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.
434 lines
11 KiB
C
434 lines
11 KiB
C
// Copyright (c) 2009-2011 Ignacio Castano <castano@gmail.com>
|
|
// Copyright (c) 2007-2009 NVIDIA Corporation -- Ignacio Castano <icastano@nvidia.com>
|
|
//
|
|
// Permission is hereby granted, free of charge, to any person
|
|
// obtaining a copy of this software and associated documentation
|
|
// files (the "Software"), to deal in the Software without
|
|
// restriction, including without limitation the rights to use,
|
|
// copy, modify, merge, publish, distribute, sublicense, and/or sell
|
|
// copies of the Software, and to permit persons to whom the
|
|
// Software is furnished to do so, subject to the following
|
|
// conditions:
|
|
//
|
|
// The above copyright notice and this permission notice shall be
|
|
// included in all copies or substantial portions of the Software.
|
|
//
|
|
// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
|
|
// EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
|
|
// OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
|
|
// NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
|
|
// HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
|
|
// WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
|
|
// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
|
|
// OTHER DEALINGS IN THE SOFTWARE.
|
|
|
|
// Math functions and operators to be used with vector types.
|
|
|
|
#ifndef CUDAMATH_H
|
|
#define CUDAMATH_H
|
|
|
|
|
|
|
|
inline __device__ __host__ float3 operator *(float3 a, float3 b)
|
|
{
|
|
return make_float3(a.x*b.x, a.y*b.y, a.z*b.z);
|
|
}
|
|
|
|
inline __device__ __host__ float3 operator *(float f, float3 v)
|
|
{
|
|
return make_float3(v.x*f, v.y*f, v.z*f);
|
|
}
|
|
|
|
inline __device__ __host__ float3 operator *(float3 v, float f)
|
|
{
|
|
return make_float3(v.x*f, v.y*f, v.z*f);
|
|
}
|
|
|
|
inline __device__ __host__ float3 operator +(float3 a, float3 b)
|
|
{
|
|
return make_float3(a.x+b.x, a.y+b.y, a.z+b.z);
|
|
}
|
|
|
|
inline __device__ __host__ void operator +=(float3 & b, float3 a)
|
|
{
|
|
b.x += a.x;
|
|
b.y += a.y;
|
|
b.z += a.z;
|
|
}
|
|
|
|
inline __device__ __host__ float3 operator -(float3 a, float3 b)
|
|
{
|
|
return make_float3(a.x-b.x, a.y-b.y, a.z-b.z);
|
|
}
|
|
|
|
inline __device__ __host__ void operator -=(float3 & b, float3 a)
|
|
{
|
|
b.x -= a.x;
|
|
b.y -= a.y;
|
|
b.z -= a.z;
|
|
}
|
|
|
|
inline __device__ __host__ float3 operator /(float3 v, float f)
|
|
{
|
|
float inv = 1.0f / f;
|
|
return v * inv;
|
|
}
|
|
|
|
inline __device__ __host__ void operator /=(float3 & b, float f)
|
|
{
|
|
float inv = 1.0f / f;
|
|
b.x *= inv;
|
|
b.y *= inv;
|
|
b.z *= inv;
|
|
}
|
|
|
|
inline __device__ __host__ bool operator ==(float3 a, float3 b)
|
|
{
|
|
return a.x == b.x && a.y == b.y && a.z == b.z;
|
|
}
|
|
|
|
|
|
// float2 operators
|
|
inline __device__ __host__ float2 operator *(float2 a, float2 b)
|
|
{
|
|
return make_float2(a.x*b.x, a.y*b.y);
|
|
}
|
|
|
|
inline __device__ __host__ float2 operator *(float f, float2 v)
|
|
{
|
|
return make_float2(v.x*f, v.y*f);
|
|
}
|
|
|
|
inline __device__ __host__ float2 operator *(float2 v, float f)
|
|
{
|
|
return make_float2(v.x*f, v.y*f);
|
|
}
|
|
|
|
inline __device__ __host__ float2 operator +(float2 a, float2 b)
|
|
{
|
|
return make_float2(a.x+b.x, a.y+b.y);
|
|
}
|
|
|
|
inline __device__ __host__ void operator +=(float2 & b, float2 a)
|
|
{
|
|
b.x += a.x;
|
|
b.y += a.y;
|
|
}
|
|
|
|
inline __device__ __host__ float2 operator -(float2 a, float2 b)
|
|
{
|
|
return make_float2(a.x-b.x, a.y-b.y);
|
|
}
|
|
|
|
inline __device__ __host__ void operator -=(float2 & b, float2 a)
|
|
{
|
|
b.x -= a.x;
|
|
b.y -= a.y;
|
|
}
|
|
|
|
inline __device__ __host__ float2 operator /(float2 v, float f)
|
|
{
|
|
float inv = 1.0f / f;
|
|
return v * inv;
|
|
}
|
|
|
|
inline __device__ __host__ void operator /=(float2 & b, float f)
|
|
{
|
|
float inv = 1.0f / f;
|
|
b.x *= inv;
|
|
b.y *= inv;
|
|
}
|
|
|
|
inline __device__ __host__ bool operator ==(float2 a, float2 b)
|
|
{
|
|
return a.x == b.x && a.y == b.y;
|
|
}
|
|
|
|
|
|
inline __device__ __host__ float dot(float2 a, float2 b)
|
|
{
|
|
return a.x * b.x + a.y * b.y;
|
|
}
|
|
|
|
inline __device__ __host__ float dot(float3 a, float3 b)
|
|
{
|
|
return a.x * b.x + a.y * b.y + a.z * b.z;
|
|
}
|
|
|
|
inline __device__ __host__ float dot(float4 a, float4 b)
|
|
{
|
|
return a.x * b.x + a.y * b.y + a.z * b.z + a.w * b.w;
|
|
}
|
|
|
|
inline __device__ __host__ float clamp(float f, float a, float b)
|
|
{
|
|
return max(a, min(f, b));
|
|
}
|
|
|
|
inline __device__ __host__ float3 clamp(float3 v, float a, float b)
|
|
{
|
|
return make_float3(clamp(v.x, a, b), clamp(v.y, a, b), clamp(v.z, a, b));
|
|
}
|
|
|
|
inline __device__ __host__ float3 clamp(float3 v, float3 a, float3 b)
|
|
{
|
|
return make_float3(clamp(v.x, a.x, b.x), clamp(v.y, a.y, b.y), clamp(v.z, a.z, b.z));
|
|
}
|
|
|
|
|
|
inline __device__ __host__ float3 normalize(float3 v)
|
|
{
|
|
float len = 1.0f / sqrtf(dot(v, v));
|
|
return make_float3(v.x * len, v.y * len, v.z * len);
|
|
}
|
|
|
|
inline __device__ __host__ float3 lerp(float3 a, float3 b, float t)
|
|
{
|
|
const float s = 1.0f - t;
|
|
return make_float3(s * a.x + t * b.x, s * a.y + t * b.y, s * a.z + t * b.z);
|
|
}
|
|
|
|
inline __device__ __host__ float lengthSquared(float3 a)
|
|
{
|
|
return dot(a, a);
|
|
}
|
|
|
|
inline __device__ __host__ float lengthSquared(float2 a)
|
|
{
|
|
return dot(a, a);
|
|
}
|
|
|
|
|
|
// Use power method to find the first eigenvector.
|
|
// http://www.miislita.com/information-retrieval-tutorial/matrix-tutorial-3-eigenvalues-eigenvectors.html
|
|
inline __device__ __host__ float3 firstEigenVector( float matrix[6] )
|
|
{
|
|
// 8 iterations seems to be more than enough.
|
|
|
|
float3 row0 = make_float3(matrix[0], matrix[1], matrix[2]);
|
|
float3 row1 = make_float3(matrix[1], matrix[3], matrix[4]);
|
|
float3 row2 = make_float3(matrix[2], matrix[4], matrix[5]);
|
|
|
|
float r0 = dot(row0, row0);
|
|
float r1 = dot(row1, row1);
|
|
float r2 = dot(row2, row2);
|
|
|
|
float3 v;
|
|
if (r0 > r1 && r0 > r2) v = row0;
|
|
else if (r1 > r2) v = row1;
|
|
else v = row2;
|
|
|
|
//float3 v = make_float3(1.0f, 1.0f, 1.0f);
|
|
for(int i = 0; i < 8; i++) {
|
|
float x = v.x * matrix[0] + v.y * matrix[1] + v.z * matrix[2];
|
|
float y = v.x * matrix[1] + v.y * matrix[3] + v.z * matrix[4];
|
|
float z = v.x * matrix[2] + v.y * matrix[4] + v.z * matrix[5];
|
|
float m = max(max(x, y), z);
|
|
float iv = 1.0f / m;
|
|
if (m == 0.0f) iv = 0.0f;
|
|
v = make_float3(x*iv, y*iv, z*iv);
|
|
}
|
|
|
|
return v;
|
|
}
|
|
|
|
|
|
inline __device__ bool singleColor(const float3 * colors)
|
|
{
|
|
#if __DEVICE_EMULATION__
|
|
bool sameColor = false;
|
|
for (int i = 0; i < 16; i++)
|
|
{
|
|
sameColor &= (colors[i] == colors[0]);
|
|
}
|
|
return sameColor;
|
|
#else
|
|
__shared__ int sameColor[16];
|
|
|
|
const int idx = threadIdx.x;
|
|
|
|
sameColor[idx] = (colors[idx] == colors[0]);
|
|
sameColor[idx] &= sameColor[idx^8];
|
|
sameColor[idx] &= sameColor[idx^4];
|
|
sameColor[idx] &= sameColor[idx^2];
|
|
sameColor[idx] &= sameColor[idx^1];
|
|
|
|
return sameColor[0];
|
|
#endif
|
|
}
|
|
|
|
inline __device__ void colorSums(const float3 * colors, float3 * sums)
|
|
{
|
|
#if __DEVICE_EMULATION__
|
|
float3 color_sum = make_float3(0.0f, 0.0f, 0.0f);
|
|
for (int i = 0; i < 16; i++)
|
|
{
|
|
color_sum += colors[i];
|
|
}
|
|
|
|
for (int i = 0; i < 16; i++)
|
|
{
|
|
sums[i] = color_sum;
|
|
}
|
|
#else
|
|
|
|
const int idx = threadIdx.x;
|
|
|
|
sums[idx] = colors[idx];
|
|
sums[idx] += sums[idx^8];
|
|
sums[idx] += sums[idx^4];
|
|
sums[idx] += sums[idx^2];
|
|
sums[idx] += sums[idx^1];
|
|
|
|
#endif
|
|
}
|
|
|
|
inline __device__ float3 bestFitLine(const float3 * colors, float3 color_sum, float3 colorMetric)
|
|
{
|
|
// Compute covariance matrix of the given colors.
|
|
#if __DEVICE_EMULATION__
|
|
float covariance[6] = {0, 0, 0, 0, 0, 0};
|
|
for (int i = 0; i < 16; i++)
|
|
{
|
|
float3 a = (colors[i] - color_sum * (1.0f / 16.0f)) * colorMetric;
|
|
covariance[0] += a.x * a.x;
|
|
covariance[1] += a.x * a.y;
|
|
covariance[2] += a.x * a.z;
|
|
covariance[3] += a.y * a.y;
|
|
covariance[4] += a.y * a.z;
|
|
covariance[5] += a.z * a.z;
|
|
}
|
|
#else
|
|
|
|
const int idx = threadIdx.x;
|
|
|
|
float3 diff = (colors[idx] - color_sum * (1.0f / 16.0f)) * colorMetric;
|
|
|
|
// @@ Eliminate two-way bank conflicts here.
|
|
// @@ It seems that doing that and unrolling the reduction doesn't help...
|
|
__shared__ float covariance[16*6];
|
|
|
|
covariance[6 * idx + 0] = diff.x * diff.x; // 0, 6, 12, 2, 8, 14, 4, 10, 0
|
|
covariance[6 * idx + 1] = diff.x * diff.y;
|
|
covariance[6 * idx + 2] = diff.x * diff.z;
|
|
covariance[6 * idx + 3] = diff.y * diff.y;
|
|
covariance[6 * idx + 4] = diff.y * diff.z;
|
|
covariance[6 * idx + 5] = diff.z * diff.z;
|
|
|
|
for(int d = 8; d > 0; d >>= 1)
|
|
{
|
|
if (idx < d)
|
|
{
|
|
covariance[6 * idx + 0] += covariance[6 * (idx+d) + 0];
|
|
covariance[6 * idx + 1] += covariance[6 * (idx+d) + 1];
|
|
covariance[6 * idx + 2] += covariance[6 * (idx+d) + 2];
|
|
covariance[6 * idx + 3] += covariance[6 * (idx+d) + 3];
|
|
covariance[6 * idx + 4] += covariance[6 * (idx+d) + 4];
|
|
covariance[6 * idx + 5] += covariance[6 * (idx+d) + 5];
|
|
}
|
|
}
|
|
|
|
#endif
|
|
|
|
// Compute first eigen vector.
|
|
return firstEigenVector(covariance);
|
|
}
|
|
|
|
|
|
// @@ For 2D this may not be the most efficient method. It's a quadratic equation, right?
|
|
inline __device__ __host__ float2 firstEigenVector2D( float matrix[3] )
|
|
{
|
|
// @@ 8 iterations is probably more than enough.
|
|
|
|
const float2 row0 = make_float2(matrix[0], matrix[1]);
|
|
const float2 row1 = make_float2(matrix[1], matrix[2]);
|
|
|
|
float r0 = lengthSquared(row0);
|
|
float r1 = lengthSquared(row1);
|
|
|
|
float2 v;
|
|
if (r0 > r1) v = row0;
|
|
v = row1;
|
|
|
|
//float2 v = make_float2(1.0f, 1.0f);
|
|
for(int i = 0; i < 8; i++) {
|
|
float x = v.x * matrix[0] + v.y * matrix[1];
|
|
float y = v.x * matrix[1] + v.y * matrix[2];
|
|
float m = max(x, y);
|
|
float iv = 1.0f / m;
|
|
if (m == 0.0f) iv = 0.0f;
|
|
v = make_float2(x*iv, y*iv);
|
|
}
|
|
|
|
return v;
|
|
}
|
|
|
|
inline __device__ void colorSums(const float2 * colors, float2 * sums)
|
|
{
|
|
#if __DEVICE_EMULATION__
|
|
float2 color_sum = make_float2(0.0f, 0.0f);
|
|
for (int i = 0; i < 16; i++)
|
|
{
|
|
color_sum += colors[i];
|
|
}
|
|
|
|
for (int i = 0; i < 16; i++)
|
|
{
|
|
sums[i] = color_sum;
|
|
}
|
|
#else
|
|
|
|
const int idx = threadIdx.x;
|
|
|
|
sums[idx] = colors[idx];
|
|
sums[idx] += sums[idx^8];
|
|
sums[idx] += sums[idx^4];
|
|
sums[idx] += sums[idx^2];
|
|
sums[idx] += sums[idx^1];
|
|
|
|
#endif
|
|
}
|
|
|
|
inline __device__ float2 bestFitLine(const float2 * colors, float2 color_sum)
|
|
{
|
|
// Compute covariance matrix of the given colors.
|
|
#if __DEVICE_EMULATION__
|
|
float covariance[3] = {0, 0, 0};
|
|
for (int i = 0; i < 16; i++)
|
|
{
|
|
float2 a = (colors[i] - color_sum * (1.0f / 16.0f));
|
|
covariance[0] += a.x * a.x;
|
|
covariance[1] += a.x * a.y;
|
|
covariance[2] += a.y * a.y;
|
|
}
|
|
#else
|
|
|
|
const int idx = threadIdx.x;
|
|
|
|
float2 diff = (colors[idx] - color_sum * (1.0f / 16.0f));
|
|
|
|
__shared__ float covariance[16*3];
|
|
|
|
covariance[3 * idx + 0] = diff.x * diff.x;
|
|
covariance[3 * idx + 1] = diff.x * diff.y;
|
|
covariance[3 * idx + 2] = diff.y * diff.y;
|
|
|
|
for(int d = 8; d > 0; d >>= 1)
|
|
{
|
|
if (idx < d)
|
|
{
|
|
covariance[3 * idx + 0] += covariance[3 * (idx+d) + 0];
|
|
covariance[3 * idx + 1] += covariance[3 * (idx+d) + 1];
|
|
covariance[3 * idx + 2] += covariance[3 * (idx+d) + 2];
|
|
}
|
|
}
|
|
|
|
#endif
|
|
|
|
// Compute first eigen vector.
|
|
return firstEigenVector2D(covariance);
|
|
}
|
|
|
|
|
|
#endif // CUDAMATH_H
|