2 )
                         {
                             x_shift3 = m_strm.getByte()*nch;
-                            y_shift = m_strm.getByte();
+                            m_strm.getByte();
                         }
 
-                        len = x_shift3 + ((y_shift * width3) & ((code == 0) - 1));
-
                         if( color )
                             data = FillUniColor( data, line_end, step, width3,
                                                  y, m_height, x_shift3,

modules/imgcodecs/src/grfmt_pam.cpp

@@ -689,7 +689,7 @@ bool PAMEncoder::write( const Mat& img, const std::vector<int>& params )
     tmp += sprintf( buffer + tmp, "MAXVAL %d\n", (1 << img.elemSize1()*8) - 1);
     if (fmt)
         tmp += sprintf( buffer + tmp, "TUPLTYPE %s\n", fmt->name );
-    tmp += sprintf( buffer + tmp, "ENDHDR\n" );
+    sprintf( buffer + tmp, "ENDHDR\n" );
 
     strm.putBytes( buffer, (int)strlen(buffer) );
     /* write data */

modules/imgcodecs/src/grfmt_tiff.cpp

@@ -255,22 +255,21 @@ bool TiffDecoder::readHeader()
             {
                 case 8:
                     m_type = CV_MAKETYPE(CV_8U, photometric > 1 ? wanted_channels : 1);
+                    result = true;
                     break;
                 case 16:
                     m_type = CV_MAKETYPE(CV_16U, photometric > 1 ? wanted_channels : 1);
+                    result = true;
                     break;
-
                 case 32:
                     m_type = CV_MAKETYPE(CV_32F, photometric > 1 ? 3 : 1);
+                    result = true;
                     break;
                 case 64:
                     m_type = CV_MAKETYPE(CV_64F, photometric > 1 ? 3 : 1);
+                    result = true;
                     break;
-
-                default:
-                    result = false;
             }
-            result = true;
         }
     }
 

modules/imgproc/src/contours.cpp

@@ -855,7 +855,6 @@ icvTraceContour_32s( int *ptr, int step, int *stop_ptr, int is_hole )
         for( ;; )
         {
             CV_Assert(i3 != NULL);
-            s_end = s;
             s = std::min(s, MAX_SIZE - 1);
 
             while( s < MAX_SIZE - 1 )
@@ -1479,7 +1478,7 @@ icvFindContoursInInterval( const CvArr* src,
     cv::Ptr<CvMemStorage> storage01;
     CvSeq* first = 0;
 
-    int i, j, k, n;
+    int j, k, n;
 
     uchar*  src_data = 0;
     int  img_step = 0;
@@ -1547,7 +1546,6 @@ icvFindContoursInInterval( const CvArr* src,
 
     // First line. None of runs is binded
     tmp.pt.y = 0;
-    i = 0;
     CV_WRITE_SEQ_ELEM( tmp, writer );
     upper_line = (CvLinkedRunPoint*)CV_GET_WRITTEN_ELEM( writer );
 
@@ -1580,7 +1578,7 @@ icvFindContoursInInterval( const CvArr* src,
     last_elem = tmp_prev;
     tmp_prev->next = 0;
 
-    for( i = 1; i < img_size.height; i++ )
+    for( int i = 1; i < img_size.height; i++ )
     {
 //------// Find runs in next line
         src_data += img_step;

modules/imgproc/src/drawing.cpp

@@ -338,7 +338,6 @@ LineAA( Mat& img, Point2l pt1, Point2l pt2, const void* color )
 
     if( ax > ay )
     {
-        dx = ax;
         dy = (dy ^ j) - j;
         pt1.x ^= pt2.x & j;
         pt2.x ^= pt1.x & j;
@@ -362,7 +361,6 @@ LineAA( Mat& img, Point2l pt1, Point2l pt2, const void* color )
     }
     else
     {
-        dy = ay;
         dx = (dx ^ i) - i;
         pt1.x ^= pt2.x & i;
         pt2.x ^= pt1.x & i;
@@ -677,7 +675,6 @@ Line2( Mat& img, Point2l pt1, Point2l pt2, const void* color)
 
     if( ax > ay )
     {
-        dx = ax;
         dy = (dy ^ j) - j;
         pt1.x ^= pt2.x & j;
         pt2.x ^= pt1.x & j;
@@ -692,7 +689,6 @@ Line2( Mat& img, Point2l pt1, Point2l pt2, const void* color)
     }
     else
     {
-        dy = ay;
         dx = (dx ^ i) - i;
         pt1.x ^= pt2.x & i;
         pt2.x ^= pt1.x & i;

modules/imgproc/src/filter.avx2.cpp

@@ -128,8 +128,6 @@ int SymmColumnVec_32f_Symm_AVX(const float** src, const float* ky, float* dst, f
         for( k = 1; k <= ksize2; k++ )
         {
             f = _mm_set1_ps(ky[k]);
-            S = src[k] + i;
-            S2 = src[-k] + i;
             x0 = _mm_add_ps(_mm_load_ps(src[k]+i), _mm_load_ps(src[-k] + i));
             s0 = _mm_add_ps(s0, _mm_mul_ps(x0, f));
         }
@@ -144,7 +142,7 @@ int SymmColumnVec_32f_Symm_AVX(const float** src, const float* ky, float* dst, f
 int SymmColumnVec_32f_Unsymm_AVX(const float** src, const float* ky, float* dst, float delta, int width, int ksize2)
 {
     int i = 0, k;
-    const float *S, *S2;
+    const float *S2;
     const __m128 d4 = _mm_set1_ps(delta);
     const __m256 d8 = _mm256_set1_ps(delta);
 
@@ -152,11 +150,10 @@ int SymmColumnVec_32f_Unsymm_AVX(const float** src, const float* ky, float* dst,
     {
         __m256 f, s0 = d8, s1 = d8;
         __m256 x0;
-        S = src[0] + i;
 
         for (k = 1; k <= ksize2; k++)
         {
-            S = src[k] + i;
+            const float *S = src[k] + i;
             S2 = src[-k] + i;
             f = _mm256_set1_ps(ky[k]);
             x0 = _mm256_sub_ps(_mm256_loadu_ps(S), _mm256_loadu_ps(S2));

modules/imgproc/src/floodfill.cpp

@@ -467,7 +467,7 @@ int cv::floodFill( InputOutputArray _image, InputOutputArray _mask,
     if( rect )
         *rect = Rect();
 
-    int i, connectivity = flags & 255;
+    int i;
     union {
         uchar b[4];
         int i[4];
@@ -491,9 +491,8 @@ int cv::floodFill( InputOutputArray _image, InputOutputArray _mask,
         CV_Error( CV_StsBadArg, "Number of channels in input image must be 1 or 3" );
     }
 
-    if( connectivity == 0 )
-        connectivity = 4;
-    else if( connectivity != 4 && connectivity != 8 )
+    const int connectivity = flags & 255;
+    if( connectivity != 0 && connectivity != 4 && connectivity != 8 )
         CV_Error( CV_StsBadFlag, "Connectivity must be 4, 0(=4) or 8" );
 
     bool is_simple = mask.empty() && (flags & FLOODFILL_MASK_ONLY) == 0;

modules/imgproc/src/histogram.cpp

@@ -1930,7 +1930,7 @@ double cv::compareHist( InputArray _H1, InputArray _H2, int method )
     Mat planes[2];
     NAryMatIterator it(arrays, planes);
     double result = 0;
-    int j, len = (int)it.size;
+    int j;
 
     CV_Assert( H1.type() == H2.type() && H1.depth() == CV_32F );
 
@@ -1946,7 +1946,7 @@ double cv::compareHist( InputArray _H1, InputArray _H2, int method )
     {
         const float* h1 = it.planes[0].ptr<float>();
         const float* h2 = it.planes[1].ptr<float>();
-        len = it.planes[0].rows*it.planes[0].cols*H1.channels();
+        const int len = it.planes[0].rows*it.planes[0].cols*H1.channels();
         j = 0;
 
         if( (method == CV_COMP_CHISQR) || (method == CV_COMP_CHISQR_ALT))

modules/imgproc/src/hough.cpp

@@ -413,7 +413,6 @@ HoughLinesSDiv( InputArray image, OutputArray lines, int type,
                 // Find peaks in maccum...
                 for( index = 0; index < sfn; index++ )
                 {
-                    i = 0;
                     int pos = (int)(lst.size() - 1);
                     if( pos < 0 || lst[pos].value < mcaccum[index] )
                     {

modules/imgproc/src/min_enclosing_triangle.cpp

@@ -401,7 +401,6 @@ static void findMinimumAreaEnclosingTriangle(const std::vector<cv::Point2f> &pol
 
     a = 1;
     b = 2;
-    c = 0;
 
     // Main algorithm steps
 

modules/ml/src/ann_mlp.cpp

@@ -1259,7 +1259,7 @@ public:
             prev_dEdw_sign[i] = Mat::zeros(weights[i].size(), CV_8S);
             dEdw[i] = Mat::zeros(weights[i].size(), CV_64F);
         }
-
+        CV_Assert(total > 0);
         int dcount0 = max_buf_size/(2*total);
         dcount0 = std::max( dcount0, 1 );
         dcount0 = std::min( dcount0, count );

modules/videoio/src/cap_ffmpeg_impl.hpp

@@ -2351,9 +2351,6 @@ AVStream* OutputMediaStream_FFMPEG::addVideoStream(AVFormatContext *oc, CV_CODEC
     c->codec_type = AVMEDIA_TYPE_VIDEO;
 
     // put sample parameters
-    unsigned long long lbit_rate = static_cast<unsigned long long>(bitrate);
-    lbit_rate += (bitrate / 4);
-    lbit_rate = std::min(lbit_rate, static_cast<unsigned long long>(std::numeric_limits<int>::max()));
     c->bit_rate = bitrate;
 
     // took advice from

modules/videoio/src/container_avi.cpp

@@ -325,9 +325,6 @@ bool AVIReadContainer::parseStrl(char stream_id, Codecs codec_)
 
     if(m_file_stream && strh.m_four_cc == STRH_CC)
     {
-        uint64_t next_strl_list = m_file_stream->tellg();
-        next_strl_list += strh.m_size;
-
         AviStreamHeader strm_hdr;
         *m_file_stream >> strm_hdr;