You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
913 lines
22 KiB
C++
913 lines
22 KiB
C++
// This code is in the public domain -- castanyo@yahoo.es
|
|
|
|
#include "FloatImage.h"
|
|
#include "Filter.h"
|
|
#include "Image.h"
|
|
|
|
#include <nvmath/Color.h>
|
|
#include <nvmath/Matrix.h>
|
|
|
|
#include <nvcore/Containers.h>
|
|
#include <nvcore/Ptr.h>
|
|
|
|
#include <math.h>
|
|
|
|
|
|
using namespace nv;
|
|
|
|
namespace
|
|
{
|
|
static int iround(float f)
|
|
{
|
|
return int(f);
|
|
}
|
|
|
|
static int ifloor(float f)
|
|
{
|
|
return int(floor(f));
|
|
}
|
|
|
|
static float frac(float f)
|
|
{
|
|
return f - floor(f);
|
|
}
|
|
|
|
static int mirror(int x, int w)
|
|
{
|
|
if (w == 1) return 0;
|
|
|
|
x = abs(x);
|
|
while (x >= w) {
|
|
x = abs(w + w - x - 2);
|
|
}
|
|
|
|
return x;
|
|
}
|
|
}
|
|
|
|
|
|
/// Ctor.
|
|
FloatImage::FloatImage() : m_width(0), m_height(0),
|
|
m_componentNum(0), m_count(0), m_mem(NULL)
|
|
{
|
|
}
|
|
|
|
/// Ctor. Init from image.
|
|
FloatImage::FloatImage(const Image * img) : m_width(0), m_height(0),
|
|
m_componentNum(0), m_count(0), m_mem(NULL)
|
|
{
|
|
initFrom(img);
|
|
}
|
|
|
|
/// Dtor.
|
|
FloatImage::~FloatImage()
|
|
{
|
|
free();
|
|
}
|
|
|
|
|
|
/// Init the floating point image from a regular image.
|
|
void FloatImage::initFrom(const Image * img)
|
|
{
|
|
nvCheck(img != NULL);
|
|
|
|
allocate(4, img->width(), img->height());
|
|
|
|
float * red_channel = channel(0);
|
|
float * green_channel = channel(1);
|
|
float * blue_channel = channel(2);
|
|
float * alpha_channel = channel(3);
|
|
|
|
const uint count = m_width * m_height;
|
|
for(uint i = 0; i < count; i++) {
|
|
Color32 pixel = img->pixel(i);
|
|
red_channel[i] = float(pixel.r) / 255.0f;
|
|
green_channel[i] = float(pixel.g) / 255.0f;
|
|
blue_channel[i] = float(pixel.b) / 255.0f;
|
|
alpha_channel[i] = float(pixel.a) / 255.0f;
|
|
}
|
|
}
|
|
|
|
/// Convert the floating point image to a regular image.
|
|
Image * FloatImage::createImage(uint base_component/*= 0*/, uint num/*= 4*/) const
|
|
{
|
|
nvCheck(num <= 4);
|
|
nvCheck(base_component + num <= m_componentNum);
|
|
|
|
AutoPtr<Image> img(new Image());
|
|
img->allocate(m_width, m_height);
|
|
|
|
const uint size = m_width * m_height;
|
|
for(uint i = 0; i < size; i++) {
|
|
|
|
uint c;
|
|
uint8 rgba[4]= {0, 0, 0, 0xff};
|
|
|
|
for(c = 0; c < num; c++) {
|
|
float f = m_mem[size * (base_component + c) + i];
|
|
rgba[c] = nv::clamp(int(255.0f * f), 0, 255);
|
|
}
|
|
|
|
img->pixel(i) = Color32(rgba[0], rgba[1], rgba[2], rgba[3]);
|
|
}
|
|
|
|
return img.release();
|
|
}
|
|
|
|
|
|
/// Convert the floating point image to a regular image. Correct gamma of rgb, but not alpha.
|
|
Image * FloatImage::createImageGammaCorrect(float gamma/*= 2.2f*/) const
|
|
{
|
|
nvCheck(m_componentNum == 4);
|
|
|
|
AutoPtr<Image> img(new Image());
|
|
img->allocate(m_width, m_height);
|
|
|
|
const float * rChannel = this->channel(0);
|
|
const float * gChannel = this->channel(1);
|
|
const float * bChannel = this->channel(2);
|
|
const float * aChannel = this->channel(3);
|
|
|
|
const uint size = m_width * m_height;
|
|
for(uint i = 0; i < size; i++)
|
|
{
|
|
const uint8 r = nv::clamp(int(255.0f * pow(rChannel[i], 1.0f/gamma)), 0, 255);
|
|
const uint8 g = nv::clamp(int(255.0f * pow(gChannel[i], 1.0f/gamma)), 0, 255);
|
|
const uint8 b = nv::clamp(int(255.0f * pow(bChannel[i], 1.0f/gamma)), 0, 255);
|
|
const uint8 a = nv::clamp(int(255.0f * aChannel[i]), 0, 255);
|
|
|
|
img->pixel(i) = Color32(r, g, b, a);
|
|
}
|
|
|
|
return img.release();
|
|
}
|
|
|
|
/// Allocate a 2d float image of the given format and the given extents.
|
|
void FloatImage::allocate(uint c, uint w, uint h)
|
|
{
|
|
free();
|
|
|
|
m_width = w;
|
|
m_height = h;
|
|
m_componentNum = c;
|
|
m_count = w * h * c;
|
|
m_mem = reinterpret_cast<float *>(::malloc(m_count * sizeof(float)));
|
|
}
|
|
|
|
/// Free the image, but don't clear the members.
|
|
void FloatImage::free()
|
|
{
|
|
::free( reinterpret_cast<void *>(m_mem) );
|
|
m_mem = NULL;
|
|
}
|
|
|
|
void FloatImage::clear(float f/*=0.0f*/)
|
|
{
|
|
for(uint i = 0; i < m_count; i++) {
|
|
m_mem[i] = f;
|
|
}
|
|
}
|
|
|
|
void FloatImage::normalize(uint base_component)
|
|
{
|
|
nvCheck(base_component + 3 <= m_componentNum);
|
|
|
|
float * xChannel = this->channel(base_component + 0);
|
|
float * yChannel = this->channel(base_component + 1);
|
|
float * zChannel = this->channel(base_component + 2);
|
|
|
|
const uint size = m_width * m_height;
|
|
for(uint i = 0; i < size; i++) {
|
|
|
|
Vector3 normal(xChannel[i], yChannel[i], zChannel[i]);
|
|
normal = normalizeSafe(normal, Vector3(zero), 0.0f);
|
|
|
|
xChannel[i] = normal.x();
|
|
yChannel[i] = normal.y();
|
|
zChannel[i] = normal.z();
|
|
}
|
|
}
|
|
|
|
void FloatImage::packNormals(uint base_component)
|
|
{
|
|
scaleBias(base_component, 3, 0.5f, 1.0f);
|
|
}
|
|
|
|
void FloatImage::expandNormals(uint base_component)
|
|
{
|
|
scaleBias(base_component, 3, 2, -0.5);
|
|
}
|
|
|
|
void FloatImage::scaleBias(uint base_component, uint num, float scale, float bias)
|
|
{
|
|
const uint size = m_width * m_height;
|
|
|
|
for(uint c = 0; c < num; c++) {
|
|
float * ptr = this->channel(base_component + c);
|
|
|
|
for(uint i = 0; i < size; i++) {
|
|
ptr[i] = scale * (ptr[i] + bias);
|
|
}
|
|
}
|
|
}
|
|
|
|
/// Clamp the elements of the image.
|
|
void FloatImage::clamp(float low, float high)
|
|
{
|
|
for(uint i = 0; i < m_count; i++) {
|
|
m_mem[i] = nv::clamp(m_mem[i], low, high);
|
|
}
|
|
}
|
|
|
|
/// From gamma to linear space.
|
|
void FloatImage::toLinear(uint base_component, uint num, float gamma /*= 2.2f*/)
|
|
{
|
|
exponentiate(base_component, num, gamma);
|
|
}
|
|
|
|
/// From linear to gamma space.
|
|
void FloatImage::toGamma(uint base_component, uint num, float gamma /*= 2.2f*/)
|
|
{
|
|
exponentiate(base_component, num, 1.0f/gamma);
|
|
}
|
|
|
|
/// Exponentiate the elements of the image.
|
|
void FloatImage::exponentiate(uint base_component, uint num, float power)
|
|
{
|
|
const uint size = m_width * m_height;
|
|
|
|
for(uint c = 0; c < num; c++) {
|
|
float * ptr = this->channel(base_component + c);
|
|
|
|
for(uint i = 0; i < size; i++) {
|
|
ptr[i] = pow(ptr[i], power);
|
|
}
|
|
}
|
|
}
|
|
|
|
float FloatImage::sampleNearest(const float x, const float y, const int c, const WrapMode wm) const
|
|
{
|
|
if( wm == WrapMode_Clamp ) return sampleNearestClamp(x, y, c);
|
|
else if( wm == WrapMode_Repeat ) return sampleNearestRepeat(x, y, c);
|
|
else /*if( wm == WrapMode_Mirror )*/ return sampleNearestMirror(x, y, c);
|
|
}
|
|
|
|
float FloatImage::sampleLinear(const float x, const float y, const int c, const WrapMode wm) const
|
|
{
|
|
if( wm == WrapMode_Clamp ) return sampleLinearClamp(x, y, c);
|
|
else if( wm == WrapMode_Repeat ) return sampleLinearRepeat(x, y, c);
|
|
else /*if( wm == WrapMode_Mirror )*/ return sampleLinearMirror(x, y, c);
|
|
}
|
|
|
|
float FloatImage::sampleNearestClamp(const float x, const float y, const int c) const
|
|
{
|
|
int ix = ::clamp(iround(x * m_width), 0, m_width-1);
|
|
int iy = ::clamp(iround(y * m_height), 0, m_height-1);
|
|
return pixel(ix, iy, c);
|
|
}
|
|
|
|
float FloatImage::sampleNearestRepeat(const float x, const float y, const int c) const
|
|
{
|
|
int ix = iround(frac(x) * m_width);
|
|
int iy = iround(frac(y) * m_height);
|
|
return pixel(ix, iy, c);
|
|
}
|
|
|
|
float FloatImage::sampleNearestMirror(const float x, const float y, const int c) const
|
|
{
|
|
int ix = mirror(iround(x * m_width), m_width);
|
|
int iy = mirror(iround(y * m_height), m_height);
|
|
return pixel(ix, iy, c);
|
|
}
|
|
|
|
float FloatImage::sampleLinearClamp(float x, float y, const int c) const
|
|
{
|
|
const int w = m_width;
|
|
const int h = m_height;
|
|
|
|
x *= w;
|
|
y *= h;
|
|
|
|
const float fracX = frac(x);
|
|
const float fracY = frac(y);
|
|
|
|
const int ix0 = ::clamp(ifloor(x), 0, w-1);
|
|
const int iy0 = ::clamp(ifloor(y), 0, h-1);
|
|
const int ix1 = ::clamp(ifloor(x)+1, 0, w-1);
|
|
const int iy1 = ::clamp(ifloor(y)+1, 0, h-1);
|
|
|
|
float f1 = pixel(ix0, iy0, c);
|
|
float f2 = pixel(ix1, iy0, c);
|
|
float f3 = pixel(ix0, iy1, c);
|
|
float f4 = pixel(ix1, iy1, c);
|
|
|
|
float i1 = lerp(f1, f2, fracX);
|
|
float i2 = lerp(f3, f4, fracX);
|
|
|
|
return lerp(i1, i2, fracY);
|
|
}
|
|
|
|
float FloatImage::sampleLinearRepeat(float x, float y, int c) const
|
|
{
|
|
const int w = m_width;
|
|
const int h = m_height;
|
|
|
|
const float fracX = frac(x * w);
|
|
const float fracY = frac(y * h);
|
|
|
|
int ix0 = ifloor(frac(x) * w);
|
|
int iy0 = ifloor(frac(y) * h);
|
|
int ix1 = ifloor(frac(x + 1.0f/w) * w);
|
|
int iy1 = ifloor(frac(y + 1.0f/h) * h);
|
|
|
|
float f1 = pixel(ix0, iy0, c);
|
|
float f2 = pixel(ix1, iy0, c);
|
|
float f3 = pixel(ix0, iy1, c);
|
|
float f4 = pixel(ix1, iy1, c);
|
|
|
|
float i1 = lerp(f1, f2, fracX);
|
|
float i2 = lerp(f3, f4, fracX);
|
|
|
|
return lerp(i1, i2, fracY);
|
|
}
|
|
|
|
float FloatImage::sampleLinearMirror(float x, float y, int c) const
|
|
{
|
|
const int w = m_width;
|
|
const int h = m_height;
|
|
|
|
x *= w;
|
|
y *= h;
|
|
|
|
const float fracX = frac(x);
|
|
const float fracY = frac(y);
|
|
|
|
int ix0 = mirror(iround(x), w);
|
|
int iy0 = mirror(iround(y), h);
|
|
int ix1 = mirror(iround(x) + 1, w);
|
|
int iy1 = mirror(iround(y) + 1, h);
|
|
|
|
float f1 = pixel(ix0, iy0, c);
|
|
float f2 = pixel(ix1, iy0, c);
|
|
float f3 = pixel(ix0, iy1, c);
|
|
float f4 = pixel(ix1, iy1, c);
|
|
|
|
float i1 = lerp(f1, f2, fracX);
|
|
float i2 = lerp(f3, f4, fracX);
|
|
|
|
return lerp(i1, i2, fracY);
|
|
}
|
|
|
|
|
|
/// Fast downsampling using box filter.
|
|
///
|
|
/// The extents of the image are divided by two and rounded down.
|
|
///
|
|
/// When the size of the image is odd, this uses a polyphase box filter as explained in:
|
|
/// http://developer.nvidia.com/object/np2_mipmapping.html
|
|
///
|
|
FloatImage * FloatImage::fastDownSample() const
|
|
{
|
|
nvDebugCheck(m_width != 1 || m_height != 1);
|
|
|
|
AutoPtr<FloatImage> dst_image( new FloatImage() );
|
|
|
|
const uint w = max(1, m_width / 2);
|
|
const uint h = max(1, m_height / 2);
|
|
dst_image->allocate(m_componentNum, w, h);
|
|
|
|
// 1D box filter.
|
|
if (m_width == 1 || m_height == 1)
|
|
{
|
|
const uint n = w * h;
|
|
|
|
if ((m_width * m_height) & 1)
|
|
{
|
|
const float scale = 1.0f / (2 * n + 1);
|
|
|
|
for(uint c = 0; c < m_componentNum; c++)
|
|
{
|
|
const float * src = this->channel(c);
|
|
float * dst = dst_image->channel(c);
|
|
|
|
for(uint x = 0; x < n; x++)
|
|
{
|
|
const float w0 = float(n - x);
|
|
const float w1 = float(n - 0);
|
|
const float w2 = float(1 + x);
|
|
|
|
*dst++ = scale * (w0 * src[0] + w1 * src[1] + w2 * src[2]);
|
|
src += 2;
|
|
}
|
|
}
|
|
}
|
|
else
|
|
{
|
|
for(uint c = 0; c < m_componentNum; c++)
|
|
{
|
|
const float * src = this->channel(c);
|
|
float * dst = dst_image->channel(c);
|
|
|
|
for(uint x = 0; x < n; x++)
|
|
{
|
|
*dst = 0.5f * (src[0] + src[1]);
|
|
dst++;
|
|
src += 2;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
// Regular box filter.
|
|
else if ((m_width & 1) == 0 && (m_height & 1) == 0)
|
|
{
|
|
for(uint c = 0; c < m_componentNum; c++)
|
|
{
|
|
const float * src = this->channel(c);
|
|
float * dst = dst_image->channel(c);
|
|
|
|
for(uint y = 0; y < h; y++)
|
|
{
|
|
for(uint x = 0; x < w; x++)
|
|
{
|
|
*dst = 0.25f * (src[0] + src[1] + src[m_width] + src[m_width + 1]);
|
|
dst++;
|
|
src += 2;
|
|
}
|
|
|
|
src += m_width;
|
|
}
|
|
}
|
|
}
|
|
|
|
// Polyphase filters.
|
|
else if (m_width & 1 && m_height & 1)
|
|
{
|
|
nvDebugCheck(m_width == 2 * w + 1);
|
|
nvDebugCheck(m_height == 2 * h + 1);
|
|
|
|
const float scale = 1.0f / (m_width * m_height);
|
|
|
|
for(uint c = 0; c < m_componentNum; c++)
|
|
{
|
|
const float * src = this->channel(c);
|
|
float * dst = dst_image->channel(c);
|
|
|
|
for(uint y = 0; y < h; y++)
|
|
{
|
|
const float v0 = float(h - y);
|
|
const float v1 = float(h - 0);
|
|
const float v2 = float(1 + y);
|
|
|
|
for (uint x = 0; x < w; x++)
|
|
{
|
|
const float w0 = float(w - x);
|
|
const float w1 = float(w - 0);
|
|
const float w2 = float(1 + x);
|
|
|
|
float f = 0.0f;
|
|
f += v0 * (w0 * src[0 * m_width + 2 * x] + w1 * src[0 * m_width + 2 * x + 1] + w2 * src[0 * m_width + 2 * x + 2]);
|
|
f += v1 * (w0 * src[1 * m_width + 2 * x] + w1 * src[1 * m_width + 2 * x + 1] + w2 * src[1 * m_width + 2 * x + 2]);
|
|
f += v2 * (w0 * src[2 * m_width + 2 * x] + w1 * src[2 * m_width + 2 * x + 1] + w2 * src[2 * m_width + 2 * x + 2]);
|
|
|
|
*dst = f * scale;
|
|
dst++;
|
|
}
|
|
|
|
src += 2 * m_width;
|
|
}
|
|
}
|
|
}
|
|
else if (m_width & 1)
|
|
{
|
|
nvDebugCheck(m_width == 2 * w + 1);
|
|
const float scale = 1.0f / (2 * m_width);
|
|
|
|
for(uint c = 0; c < m_componentNum; c++)
|
|
{
|
|
const float * src = this->channel(c);
|
|
float * dst = dst_image->channel(c);
|
|
|
|
for(uint y = 0; y < h; y++)
|
|
{
|
|
for (uint x = 0; x < w; x++)
|
|
{
|
|
const float w0 = float(w - x);
|
|
const float w1 = float(w - 0);
|
|
const float w2 = float(1 + x);
|
|
|
|
float f = 0.0f;
|
|
f += w0 * (src[2 * x + 0] + src[m_width + 2 * x + 0]);
|
|
f += w1 * (src[2 * x + 1] + src[m_width + 2 * x + 1]);
|
|
f += w2 * (src[2 * x + 2] + src[m_width + 2 * x + 2]);
|
|
|
|
*dst = f * scale;
|
|
dst++;
|
|
}
|
|
|
|
src += 2 * m_width;
|
|
}
|
|
}
|
|
}
|
|
else if (m_height & 1)
|
|
{
|
|
nvDebugCheck(m_height == 2 * h + 1);
|
|
|
|
const float scale = 1.0f / (2 * m_height);
|
|
|
|
for(uint c = 0; c < m_componentNum; c++)
|
|
{
|
|
const float * src = this->channel(c);
|
|
float * dst = dst_image->channel(c);
|
|
|
|
for(uint y = 0; y < h; y++)
|
|
{
|
|
const float v0 = float(h - y);
|
|
const float v1 = float(h - 0);
|
|
const float v2 = float(1 + y);
|
|
|
|
for (uint x = 0; x < w; x++)
|
|
{
|
|
float f = 0.0f;
|
|
f += v0 * (src[0 * m_width + 2 * x] + src[0 * m_width + 2 * x + 1]);
|
|
f += v1 * (src[1 * m_width + 2 * x] + src[1 * m_width + 2 * x + 1]);
|
|
f += v2 * (src[2 * m_width + 2 * x] + src[2 * m_width + 2 * x + 1]);
|
|
|
|
*dst = f * scale;
|
|
dst++;
|
|
}
|
|
|
|
src += 2 * m_width;
|
|
}
|
|
}
|
|
}
|
|
|
|
return dst_image.release();
|
|
}
|
|
|
|
/// Downsample applying a 1D kernel separately in each dimension.
|
|
FloatImage * FloatImage::downSample(const Filter & filter, WrapMode wm) const
|
|
{
|
|
const uint w = max(1, m_width / 2);
|
|
const uint h = max(1, m_height / 2);
|
|
|
|
return resize(filter, w, h, wm);
|
|
}
|
|
|
|
/// Downsample applying a 1D kernel separately in each dimension.
|
|
FloatImage * FloatImage::downSample(const Filter & filter, WrapMode wm, uint alpha) const
|
|
{
|
|
const uint w = max(1, m_width / 2);
|
|
const uint h = max(1, m_height / 2);
|
|
|
|
return resize(filter, w, h, wm, alpha);
|
|
}
|
|
|
|
|
|
/// Downsample applying a 1D kernel separately in each dimension.
|
|
FloatImage * FloatImage::resize(const Filter & filter, uint w, uint h, WrapMode wm) const
|
|
{
|
|
// @@ Use monophase filters when frac(m_width / w) == 0
|
|
|
|
AutoPtr<FloatImage> tmp_image( new FloatImage() );
|
|
AutoPtr<FloatImage> dst_image( new FloatImage() );
|
|
|
|
PolyphaseKernel xkernel(filter, m_width, w, 32);
|
|
PolyphaseKernel ykernel(filter, m_height, h, 32);
|
|
|
|
// @@ Select fastest filtering order:
|
|
//if (w * m_height <= h * m_width)
|
|
{
|
|
tmp_image->allocate(m_componentNum, w, m_height);
|
|
dst_image->allocate(m_componentNum, w, h);
|
|
|
|
Array<float> tmp_column(h);
|
|
tmp_column.resize(h);
|
|
|
|
for (uint c = 0; c < m_componentNum; c++)
|
|
{
|
|
float * tmp_channel = tmp_image->channel(c);
|
|
|
|
for (uint y = 0; y < m_height; y++) {
|
|
this->applyKernelHorizontal(xkernel, y, c, wm, tmp_channel + y * w);
|
|
}
|
|
|
|
float * dst_channel = dst_image->channel(c);
|
|
|
|
for (uint x = 0; x < w; x++) {
|
|
tmp_image->applyKernelVertical(ykernel, x, c, wm, tmp_column.unsecureBuffer());
|
|
|
|
for (uint y = 0; y < h; y++) {
|
|
dst_channel[y * w + x] = tmp_column[y];
|
|
}
|
|
}
|
|
}
|
|
}
|
|
/*else
|
|
{
|
|
tmp_image->allocate(m_componentNum, m_width, h);
|
|
dst_image->allocate(m_componentNum, w, h);
|
|
|
|
Array<float> tmp_column(h);
|
|
tmp_column.resize(h);
|
|
|
|
for (uint c = 0; c < m_componentNum; c++)
|
|
{
|
|
float * tmp_channel = tmp_image->channel(c);
|
|
|
|
for (uint x = 0; x < w; x++) {
|
|
tmp_image->applyKernelVertical(ykernel, x, c, wm, tmp_column.unsecureBuffer());
|
|
|
|
for (uint y = 0; y < h; y++) {
|
|
tmp_channel[y * w + x] = tmp_column[y];
|
|
}
|
|
}
|
|
|
|
float * dst_channel = dst_image->channel(c);
|
|
|
|
for (uint y = 0; y < m_height; y++) {
|
|
this->applyKernelHorizontal(xkernel, y, c, wm, dst_channel + y * w);
|
|
}
|
|
}
|
|
}*/
|
|
|
|
return dst_image.release();
|
|
}
|
|
|
|
/// Downsample applying a 1D kernel separately in each dimension.
|
|
FloatImage * FloatImage::resize(const Filter & filter, uint w, uint h, WrapMode wm, uint alpha) const
|
|
{
|
|
nvCheck(alpha < m_componentNum);
|
|
|
|
AutoPtr<FloatImage> tmp_image( new FloatImage() );
|
|
AutoPtr<FloatImage> dst_image( new FloatImage() );
|
|
|
|
PolyphaseKernel xkernel(filter, m_width, w, 32);
|
|
PolyphaseKernel ykernel(filter, m_height, h, 32);
|
|
|
|
{
|
|
tmp_image->allocate(m_componentNum, w, m_height);
|
|
dst_image->allocate(m_componentNum, w, h);
|
|
|
|
Array<float> tmp_column(h);
|
|
tmp_column.resize(h);
|
|
|
|
for (uint c = 0; c < m_componentNum; c++)
|
|
{
|
|
float * tmp_channel = tmp_image->channel(c);
|
|
|
|
for (uint y = 0; y < m_height; y++) {
|
|
this->applyKernelHorizontal(xkernel, y, c, alpha, wm, tmp_channel + y * w);
|
|
}
|
|
}
|
|
|
|
// Process all channels before applying vertical kernel to make sure alpha has been computed.
|
|
|
|
for (uint c = 0; c < m_componentNum; c++)
|
|
{
|
|
float * dst_channel = dst_image->channel(c);
|
|
|
|
for (uint x = 0; x < w; x++) {
|
|
tmp_image->applyKernelVertical(ykernel, x, c, alpha, wm, tmp_column.unsecureBuffer());
|
|
|
|
for (uint y = 0; y < h; y++) {
|
|
dst_channel[y * w + x] = tmp_column[y];
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
return dst_image.release();
|
|
}
|
|
|
|
|
|
|
|
/// Apply 2D kernel at the given coordinates and return result.
|
|
float FloatImage::applyKernel(const Kernel2 * k, int x, int y, uint c, WrapMode wm) const
|
|
{
|
|
nvDebugCheck(k != NULL);
|
|
|
|
const uint kernelWindow = k->windowSize();
|
|
const int kernelOffset = int(kernelWindow / 2) - 1;
|
|
|
|
const float * channel = this->channel(c);
|
|
|
|
float sum = 0.0f;
|
|
for (uint i = 0; i < kernelWindow; i++)
|
|
{
|
|
const int src_y = int(y + i) - kernelOffset;
|
|
|
|
for (uint e = 0; e < kernelWindow; e++)
|
|
{
|
|
const int src_x = int(x + e) - kernelOffset;
|
|
|
|
int idx = this->index(src_x, src_y, wm);
|
|
|
|
sum += k->valueAt(e, i) * channel[idx];
|
|
}
|
|
}
|
|
|
|
return sum;
|
|
}
|
|
|
|
|
|
/// Apply 1D vertical kernel at the given coordinates and return result.
|
|
float FloatImage::applyKernelVertical(const Kernel1 * k, int x, int y, uint c, WrapMode wm) const
|
|
{
|
|
nvDebugCheck(k != NULL);
|
|
|
|
const uint kernelWindow = k->windowSize();
|
|
const int kernelOffset = int(kernelWindow / 2) - 1;
|
|
|
|
const float * channel = this->channel(c);
|
|
|
|
float sum = 0.0f;
|
|
for (uint i = 0; i < kernelWindow; i++)
|
|
{
|
|
const int src_y = int(y + i) - kernelOffset;
|
|
const int idx = this->index(x, src_y, wm);
|
|
|
|
sum += k->valueAt(i) * channel[idx];
|
|
}
|
|
|
|
return sum;
|
|
}
|
|
|
|
/// Apply 1D horizontal kernel at the given coordinates and return result.
|
|
float FloatImage::applyKernelHorizontal(const Kernel1 * k, int x, int y, uint c, WrapMode wm) const
|
|
{
|
|
nvDebugCheck(k != NULL);
|
|
|
|
const uint kernelWindow = k->windowSize();
|
|
const int kernelOffset = int(kernelWindow / 2) - 1;
|
|
|
|
const float * channel = this->channel(c);
|
|
|
|
float sum = 0.0f;
|
|
for (uint e = 0; e < kernelWindow; e++)
|
|
{
|
|
const int src_x = int(x + e) - kernelOffset;
|
|
const int idx = this->index(src_x, y, wm);
|
|
|
|
sum += k->valueAt(e) * channel[idx];
|
|
}
|
|
|
|
return sum;
|
|
}
|
|
|
|
|
|
/// Apply 1D vertical kernel at the given coordinates and return result.
|
|
void FloatImage::applyKernelVertical(const PolyphaseKernel & k, int x, uint c, WrapMode wm, float * __restrict output) const
|
|
{
|
|
const uint length = k.length();
|
|
const float scale = float(length) / float(m_height);
|
|
const float iscale = 1.0f / scale;
|
|
|
|
const float width = k.width();
|
|
const int windowSize = k.windowSize();
|
|
|
|
const float * channel = this->channel(c);
|
|
|
|
for (uint i = 0; i < length; i++)
|
|
{
|
|
const float center = (0.5f + i) * iscale;
|
|
|
|
const int left = (int)floorf(center - width);
|
|
const int right = (int)ceilf(center + width);
|
|
nvCheck(right - left <= windowSize);
|
|
|
|
float sum = 0;
|
|
for (int j = 0; j < windowSize; ++j)
|
|
{
|
|
const int idx = this->index(x, j+left, wm);
|
|
|
|
sum += k.valueAt(i, j) * channel[idx];
|
|
}
|
|
|
|
output[i] = sum;
|
|
}
|
|
}
|
|
|
|
/// Apply 1D horizontal kernel at the given coordinates and return result.
|
|
void FloatImage::applyKernelHorizontal(const PolyphaseKernel & k, int y, uint c, WrapMode wm, float * __restrict output) const
|
|
{
|
|
const uint length = k.length();
|
|
const float scale = float(length) / float(m_width);
|
|
const float iscale = 1.0f / scale;
|
|
|
|
const float width = k.width();
|
|
const int windowSize = k.windowSize();
|
|
|
|
const float * channel = this->channel(c);
|
|
|
|
for (uint i = 0; i < length; i++)
|
|
{
|
|
const float center = (0.5f + i) * iscale;
|
|
|
|
const int left = (int)floorf(center - width);
|
|
const int right = (int)ceilf(center + width);
|
|
nvDebugCheck(right - left <= windowSize);
|
|
|
|
float sum = 0;
|
|
for (int j = 0; j < windowSize; ++j)
|
|
{
|
|
const int idx = this->index(left + j, y, wm);
|
|
|
|
sum += k.valueAt(i, j) * channel[idx];
|
|
}
|
|
|
|
output[i] = sum;
|
|
}
|
|
}
|
|
|
|
|
|
/// Apply 1D vertical kernel at the given coordinates and return result.
|
|
void FloatImage::applyKernelVertical(const PolyphaseKernel & k, int x, uint c, uint a, WrapMode wm, float * __restrict output) const
|
|
{
|
|
const uint length = k.length();
|
|
const float scale = float(length) / float(m_height);
|
|
const float iscale = 1.0f / scale;
|
|
|
|
const float width = k.width();
|
|
const int windowSize = k.windowSize();
|
|
|
|
const float * channel = this->channel(c);
|
|
const float * alpha = this->channel(a);
|
|
|
|
for (uint i = 0; i < length; i++)
|
|
{
|
|
const float center = (0.5f + i) * iscale;
|
|
|
|
const int left = (int)floorf(center - width);
|
|
const int right = (int)ceilf(center + width);
|
|
nvCheck(right - left <= windowSize);
|
|
|
|
float norm = 0;
|
|
float sum = 0;
|
|
for (int j = 0; j < windowSize; ++j)
|
|
{
|
|
const int idx = this->index(x, j+left, wm);
|
|
|
|
float w = k.valueAt(i, j) * (alpha[idx] + (1.0f / 256.0f));
|
|
norm += w;
|
|
sum += w * channel[idx];
|
|
}
|
|
|
|
output[i] = sum / norm;
|
|
}
|
|
}
|
|
|
|
/// Apply 1D horizontal kernel at the given coordinates and return result.
|
|
void FloatImage::applyKernelHorizontal(const PolyphaseKernel & k, int y, uint c, uint a, WrapMode wm, float * __restrict output) const
|
|
{
|
|
const uint length = k.length();
|
|
const float scale = float(length) / float(m_width);
|
|
const float iscale = 1.0f / scale;
|
|
|
|
const float width = k.width();
|
|
const int windowSize = k.windowSize();
|
|
|
|
const float * channel = this->channel(c);
|
|
const float * alpha = this->channel(a);
|
|
|
|
for (uint i = 0; i < length; i++)
|
|
{
|
|
const float center = (0.5f + i) * iscale;
|
|
|
|
const int left = (int)floorf(center - width);
|
|
const int right = (int)ceilf(center + width);
|
|
nvDebugCheck(right - left <= windowSize);
|
|
|
|
float norm = 0.0f;
|
|
float sum = 0;
|
|
for (int j = 0; j < windowSize; ++j)
|
|
{
|
|
const int idx = this->index(left + j, y, wm);
|
|
|
|
float w = k.valueAt(i, j) * (alpha[idx] + (1.0f / 256.0f));
|
|
norm += w;
|
|
sum += w * channel[idx];
|
|
}
|
|
|
|
output[i] = sum / norm;
|
|
}
|
|
}
|
|
|
|
FloatImage* FloatImage::clone() const
|
|
{
|
|
FloatImage* copy = new FloatImage();
|
|
copy->m_width = m_width;
|
|
copy->m_height = m_height;
|
|
copy->m_componentNum = m_componentNum;
|
|
copy->m_count = m_count;
|
|
|
|
if(m_mem)
|
|
{
|
|
copy->allocate(m_componentNum, m_width, m_height);
|
|
memcpy(copy->m_mem, m_mem, m_count * sizeof(float));
|
|
}
|
|
|
|
return copy;
|
|
}
|
|
|