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Fixed several issues found by static analysis

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This commit is contained in:
Maksim Shabunin 2021-12-15 12:49:13 +03:00 committed by Maksim Shabunin
parent d8b1fc45aa
commit a079c2eb7c
5 changed files with 11 additions and 5 deletions
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5
modules/dnn/src/int8layers/eltwise_layer.cpp
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@ -238,7 +238,7 @@ public:
EltwiseInvoker(EltwiseLayerInt8Impl& self_) EltwiseInvoker(EltwiseLayerInt8Impl& self_)
: self(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) , planeSize(0), offset(0)
{} {}
@ -345,7 +345,8 @@ public:
int8_t* dstptr0 = dst->ptr<int8_t>(); int8_t* dstptr0 = dst->ptr<int8_t>();
float* bufptr0 = buf->ptr<float>(); float* bufptr0 = buf->ptr<float>();
int blockSize0 = 1 << 12; int blockSize0 = 1 << 12;
CV_Assert(op != PROD || zeropointsptr);
CV_Assert((op != PROD && op != SUM) || coeffsptr);
for (size_t ofs = stripeStart; ofs < stripeEnd; ) for (size_t ofs = stripeStart; ofs < stripeEnd; )
{ {
int sampleIdx = (int)(ofs / planeSize); int sampleIdx = (int)(ofs / planeSize);

4
modules/dnn/src/onnx/onnx_importer.cpp
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@ -2023,7 +2023,9 @@ void ONNXImporter::parseUnsqueeze(LayerParams& layerParams, const opencv_onnx::N
} }
CV_Assert(axes.getIntValue(axes.size()-1) <= dims.size()); CV_Assert(axes.getIntValue(axes.size()-1) <= dims.size());
for (int j = 0; j < axes.size(); j++) { 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); Mat out = input.reshape(0, dims);

1
modules/objdetect/src/face_detect.cpp
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@ -142,6 +142,7 @@ private:
{64.0f, 96.0f}, {64.0f, 96.0f},
{128.0f, 192.0f, 256.0f} {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 }; const std::vector<int> steps = { 8, 16, 32, 64 };
// Generate priors // Generate priors

4
modules/objdetect/src/face_recognize.cpp
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@ -45,8 +45,8 @@ public:
double match(InputArray _face_feature1, InputArray _face_feature2, int dis_type) const override double match(InputArray _face_feature1, InputArray _face_feature2, int dis_type) const override
{ {
Mat face_feature1 = _face_feature1.getMat(), face_feature2 = _face_feature2.getMat(); Mat face_feature1 = _face_feature1.getMat(), face_feature2 = _face_feature2.getMat();
face_feature1 /= norm(face_feature1); normalize(face_feature1, face_feature1);
face_feature2 /= norm(face_feature2); normalize(face_feature2, face_feature2);
if(dis_type == DisType::FR_COSINE){ if(dis_type == DisType::FR_COSINE){
return sum(face_feature1.mul(face_feature2))[0]; return sum(face_feature1.mul(face_feature2))[0];

2
modules/objdetect/src/qrcode_encoder.cpp
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@ -881,6 +881,8 @@ void QRCodeEncoderImpl::findAutoMaskType()
total_modules += 1; total_modules += 1;
} }
} }
if (total_modules == 0)
continue; // TODO: refactor, extract functions to reduce complexity
int modules_percent = dark_modules * 100 / total_modules; int modules_percent = dark_modules * 100 / total_modules;
int lower_bound = 45; int lower_bound = 45;
int upper_bound = 55; int upper_bound = 55;