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Eliminate use of 32-bit floating pt type
Replace all "float" by "double" (64-bit) to avoid "lose precision" warnings.
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@ -10,22 +10,22 @@ namespace cv{namespace optim{
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class AddFloatToCharScaled{
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public:
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AddFloatToCharScaled(float scale):_scale(scale){}
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inline float operator()(float a,uchar b){
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return a+_scale*((float)b);
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AddFloatToCharScaled(double scale):_scale(scale){}
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inline double operator()(double a,uchar b){
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return a+_scale*((double)b);
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}
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private:
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float _scale;
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double _scale;
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};
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void denoise_TVL1(const std::vector<Mat>& observations,Mat& result, double lambda, int niters){
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CV_Assert(observations.size()>0 && niters>0 && lambda>0);
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const float L2 = 8.0f, tau = 0.02f, sigma = 1./(L2*tau), theta = 1.f;
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float clambda = (float)lambda;
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float s=0;
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const int workdepth = CV_32F;
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const double L2 = 8.0, tau = 0.02, sigma = 1./(L2*tau), theta = 1.0;
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double clambda = (double)lambda;
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double s=0;
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const int workdepth = CV_64F;
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int i, x, y, rows=observations[0].rows, cols=observations[0].cols,count;
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for(i=1;i<(int)observations.size();i++){
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@ -34,41 +34,41 @@ namespace cv{namespace optim{
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Mat X, P = Mat::zeros(rows, cols, CV_MAKETYPE(workdepth, 2));
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observations[0].convertTo(X, workdepth, 1./255);
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std::vector< Mat_<float> > Rs(observations.size());
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std::vector< Mat_<double> > Rs(observations.size());
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for(count=0;count<(int)Rs.size();count++){
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Rs[count]=Mat::zeros(rows,cols,workdepth);
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}
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for( i = 0; i < niters; i++ )
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{
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float currsigma = i == 0 ? 1 + sigma : sigma;
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double currsigma = i == 0 ? 1 + sigma : sigma;
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// P_ = P + sigma*nabla(X)
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// P(x,y) = P_(x,y)/max(||P(x,y)||,1)
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for( y = 0; y < rows; y++ )
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{
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const float* x_curr = X.ptr<float>(y);
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const float* x_next = X.ptr<float>(std::min(y+1, rows-1));
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Point2f* p_curr = P.ptr<Point2f>(y);
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float dx, dy, m;
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const double* x_curr = X.ptr<double>(y);
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const double* x_next = X.ptr<double>(std::min(y+1, rows-1));
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Point2d* p_curr = P.ptr<Point2d>(y);
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double dx, dy, m;
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for( x = 0; x < cols-1; x++ )
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{
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dx = (x_curr[x+1] - x_curr[x])*currsigma + p_curr[x].x;
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dy = (x_next[x] - x_curr[x])*currsigma + p_curr[x].y;
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m = 1.f/std::max(std::sqrt(dx*dx + dy*dy), 1.f);
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m = 1.0/std::max(std::sqrt(dx*dx + dy*dy), 1.0);
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p_curr[x].x = dx*m;
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p_curr[x].y = dy*m;
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}
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dy = (x_next[x] - x_curr[x])*currsigma + p_curr[x].y;
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m = 1.f/std::max(std::abs(dy), 1.f);
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p_curr[x].x = 0.f;
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m = 1.0/std::max(std::abs(dy), 1.0);
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p_curr[x].x = 0.0;
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p_curr[x].y = dy*m;
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}
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//Rs = clip(Rs + sigma*(X-imgs), -clambda, clambda)
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for(count=0;count<(int)Rs.size();count++){
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std::transform<MatIterator_<float>,MatConstIterator_<uchar>,MatIterator_<float>,AddFloatToCharScaled>(
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std::transform<MatIterator_<double>,MatConstIterator_<uchar>,MatIterator_<double>,AddFloatToCharScaled>(
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Rs[count].begin(),Rs[count].end(),observations[count].begin<uchar>(),
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Rs[count].begin(),AddFloatToCharScaled(-sigma/255.0));
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Rs[count]+=sigma*X;
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@ -78,9 +78,9 @@ namespace cv{namespace optim{
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for( y = 0; y < rows; y++ )
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{
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float* x_curr = X.ptr<float>(y);
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const Point2f* p_curr = P.ptr<Point2f>(y);
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const Point2f* p_prev = P.ptr<Point2f>(std::max(y - 1, 0));
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double* x_curr = X.ptr<double>(y);
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const Point2d* p_curr = P.ptr<Point2d>(y);
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const Point2d* p_prev = P.ptr<Point2d>(std::max(y - 1, 0));
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// X1 = X + tau*(-nablaT(P))
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x = 0;
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@ -88,7 +88,7 @@ namespace cv{namespace optim{
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for(count=0;count<(int)Rs.size();count++){
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s=s+Rs[count](y,x);
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}
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float x_new = x_curr[x] + tau*(p_curr[x].y - p_prev[x].y)-tau*s;
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double x_new = x_curr[x] + tau*(p_curr[x].y - p_prev[x].y)-tau*s;
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// X = X2 + theta*(X2 - X)
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x_curr[x] = x_new + theta*(x_new - x_curr[x]);
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@ -8,7 +8,7 @@ void make_noisy(const cv::Mat& img, cv::Mat& noisy, double sigma, double pepper_
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cv::addWeighted(img, 1, noise, 1, -128, noisy);
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cv::randn(noise, cv::Scalar::all(0), cv::Scalar::all(2));
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noise *= 255;
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cv::randu(mask, 0, round(1./pepper_salt_ratio));
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cv::randu(mask, 0, cvRound(1./pepper_salt_ratio));
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cv::Mat half = mask.colRange(0, img.cols/2);
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half = cv::Scalar::all(1);
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noise.setTo(128, mask);
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