template <class P1, class P2> static
void ApplyFilter_2( GenericImage<P1>& image, const GenericImage<P2>& sharp,
float amount, float threshold, float deringing,
float rangeLow, float rangeHigh, pcl_bool disableExtension, int c, pcl_bool highPass )
{
float rangeWidth = 1 + rangeHigh + rangeLow;
bool isRange = rangeWidth + 1 != 1;
StandardStatus callback;
StatusMonitor monitor;
monitor.SetCallback( &callback );
monitor.Initialize( "<end><cbr>Larson-Sekanina filter", image.NumberOfPixels() );
for ( int x = 0; x < image.Width(); ++x )
for ( int y = 0; y < image.Height(); ++y, ++monitor )
{
double f1, f2;
P1::FromSample( f1, image.Pixel( x, y, c ) );
P2::FromSample( f2, sharp.Pixel( x, y ) );
Apply_PixelValues( f1, f2, threshold, deringing, amount, highPass );
if ( disableExtension )
image.Pixel( x, y, c ) = P1::ToSample( f1 );
else
{
if ( isRange )
f1 = (f1 + rangeLow)/rangeWidth;
image.Pixel( x, y, c ) = P1::ToSample( pcl::Range( f1, 0.0, 1.0 ) );
}
}
if ( disableExtension )
Console().WarningLn( "<end><cbr>*** Warning: Dynamic range extension has been disabled - check pixel values!" );
}
template <class P> static
void Rotate90CW( GenericImage<P>& image )
{
image.SetUnique();
int w = image.Width();
int h = image.Height();
int h1 = h - 1;
int n = image.NumberOfChannels();
size_type N = image.NumberOfPixels();
typename GenericImage<P>::color_space cs0 = image.ColorSpace();
StatusMonitor status = image.Status();
typename P::sample** f0 = 0;
try
{
if ( image.Status().IsInitializationEnabled() )
status.Initialize( "Rotate 90 degrees, clockwise", n*N );
f0 = image.ReleaseData();
typename GenericImage<P>::sample_array tmp( N );
for ( int c = 0; c < n; ++c, status += N )
{
typename P::sample* f = f0[c];
typename P::sample* t = tmp.Begin();
::memcpy( t, f, N*P::BytesPerSample() );
for ( int y = 0; y < h; ++y )
for ( int x = 0, h1y = h1-y; x < w; ++x, ++t )
f[x*h + h1y] = *t;
}
image.ImportData( f0, h, w, n, cs0 ).Status() = status;
}
catch ( ... )
{
if ( f0 != 0 )
{
for ( int c = 0; c < n; ++c )
if ( f0[c] != 0 )
image.Allocator().Deallocate( f0[c] );
image.Allocator().Deallocate( f0 );
image.FreeData();
}
throw;
}
}
template <class P> inline
static void Apply( GenericImage<P>& image, const IntegerResample& Z )
{
int width = image.Width();
int w0 = width;
int height = image.Height();
int h0 = height;
Z.GetNewSizes( width, height );
if ( width == w0 && height == h0 )
return;
if ( width == 0 || height == 0 )
{
image.FreeData();
return;
}
image.EnsureUnique();
typename P::sample* f = 0;
typename P::sample** f0 = 0;
int n = image.NumberOfChannels();
size_type N = image.NumberOfPixels();
typename GenericImage<P>::color_space cs0 = image.ColorSpace();
StatusMonitor status = image.Status();
int z = pcl::Abs( Z.ZoomFactor() );
int z2 = z*z;
int n2 = z2 >> 1;
try
{
if ( status.IsInitializationEnabled() )
{
String info = (Z.ZoomFactor() > 0) ? "Upsampling" : "Downsampling";
info.AppendFormat( " %d:%d, %dx%d",
(Z.ZoomFactor() > 0) ? z : 1, (Z.ZoomFactor() > 0) ? 1 : z, width, height );
if ( Z.ZoomFactor() < 0 )
{
info += ", ";
switch ( Z.DownsampleMode() )
{
default:
case IntegerDownsampleMode::Average: info += "average"; break;
case IntegerDownsampleMode::Median: info += "median"; break;
case IntegerDownsampleMode::Maximum: info += "maximum"; break;
case IntegerDownsampleMode::Minimum: info += "minimum"; break;
}
}
status.Initialize( info, n*N );
}
GenericVector<typename P::sample> fm;
if ( Z.ZoomFactor() < 0 && Z.DownsampleMode() == IntegerDownsampleMode::Median )
fm = GenericVector<typename P::sample>( z2 );
f0 = image.ReleaseData();
for ( int c = 0; c < n; ++c, status += N )
{
f = image.Allocator().AllocatePixels( width, height );
if ( Z.ZoomFactor() > 0 )
{
const typename P::sample* f0c = f0[c];
for ( int y = 0; y < h0; ++y )
{
int yz = y*z;
for ( int x = 0; x < w0; ++x )
{
int xz = x*z;
typename P::sample v = *f0c++;
for ( int i = 0; i < z; ++i )
{
typename P::sample* fi = f + (size_type( yz + i )*width + xz);
for ( int j = 0; j < z; ++j )
*fi++ = v;
}
}
}
}
else
{
typename P::sample* fz = f;
for ( int y = 0; y < height; ++y )
{
const typename P::sample* fy = f0[c] + size_type( y )*z*w0;
for ( int x = 0; x < width; ++x )
{
const typename P::sample* fyx = fy + x*z;
switch ( Z.DownsampleMode() )
{
default:
case IntegerDownsampleMode::Average:
{
double s = 0;
for ( int i = 0; i < z; ++i, fyx += w0 )
for ( int j = 0; j < z; ++j )
s += fyx[j];
*fz++ = typename P::sample( P::IsFloatSample() ? s/z2 : Round( s/z2 ) );
}
break;
case IntegerDownsampleMode::Median:
{
typename P::sample* fmi = *fm;
for ( int i = 0; i < z; ++i, fyx += w0 )
for ( int j = 0; j < z; ++j )
*fmi++ = fyx[j];
*fz++ = (z & 1) ?
*Select( *fm, fm.At( z2 ), n2 ) :
P::FloatToSample( 0.5*(double( *Select( *fm, fm.At( z2 ), n2 ) ) +
double( *Select( *fm, fm.At( z2 ), n2-1 ) )) );
}
break;
case IntegerDownsampleMode::Maximum:
{
*fz = P::MinSampleValue();
for ( int i = 0; i < z; ++i, fyx += w0 )
for ( int j = 0; j < z; ++j )
if ( fyx[j] > *fz )
*fz = fyx[j];
++fz;
}
break;
case IntegerDownsampleMode::Minimum:
{
*fz = P::MaxSampleValue();
for ( int i = 0; i < z; ++i, fyx += w0 )
for ( int j = 0; j < z; ++j )
if ( fyx[j] < *fz )
*fz = fyx[j];
++fz;
}
break;
}
}
}
}
image.Allocator().Deallocate( f0[c] );
f0[c] = f;
f = 0;
}
image.ImportData( f0, width, height, n, cs0 ).Status() = status;
}
catch ( ... )
{
if ( f != 0 )
image.Allocator().Deallocate( f );
if ( f0 != 0 )
{
for ( int c = 0; c < n; ++c )
if ( f0[c] != 0 )
image.Allocator().Deallocate( f0[c] );
image.Allocator().Deallocate( f0 );
}
image.FreeData();
throw;
}
}
template <class P> static
void Apply( GenericImage<P>& image, const Translation& translation )
{
if ( translation.Delta() == 0.0 )
return;
int width = image.Width();
int height = image.Height();
if ( width == 0 || height == 0 )
return;
image.EnsureUnique();
typename P::sample* f = nullptr;
typename P::sample** f0 = nullptr;
int n = image.NumberOfChannels();
typename GenericImage<P>::color_space cs0 = image.ColorSpace();
StatusMonitor status = image.Status();
int numberOfThreads = translation.IsParallelProcessingEnabled() ?
Min( translation.MaxProcessors(), pcl::Thread::NumberOfThreads( height, 1 ) ) : 1;
int rowsPerThread = height/numberOfThreads;
try
{
size_type N = size_type( width )*size_type( height );
if ( status.IsInitializationEnabled() )
status.Initialize( String().Format( "Translate dx=%.3lf, dy=%.3lf, ",
translation.Delta().x, translation.Delta().y ) + translation.Interpolation().Description(),
size_type( n )*N );
f0 = image.ReleaseData();
for ( int c = 0; c < n; ++c )
{
ThreadData<P> data( translation.Delta(), width, height, status, N );
data.f = f = image.Allocator().AllocatePixels( size_type( width )*size_type( height ) );
data.fillValue = (c < translation.FillValues().Length()) ? P::ToSample( translation.FillValues()[c] ) : P::MinSampleValue();
ReferenceArray<Thread<P> > threads;
for ( int i = 0, j = 1; i < numberOfThreads; ++i, ++j )
threads.Add( new Thread<P>( data,
translation.Interpolation().NewInterpolator<P>( f0[c], width, height ),
i*rowsPerThread,
(j < numberOfThreads) ? j*rowsPerThread : height ) );
AbstractImage::RunThreads( threads, data );
threads.Destroy();
image.Allocator().Deallocate( f0[c] );
f0[c] = f;
f = nullptr;
status = data.status;
}
image.ImportData( f0, width, height, n, cs0 ).Status() = status;
}
catch ( ... )
{
if ( f != nullptr )
image.Allocator().Deallocate( f );
if ( f0 != nullptr )
{
for ( int c = 0; c < n; ++c )
if ( f0[c] != nullptr )
image.Allocator().Deallocate( f0[c] );
image.Allocator().Deallocate( f0 );
}
image.FreeData();
throw;
}
}
template <class P1, class P2> static
void Convolve_2( const GenericImage<P1>& image, GenericImage<P2>& sharp,
pcl_enum interpolation, float dR, float angleD, DPoint center, int c )
{
PixelInterpolation* P = 0;
PixelInterpolation::Interpolator<P1>* interpolator = 0;
try
{
switch ( interpolation )
{
case LSInterpolation::Bilinear:
P = new BilinearPixelInterpolation();
break;
default:
case LSInterpolation::Bicubic:
P = new BicubicPixelInterpolation();
break;
case LSInterpolation::BicubicSpline:
P = new BicubicSplinePixelInterpolation();
break;
case LSInterpolation::BicubicBSpline:
P = new BicubicBSplinePixelInterpolation();
break;
}
interpolator = P->NewInterpolator<P1>( image[c], image.Width(), image.Height() );
int w = image.Width() - 1;
int h = image.Height() - 1;
double fimg, fsharp;
StatusMonitor monitor;
monitor.Initialize( "<end><cbr>High-pass Larson-Sekanina filter", image.NumberOfPixels() );
sharp.Zero();
float dAlpha = Rad( angleD );
for ( int x = 0; x < image.Width(); ++x )
for ( int y = 0; y < image.Height(); ++y, ++monitor )
{
// Get the central value
P1::FromSample( fimg, image.Pixel( x, y, c ) );
fsharp = fimg+fimg;
double r, theta;
ToPolar( x, y, center, r, theta);
DPoint delta;
// Positive differential
ToCartesian( r-dR, theta+dAlpha, center, delta );
if ( delta.x < 0 )
delta.x = Abs( delta.x );
else if ( delta.x > w )
delta.x = 2*w - delta.x;
if ( delta.y < 0 )
delta.y = Abs( delta.y );
else if ( delta.y > h )
delta.y = 2*h - delta.y;
P1::FromSample( fimg, (*interpolator)( delta ) );
fsharp -= fimg;
//Negative differential
ToCartesian( r-dR, theta-dAlpha, center, delta );
if ( delta.x < 0 )
delta.x = Abs( delta.x );
else if ( delta.x > w )
delta.x = 2*w - delta.x;
if ( delta.y < 0 )
delta.y = Abs( delta.y );
else if ( delta.y > h )
delta.y = 2*h - delta.y;
P1::FromSample( fimg, (*interpolator)( delta ) );
fsharp -= fimg;
sharp.Pixel( x, y ) = P2::ToSample( fsharp );
}
delete interpolator;
delete P;
}
catch ( ... )
{
if ( interpolator != 0 )
delete interpolator;
if ( P != 0 )
delete P;
throw;
}
}
template <class P> static
void Apply( GenericImage<P>& image, const Resample& resample )
{
int width = image.Width();
int w0 = width;
int height = image.Height();
int h0 = height;
resample.GetNewSizes( width, height );
if ( width == w0 && height == h0 )
return;
if ( width <= 0 || height <= 0 )
{
image.FreeData();
return;
}
image.EnsureUnique();
typename P::sample* f = nullptr;
typename P::sample** f0 = nullptr;
int n = image.NumberOfChannels();
typename GenericImage<P>::color_space cs0 = image.ColorSpace();
double rx = double( w0 )/width;
double ry = double( h0 )/height;
StatusMonitor status = image.Status();
int numberOfThreads = resample.IsParallelProcessingEnabled() ?
Min( resample.MaxProcessors(), pcl::Thread::NumberOfThreads( height, 1 ) ) : 1;
int rowsPerThread = height/numberOfThreads;
try
{
size_type N = size_type( width )*size_type( height );
if ( status.IsInitializationEnabled() )
status.Initialize( String().Format( "Resampling to %dx%d px, ", width, height )
+ resample.Interpolation().Description(), size_type( n )*N );
f0 = image.ReleaseData();
for ( int c = 0; c < n; ++c )
{
ThreadData<P> data( rx, ry, width, status, N );
data.f = f = image.Allocator().AllocatePixels( width, height );
ReferenceArray<Thread<P> > threads;
for ( int i = 0, j = 1; i < numberOfThreads; ++i, ++j )
threads.Add( new Thread<P>( data, resample.Interpolation().NewInterpolator<P>( f0[c], w0, h0 ),
i*rowsPerThread,
(j < numberOfThreads) ? j*rowsPerThread : height ) );
AbstractImage::RunThreads( threads, data );
threads.Destroy();
image.Allocator().Deallocate( f0[c] );
f0[c] = f;
f = nullptr;
status = data.status;
}
image.ImportData( f0, width, height, n, cs0 ).Status() = status;
}
catch ( ... )
{
if ( f != nullptr )
image.Allocator().Deallocate( f );
if ( f0 != nullptr )
{
for ( int c = 0; c < n; ++c )
if ( f0[c] != nullptr )
image.Allocator().Deallocate( f0[c] );
image.Allocator().Deallocate( f0 );
}
image.FreeData();
throw;
}
}