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cudaSiftD.cu
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/**
* Author: Marten Bjorkman
* Contributor: Ethan Stewart
*
* This file consists of a number of functions related to sift.
*/
#include "cudaSiftD.h"
#include "cudautils.h"
#include "cudaSift.h"
///////////////////////////////////////////////////////////////////////////////
// Kernel configuration
///////////////////////////////////////////////////////////////////////////////
__constant__ int d_MaxNumPoints;
__device__ unsigned int d_PointCounter[8 * 2 + 1];
__constant__ float d_ScaleDownKernel[5];
__constant__ float d_LowPassKernel[2 * LOWPASS_R + 1];
__constant__ float d_LaplaceKernel[8 * 12 * 16];
///////////////////////////////////////////////////////////////////////////////
// Lowpass filter and subsample image
///////////////////////////////////////////////////////////////////////////////
__global__ void ScaleDownDenseShift(float *d_Result, float *d_Data, int width, int pitch, int height, int newpitch) {
#define BW (SCALEDOWN_W+4)
#define BH (SCALEDOWN_H+4)
#define W2 (SCALEDOWN_W/2)
#define H2 (SCALEDOWN_H/2)
__shared__ float brows[BH * BW];
const int tx = threadIdx.x;
const int ty = threadIdx.y;
const int xp = blockIdx.x * SCALEDOWN_W + tx;
const int yp = blockIdx.y * SCALEDOWN_H + ty;
const float k0 = d_ScaleDownKernel[0];
const float k1 = d_ScaleDownKernel[1];
const float k2 = d_ScaleDownKernel[2];
const int xl = min(width - 1, max(0, xp - 2));
const int yl = min(height - 1, max(0, yp - 2));
if (xp < (width + 4) && yp < (height + 4)) {
float v = d_Data[yl * pitch + xl];
brows[BW * ty + tx] =
k0 * (v + ShiftDown(v, 4)) + k1 * (ShiftDown(v, 1) + ShiftDown(v, 3)) + k2 * ShiftDown(v, 2);
}
__syncthreads();
const int xs = blockIdx.x * W2 + tx;
const int ys = blockIdx.y * H2 + ty;
if (tx < W2 && ty < H2 && xs < (width / 2) && ys < (height / 2)) {
float *ptr = &brows[BW * (ty * 2) + (tx * 2)];
d_Result[ys * newpitch + xs] =
k0 * (ptr[0] + ptr[4 * BW]) + k1 * (ptr[1 * BW] + ptr[3 * BW]) + k2 * ptr[2 * BW];
}
}
__global__ void ScaleDownDense(float *d_Result, float *d_Data, int width, int pitch, int height, int newpitch) {
#define BW (SCALEDOWN_W+4)
#define BH (SCALEDOWN_H+4)
#define W2 (SCALEDOWN_W/2)
#define H2 (SCALEDOWN_H/2)
__shared__ float irows[BH * BW];
__shared__ float brows[BH * W2];
const int tx = threadIdx.x;
const int ty = threadIdx.y;
const int xp = blockIdx.x * SCALEDOWN_W + tx;
const int yp = blockIdx.y * SCALEDOWN_H + ty;
const int xl = min(width - 1, max(0, xp - 2));
const int yl = min(height - 1, max(0, yp - 2));
const float k0 = d_ScaleDownKernel[0];
const float k1 = d_ScaleDownKernel[1];
const float k2 = d_ScaleDownKernel[2];
if (xp < (width + 4) && yp < (height + 4))
irows[BW * ty + tx] = d_Data[yl * pitch + xl];
__syncthreads();
if (yp < (height + 4) && tx < W2) {
float *ptr = &irows[BW * ty + 2 * tx];
brows[W2 * ty + tx] = k0 * (ptr[0] + ptr[4]) + k1 * (ptr[1] + ptr[3]) + k2 * ptr[2];
}
__syncthreads();
const int xs = blockIdx.x * W2 + tx;
const int ys = blockIdx.y * H2 + ty;
if (tx < W2 && ty < H2 && xs < (width / 2) && ys < (height / 2)) {
float *ptr = &brows[W2 * (ty * 2) + tx];
d_Result[ys * newpitch + xs] =
k0 * (ptr[0] + ptr[4 * W2]) + k1 * (ptr[1 * W2] + ptr[3 * W2]) + k2 * ptr[2 * W2];
}
}
__global__ void ScaleDown(float *d_Result, float *d_Data, int width, int pitch, int height, int newpitch) {
__shared__ float inrow[SCALEDOWN_W + 4];
__shared__ float brow[5 * (SCALEDOWN_W / 2)];
__shared__ int yRead[SCALEDOWN_H + 4];
__shared__ int yWrite[SCALEDOWN_H + 4];
#define dx2 (SCALEDOWN_W/2)
const int tx = threadIdx.x;
const int tx0 = tx + 0 * dx2;
const int tx1 = tx + 1 * dx2;
const int tx2 = tx + 2 * dx2;
const int tx3 = tx + 3 * dx2;
const int tx4 = tx + 4 * dx2;
const int xStart = blockIdx.x * SCALEDOWN_W;
const int yStart = blockIdx.y * SCALEDOWN_H;
const int xWrite = xStart / 2 + tx;
float k0 = d_ScaleDownKernel[0];
float k1 = d_ScaleDownKernel[1];
float k2 = d_ScaleDownKernel[2];
if (tx < SCALEDOWN_H + 4) {
int y = yStart + tx - 2;
y = (y < 0 ? 0 : y);
y = (y >= height ? height - 1 : y);
yRead[tx] = y * pitch;
yWrite[tx] = (yStart + tx - 4) / 2 * newpitch;
}
__syncthreads();
int xRead = xStart + tx - 2;
xRead = (xRead < 0 ? 0 : xRead);
xRead = (xRead >= width ? width - 1 : xRead);
int maxtx = min(dx2, width / 2 - xStart / 2);
for (int dy = 0; dy < SCALEDOWN_H + 4; dy += 5) {
{
inrow[tx] = d_Data[yRead[dy + 0] + xRead];
__syncthreads();
if (tx < maxtx) {
brow[tx4] = k0 * (inrow[2 * tx] + inrow[2 * tx + 4]) + k1 * (inrow[2 * tx + 1] + inrow[2 * tx + 3]) +
k2 * inrow[2 * tx + 2];
if (dy >= 4 && !(dy & 1))
d_Result[yWrite[dy + 0] + xWrite] =
k2 * brow[tx2] + k0 * (brow[tx0] + brow[tx4]) + k1 * (brow[tx1] + brow[tx3]);
}
__syncthreads();
}
if (dy < (SCALEDOWN_H + 3)) {
inrow[tx] = d_Data[yRead[dy + 1] + xRead];
__syncthreads();
if (tx < maxtx) {
brow[tx0] = k0 * (inrow[2 * tx] + inrow[2 * tx + 4]) + k1 * (inrow[2 * tx + 1] + inrow[2 * tx + 3]) +
k2 * inrow[2 * tx + 2];
if (dy >= 3 && (dy & 1))
d_Result[yWrite[dy + 1] + xWrite] =
k2 * brow[tx3] + k0 * (brow[tx1] + brow[tx0]) + k1 * (brow[tx2] + brow[tx4]);
}
__syncthreads();
}
if (dy < (SCALEDOWN_H + 2)) {
inrow[tx] = d_Data[yRead[dy + 2] + xRead];
__syncthreads();
if (tx < maxtx) {
brow[tx1] = k0 * (inrow[2 * tx] + inrow[2 * tx + 4]) + k1 * (inrow[2 * tx + 1] + inrow[2 * tx + 3]) +
k2 * inrow[2 * tx + 2];
if (dy >= 2 && !(dy & 1))
d_Result[yWrite[dy + 2] + xWrite] =
k2 * brow[tx4] + k0 * (brow[tx2] + brow[tx1]) + k1 * (brow[tx3] + brow[tx0]);
}
__syncthreads();
}
if (dy < (SCALEDOWN_H + 1)) {
inrow[tx] = d_Data[yRead[dy + 3] + xRead];
__syncthreads();
if (tx < maxtx) {
brow[tx2] = k0 * (inrow[2 * tx] + inrow[2 * tx + 4]) + k1 * (inrow[2 * tx + 1] + inrow[2 * tx + 3]) +
k2 * inrow[2 * tx + 2];
if (dy >= 1 && (dy & 1))
d_Result[yWrite[dy + 3] + xWrite] =
k2 * brow[tx0] + k0 * (brow[tx3] + brow[tx2]) + k1 * (brow[tx4] + brow[tx1]);
}
__syncthreads();
}
if (dy < SCALEDOWN_H) {
inrow[tx] = d_Data[yRead[dy + 4] + xRead];
__syncthreads();
if (tx < dx2 && xWrite < width / 2) {
brow[tx3] = k0 * (inrow[2 * tx] + inrow[2 * tx + 4]) + k1 * (inrow[2 * tx + 1] + inrow[2 * tx + 3]) +
k2 * inrow[2 * tx + 2];
if (!(dy & 1))
d_Result[yWrite[dy + 4] + xWrite] =
k2 * brow[tx1] + k0 * (brow[tx4] + brow[tx3]) + k1 * (brow[tx0] + brow[tx2]);
}
__syncthreads();
}
}
}
__global__ void ScaleUp(float *d_Result, float *d_Data, int width, int pitch, int height, int newpitch) {
const int tx = threadIdx.x;
const int ty = threadIdx.y;
int x = blockIdx.x * SCALEUP_W + 2 * tx;
int y = blockIdx.y * SCALEUP_H + 2 * ty;
if (x < 2 * width && y < 2 * height) {
int xl = blockIdx.x * (SCALEUP_W / 2) + tx;
int yu = blockIdx.y * (SCALEUP_H / 2) + ty;
int xr = min(xl + 1, width - 1);
int yd = min(yu + 1, height - 1);
float vul = d_Data[yu * pitch + xl];
float vur = d_Data[yu * pitch + xr];
float vdl = d_Data[yd * pitch + xl];
float vdr = d_Data[yd * pitch + xr];
d_Result[(y + 0) * newpitch + x + 0] = vul;
d_Result[(y + 0) * newpitch + x + 1] = 0.50f * (vul + vur);
d_Result[(y + 1) * newpitch + x + 0] = 0.50f * (vul + vdl);
d_Result[(y + 1) * newpitch + x + 1] = 0.25f * (vul + vur + vdl + vdr);
}
}
__global__ void ExtractSiftDescriptors(cudaTextureObject_t texObj, SiftPoint *d_sift, int fstPts, float subsampling) {
__shared__ float gauss[16];
__shared__ float buffer[128];
__shared__ float sums[4];
const int tx = threadIdx.x; // 0 -> 16
const int ty = threadIdx.y; // 0 -> 8
const int idx = ty * 16 + tx;
const int bx = blockIdx.x + fstPts; // 0 -> numPts
if (ty == 0)
gauss[tx] = exp(-(tx - 7.5f) * (tx - 7.5f) / 128.0f);
buffer[idx] = 0.0f;
__syncthreads();
// Compute angles and gradients
float theta = 2.0f * 3.1415f / 360.0f * d_sift[bx].orientation;
float sina = sinf(theta); // cosa -sina
float cosa = cosf(theta); // sina cosa
float scale = 12.0f / 16.0f * d_sift[bx].scale;
float ssina = scale * sina;
float scosa = scale * cosa;
for (int y = ty; y < 16; y += 8) {
float xpos = d_sift[bx].xpos + (tx - 7.5f) * scosa - (y - 7.5f) * ssina + 0.5f;
float ypos = d_sift[bx].ypos + (tx - 7.5f) * ssina + (y - 7.5f) * scosa + 0.5f;
float dx = tex2D<float>(texObj, xpos + cosa, ypos + sina) -
tex2D<float>(texObj, xpos - cosa, ypos - sina);
float dy = tex2D<float>(texObj, xpos - sina, ypos + cosa) -
tex2D<float>(texObj, xpos + sina, ypos - cosa);
float grad = gauss[y] * gauss[tx] * sqrtf(dx * dx + dy * dy);
float angf = 4.0f / 3.1415f * atan2f(dy, dx) + 4.0f;
int hori = (tx + 2) / 4 - 1; // Convert from (tx,y,angle) to bins
float horf = (tx - 1.5f) / 4.0f - hori;
float ihorf = 1.0f - horf;
int veri = (y + 2) / 4 - 1;
float verf = (y - 1.5f) / 4.0f - veri;
float iverf = 1.0f - verf;
int angi = angf;
int angp = (angi < 7 ? angi + 1 : 0);
angf -= angi;
float iangf = 1.0f - angf;
int hist = 8 * (4 * veri + hori); // Each gradient measure is interpolated
int p1 = angi + hist; // in angles, xpos and ypos -> 8 stores
int p2 = angp + hist;
if (tx >= 2) {
float grad1 = ihorf * grad;
if (y >= 2) { // Upper left
float grad2 = iverf * grad1;
atomicAdd(buffer + p1, iangf * grad2);
atomicAdd(buffer + p2, angf * grad2);
}
if (y <= 13) { // Lower left
float grad2 = verf * grad1;
atomicAdd(buffer + p1 + 32, iangf * grad2);
atomicAdd(buffer + p2 + 32, angf * grad2);
}
}
if (tx <= 13) {
float grad1 = horf * grad;
if (y >= 2) { // Upper right
float grad2 = iverf * grad1;
atomicAdd(buffer + p1 + 8, iangf * grad2);
atomicAdd(buffer + p2 + 8, angf * grad2);
}
if (y <= 13) { // Lower right
float grad2 = verf * grad1;
atomicAdd(buffer + p1 + 40, iangf * grad2);
atomicAdd(buffer + p2 + 40, angf * grad2);
}
}
}
__syncthreads();
// Normalize twice and suppress peaks first time
float sum = buffer[idx] * buffer[idx];
for (int i = 16; i > 0; i /= 2)
sum += ShiftDown(sum, i);
if ((idx & 31) == 0)
sums[idx / 32] = sum;
__syncthreads();
float tsum1 = sums[0] + sums[1] + sums[2] + sums[3];
tsum1 = min(buffer[idx] * rsqrtf(tsum1), 0.2f);
sum = tsum1 * tsum1;
for (int i = 16; i > 0; i /= 2)
sum += ShiftDown(sum, i);
if ((idx & 31) == 0)
sums[idx / 32] = sum;
__syncthreads();
float tsum2 = sums[0] + sums[1] + sums[2] + sums[3];
float *desc = d_sift[bx].data;
desc[idx] = tsum1 * rsqrtf(tsum2);
if (idx == 0) {
d_sift[bx].xpos *= subsampling;
d_sift[bx].ypos *= subsampling;
d_sift[bx].scale *= subsampling;
}
}
__device__ float FastAtan2(float y, float x) {
float absx = abs(x);
float absy = abs(y);
float a = __fdiv_rn(min(absx, absy), max(absx, absy));
float s = a * a;
float r = ((-0.0464964749f * s + 0.15931422f) * s - 0.327622764f) * s * a + a;
r = (absy > absx ? 1.57079637f - r : r);
r = (x < 0 ? 3.14159274f - r : r);
r = (y < 0 ? -r : r);
return r;
}
__global__ void
ExtractSiftDescriptorsCONSTNew(cudaTextureObject_t texObj, SiftPoint *d_sift, float subsampling, int octave) {
__shared__ float gauss[16];
__shared__ float buffer[128];
__shared__ float sums[4];
const int tx = threadIdx.x; // 0 -> 16
const int ty = threadIdx.y; // 0 -> 8
const int idx = ty * 16 + tx;
if (ty == 0)
gauss[tx] = __expf(-(tx - 7.5f) * (tx - 7.5f) / 128.0f);
int fstPts = min(d_PointCounter[2 * octave - 1], d_MaxNumPoints);
int totPts = min(d_PointCounter[2 * octave + 1], d_MaxNumPoints);
//if (tx==0 && ty==0)
// printf("%d %d %d %d\n", octave, fstPts, min(d_PointCounter[2*octave], d_MaxNumPoints), totPts);
for (int bx = blockIdx.x + fstPts; bx < totPts; bx += gridDim.x) {
buffer[idx] = 0.0f;
__syncthreads();
// Compute angles and gradients
float theta = 2.0f * 3.1415f / 360.0f * d_sift[bx].orientation;
float sina = __sinf(theta); // cosa -sina
float cosa = __cosf(theta); // sina cosa
float scale = 12.0f / 16.0f * d_sift[bx].scale;
float ssina = scale * sina;
float scosa = scale * cosa;
for (int y = ty; y < 16; y += 8) {
float xpos = d_sift[bx].xpos + (tx - 7.5f) * scosa - (y - 7.5f) * ssina + 0.5f;
float ypos = d_sift[bx].ypos + (tx - 7.5f) * ssina + (y - 7.5f) * scosa + 0.5f;
float dx = tex2D<float>(texObj, xpos + cosa, ypos + sina) -
tex2D<float>(texObj, xpos - cosa, ypos - sina);
float dy = tex2D<float>(texObj, xpos - sina, ypos + cosa) -
tex2D<float>(texObj, xpos + sina, ypos - cosa);
float grad = gauss[y] * gauss[tx] * __fsqrt_rn(dx * dx + dy * dy);
float angf = 4.0f / 3.1415f * FastAtan2(dy, dx) + 4.0f;
int hori = (tx + 2) / 4 - 1; // Convert from (tx,y,angle) to bins
float horf = (tx - 1.5f) / 4.0f - hori;
float ihorf = 1.0f - horf;
int veri = (y + 2) / 4 - 1;
float verf = (y - 1.5f) / 4.0f - veri;
float iverf = 1.0f - verf;
int angi = angf;
int angp = (angi < 7 ? angi + 1 : 0);
angf -= angi;
float iangf = 1.0f - angf;
int hist = 8 * (4 * veri + hori); // Each gradient measure is interpolated
int p1 = angi + hist; // in angles, xpos and ypos -> 8 stores
int p2 = angp + hist;
if (tx >= 2) {
float grad1 = ihorf * grad;
if (y >= 2) { // Upper left
float grad2 = iverf * grad1;
atomicAdd(buffer + p1, iangf * grad2);
atomicAdd(buffer + p2, angf * grad2);
}
if (y <= 13) { // Lower left
float grad2 = verf * grad1;
atomicAdd(buffer + p1 + 32, iangf * grad2);
atomicAdd(buffer + p2 + 32, angf * grad2);
}
}
if (tx <= 13) {
float grad1 = horf * grad;
if (y >= 2) { // Upper right
float grad2 = iverf * grad1;
atomicAdd(buffer + p1 + 8, iangf * grad2);
atomicAdd(buffer + p2 + 8, angf * grad2);
}
if (y <= 13) { // Lower right
float grad2 = verf * grad1;
atomicAdd(buffer + p1 + 40, iangf * grad2);
atomicAdd(buffer + p2 + 40, angf * grad2);
}
}
}
__syncthreads();
// Normalize twice and suppress peaks first time
float sum = buffer[idx] * buffer[idx];
for (int i = 16; i > 0; i /= 2)
sum += ShiftDown(sum, i);
if ((idx & 31) == 0)
sums[idx / 32] = sum;
__syncthreads();
float tsum1 = sums[0] + sums[1] + sums[2] + sums[3];
tsum1 = min(buffer[idx] * rsqrtf(tsum1), 0.2f);
sum = tsum1 * tsum1;
for (int i = 16; i > 0; i /= 2)
sum += ShiftDown(sum, i);
if ((idx & 31) == 0)
sums[idx / 32] = sum;
__syncthreads();
float tsum2 = sums[0] + sums[1] + sums[2] + sums[3];
float *desc = d_sift[bx].data;
desc[idx] = tsum1 * rsqrtf(tsum2);
if (idx == 0) {
d_sift[bx].xpos *= subsampling;
d_sift[bx].ypos *= subsampling;
d_sift[bx].scale *= subsampling;
}
__syncthreads();
}
}
__global__ void
ExtractSiftDescriptorsCONST(cudaTextureObject_t texObj, SiftPoint *d_sift, float subsampling, int octave) {
__shared__ float gauss[16];
__shared__ float buffer[128];
__shared__ float sums[4];
const int tx = threadIdx.x; // 0 -> 16
const int ty = threadIdx.y; // 0 -> 8
const int idx = ty * 16 + tx;
if (ty == 0)
gauss[tx] = exp(-(tx - 7.5f) * (tx - 7.5f) / 128.0f);
int fstPts = min(d_PointCounter[2 * octave - 1], d_MaxNumPoints);
int totPts = min(d_PointCounter[2 * octave + 1], d_MaxNumPoints);
//if (tx==0 && ty==0)
// printf("%d %d %d %d\n", octave, fstPts, min(d_PointCounter[2*octave], d_MaxNumPoints), totPts);
for (int bx = blockIdx.x + fstPts; bx < totPts; bx += gridDim.x) {
buffer[idx] = 0.0f;
__syncthreads();
// Compute angles and gradients
float theta = 2.0f * 3.1415f / 360.0f * d_sift[bx].orientation;
float sina = sinf(theta); // cosa -sina
float cosa = cosf(theta); // sina cosa
float scale = 12.0f / 16.0f * d_sift[bx].scale;
float ssina = scale * sina;
float scosa = scale * cosa;
for (int y = ty; y < 16; y += 8) {
float xpos = d_sift[bx].xpos + (tx - 7.5f) * scosa - (y - 7.5f) * ssina + 0.5f;
float ypos = d_sift[bx].ypos + (tx - 7.5f) * ssina + (y - 7.5f) * scosa + 0.5f;
float dx = tex2D<float>(texObj, xpos + cosa, ypos + sina) -
tex2D<float>(texObj, xpos - cosa, ypos - sina);
float dy = tex2D<float>(texObj, xpos - sina, ypos + cosa) -
tex2D<float>(texObj, xpos + sina, ypos - cosa);
float grad = gauss[y] * gauss[tx] * sqrtf(dx * dx + dy * dy);
float angf = 4.0f / 3.1415f * atan2f(dy, dx) + 4.0f;
int hori = (tx + 2) / 4 - 1; // Convert from (tx,y,angle) to bins
float horf = (tx - 1.5f) / 4.0f - hori;
float ihorf = 1.0f - horf;
int veri = (y + 2) / 4 - 1;
float verf = (y - 1.5f) / 4.0f - veri;
float iverf = 1.0f - verf;
int angi = angf;
int angp = (angi < 7 ? angi + 1 : 0);
angf -= angi;
float iangf = 1.0f - angf;
int hist = 8 * (4 * veri + hori); // Each gradient measure is interpolated
int p1 = angi + hist; // in angles, xpos and ypos -> 8 stores
int p2 = angp + hist;
if (tx >= 2) {
float grad1 = ihorf * grad;
if (y >= 2) { // Upper left
float grad2 = iverf * grad1;
atomicAdd(buffer + p1, iangf * grad2);
atomicAdd(buffer + p2, angf * grad2);
}
if (y <= 13) { // Lower left
float grad2 = verf * grad1;
atomicAdd(buffer + p1 + 32, iangf * grad2);
atomicAdd(buffer + p2 + 32, angf * grad2);
}
}
if (tx <= 13) {
float grad1 = horf * grad;
if (y >= 2) { // Upper right
float grad2 = iverf * grad1;
atomicAdd(buffer + p1 + 8, iangf * grad2);
atomicAdd(buffer + p2 + 8, angf * grad2);
}
if (y <= 13) { // Lower right
float grad2 = verf * grad1;
atomicAdd(buffer + p1 + 40, iangf * grad2);
atomicAdd(buffer + p2 + 40, angf * grad2);
}
}
}
__syncthreads();
// Normalize twice and suppress peaks first time
float sum = buffer[idx] * buffer[idx];
for (int i = 16; i > 0; i /= 2)
sum += ShiftDown(sum, i);
if ((idx & 31) == 0)
sums[idx / 32] = sum;
__syncthreads();
float tsum1 = sums[0] + sums[1] + sums[2] + sums[3];
tsum1 = min(buffer[idx] * rsqrtf(tsum1), 0.2f);
sum = tsum1 * tsum1;
for (int i = 16; i > 0; i /= 2)
sum += ShiftDown(sum, i);
if ((idx & 31) == 0)
sums[idx / 32] = sum;
__syncthreads();
float tsum2 = sums[0] + sums[1] + sums[2] + sums[3];
float *desc = d_sift[bx].data;
desc[idx] = tsum1 * rsqrtf(tsum2);
if (idx == 0) {
d_sift[bx].xpos *= subsampling;
d_sift[bx].ypos *= subsampling;
d_sift[bx].scale *= subsampling;
}
__syncthreads();
}
}
__global__ void
ExtractSiftDescriptorsOld(cudaTextureObject_t texObj, SiftPoint *d_sift, int fstPts, float subsampling) {
__shared__ float gauss[16];
__shared__ float buffer[128];
__shared__ float sums[128];
const int tx = threadIdx.x; // 0 -> 16
const int ty = threadIdx.y; // 0 -> 8
const int idx = ty * 16 + tx;
const int bx = blockIdx.x + fstPts; // 0 -> numPts
if (ty == 0)
gauss[tx] = exp(-(tx - 7.5f) * (tx - 7.5f) / 128.0f);
buffer[idx] = 0.0f;
__syncthreads();
// Compute angles and gradients
float theta = 2.0f * 3.1415f / 360.0f * d_sift[bx].orientation;
float sina = sinf(theta); // cosa -sina
float cosa = cosf(theta); // sina cosa
float scale = 12.0f / 16.0f * d_sift[bx].scale;
float ssina = scale * sina;
float scosa = scale * cosa;
for (int y = ty; y < 16; y += 8) {
float xpos = d_sift[bx].xpos + (tx - 7.5f) * scosa - (y - 7.5f) * ssina + 0.5f;
float ypos = d_sift[bx].ypos + (tx - 7.5f) * ssina + (y - 7.5f) * scosa + 0.5f;
float dx = tex2D<float>(texObj, xpos + cosa, ypos + sina) -
tex2D<float>(texObj, xpos - cosa, ypos - sina);
float dy = tex2D<float>(texObj, xpos - sina, ypos + cosa) -
tex2D<float>(texObj, xpos + sina, ypos - cosa);
float grad = gauss[y] * gauss[tx] * sqrtf(dx * dx + dy * dy);
float angf = 4.0f / 3.1415f * atan2f(dy, dx) + 4.0f;
int hori = (tx + 2) / 4 - 1; // Convert from (tx,y,angle) to bins
float horf = (tx - 1.5f) / 4.0f - hori;
float ihorf = 1.0f - horf;
int veri = (y + 2) / 4 - 1;
float verf = (y - 1.5f) / 4.0f - veri;
float iverf = 1.0f - verf;
int angi = angf;
int angp = (angi < 7 ? angi + 1 : 0);
angf -= angi;
float iangf = 1.0f - angf;
int hist = 8 * (4 * veri + hori); // Each gradient measure is interpolated
int p1 = angi + hist; // in angles, xpos and ypos -> 8 stores
int p2 = angp + hist;
if (tx >= 2) {
float grad1 = ihorf * grad;
if (y >= 2) { // Upper left
float grad2 = iverf * grad1;
atomicAdd(buffer + p1, iangf * grad2);
atomicAdd(buffer + p2, angf * grad2);
}
if (y <= 13) { // Lower left
float grad2 = verf * grad1;
atomicAdd(buffer + p1 + 32, iangf * grad2);
atomicAdd(buffer + p2 + 32, angf * grad2);
}
}
if (tx <= 13) {
float grad1 = horf * grad;
if (y >= 2) { // Upper right
float grad2 = iverf * grad1;
atomicAdd(buffer + p1 + 8, iangf * grad2);
atomicAdd(buffer + p2 + 8, angf * grad2);
}
if (y <= 13) { // Lower right
float grad2 = verf * grad1;
atomicAdd(buffer + p1 + 40, iangf * grad2);
atomicAdd(buffer + p2 + 40, angf * grad2);
}
}
}
__syncthreads();
// Normalize twice and suppress peaks first time
if (idx < 64)
sums[idx] = buffer[idx] * buffer[idx] + buffer[idx + 64] * buffer[idx + 64];
__syncthreads();
if (idx < 32) sums[idx] = sums[idx] + sums[idx + 32];
__syncthreads();
if (idx < 16) sums[idx] = sums[idx] + sums[idx + 16];
__syncthreads();
if (idx < 8) sums[idx] = sums[idx] + sums[idx + 8];
__syncthreads();
if (idx < 4) sums[idx] = sums[idx] + sums[idx + 4];
__syncthreads();
float tsum1 = sums[0] + sums[1] + sums[2] + sums[3];
buffer[idx] = buffer[idx] * rsqrtf(tsum1);
if (buffer[idx] > 0.2f)
buffer[idx] = 0.2f;
__syncthreads();
if (idx < 64)
sums[idx] = buffer[idx] * buffer[idx] + buffer[idx + 64] * buffer[idx + 64];
__syncthreads();
if (idx < 32) sums[idx] = sums[idx] + sums[idx + 32];
__syncthreads();
if (idx < 16) sums[idx] = sums[idx] + sums[idx + 16];
__syncthreads();
if (idx < 8) sums[idx] = sums[idx] + sums[idx + 8];
__syncthreads();
if (idx < 4) sums[idx] = sums[idx] + sums[idx + 4];
__syncthreads();
float tsum2 = sums[0] + sums[1] + sums[2] + sums[3];
float *desc = d_sift[bx].data;
desc[idx] = buffer[idx] * rsqrtf(tsum2);
if (idx == 0) {
d_sift[bx].xpos *= subsampling;
d_sift[bx].ypos *= subsampling;
d_sift[bx].scale *= subsampling;
}
}
__device__ void
ExtractSiftDescriptor(cudaTextureObject_t texObj, SiftPoint *d_sift, float subsampling, int octave, int bx) {
__shared__ float gauss[16];
__shared__ float buffer[128];
__shared__ float sums[4];
const int idx = threadIdx.x;
const int tx = idx & 15; // 0 -> 16
const int ty = idx / 16; // 0 -> 8
if (ty == 0)
gauss[tx] = exp(-(tx - 7.5f) * (tx - 7.5f) / 128.0f);
buffer[idx] = 0.0f;
__syncthreads();
// Compute angles and gradients
float theta = 2.0f * 3.1415f / 360.0f * d_sift[bx].orientation;
float sina = sinf(theta); // cosa -sina
float cosa = cosf(theta); // sina cosa
float scale = 12.0f / 16.0f * d_sift[bx].scale;
float ssina = scale * sina;
float scosa = scale * cosa;
for (int y = ty; y < 16; y += 8) {
float xpos = d_sift[bx].xpos + (tx - 7.5f) * scosa - (y - 7.5f) * ssina + 0.5f;
float ypos = d_sift[bx].ypos + (tx - 7.5f) * ssina + (y - 7.5f) * scosa + 0.5f;
float dx = tex2D<float>(texObj, xpos + cosa, ypos + sina) -
tex2D<float>(texObj, xpos - cosa, ypos - sina);
float dy = tex2D<float>(texObj, xpos - sina, ypos + cosa) -
tex2D<float>(texObj, xpos + sina, ypos - cosa);
float grad = gauss[y] * gauss[tx] * sqrtf(dx * dx + dy * dy);
float angf = 4.0f / 3.1415f * atan2f(dy, dx) + 4.0f;
int hori = (tx + 2) / 4 - 1; // Convert from (tx,y,angle) to bins
float horf = (tx - 1.5f) / 4.0f - hori;
float ihorf = 1.0f - horf;
int veri = (y + 2) / 4 - 1;
float verf = (y - 1.5f) / 4.0f - veri;
float iverf = 1.0f - verf;
int angi = angf;
int angp = (angi < 7 ? angi + 1 : 0);
angf -= angi;
float iangf = 1.0f - angf;
int hist = 8 * (4 * veri + hori); // Each gradient measure is interpolated
int p1 = angi + hist; // in angles, xpos and ypos -> 8 stores
int p2 = angp + hist;
if (tx >= 2) {
float grad1 = ihorf * grad;
if (y >= 2) { // Upper left
float grad2 = iverf * grad1;
atomicAdd(buffer + p1, iangf * grad2);
atomicAdd(buffer + p2, angf * grad2);
}
if (y <= 13) { // Lower left
float grad2 = verf * grad1;
atomicAdd(buffer + p1 + 32, iangf * grad2);
atomicAdd(buffer + p2 + 32, angf * grad2);
}
}
if (tx <= 13) {
float grad1 = horf * grad;
if (y >= 2) { // Upper right
float grad2 = iverf * grad1;
atomicAdd(buffer + p1 + 8, iangf * grad2);
atomicAdd(buffer + p2 + 8, angf * grad2);
}
if (y <= 13) { // Lower right
float grad2 = verf * grad1;
atomicAdd(buffer + p1 + 40, iangf * grad2);
atomicAdd(buffer + p2 + 40, angf * grad2);
}
}
}
__syncthreads();
// Normalize twice and suppress peaks first time
float sum = buffer[idx] * buffer[idx];
for (int i = 16; i > 0; i /= 2)
sum += ShiftDown(sum, i);
if ((idx & 31) == 0)
sums[idx / 32] = sum;
__syncthreads();
float tsum1 = sums[0] + sums[1] + sums[2] + sums[3];
tsum1 = min(buffer[idx] * rsqrtf(tsum1), 0.2f);
sum = tsum1 * tsum1;
for (int i = 16; i > 0; i /= 2)
sum += ShiftDown(sum, i);
if ((idx & 31) == 0)
sums[idx / 32] = sum;
__syncthreads();
float tsum2 = sums[0] + sums[1] + sums[2] + sums[3];
float *desc = d_sift[bx].data;
desc[idx] = tsum1 * rsqrtf(tsum2);
if (idx == 0) {
d_sift[bx].xpos *= subsampling;
d_sift[bx].ypos *= subsampling;
d_sift[bx].scale *= subsampling;
}
__syncthreads();
}
__global__ void RescalePositions(SiftPoint *d_sift, int numPts, float scale) {
int num = blockIdx.x * blockDim.x + threadIdx.x;
if (num < numPts) {
d_sift[num].xpos *= scale;
d_sift[num].ypos *= scale;
d_sift[num].scale *= scale;
}
}
__global__ void ComputeOrientations(cudaTextureObject_t texObj, SiftPoint *d_Sift, int fstPts) {
__shared__ float hist[64];
__shared__ float gauss[11];
const int tx = threadIdx.x;
const int bx = blockIdx.x + fstPts;
float i2sigma2 = -1.0f / (4.5f * d_Sift[bx].scale * d_Sift[bx].scale);
if (tx < 11)
gauss[tx] = exp(i2sigma2 * (tx - 5) * (tx - 5));
if (tx < 64)
hist[tx] = 0.0f;
__syncthreads();
float xp = d_Sift[bx].xpos - 4.5f;
float yp = d_Sift[bx].ypos - 4.5f;
int yd = tx / 11;
int xd = tx - yd * 11;
float xf = xp + xd;
float yf = yp + yd;
if (yd < 11) {
float dx = tex2D<float>(texObj, xf + 1.0, yf) - tex2D<float>(texObj, xf - 1.0, yf);
float dy = tex2D<float>(texObj, xf, yf + 1.0) - tex2D<float>(texObj, xf, yf - 1.0);
int bin = 16.0f * atan2f(dy, dx) / 3.1416f + 16.5f;
if (bin > 31)
bin = 0;
float grad = sqrtf(dx * dx + dy * dy);
atomicAdd(&hist[bin], grad * gauss[xd] * gauss[yd]);
}
__syncthreads();
int x1m = (tx >= 1 ? tx - 1 : tx + 31);
int x1p = (tx <= 30 ? tx + 1 : tx - 31);
if (tx < 32) {
int x2m = (tx >= 2 ? tx - 2 : tx + 30);
int x2p = (tx <= 29 ? tx + 2 : tx - 30);
hist[tx + 32] = 6.0f * hist[tx] + 4.0f * (hist[x1m] + hist[x1p]) + (hist[x2m] + hist[x2p]);
}
__syncthreads();
if (tx < 32) {
float v = hist[32 + tx];
hist[tx] = (v > hist[32 + x1m] && v >= hist[32 + x1p] ? v : 0.0f);
}
__syncthreads();
if (tx == 0) {
float maxval1 = 0.0;
float maxval2 = 0.0;
int i1 = -1;
int i2 = -1;
for (int i = 0; i < 32; i++) {
float v = hist[i];
if (v > maxval1) {
maxval2 = maxval1;
maxval1 = v;
i2 = i1;
i1 = i;
} else if (v > maxval2) {
maxval2 = v;
i2 = i;
}
}
float val1 = hist[32 + ((i1 + 1) & 31)];
float val2 = hist[32 + ((i1 + 31) & 31)];
float peak = i1 + 0.5f * (val1 - val2) / (2.0f * maxval1 - val1 - val2);
d_Sift[bx].orientation = 11.25f * (peak < 0.0f ? peak + 32.0f : peak);
if (maxval2 > 0.8f * maxval1) {
float val1 = hist[32 + ((i2 + 1) & 31)];
float val2 = hist[32 + ((i2 + 31) & 31)];
float peak = i2 + 0.5f * (val1 - val2) / (2.0f * maxval2 - val1 - val2);
unsigned int idx = atomicInc(d_PointCounter, 0x7fffffff);
if (idx < d_MaxNumPoints) {
d_Sift[idx].xpos = d_Sift[bx].xpos;
d_Sift[idx].ypos = d_Sift[bx].ypos;
d_Sift[idx].scale = d_Sift[bx].scale;
d_Sift[idx].sharpness = d_Sift[bx].sharpness;
d_Sift[idx].edgeness = d_Sift[bx].edgeness;
d_Sift[idx].orientation = 11.25f * (peak < 0.0f ? peak + 32.0f : peak);;
d_Sift[idx].subsampling = d_Sift[bx].subsampling;
}
}
}
}
// With constant number of blocks
__global__ void ComputeOrientationsCONSTNew(float *image, int w, int p, int h, SiftPoint *d_Sift, int octave) {
#define RAD 9
#define WID (2*RAD + 1)
#define LEN 32 //%%%% Note: Lowe suggests 36, not 32
__shared__ float img[WID][WID], tmp[WID][WID];
__shared__ float hist[2 * LEN];
__shared__ float gaussx[WID], gaussy[WID];
const int tx = threadIdx.x;
int fstPts = min(d_PointCounter[2 * octave - 1], d_MaxNumPoints);
int totPts = min(d_PointCounter[2 * octave + 0], d_MaxNumPoints);
for (int bx = blockIdx.x + fstPts; bx < totPts; bx += gridDim.x) {
float sc = d_Sift[bx].scale;
for (int i = tx; i < 2 * LEN; i += blockDim.x)
hist[i] = 0.0f;
float xp = d_Sift[bx].xpos;
float yp = d_Sift[bx].ypos;
int xi = (int) xp;
int yi = (int) yp;
float xf = xp - xi;
float yf = yp - yi;
for (int i = tx; i < WID * WID; i += blockDim.x) {
int y = i / WID;
int x = i - y * WID;
int xp = max(min(x - RAD + xi, w - 1), 0);
int yp = max(min(y - RAD + yi, h - 1), 0);
img[y][x] = image[yp * p + xp];
}
float fac[5];
fac[1] = fac[3] = (sc > 0.5f ? __expf(-1.0f / (2.0f * (sc * sc - 0.25f))) : 0.0f);
fac[0] = fac[4] = (sc > 0.5f ? __expf(-4.0f / (2.0f * (sc * sc - 0.25f))) : 0.0f);
fac[2] = 1.0f;
float i2sigma2 = -1.0f / (2.0f * 2.0f * 2.0f * sc * sc); //%%%% Note: Lowe suggests 1.5, not 2.0
if (tx < WID) {
gaussx[tx] = __expf(i2sigma2 * (tx - RAD - xf) * (tx - RAD - xf));
gaussy[tx] = __expf(i2sigma2 * (tx - RAD - yf) * (tx - RAD - yf));
}
__syncthreads();
for (int i = tx; i < (WID - 4) * WID; i += blockDim.x) {
int y = i / WID;
int x = i - y * WID;
y += 2;
tmp[y][x] = img[y][x] + fac[1] * (img[y - 1][x] + img[y + 1][x]) +
fac[0] * (img[y - 2][x] + img[y + 2][x]);
}
__syncthreads();
for (int i = tx; i < (WID - 4) * (WID - 4); i += blockDim.x) {
int y = i / (WID - 4);
int x = i - y * (WID - 4);
x += 2;
y += 2;
img[y][x] = tmp[y][x] + fac[1] * (tmp[y][x - 1] + tmp[y][x + 1]) +
fac[0] * (tmp[y][x - 2] + tmp[y][x + 2]);
}
__syncthreads();
for (int i = tx; i < (WID - 6) * (WID - 6); i += blockDim.x) {
int y = i / (WID - 6);
int x = i - y * (WID - 6);
x += 3;
y += 3;
float dx = img[y][x + 1] - img[y][x - 1];
float dy = img[y + 1][x] - img[y - 1][x];
int bin = (int) ((LEN / 2) * atan2f(dy, dx) / 3.1416f + (LEN / 2) + 0.5f) % LEN;
float grad = __fsqrt_rn(dx * dx + dy * dy);
atomicAdd(&hist[LEN + bin], grad * gaussx[x] * gaussy[y]);
}
__syncthreads();
int x1m = (tx >= 1 ? tx - 1 : tx + LEN - 1);
int x1p = (tx < (LEN - 1) ? tx + 1 : tx - LEN + 1);
int x2m = (tx >= 2 ? tx - 2 : tx + LEN - 2);
int x2p = (tx < (LEN - 2) ? tx + 2 : tx - LEN + 2);
if (tx < LEN) {
hist[tx] = 6.0f * hist[tx + LEN] + 4.0f * (hist[x1m + LEN] + hist[x1p + LEN]) +
1.0f * (hist[x2m + LEN] + hist[x2p + LEN]);
hist[tx + LEN] = 8.0f * hist[tx] + 4.0f * (hist[x1m] + hist[x1p]) +
0.0f * (hist[x2m] + hist[x2p]);
float val = hist[tx + LEN];
hist[tx] = (val > hist[x1m + LEN] && val >= hist[x1p + LEN] ? val : 0.0f);
}
__syncthreads();
if (tx == 0) {
float maxval1 = 0.0;
float maxval2 = 0.0;
int i1 = -1;
int i2 = -1;
for (int i = 0; i < LEN; i++) {
float v = hist[i];
if (v > maxval1) {
maxval2 = maxval1;
maxval1 = v;
i2 = i1;
i1 = i;
} else if (v > maxval2) {
maxval2 = v;
i2 = i;
}
}
float val1 = hist[LEN + ((i1 + 1) % LEN)];
float val2 = hist[LEN + ((i1 + LEN - 1) % LEN)];
float peak = i1 + 0.5f * (val1 - val2) / (2.0f * maxval1 - val1 - val2);
d_Sift[bx].orientation = 360.0f * (peak < 0.0f ? peak + LEN : peak) / LEN;
atomicMax(&d_PointCounter[2 * octave + 1], d_PointCounter[2 * octave + 0]);
if (maxval2 > 0.8f * maxval1 && true) {
float val1 = hist[LEN + ((i2 + 1) % LEN)];
float val2 = hist[LEN + ((i2 + LEN - 1) % LEN)];
float peak = i2 + 0.5f * (val1 - val2) / (2.0f * maxval2 - val1 - val2);
unsigned int idx = atomicInc(&d_PointCounter[2 * octave + 1], 0x7fffffff);
if (idx < d_MaxNumPoints) {
d_Sift[idx].xpos = d_Sift[bx].xpos;
d_Sift[idx].ypos = d_Sift[bx].ypos;
d_Sift[idx].scale = sc;
d_Sift[idx].sharpness = d_Sift[bx].sharpness;
d_Sift[idx].edgeness = d_Sift[bx].edgeness;
d_Sift[idx].orientation = 360.0f * (peak < 0.0f ? peak + LEN : peak) / LEN;
d_Sift[idx].subsampling = d_Sift[bx].subsampling;
}
}
}
}
#undef RAD
#undef WID
#undef LEN
}
// With constant number of blocks
__global__ void ComputeOrientationsCONST(cudaTextureObject_t texObj, SiftPoint *d_Sift, int octave) {
__shared__ float hist[64];
__shared__ float gauss[11];
const int tx = threadIdx.x;
int fstPts = min(d_PointCounter[2 * octave - 1], d_MaxNumPoints);
int totPts = min(d_PointCounter[2 * octave + 0], d_MaxNumPoints);
for (int bx = blockIdx.x + fstPts; bx < totPts; bx += gridDim.x) {
float i2sigma2 = -1.0f / (2.0f * 1.5f * 1.5f * d_Sift[bx].scale * d_Sift[bx].scale);
if (tx < 11)
gauss[tx] = exp(i2sigma2 * (tx - 5) * (tx - 5));
if (tx < 64)
hist[tx] = 0.0f;
__syncthreads();
float xp = d_Sift[bx].xpos - 4.5f;
float yp = d_Sift[bx].ypos - 4.5f;
int yd = tx / 11;
int xd = tx - yd * 11;
float xf = xp + xd;
float yf = yp + yd;
if (yd < 11) {