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Eliminate use of 32-bit floating pt type

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Replace all "float" by "double" (64-bit) to avoid "lose precision"
warnings.
This commit is contained in:
Alex Leontiev 2013-09-01 13:59:15 +08:00
parent 1207cd132b
commit a29863ee7b
2 changed files with 23 additions and 23 deletions
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44
modules/optim/src/denoise_tvl1.cpp
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@ -10,22 +10,22 @@ namespace cv{namespace optim{
class AddFloatToCharScaled{ class AddFloatToCharScaled{
public: public:
AddFloatToCharScaled(float scale):_scale(scale){} AddFloatToCharScaled(double scale):_scale(scale){}
inline float operator()(float a,uchar b){ inline double operator()(double a,uchar b){
return a+_scale*((float)b); return a+_scale*((double)b);
} }
private: private:
float _scale; double _scale;
}; };
void denoise_TVL1(const std::vector<Mat>& observations,Mat& result, double lambda, int niters){ void denoise_TVL1(const std::vector<Mat>& observations,Mat& result, double lambda, int niters){
CV_Assert(observations.size()>0 && niters>0 && lambda>0); CV_Assert(observations.size()>0 && niters>0 && lambda>0);
const float L2 = 8.0f, tau = 0.02f, sigma = 1./(L2*tau), theta = 1.f; const double L2 = 8.0, tau = 0.02, sigma = 1./(L2*tau), theta = 1.0;
float clambda = (float)lambda; double clambda = (double)lambda;
float s=0; double s=0;
const int workdepth = CV_32F; const int workdepth = CV_64F;
int i, x, y, rows=observations[0].rows, cols=observations[0].cols,count; int i, x, y, rows=observations[0].rows, cols=observations[0].cols,count;
for(i=1;i<(int)observations.size();i++){ for(i=1;i<(int)observations.size();i++){
@ -34,41 +34,41 @@ namespace cv{namespace optim{
Mat X, P = Mat::zeros(rows, cols, CV_MAKETYPE(workdepth, 2)); Mat X, P = Mat::zeros(rows, cols, CV_MAKETYPE(workdepth, 2));
observations[0].convertTo(X, workdepth, 1./255); observations[0].convertTo(X, workdepth, 1./255);
std::vector< Mat_<float> > Rs(observations.size()); std::vector< Mat_<double> > Rs(observations.size());
for(count=0;count<(int)Rs.size();count++){ for(count=0;count<(int)Rs.size();count++){
Rs[count]=Mat::zeros(rows,cols,workdepth); Rs[count]=Mat::zeros(rows,cols,workdepth);
} }
for( i = 0; i < niters; i++ ) for( i = 0; i < niters; i++ )
{ {
float currsigma = i == 0 ? 1 + sigma : sigma; double currsigma = i == 0 ? 1 + sigma : sigma;
// P_ = P + sigma*nabla(X) // P_ = P + sigma*nabla(X)
// P(x,y) = P_(x,y)/max(||P(x,y)||,1) // P(x,y) = P_(x,y)/max(||P(x,y)||,1)
for( y = 0; y < rows; y++ ) for( y = 0; y < rows; y++ )
{ {
const float* x_curr = X.ptr<float>(y); const double* x_curr = X.ptr<double>(y);
const float* x_next = X.ptr<float>(std::min(y+1, rows-1)); const double* x_next = X.ptr<double>(std::min(y+1, rows-1));
Point2f* p_curr = P.ptr<Point2f>(y); Point2d* p_curr = P.ptr<Point2d>(y);
float dx, dy, m; double dx, dy, m;
for( x = 0; x < cols-1; x++ ) for( x = 0; x < cols-1; x++ )
{ {
dx = (x_curr[x+1] - x_curr[x])*currsigma + p_curr[x].x; dx = (x_curr[x+1] - x_curr[x])*currsigma + p_curr[x].x;
dy = (x_next[x] - x_curr[x])*currsigma + p_curr[x].y; dy = (x_next[x] - x_curr[x])*currsigma + p_curr[x].y;
m = 1.f/std::max(std::sqrt(dx*dx + dy*dy), 1.f); m = 1.0/std::max(std::sqrt(dx*dx + dy*dy), 1.0);
p_curr[x].x = dx*m; p_curr[x].x = dx*m;
p_curr[x].y = dy*m; p_curr[x].y = dy*m;
} }
dy = (x_next[x] - x_curr[x])*currsigma + p_curr[x].y; dy = (x_next[x] - x_curr[x])*currsigma + p_curr[x].y;
m = 1.f/std::max(std::abs(dy), 1.f); m = 1.0/std::max(std::abs(dy), 1.0);
p_curr[x].x = 0.f; p_curr[x].x = 0.0;
p_curr[x].y = dy*m; p_curr[x].y = dy*m;
} }
//Rs = clip(Rs + sigma*(X-imgs), -clambda, clambda) //Rs = clip(Rs + sigma*(X-imgs), -clambda, clambda)
for(count=0;count<(int)Rs.size();count++){ for(count=0;count<(int)Rs.size();count++){
std::transform<MatIterator_<float>,MatConstIterator_<uchar>,MatIterator_<float>,AddFloatToCharScaled>( std::transform<MatIterator_<double>,MatConstIterator_<uchar>,MatIterator_<double>,AddFloatToCharScaled>(
Rs[count].begin(),Rs[count].end(),observations[count].begin<uchar>(), Rs[count].begin(),Rs[count].end(),observations[count].begin<uchar>(),
Rs[count].begin(),AddFloatToCharScaled(-sigma/255.0)); Rs[count].begin(),AddFloatToCharScaled(-sigma/255.0));
Rs[count]+=sigma*X; Rs[count]+=sigma*X;
@ -78,9 +78,9 @@ namespace cv{namespace optim{
for( y = 0; y < rows; y++ ) for( y = 0; y < rows; y++ )
{ {
float* x_curr = X.ptr<float>(y); double* x_curr = X.ptr<double>(y);
const Point2f* p_curr = P.ptr<Point2f>(y); const Point2d* p_curr = P.ptr<Point2d>(y);
const Point2f* p_prev = P.ptr<Point2f>(std::max(y - 1, 0)); const Point2d* p_prev = P.ptr<Point2d>(std::max(y - 1, 0));
// X1 = X + tau*(-nablaT(P)) // X1 = X + tau*(-nablaT(P))
x = 0; x = 0;
@ -88,7 +88,7 @@ namespace cv{namespace optim{
for(count=0;count<(int)Rs.size();count++){ for(count=0;count<(int)Rs.size();count++){
s=s+Rs[count](y,x); s=s+Rs[count](y,x);
} }
float x_new = x_curr[x] + tau*(p_curr[x].y - p_prev[x].y)-tau*s; double x_new = x_curr[x] + tau*(p_curr[x].y - p_prev[x].y)-tau*s;
// X = X2 + theta*(X2 - X) // X = X2 + theta*(X2 - X)
x_curr[x] = x_new + theta*(x_new - x_curr[x]); x_curr[x] = x_new + theta*(x_new - x_curr[x]);

2
modules/optim/test/test_denoise_tvl1.cpp
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@ -8,7 +8,7 @@ void make_noisy(const cv::Mat& img, cv::Mat& noisy, double sigma, double pepper_
cv::addWeighted(img, 1, noise, 1, -128, noisy); cv::addWeighted(img, 1, noise, 1, -128, noisy);
cv::randn(noise, cv::Scalar::all(0), cv::Scalar::all(2)); cv::randn(noise, cv::Scalar::all(0), cv::Scalar::all(2));
noise *= 255; noise *= 255;
cv::randu(mask, 0, round(1./pepper_salt_ratio)); cv::randu(mask, 0, cvRound(1./pepper_salt_ratio));
cv::Mat half = mask.colRange(0, img.cols/2); cv::Mat half = mask.colRange(0, img.cols/2);
half = cv::Scalar::all(1); half = cv::Scalar::all(1);
noise.setTo(128, mask); noise.setTo(128, mask);