Fixed several issues found by static analysis

modules/dnn/src/int8layers/eltwise_layer.cpp

@@ -238,7 +238,7 @@ public:
 
         EltwiseInvoker(EltwiseLayerInt8Impl& self_)
             : self(self_)
-            , nsrcs(0), dst(0), buf(0), nstripes(0), activ(0), channels(0)
+            , nsrcs(0), dst(0), buf(0), nstripes(0), activLUT(0), activ(0), channels(0)
             , planeSize(0), offset(0)
         {}
 
@@ -345,7 +345,8 @@ public:
             int8_t* dstptr0 = dst->ptr<int8_t>();
             float* bufptr0 = buf->ptr<float>();
             int blockSize0 = 1 << 12;
-
+            CV_Assert(op != PROD || zeropointsptr);
+            CV_Assert((op != PROD && op != SUM) || coeffsptr);
             for (size_t ofs = stripeStart; ofs < stripeEnd; )
             {
                 int sampleIdx = (int)(ofs / planeSize);

modules/dnn/src/onnx/onnx_importer.cpp

@@ -2023,7 +2023,9 @@ void ONNXImporter::parseUnsqueeze(LayerParams& layerParams, const opencv_onnx::N
         }
         CV_Assert(axes.getIntValue(axes.size()-1) <= dims.size());
         for (int j = 0; j < axes.size(); j++) {
-            dims.insert(dims.begin() + axes.getIntValue(j), 1);
+            const int idx = axes.getIntValue(j);
+            CV_Assert(idx <= dims.size());
+            dims.insert(dims.begin() + idx, 1);
         }
 
         Mat out = input.reshape(0, dims);

modules/objdetect/src/face_detect.cpp

@@ -142,6 +142,7 @@ private:
             {64.0f,  96.0f},
             {128.0f, 192.0f, 256.0f}
         };
+        CV_Assert(min_sizes.size() == feature_map_sizes.size()); // just to keep vectors in sync
         const std::vector<int> steps = { 8, 16, 32, 64 };
 
         // Generate priors

modules/objdetect/src/face_recognize.cpp

@@ -45,8 +45,8 @@ public:
     double match(InputArray _face_feature1, InputArray _face_feature2, int dis_type) const override
     {
         Mat face_feature1 = _face_feature1.getMat(), face_feature2 = _face_feature2.getMat();
-        face_feature1 /= norm(face_feature1);
-        face_feature2 /= norm(face_feature2);
+        normalize(face_feature1, face_feature1);
+        normalize(face_feature2, face_feature2);
 
         if(dis_type == DisType::FR_COSINE){
             return sum(face_feature1.mul(face_feature2))[0];

modules/objdetect/src/qrcode_encoder.cpp

@@ -881,6 +881,8 @@ void QRCodeEncoderImpl::findAutoMaskType()
                 total_modules += 1;
             }
         }
+        if (total_modules == 0)
+            continue; // TODO: refactor, extract functions to reduce complexity
         int modules_percent = dark_modules * 100 / total_modules;
         int lower_bound = 45;
         int upper_bound = 55;