Suppressed hundreds of useless MSVC warnings (can be reenabled setting cmake variable ENABLE_NOISY_WARNINGS to ON). Fixed some of remaining warnings.
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@@ -166,7 +166,7 @@ namespace colormap
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static Mat linear_colormap(InputArray X,
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InputArray r, InputArray g, InputArray b,
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float begin, float end, float n) {
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return linear_colormap(X,r,g,b,linspace(begin,end,n));
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return linear_colormap(X,r,g,b,linspace(begin,end, cvRound(n)));
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}
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// Interpolates from a base colormap.
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@@ -117,7 +117,7 @@ public:
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void train(InputArray src, InputArray labels);
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// Predicts the label of a query image in src.
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int predict(const InputArray src) const;
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int predict(InputArray src) const;
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// See FaceRecognizer::load.
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void load(const FileStorage& fs);
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@@ -495,8 +495,8 @@ inline void elbp_(InputArray _src, OutputArray _dst, int radius, int neighbors)
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dst.setTo(0);
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for(int n=0; n<neighbors; n++) {
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// sample points
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float x = static_cast<float>(-radius) * sin(2.0*CV_PI*n/static_cast<float>(neighbors));
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float y = static_cast<float>(radius) * cos(2.0*CV_PI*n/static_cast<float>(neighbors));
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float x = static_cast<float>(-radius * sin(2.0*CV_PI*n/static_cast<float>(neighbors)));
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float y = static_cast<float>(radius * cos(2.0*CV_PI*n/static_cast<float>(neighbors)));
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// relative indices
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int fx = static_cast<int>(floor(x));
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int fy = static_cast<int>(floor(y));
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@@ -514,7 +514,7 @@ inline void elbp_(InputArray _src, OutputArray _dst, int radius, int neighbors)
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for(int i=radius; i < src.rows-radius;i++) {
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for(int j=radius;j < src.cols-radius;j++) {
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// calculate interpolated value
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float t = w1*src.at<_Tp>(i+fy,j+fx) + w2*src.at<_Tp>(i+fy,j+cx) + w3*src.at<_Tp>(i+cy,j+fx) + w4*src.at<_Tp>(i+cy,j+cx);
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float t = static_cast<float>(w1*src.at<_Tp>(i+fy,j+fx) + w2*src.at<_Tp>(i+fy,j+cx) + w3*src.at<_Tp>(i+cy,j+fx) + w4*src.at<_Tp>(i+cy,j+cx));
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// floating point precision, so check some machine-dependent epsilon
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dst.at<int>(i-radius,j-radius) += ((t > src.at<_Tp>(i,j)) || (std::abs(t-src.at<_Tp>(i,j)) < std::numeric_limits<float>::epsilon())) << n;
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}
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@@ -543,7 +543,7 @@ histc_(const Mat& src, int minVal=0, int maxVal=255, bool normed=false)
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// Establish the number of bins.
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int histSize = maxVal-minVal+1;
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// Set the ranges.
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float range[] = { minVal, maxVal } ;
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float range[] = { static_cast<float>(minVal), static_cast<float>(maxVal) };
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const float* histRange = { range };
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// calc histogram
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calcHist(&src, 1, 0, Mat(), result, 1, &histSize, &histRange, true, false);
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@@ -442,7 +442,7 @@ void cv::Mesh3D::clearOctree(){ octree = Octree(); }
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float cv::Mesh3D::estimateResolution(float tryRatio)
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{
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#if 0
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#if 0
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const int neighbors = 3;
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const int minReasonable = 10;
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@@ -476,10 +476,10 @@ float cv::Mesh3D::estimateResolution(float tryRatio)
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sort(dist, less<double>());
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return resolution = (float)dist[ dist.size() / 2 ];
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#else
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#else
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CV_Error(CV_StsNotImplemented, "");
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return 1.f;
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#endif
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#endif
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}
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@@ -1182,14 +1182,14 @@ private:
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left.erase(pos);
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}
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else
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break;
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break;
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}
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if (group.size() >= 4)
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groups.push_back(group);
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groups.push_back(group);
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}
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/* converting the data to final result */
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/* converting the data to final result */
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for(size_t i = 0; i < groups.size(); ++i)
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{
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const group_t& group = groups[i];
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@@ -1197,7 +1197,7 @@ private:
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vector< Vec2i > outgrp;
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for(citer pos = group.begin(); pos != group.end(); ++pos)
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{
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const Match& m = allMatches[*pos];
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const Match& m = allMatches[*pos];
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outgrp.push_back(Vec2i(subset[m.modelInd], scene.subset[m.sceneInd]));
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}
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result.push_back(outgrp);
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