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kernels_no_3d_write.cl
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kernels_no_3d_write.cl
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#include "HistogramPyramids.clh"
__constant sampler_t sampler = CLK_NORMALIZED_COORDS_FALSE | CLK_ADDRESS_CLAMP_TO_EDGE | CLK_FILTER_NEAREST;
__constant sampler_t interpolationSampler = CLK_NORMALIZED_COORDS_FALSE | CLK_ADDRESS_CLAMP_TO_EDGE | CLK_FILTER_LINEAR;
__constant sampler_t samplerClamp = CLK_NORMALIZED_COORDS_FALSE | CLK_ADDRESS_CLAMP | CLK_FILTER_NEAREST;
#define LPOS(pos) pos.x+pos.y*get_global_size(0)+pos.z*get_global_size(0)*get_global_size(1)
#ifdef VECTORS_16BIT
#define FLOAT_TO_SNORM16_4(vector) convert_short4_sat_rte(vector * 32767.0f)
#define SNORM16_TO_FLOAT_4(vector) max(-1.0f, convert_float4(vector) / 32767.0f)
#define FLOAT_TO_SNORM16_3(vector) convert_short3_sat_rte(vector * 32767.0f)
#define SNORM16_TO_FLOAT_3(vector) max(-1.0f, convert_float3(vector) / 32767.0f)
#define FLOAT_TO_SNORM16_2(vector) convert_short2_sat_rte(vector * 32767.0f)
#define SNORM16_TO_FLOAT_2(vector) max(-1.0f, convert_float2(vector) / 32767.0f)
#define FLOAT_TO_SNORM16(vector) convert_short_sat_rte(vector * 32767.0f)
#define SNORM16_TO_FLOAT(vector) max(-1.0f, convert_float(vector) / 32767.0f)
#define VECTOR_FIELD_TYPE short
#define UNORM16_TO_FLOAT(v) (float)v / 65535.0f
#define FLOAT_TO_UNORM16(v) convert_ushort_sat_rte(v * 65535.0f)
#define TDF_TYPE ushort
#else
#define FLOAT_TO_SNORM16_4(vector) vector
#define SNORM16_TO_FLOAT_4(vector) vector
#define FLOAT_TO_SNORM16_3(vector) vector
#define SNORM16_TO_FLOAT_3(vector) vector
#define FLOAT_TO_SNORM16_2(vector) vector
#define SNORM16_TO_FLOAT_2(vector) vector
#define FLOAT_TO_SNORM16(vector) vector
#define SNORM16_TO_FLOAT(vector) vector
#define VECTOR_FIELD_TYPE float
#define UNORM16_TO_FLOAT(v) v
#define FLOAT_TO_UNORM16(v) v
#define TDF_TYPE float
#endif
// Intialize 2D image to 0
__kernel void init2DImage(
__write_only image2d_t image
) {
write_imageui(image, (int2)(get_global_id(0), get_global_id(1)), (uint4)(0,0,0,0));
}
// Intialize int buffer to 0
__kernel void initIntBuffer(
__global int * buffer
) {
buffer[get_global_id(0)] = 0;
}
// Intialize char buffer to 0
__kernel void initCharBuffer(
__global char * buffer
) {
buffer[get_global_id(0)] = 0;
}
// Intialize int buffer to its ID
__kernel void initIntBufferID(
__global int * buffer,
__private int sum
) {
int id = get_global_id(0);
if(id >= sum)
id = 0;
buffer[id] = id;
}
// Forward declaration of eigen_decomp function
void eigen_decomposition(float M[3][3], float V[3][3], float e[3]);
float3 gradientNormalized(
__read_only image3d_t volume, // Volume to perform gradient on
int4 pos, // Position to perform gradient on
int volumeComponent, // The volume component to perform gradient on: 0, 1 or 2
int dimensions // The number of dimensions to perform gradient in: 1, 2 or 3
) {
float f100, f_100, f010, f0_10, f001, f00_1;
switch(volumeComponent) {
case 0:
f100 = read_imagef(volume, sampler, pos + (int4)(1,0,0,0)).x;
f_100 = read_imagef(volume, sampler, pos - (int4)(1,0,0,0)).x;
if(dimensions > 1) {
f010 = read_imagef(volume, sampler, pos + (int4)(0,1,0,0)).x;
f0_10 = read_imagef(volume, sampler, pos - (int4)(0,1,0,0)).x;
}
if(dimensions > 2) {
f001 = read_imagef(volume, sampler, pos + (int4)(0,0,1,0)).x;
f00_1 = read_imagef(volume, sampler, pos - (int4)(0,0,1,0)).x;
}
break;
case 1:
f100 = read_imagef(volume, sampler, pos + (int4)(1,0,0,0)).y;
f_100 = read_imagef(volume, sampler, pos - (int4)(1,0,0,0)).y;
if(dimensions > 1) {
f010 = read_imagef(volume, sampler, pos + (int4)(0,1,0,0)).y;
f0_10 = read_imagef(volume, sampler, pos - (int4)(0,1,0,0)).y;
}
if(dimensions > 2) {
f001 = read_imagef(volume, sampler, pos + (int4)(0,0,1,0)).y;
f00_1 = read_imagef(volume, sampler, pos - (int4)(0,0,1,0)).y;
}
break;
case 2:
f100 = read_imagef(volume, sampler, pos + (int4)(1,0,0,0)).z;
f_100 = read_imagef(volume, sampler, pos - (int4)(1,0,0,0)).z;
if(dimensions > 1) {
f010 = read_imagef(volume, sampler, pos + (int4)(0,1,0,0)).z;
f0_10 = read_imagef(volume, sampler, pos - (int4)(0,1,0,0)).z;
}
if(dimensions > 2) {
f001 = read_imagef(volume, sampler, pos + (int4)(0,0,1,0)).z;
f00_1 = read_imagef(volume, sampler, pos - (int4)(0,0,1,0)).z;
}
break;
}
float3 grad = {
0.5f*(f100/read_imagef(volume, sampler, pos+(int4)(1,0,0,0)).w-f_100/read_imagef(volume, sampler, pos-(int4)(1,0,0,0)).w),
0.5f*(f010/read_imagef(volume, sampler, pos+(int4)(0,1,0,0)).w-f0_10/read_imagef(volume, sampler, pos-(int4)(0,1,0,0)).w),
0.5f*(f001/read_imagef(volume, sampler, pos+(int4)(0,0,1,0)).w-f00_1/read_imagef(volume, sampler, pos-(int4)(0,0,1,0)).w)
};
return grad;
}
float3 gradient(
__read_only image3d_t volume, // Volume to perform gradient on
int4 pos, // Position to perform gradient on
int volumeComponent, // The volume component to perform gradient on: 0, 1 or 2
int dimensions // The number of dimensions to perform gradient in: 1, 2 or 3
) {
float f100, f_100, f010, f0_10, f001, f00_1;
switch(volumeComponent) {
case 0:
f100 = read_imagef(volume, sampler, pos + (int4)(1,0,0,0)).x;
f_100 = read_imagef(volume, sampler, pos - (int4)(1,0,0,0)).x;
if(dimensions > 1) {
f010 = read_imagef(volume, sampler, pos + (int4)(0,1,0,0)).x;
f0_10 = read_imagef(volume, sampler, pos - (int4)(0,1,0,0)).x;
}
if(dimensions > 2) {
f001 = read_imagef(volume, sampler, pos + (int4)(0,0,1,0)).x;
f00_1 = read_imagef(volume, sampler, pos - (int4)(0,0,1,0)).x;
}
break;
case 1:
f100 = read_imagef(volume, sampler, pos + (int4)(1,0,0,0)).y;
f_100 = read_imagef(volume, sampler, pos - (int4)(1,0,0,0)).y;
if(dimensions > 1) {
f010 = read_imagef(volume, sampler, pos + (int4)(0,1,0,0)).y;
f0_10 = read_imagef(volume, sampler, pos - (int4)(0,1,0,0)).y;
}
if(dimensions > 2) {
f001 = read_imagef(volume, sampler, pos + (int4)(0,0,1,0)).y;
f00_1 = read_imagef(volume, sampler, pos - (int4)(0,0,1,0)).y;
}
break;
case 2:
f100 = read_imagef(volume, sampler, pos + (int4)(1,0,0,0)).z;
f_100 = read_imagef(volume, sampler, pos - (int4)(1,0,0,0)).z;
if(dimensions > 1) {
f010 = read_imagef(volume, sampler, pos + (int4)(0,1,0,0)).z;
f0_10 = read_imagef(volume, sampler, pos - (int4)(0,1,0,0)).z;
}
if(dimensions > 2) {
f001 = read_imagef(volume, sampler, pos + (int4)(0,0,1,0)).z;
f00_1 = read_imagef(volume, sampler, pos - (int4)(0,0,1,0)).z;
}
break;
}
float3 grad = {
0.5f*(f100-f_100),
0.5f*(f010-f0_10),
0.5f*(f001-f00_1)
};
return grad;
}
__kernel void linkLengths(
__global int const * restrict positions,
__write_only image2d_t lengths
) {
const float3 xa = convert_float3(vload3(get_global_id(0), positions));
const float3 xb = convert_float3(vload3(get_global_id(1), positions));
write_imagef(lengths, (int2)(get_global_id(0), get_global_id(1)), (float4)(distance(xa,xb),0.0f,0.0f,0.0f));
}
__kernel void compact(
__read_only image2d_t lengths,
volatile __global int * incs,
__write_only image2d_t compacted_lengths,
__private float maxDistance
) {
const int i = get_global_id(0);
const int j = get_global_id(1);
float length = read_imagef(lengths, sampler, (int2)(i,j)).x;
if(length < maxDistance && length > 0.0f) {
volatile int nr = atomic_inc(&(incs[i]));
write_imagef(compacted_lengths, (int2)(i,nr), (float4)(length, j, 0, 0));
}
}
__kernel void linkCenterpoints(
__read_only image3d_t TDF,
__global int const * restrict positions,
__write_only image2d_t edges,
__read_only image2d_t compacted_lengths,
__private int sum,
__private float minAvgTDF,
__private float maxDistance
) {
int id = get_global_id(0);
if(id >= sum)
id = 0;
float3 xa = convert_float3(vload3(id, positions));
//printf("%f %f %f\n",xa.x,xa.y,xa.z);
int2 bestPair;
float shortestDistance = maxDistance*2;
bool validPairFound = false;
for(int i = 0; i < sum; i++) {
float2 cl = read_imagef(compacted_lengths, sampler, (int2)(id,i)).xy;
// reached the end?
if(cl.x == 0.0f)
break;
float3 xb = convert_float3(vload3(cl.y, positions));
int db = round(cl.x);
if(db >= shortestDistance)
continue;
for(int j = 0; j < i; j++) {
float2 cl2 = read_imagef(compacted_lengths, sampler, (int2)(id,j)).xy;
if(cl2.y == cl.y)
continue;
// reached the end?
if(cl2.x == 0.0f)
break;
float3 xc = convert_float3(vload3(cl2.y, positions));
// Check distance between xa and xb
int dc = round(cl2.x);
float minTDF = 0.0f;
float maxVarTDF = 1.005f;
float maxIntensity = 1.3f;
float maxAvgIntensity = 1.2f;
float maxVarIntensity = 1.005f;
if(db+dc < shortestDistance) {
// Check angle
float3 ab = (xb-xa);
float3 ac = (xc-xa);
float angle = acos(dot(normalize(ab), normalize(ac)));
if(angle < 2.0f) // 120 degrees
continue;
// Check TDF
float avgTDF = 0.0f;
float avgIntensity = 0.0f;
for(int k = 0; k <= db; k++) {
float alpha = (float)k/db;
float3 p = xa+ab*alpha;
float t = read_imagef(TDF, interpolationSampler, p.xyzz).x;
avgTDF += t;
}
avgTDF /= db+1;
if(avgTDF < minAvgTDF)
continue;
/*
float varTDF = 0.0f;
for(int k = 0; k <= db; k++) {
float alpha = (float)k/db;
float3 p = xa+ab*alpha;
float t = read_imagef(TDF, interpolationSampler, p.xyzz).x;
float i = read_imagef(intensity, interpolationSampler, p.xyzz).x;
varIntensity += (i-avgIntensity)*(i-avgIntensity);
varTDF += (t-avgTDF)*(t-avgTDF);
if(i > maxIntensity || t < minTDF) {
invalid = true;
break;
}
}
if(invalid)
continue;
if(db > 4 && varTDF / (db+1) > maxVarTDF)
continue;
*/
avgTDF = 0.0f;
for(int k = 0; k <= dc; k++) {
float alpha = (float)k/dc;
float3 p = xa+ac*alpha;
float t = read_imagef(TDF, interpolationSampler, p.xyzz).x;
avgTDF += t;
}
avgTDF /= dc+1;
if(avgTDF < minAvgTDF)
continue;
/*
for(int k = 0; k <= dc; k++) {
float alpha = (float)k/dc;
float3 p = xa+ac*alpha;
float t = read_imagef(TDF, interpolationSampler, p.xyzz).x;
varTDF += (t-avgTDF)*(t-avgTDF);
}
if(dc > 4 && varIntensity / (dc+1) > maxVarIntensity)
continue;
if(dc > 4 && varTDF / (dc+1) > maxVarTDF)
continue;
*/
validPairFound = true;
bestPair.x = cl.y;
bestPair.y = cl2.y;
shortestDistance = db+dc;
}
}}
if(validPairFound) {
// Store edges
int2 edge = {id, bestPair.x};
int2 edge2 = {id, bestPair.y};
write_imageui(edges, edge, (uint4)(1,0,0,0));
write_imageui(edges, edge2, (uint4)(1,0,0,0));
}
}
__kernel void graphComponentLabeling(
__global int const * restrict edges,
volatile __global int * C,
__global int * m,
__private int sum
) {
int id = get_global_id(0);
if(id >= sum)
id = 0;
int2 edge = vload2(id, edges);
const int ca = C[edge.x];
const int cb = C[edge.y];
// Find the smallest C value and store in C in the others
if(ca == cb) {
return;
} else {
if(ca < cb) {
// ca is smallest
atomic_min(&C[edge.y], ca);
} else {
// cb is smallest
atomic_min(&C[edge.x], cb);
}
m[0] = 1; // register change
}
}
__kernel void calculateTreeLength(
__global int const * restrict C,
volatile __global int * S
) {
const int id = get_global_id(0);
atomic_inc(&S[C[id]]);
}
__kernel void removeSmallTrees(
__global int const * restrict edges,
__global int const * restrict vertices,
__global int const * restrict C,
__global int const * restrict S,
__private int minTreeLength,
__global char * centerlines,
__private int width,
__private int height
) {
// Find the edges that are part of the large trees
const int id = get_global_id(0);
int2 edge = vload2(id, edges);
const int ca = C[edge.x];
if(S[ca] >= minTreeLength) {
const float3 xa = convert_float3(vload3(edge.x, vertices));
const float3 xb = convert_float3(vload3(edge.y, vertices));
int l = round(length(xb-xa));
for(int i = 0; i < l; i++) {
const float alpha = (float)i/l;
//write_imagei(centerlines, convert_int3(round(xa+(xb-xa)*alpha)).xyzz, 1);
const int3 pos = convert_int3(round(xa+(xb-xa)*alpha));
centerlines[pos.x+pos.y*width+pos.z*width*height] = 1;
}
}
}
__kernel void removeDuplicateEdges(
__read_only image2d_t edgeTuples,
__write_only image2d_t edgeTuples2
) {
const int2 pos = {get_global_id(0), get_global_id(1)};
if(read_imageui(edgeTuples, sampler, pos).x == 0) {
write_imageui(edgeTuples2,pos, (uint4)(0,0,0,0));
} else if(pos.x > pos.y) {
// Check if opposite is an edge
if(read_imageui(edgeTuples, sampler, (int2)(pos.y,pos.x)).x == 1) {
// opposite exists => remove this one
write_imageui(edgeTuples2,pos, (uint4)(0,0,0,0));
} else {
// opposite doesn't exist => save this one
write_imageui(edgeTuples2,pos, (uint4)(1,0,0,0));
}
} else {
write_imageui(edgeTuples2,pos, (uint4)(1,0,0,0));
}
}
#define SQR_MAG(pos) read_imagef(vectorField, sampler, pos).w
__kernel void dd(
__read_only image3d_t TDF,
__read_only image3d_t centerpointCandidates,
__global uchar * centerpoints,
__private int cubeSize
) {
int4 bestPos;
float bestTDF = 0.0f;
int4 readPos = {
get_global_id(0)*cubeSize,
get_global_id(1)*cubeSize,
get_global_id(2)*cubeSize,
0
};
bool found = false;
for(int a = 0; a < cubeSize; a++) {
for(int b = 0; b < cubeSize; b++) {
for(int c = 0; c < cubeSize; c++) {
int4 pos = readPos + (int4)(a,b,c,0);
if(read_imagei(centerpointCandidates, sampler, pos).x == 1) {
float tdf = read_imagef(TDF, sampler, pos).x;
if(tdf > bestTDF) {
found = true;
bestTDF = tdf;
bestPos = pos;
}
}
}}}
if(found) {
centerpoints[bestPos.x+bestPos.y*get_image_width(TDF)+bestPos.z*get_image_width(TDF)*get_image_height(TDF)] = 1;
}
}
__kernel void findCandidateCenterpoints(
__read_only image3d_t TDF,
__global uchar * centerpoints,
__private float TDFlimit
) {
const int4 pos = {get_global_id(0), get_global_id(1), get_global_id(2), 0};
if(read_imagef(TDF, sampler, pos).x < TDFlimit) {
centerpoints[LPOS(pos)] = 0;
} else {
centerpoints[LPOS(pos)] = 1;
}
}
__kernel void findCandidateCenterpoints2(
__read_only image3d_t TDF,
__read_only image3d_t radius,
__read_only image3d_t vectorField,
__global char * centerpoints,
__private int HP_SIZE,
__private int sum,
__global uchar * hp0, // Largest HP
__global uchar * hp1,
__global ushort * hp2,
__global ushort * hp3,
__global ushort * hp4,
__global int * hp5,
__global int * hp6,
__global int * hp7,
__global int * hp8,
__global int * hp9
) {
int target = get_global_id(0);
if(target >= sum)
target = 0;
uint3 size = {get_image_width(TDF),get_image_height(TDF),get_image_depth(TDF)};
int4 pos = traverseHP3DBuffer(size,target,HP_SIZE,hp0,hp1,hp2,hp3,hp4,hp5,hp6,hp7,hp8,hp9);
const float thetaLimit = 0.5f;
const float radii = read_imagef(radius, sampler, pos).x;
const int maxD = max(min(round(radii), 5.0f), 1.0f);
bool invalid = false;
// Find Hessian Matrix
float3 Fx, Fy, Fz;
Fx = gradientNormalized(vectorField, pos, 0, 1);
Fy = gradientNormalized(vectorField, pos, 1, 2);
Fz = gradientNormalized(vectorField, pos, 2, 3);
float Hessian[3][3] = {
{Fx.x, Fy.x, Fz.x},
{Fy.x, Fy.y, Fz.y},
{Fz.x, Fz.y, Fz.z}
};
// Eigen decomposition
float eigenValues[3];
float eigenVectors[3][3];
eigen_decomposition(Hessian, eigenVectors, eigenValues);
const float3 e1 = {eigenVectors[0][0], eigenVectors[1][0], eigenVectors[2][0]};
for(int a = -maxD; a <= maxD; a++) {
for(int b = -maxD; b <= maxD; b++) {
for(int c = -maxD; c <= maxD; c++) {
if(a == 0 && b == 0 && c == 0)
continue;
const int4 n = pos + (int4)(a,b,c,0);
const float3 r = {a,b,c};
const float dp = dot(e1,r);
const float3 r_projected = r-e1*dp;
const float theta = acos(dot(normalize(r), normalize(r_projected)));
if(theta < thetaLimit && length(r) < maxD) {
if(SQR_MAG(n) < SQR_MAG(pos)) {
invalid = true;
}
}
}}}
centerpoints[pos.x+pos.y*get_image_width(TDF)+pos.z*get_image_width(TDF)*get_image_height(TDF)] = invalid ? 0:1;
}
__kernel void combine(
__global TDF_TYPE * TDFsmall,
__global float * radiusSmall,
__global TDF_TYPE * TDFlarge,
__global float * radiusLarge
) {
int i = get_global_id(0);
if(TDFlarge[i] < TDFsmall[i]) {
TDFlarge[i] = TDFsmall[i];
radiusLarge[i] = radiusSmall[i];
}
}
__kernel void initGrowing(
__read_only image3d_t centerline,
__global char * initSegmentation,
__read_only image3d_t avgRadius
) {
int4 pos = {get_global_id(0), get_global_id(1), get_global_id(2), 0};
if(read_imagei(centerline, sampler, pos).x == 1) {
float radius = read_imagef(avgRadius, sampler, pos).x;
int N = min(max(1, (int)round(radius/2.0f)), 4);
for(int a = -N; a < N+1; a++) {
for(int b = -N; b < N+1; b++) {
for(int c = -N; c < N+1; c++) {
int4 n;
n.x = pos.x + a;
n.y = pos.y + b;
n.z = pos.z + c;
if(read_imagei(centerline, sampler, n).x == 0 &&
n.x >= 0 && n.y >= 0 && n.z >= 0 &&
n.x < get_global_size(0) && n.y < get_global_size(1) && n.z < get_global_size(2))
initSegmentation[LPOS(n)] = 2;
}}}
}
}
__kernel void grow(
__read_only image3d_t currentSegmentation,
__read_only image3d_t gvf,
__global char * nextSegmentation,
__global int * stop
) {
int4 X = {get_global_id(0), get_global_id(1), get_global_id(2), 0};
char value = read_imagei(currentSegmentation, sampler, X).x;
// value of 2, means to check it, 1 means it is already accepted
if(value == 1) {
nextSegmentation[LPOS(X)] = 1;
}else if(value == 2){
float FNXw = read_imagef(gvf, sampler, X).w;
bool continueGrowing = false;
for(int a = -1; a < 2; a++) {
for(int b = -1; b < 2; b++) {
for(int c = -1; c < 2; c++) {
if(a == 0 && b == 0 && c == 0)
continue;
int4 Y;
Y.x = X.x + a;
Y.y = X.y + b;
Y.z = X.z + c;
char valueY = read_imagei(currentSegmentation, sampler, Y).x;
if(valueY != 1) {
float4 FNY = read_imagef(gvf, sampler, Y);
FNY.x /= FNY.w;
FNY.y /= FNY.w;
FNY.z /= FNY.w;
if(FNY.w > FNXw /*|| FNXw < 0.1f*/) {
int4 Z;
float maxDotProduct = -2.0f;
for(int a2 = -1; a2 < 2; a2++) {
for(int b2 = -1; b2 < 2; b2++) {
for(int c2 = -1; c2 < 2; c2++) {
if(a2 == 0 && b2 == 0 && c2 == 0)
continue;
int4 Zc;
Zc.x = Y.x+a2;
Zc.y = Y.y+b2;
Zc.z = Y.z+c2;
float3 YZ;
YZ.x = Zc.x-Y.x;
YZ.y = Zc.y-Y.y;
YZ.z = Zc.z-Y.z;
YZ = normalize(YZ);
const float v = FNY.x*YZ.x+FNY.y*YZ.y+FNY.z*YZ.z;
if(v > maxDotProduct) {
maxDotProduct = v;
Z = Zc;
}
}}}
if(Z.x == X.x && Z.y == X.y && Z.z == X.z) {
nextSegmentation[LPOS(X)] = 1;
// Check if in bounds
if(Y.x >= 0 && Y.y >= 0 && Y.z >= 0 &&
Y.x < get_global_size(0) && Y.y < get_global_size(1) && Y.z < get_global_size(2)) {
nextSegmentation[LPOS(Y)] = 2;
continueGrowing = true;
}
}
}}
}}}
if(continueGrowing) {
// Added new items to list (values of 2)
stop[0] = 0;
} else {
// X was not accepted
nextSegmentation[LPOS(X)] = 0;
}
}
}
__kernel void sphereSegmentation(
__read_only image3d_t centerlines,
__read_only image3d_t radius,
__global char * segmentation
) {
int4 pos = {get_global_id(0), get_global_id(1), get_global_id(2), 0};
if(read_imagei(centerlines,sampler,pos).x == 0)
return;
float r = read_imagef(radius, sampler ,pos).x;
int N = ceil(r);
for(int x = -N; x <= N; x++) {
for(int y = -N; y <= N; y++) {
for(int z = -N; z <= N; z++) {
// calculate distance
if(length((float3)(x,y,z)) < r) {
int4 posN = pos + (int4)(x,y,z,0);
if(posN.x >= 0 && posN.y >= 0 && posN.z >= 0 &&
posN.x < get_global_size(0) && posN.y < get_global_size(1) && posN.z < get_global_size(2)) {
segmentation[posN.x+posN.y*get_global_size(0)+posN.z*get_global_size(0)*get_global_size(1)] = 1;
}
}
}}}
}
__kernel void cropDatasetThreshold(
__read_only image3d_t volume,
__global short * scanLinesInside,
__private int sliceDirection,
__private float threshold,
__private int type
) {
int sliceNr = get_global_id(0);
short scanLines = 0;
int scanLineSize, scanLineElementSize;
if(sliceDirection == 0) {
scanLineSize = get_image_height(volume);
scanLineElementSize = get_image_depth(volume);
} else if(sliceDirection == 1) {
scanLineSize = get_image_width(volume);
scanLineElementSize = get_image_depth(volume);
} else {
scanLineSize = get_image_height(volume);
scanLineElementSize = get_image_width(volume);
}
for(int scanLine = 0; scanLine < scanLineSize; scanLine++) {
bool found = false;
for(int scanLineElement = 0; scanLineElement < scanLineElementSize; scanLineElement ++) {
int4 pos;
if(sliceDirection == 0) {
pos.x = sliceNr;
pos.y = scanLine;
pos.z = scanLineElement;
} else if(sliceDirection == 1) {
pos.x = scanLine;
pos.y = sliceNr;
pos.z = scanLineElement;
} else {
pos.x = scanLineElement;
pos.y = scanLine;
pos.z = sliceNr;
}
if(type == 1) {
if(read_imagei(volume,sampler,pos).x > threshold)
found = true;
} else if(type == 2) {
if(read_imageui(volume,sampler,pos).x > threshold)
found = true;
} else {
if(read_imagef(volume,sampler,pos).x > threshold)
found = true;
}
} // End scan line
if(found)
scanLines++;
}
scanLinesInside[sliceNr] = scanLines;
}
__kernel void cropDatasetLung(
__read_only image3d_t volume,
__global short * scanLinesInside,
__private int sliceDirection,
__private int type
) {
short HUlimit = -150;
if(type == 2)
HUlimit += 1024;
int Wlimit = 30;
int Blimit = 30;
int sliceNr = get_global_id(0);
short scanLines = 0;
int scanLineSize, scanLineElementSize;
if(sliceDirection == 0) {
scanLineSize = get_image_height(volume);
scanLineElementSize = get_image_depth(volume);
} else if(sliceDirection == 1) {
scanLineSize = get_image_width(volume);
scanLineElementSize = get_image_depth(volume);
} else {
scanLineSize = get_image_height(volume);
scanLineElementSize = get_image_width(volume);
}
for(int scanLine = 0; scanLine < scanLineSize; scanLine++) {
int currentWcount = 0,
currentBcount = 0,
detectedBlackAreas = 0,
detectedWhiteAreas = 0;
for(int scanLineElement = 0; scanLineElement < scanLineElementSize; scanLineElement ++) {
int4 pos;
if(sliceDirection == 0) {
pos.x = sliceNr;
pos.y = scanLine;
pos.z = scanLineElement;
} else if(sliceDirection == 1) {
pos.x = scanLine;
pos.y = sliceNr;
pos.z = scanLineElement;
} else {
pos.x = scanLineElement;
pos.y = scanLine;
pos.z = sliceNr;
}
short HU;
if(type == 1) {
HU = read_imagei(volume, sampler, pos).x;
}else{
HU = read_imageui(volume, sampler, pos).x;
}
if(HU > HUlimit) {
if(currentWcount == Wlimit) {
detectedWhiteAreas++;
currentBcount = 0;
}
currentWcount++;
} else {
if(currentBcount == Blimit) {
detectedBlackAreas++;
currentWcount = 0;
}
currentBcount++;
}
}
if((detectedWhiteAreas == 2 && detectedBlackAreas == 1) ||
(detectedBlackAreas > 1 && detectedWhiteAreas > 1)) {
scanLines++;
} // End scan line
}
scanLinesInside[sliceNr] = scanLines;
}
__kernel void dilate(
__read_only image3d_t volume,
__global char * result
) {
int4 pos = {get_global_id(0), get_global_id(1), get_global_id(2), 0};
if(read_imagei(volume, sampler, pos).x == 1) {
for(int a = -1; a < 2 ; a++) {
for(int b = -1; b < 2 ; b++) {
for(int c = -1; c < 2 ; c++) {
int4 nPos = pos + (int4)(a,b,c,0);
// Check if in bounds
if(nPos.x >= 0 && nPos.y >= 0 && nPos.z >= 0 &&
nPos.x < get_global_size(0) && nPos.y < get_global_size(1) && nPos.z < get_global_size(2))
result[LPOS(nPos)] = 1;
}
}
}
}
}
__kernel void erode(
__read_only image3d_t volume,
__global char * result
) {
int4 pos = {get_global_id(0), get_global_id(1), get_global_id(2), 0};
int value = read_imagei(volume, sampler, pos).x;
if(value == 1) {
bool keep = true;
for(int a = -1; a < 2 ; a++) {
for(int b = -1; b < 2 ; b++) {
for(int c = -1; c < 2 ; c++) {
keep = (read_imagei(volume, sampler, pos + (int4)(a,b,c,0)).x == 1 && keep);
}
}
}
result[LPOS(pos)] = keep ? 1 : 0;
} else {
result[LPOS(pos)] = 0;
}
}
__kernel void toFloat(
__read_only image3d_t volume,
__global float * processedVolume,
__private float minimum,
__private float maximum,
__private int type
) {
int4 pos = {get_global_id(0), get_global_id(1), get_global_id(2), 0};
float v;
if(type == 1) {
v = read_imagei(volume, sampler, pos).x;
} else if(type == 2) {
v = read_imageui(volume, sampler, pos).x;
} else {
v = read_imagef(volume, sampler, pos).x;
}
v = v > maximum ? maximum : v;
v = v < minimum ? minimum : v;
// Convert to floating point representation 0 to 1
float value = (float)(v - minimum) / (float)(maximum - minimum);
//printf("%f \n", value);
// Store value
processedVolume[LPOS(pos)] = value;
}
__kernel void blurVolumeWithGaussian(
__read_only image3d_t volume,
__global float * blurredVolume,
__private int maskSize,
__constant float * mask
) {
const int4 pos = {get_global_id(0), get_global_id(1), get_global_id(2), 0};
// TODO: need to take into account spacing here?
// Collect neighbor values and multiply with gaussian
float sum = 0.0f;
// Calculate the mask size based on sigma (larger sigma, larger mask)
for(int a = -maskSize; a < maskSize+1; a++) {
for(int b = -maskSize; b < maskSize+1; b++) {
for(int c = -maskSize; c < maskSize+1; c++) {
sum += mask[a+maskSize+(b+maskSize)*(maskSize*2+1)+(c+maskSize)*(maskSize*2+1)*(maskSize*2+1)]
*read_imagef(volume, sampler, pos + (int4)(a,b,c,0)).x;
}
}
}
blurredVolume[LPOS(pos)] = sum;
}
#define SELECT_BUFFER(vec1,vec2,z,maxZ) z < maxZ ? vec1:vec2
#define SELECT_POS(pos,maxZ) pos.z < maxZ ? pos.x+pos.y*get_global_size(0)+pos.z*get_global_size(0)*get_global_size(1) : pos.x + pos.y*get_global_size(0) + (pos.z-maxZ)*get_global_size(0)*get_global_size(1)
__kernel void createVectorField(
__read_only image3d_t volume,
__global VECTOR_FIELD_TYPE * vectorField,
__global VECTOR_FIELD_TYPE * vectorField2,
__private float Fmax,
__private int vectorSign,
__private int maxZ
) {
const int4 pos = {get_global_id(0), get_global_id(1), get_global_id(2), 0};
// Gradient of volume
float4 F;
F.xyz = gradient(volume, pos, 0, 3); // The sign here is important
F.w = 0.0f;
F.x = vectorSign*F.x;
F.y = vectorSign*F.y;
F.z = vectorSign*F.z;
// Fmax normalization
const float l = length(F);
F = l < Fmax ? F/(Fmax) : F / (l);
F.w = 1.0f;
// Store vector field
vstore4(FLOAT_TO_SNORM16_4(F), SELECT_POS(pos,maxZ), SELECT_BUFFER(vectorField,vectorField2,pos.z,maxZ));
}
__constant float cosValues[32] = {1.0f, 0.540302f, -0.416147f, -0.989992f, -0.653644f, 0.283662f, 0.96017f, 0.753902f, -0.1455f, -0.91113f, -0.839072f, 0.0044257f, 0.843854f, 0.907447f, 0.136737f, -0.759688f, -0.957659f, -0.275163f, 0.660317f, 0.988705f, 0.408082f, -0.547729f, -0.999961f, -0.532833f, 0.424179f, 0.991203f, 0.646919f, -0.292139f, -0.962606f, -0.748058f, 0.154251f, 0.914742f};
__constant float sinValues[32] = {0.0f, 0.841471f, 0.909297f, 0.14112f, -0.756802f, -0.958924f, -0.279415f, 0.656987f, 0.989358f, 0.412118f, -0.544021f, -0.99999f, -0.536573f, 0.420167f, 0.990607f, 0.650288f, -0.287903f, -0.961397f, -0.750987f, 0.149877f, 0.912945f, 0.836656f, -0.00885131f, -0.84622f, -0.905578f, -0.132352f, 0.762558f, 0.956376f, 0.270906f, -0.663634f, -0.988032f, -0.404038f};
__kernel void circleFittingTDF(
__read_only image3d_t vectorField,
__global TDF_TYPE * T,
__global float * Radius,
__private float rMin,
__private float rMax,
__private float rStep
) {
const int4 pos = {get_global_id(0), get_global_id(1), get_global_id(2), 0};
// Find Hessian Matrix
float3 Fx, Fy, Fz;
if(rMax < 4) {
Fx = gradient(vectorField, pos, 0, 1);
Fy = gradient(vectorField, pos, 1, 2);
Fz = gradient(vectorField, pos, 2, 3);
} else {
Fx = gradientNormalized(vectorField, pos, 0, 1);
Fy = gradientNormalized(vectorField, pos, 1, 2);
Fz = gradientNormalized(vectorField, pos, 2, 3);
}
float Hessian[3][3] = {
{Fx.x, Fy.x, Fz.x},
{Fy.x, Fy.y, Fz.y},
{Fz.x, Fz.y, Fz.z}
};
// Eigen decomposition
float eigenValues[3];
float eigenVectors[3][3];
eigen_decomposition(Hessian, eigenVectors, eigenValues);
//const float3 lambda = {eigenValues[0], eigenValues[1], eigenValues[2]};
//const float3 e1 = {eigenVectors[0][0], eigenVectors[1][0], eigenVectors[2][0]};
const float3 e2 = {eigenVectors[0][1], eigenVectors[1][1], eigenVectors[2][1]};
const float3 e3 = {eigenVectors[0][2], eigenVectors[1][2], eigenVectors[2][2]};
// Circle Fitting
float maxSum = 0.0f;
float maxRadius = 0.0f;