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FLIP.h
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FLIP.h
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/*
* Copyright (c) 2020-2024, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
*
* 1. Redistributions of source code must retain the above copyright notice, this
* list of conditions and the following disclaimer.
*
* 2. Redistributions in binary form must reproduce the above copyright notice,
* this list of conditions and the following disclaimer in the documentation
* and/or other materials provided with the distribution.
*
* 3. Neither the name of the copyright holder nor the names of its
* contributors may be used to endorse or promote products derived from
* this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
* AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
* DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
* DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
* SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
* OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*
* SPDX-FileCopyrightText: Copyright (c) 2020-2024 NVIDIA CORPORATION & AFFILIATES
* SPDX-License-Identifier: BSD-3-Clause
*/
// Visualizing and Communicating Errors in Rendered Images
// Ray Tracing Gems II, 2021,
// by Pontus Andersson, Jim Nilsson, and Tomas Akenine-Moller.
// Pointer to the chapter: https://research.nvidia.com/publication/2021-08_Visualizing-and-Communicating.
// Visualizing Errors in Rendered High Dynamic Range Images
// Eurographics 2021,
// by Pontus Andersson, Jim Nilsson, Peter Shirley, and Tomas Akenine-Moller.
// Pointer to the paper: https://research.nvidia.com/publication/2021-05_HDR-FLIP.
// FLIP: A Difference Evaluator for Alternating Images
// High Performance Graphics 2020,
// by Pontus Andersson, Jim Nilsson, Tomas Akenine-Moller,
// Magnus Oskarsson, Kalle Astrom, and Mark D. Fairchild.
// Pointer to the paper: https://research.nvidia.com/publication/2020-07_FLIP.
// Single header code by Pontus Ebelin (formerly Andersson) and Tomas Akenine-Moller.
//
// We provide the following FLIP::evaluate() functions with different in/out parameters (see bottom of this file for more explanations):
//
// 1. FLIP::evaluate(const bool useHDR, FLIP::Parameters& parameters, FLIP::image<FLIP::color3>& referenceImageInput, FLIP::image<FLIP::color3>& testImageInput,
// FLIP::image<float>& errorMapFLIPOutput, FLIP::image<float>& maxErrorExposureMapOutput,
// const bool returnIntermediateLDRFLIPImages, std::vector<FLIP::image<float>*>& intermediateLDRFLIPImages,
// const bool returnIntermediateLDRImages, std::vector<FLIP::image<FLIP::color3>*>& intermediateLDRImages)
//
// # This is the one with most parameters and is used by FLIP-tool.cpp in main().
// # See the function at the bottom of this file for detailed description of the parameters.
//
// 2. FLIP::evaluate(const bool useHDR, FLIP::Parameters& parameters, FLIP::image<FLIP::color3>& referenceImageInput, FLIP::image<FLIP::color3>& testImageInput,
// FLIP::image<float>& errorMapFLIPOutput, FLIP::image<float>& maxErrorExposureMap);
//
// # We do not expect that many user will want the LDR-FLIP images and the tonemappe LDR images computed during HDR-FLIP, so provide this simpler function.
//
// 3.FLIP::evaluate(const bool useHDR, FLIP::Parameters& parameters, FLIP::image<FLIP::color3>& referenceImageInput, FLIP::image<FLIP::color3>& testImageInput,
// FLIP::image<float>& errorMapFLIPOutput);
//
// # This one also excludes the exposure map for HDR-FLIP, in case it is not used.
//
// 4. FLIP::evaluate(const bool useHDR, FLIP::Parameters& parameters, const int imageWidth, const int imageHeight,
// const float* referenceThreeChannelImage, const float* testThreeChannelImage, const bool applyMagmaMapToOutput, float** errorMapFLIPOutput)
//
// # An even simpler function that does not use any of our image classes to input the images.
#pragma once
#include <algorithm>
#include <cstdlib>
#include <cstring>
#include <iostream>
#include <string>
#include <cmath>
#include <vector>
#include <sstream>
#include <fstream>
#include <limits>
#ifdef FLIP_ENABLE_CUDA
#include "cuda_runtime.h"
#include "device_launch_parameters.h"
#endif
#ifdef FLIP_ENABLE_CUDA
#define HOST_DEVICE_FOR_CUDA __host__ __device__
#else
#define HOST_DEVICE_FOR_CUDA
#endif
namespace FLIP
{
const float PI = 3.14159265358979f;
#define Max(x, y) ((x) > (y) ? (x) : (y))
#define Min(x, y) ((x) > (y) ? (y) : (x))
#define DEFAULT_ILLUMINANT { 0.950428545f, 1.000000000f, 1.088900371f }
#define INV_DEFAULT_ILLUMINANT { 1.052156925f, 1.000000000f, 0.918357670f }
// Pixels per degree (PPD).
inline float calculatePPD(const float dist, const float resolutionX, const float monitorWidth)
{
return dist * (resolutionX / monitorWidth) * (float(FLIP::PI) / 180.0f);
}
struct Parameters
{
Parameters() = default;
float PPD = FLIP::calculatePPD(0.7f, 3840.0f, 0.7f); // Populate PPD with default values based on 0.7 meters = distance to screen, 3840 pixels screen width, 0.7 meters monitor width.
float startExposure = std::numeric_limits<float>::infinity(); // Used when the input is HDR.
float stopExposure = std::numeric_limits<float>::infinity(); // Used when the input is HDR.
int numExposures = -1; // Used when the input is HDR.
std::string tonemapper = "aces"; // Default tonemapper (used for HDR).
};
static const struct xFLIPConstants
{
xFLIPConstants() = default;
float gqc = 0.7f;
float gpc = 0.4f;
float gpt = 0.95f;
float gw = 0.082f;
float gqf = 0.5f;
} FLIPConstants;
#ifndef FLIP_ENABLE_CUDA
static const float ToneMappingCoefficients[3][6] =
#else
__device__ const float ToneMappingCoefficients[3][6] =
#endif
{
{ 0.0f, 1.0f, 0.0f, 0.0f, 1.0f, 1.0f }, // Reinhard.
{ 0.6f * 0.6f * 2.51f, 0.6f * 0.03f, 0.0f, 0.6f * 0.6f * 2.43f, 0.6f * 0.59f, 0.14f }, // ACES, 0.6 is pre-exposure cancellation.
{ 0.231683f, 0.013791f, 0.0f, 0.18f, 0.3f, 0.018f }, // Hable.
};
#ifndef FLIP_ENABLE_CUDA
union int3
{
struct { int x, y, z; };
};
#else // FLIP_ENABLE_CUDA
__constant__ struct
{
float gqc = 0.7f;
float gpc = 0.4f;
float gpt = 0.95f;
float gw = 0.082f;
float gqf = 0.5f;
} DeviceFLIPConstants;
const dim3 DEFAULT_KERNEL_BLOCK_DIM = { 32, 32, 1 };
enum class CudaTensorState
{
UNINITIALIZED,
ALLOCATED,
HOST_ONLY,
DEVICE_ONLY,
SYNCHRONIZED
};
#endif
class color3
{
public:
union
{
struct { float r, g, b; };
struct { float x, y, z; };
struct { float h, s, v; };
};
public:
HOST_DEVICE_FOR_CUDA color3(void)
{
this->x = 0.0f;
this->y = 0.0f;
this->z = 0.0f;
}
HOST_DEVICE_FOR_CUDA color3(float v)
{
this->x = v;
this->y = v;
this->z = v;
}
HOST_DEVICE_FOR_CUDA color3(const float* pColor)
{
this->x = pColor[0];
this->y = pColor[1];
this->z = pColor[2];
}
HOST_DEVICE_FOR_CUDA color3(const unsigned char* pColor)
{
this->x = float(pColor[0]);
this->y = float(pColor[1]);
this->z = float(pColor[2]);
*this /= 255.0f;
}
HOST_DEVICE_FOR_CUDA color3(float _x, float _y, float _z)
{
this->x = _x;
this->y = _y;
this->z = _z;
}
HOST_DEVICE_FOR_CUDA color3(const color3& c)
{
this->x = c.x;
this->y = c.y;
this->z = c.z;
}
HOST_DEVICE_FOR_CUDA bool operator==(const color3 c) const
{
return this->x == c.x && this->y == c.y && this->z == c.z;
}
HOST_DEVICE_FOR_CUDA bool operator!=(const color3 c) const
{
return !(*this == c);
}
HOST_DEVICE_FOR_CUDA color3 operator+(const color3 c) const
{
return color3(this->x + c.x, this->y + c.y, this->z + c.z);
}
HOST_DEVICE_FOR_CUDA color3 operator-(const color3 c) const
{
return color3(this->x - c.x, this->y - c.y, this->z - c.z);
}
HOST_DEVICE_FOR_CUDA color3 operator*(const float c) const
{
return color3(this->x * c, this->y * c, this->z * c);
}
HOST_DEVICE_FOR_CUDA color3 operator*(const color3 c) const
{
return color3(this->x * c.x, this->y * c.y, this->z * c.z);
}
HOST_DEVICE_FOR_CUDA color3 operator/(const float c) const
{
return color3(this->x / c, this->y / c, this->z / c);
}
HOST_DEVICE_FOR_CUDA color3 operator/(const color3 c) const
{
return color3(this->x / c.x, this->y / c.y, this->z / c.z);
}
HOST_DEVICE_FOR_CUDA color3 operator+=(const color3 c)
{
this->x += c.x;
this->y += c.y;
this->z += c.z;
return *this;
}
HOST_DEVICE_FOR_CUDA color3 operator*=(const color3 c)
{
this->x *= c.x;
this->y *= c.y;
this->z *= c.z;
return *this;
}
HOST_DEVICE_FOR_CUDA color3 operator/=(const color3 c)
{
this->x /= c.x;
this->y /= c.y;
this->z /= c.z;
return *this;
}
HOST_DEVICE_FOR_CUDA void clear(const color3 c = { 0.0f, 0.0f, 0.0f })
{
this->x = c.x;
this->y = c.y;
this->z = c.z;
}
HOST_DEVICE_FOR_CUDA static inline color3 min(color3 v0, color3 v1)
{
return color3(Min(v0.x, v1.x), Min(v0.y, v1.y), Min(v0.z, v1.z));
}
HOST_DEVICE_FOR_CUDA static inline color3 max(color3 v0, color3 v1)
{
return color3(Max(v0.x, v1.x), Max(v0.y, v1.y), Max(v0.z, v1.z));
}
HOST_DEVICE_FOR_CUDA static inline color3 abs(color3 v)
{
return color3(std::abs(v.x), std::abs(v.y), std::abs(v.z));
}
HOST_DEVICE_FOR_CUDA static inline color3 sqrt(color3 v)
{
return color3(std::sqrt(v.x), std::sqrt(v.y), std::sqrt(v.z));
}
HOST_DEVICE_FOR_CUDA static inline color3 clamp(color3 v, float _min = 0.0f, float _max = 1.0f)
{
return color3(Min(Max(v.x, _min), _max), Min(Max(v.y, _min), _max), Min(Max(v.z, _min), _max));
}
HOST_DEVICE_FOR_CUDA static inline float linearRGBToLuminance(color3 linearRGB)
{
return 0.2126f * linearRGB.r + 0.7152f * linearRGB.g + 0.0722f * linearRGB.b;
}
HOST_DEVICE_FOR_CUDA static inline float sRGBToLinearRGB(float sC)
{
if (sC <= 0.04045f)
{
return sC / 12.92f;
}
return powf((sC + 0.055f) / 1.055f, 2.4f);
}
HOST_DEVICE_FOR_CUDA static inline float LinearRGBTosRGB(float lC)
{
if (lC <= 0.0031308f)
{
return lC * 12.92f;
}
return 1.055f * powf(lC, 1.0f / 2.4f) - 0.055f;
}
HOST_DEVICE_FOR_CUDA static inline color3 sRGBToLinearRGB(color3 sRGB)
{
float R = sRGBToLinearRGB(sRGB.x);
float G = sRGBToLinearRGB(sRGB.y);
float B = sRGBToLinearRGB(sRGB.z);
return color3(R, G, B);
}
HOST_DEVICE_FOR_CUDA static inline color3 LinearRGBTosRGB(color3 RGB)
{
float sR = LinearRGBTosRGB(RGB.x);
float sG = LinearRGBTosRGB(RGB.y);
float sB = LinearRGBTosRGB(RGB.z);
return color3(sR, sG, sB);
}
HOST_DEVICE_FOR_CUDA static inline color3 LinearRGBToXYZ(color3 RGB)
{
// Source: https://www.image-engineering.de/library/technotes/958-how-to-convert-between-srgb-and-ciexyz
// Assumes D65 standard illuminant.
const float a11 = 10135552.0f / 24577794.0f;
const float a12 = 8788810.0f / 24577794.0f;
const float a13 = 4435075.0f / 24577794.0f;
const float a21 = 2613072.0f / 12288897.0f;
const float a22 = 8788810.0f / 12288897.0f;
const float a23 = 887015.0f / 12288897.0f;
const float a31 = 1425312.0f / 73733382.0f;
const float a32 = 8788810.0f / 73733382.0f;
const float a33 = 70074185.0f / 73733382.0f;
color3 XYZ;
XYZ.x = a11 * RGB.x + a12 * RGB.y + a13 * RGB.z;
XYZ.y = a21 * RGB.x + a22 * RGB.y + a23 * RGB.z;
XYZ.z = a31 * RGB.x + a32 * RGB.y + a33 * RGB.z;
return XYZ;
}
HOST_DEVICE_FOR_CUDA static inline color3 XYZToLinearRGB(color3 XYZ)
{
// Return values in linear RGB, assuming D65 standard illuminant.
const float a11 = 3.241003275f;
const float a12 = -1.537398934f;
const float a13 = -0.498615861f;
const float a21 = -0.969224334f;
const float a22 = 1.875930071f;
const float a23 = 0.041554224f;
const float a31 = 0.055639423f;
const float a32 = -0.204011202f;
const float a33 = 1.057148933f;
color3 RGB;
RGB.x = a11 * XYZ.x + a12 * XYZ.y + a13 * XYZ.z;
RGB.y = a21 * XYZ.x + a22 * XYZ.y + a23 * XYZ.z;
RGB.z = a31 * XYZ.x + a32 * XYZ.y + a33 * XYZ.z;
return RGB;
}
HOST_DEVICE_FOR_CUDA static inline color3 XYZToCIELab(color3 XYZ, const color3 invReferenceIlluminant = INV_DEFAULT_ILLUMINANT)
{
const float delta = 6.0f / 29.0f;
const float deltaSquare = delta * delta;
const float deltaCube = delta * deltaSquare;
const float factor = 1.0f / (3.0f * deltaSquare);
const float term = 4.0f / 29.0f;
// The default illuminant is D65.
XYZ = XYZ * invReferenceIlluminant;
XYZ.x = (XYZ.x > deltaCube ? powf(XYZ.x, 1.0f / 3.0f) : factor * XYZ.x + term);
XYZ.y = (XYZ.y > deltaCube ? powf(XYZ.y, 1.0f / 3.0f) : factor * XYZ.y + term);
XYZ.z = (XYZ.z > deltaCube ? powf(XYZ.z, 1.0f / 3.0f) : factor * XYZ.z + term);
float L = 116.0f * XYZ.y - 16.0f;
float a = 500.0f * (XYZ.x - XYZ.y);
float b = 200.0f * (XYZ.y - XYZ.z);
return color3(L, a, b);
}
HOST_DEVICE_FOR_CUDA static inline color3 CIELabToXYZ(color3 Lab, const color3 referenceIlluminant = DEFAULT_ILLUMINANT)
{
// The default illuminant is D65.
float Y = (Lab.x + 16.0f) / 116.0f;
float X = Lab.y / 500.0f + Y;
float Z = Y - Lab.z / 200.0f;
const float delta = 6.0f / 29.0f;
const float factor = 3.0f * delta * delta;
const float term = 4.0f / 29.0f;
X = (X > delta ? X * X * X : (X - term) * factor);
Y = (Y > delta ? Y * Y * Y : (Y - term) * factor);
Z = (Z > delta ? Z * Z * Z : (Z - term) * factor);
return color3(X, Y, Z) * referenceIlluminant;
}
HOST_DEVICE_FOR_CUDA static inline color3 XYZToYCxCz(color3 XYZ, const color3 invReferenceIlluminant = INV_DEFAULT_ILLUMINANT)
{
// The default illuminant is D65.
XYZ = XYZ * invReferenceIlluminant;
float Y = 116.0f * XYZ.y - 16.0f;
float Cx = 500.0f * (XYZ.x - XYZ.y);
float Cz = 200.0f * (XYZ.y - XYZ.z);
return color3(Y, Cx, Cz);
}
HOST_DEVICE_FOR_CUDA static inline color3 YCxCzToXYZ(color3 YCxCz, const color3 referenceIlluminant = DEFAULT_ILLUMINANT)
{
// The default illuminant is D65.
const float Y = (YCxCz.x + 16.0f) / 116.0f;
const float Cx = YCxCz.y / 500.0f;
const float Cz = YCxCz.z / 200.0f;
float X = Y + Cx;
float Z = Y - Cz;
return color3(X, Y, Z) * referenceIlluminant;
}
HOST_DEVICE_FOR_CUDA static inline float YCxCzToGray(color3 YCxCz)
{
return (YCxCz.x + 16.0f) / 116.0f; // Make it [0,1].
}
// FLIP-specific functions below.
HOST_DEVICE_FOR_CUDA static inline float Hunt(const float luminance, const float chrominance)
{
return 0.01f * luminance * chrominance;
}
HOST_DEVICE_FOR_CUDA static inline float HyAB(color3& refPixel, color3& testPixel)
{
float cityBlockDistanceL = std::fabs(refPixel.x - testPixel.x);
float euclideanDistanceAB = std::sqrt((refPixel.y - testPixel.y) * (refPixel.y - testPixel.y) + (refPixel.z - testPixel.z) * (refPixel.z - testPixel.z));
return cityBlockDistanceL + euclideanDistanceAB;
}
HOST_DEVICE_FOR_CUDA static inline float computeMaxDistance(float gqc)
{
color3 greenLab = color3::XYZToCIELab(color3::LinearRGBToXYZ(color3(0.0f, 1.0f, 0.0f)));
color3 blueLab = color3::XYZToCIELab(color3::LinearRGBToXYZ(color3(0.0f, 0.0f, 1.0f)));
color3 greenLabHunt = color3(greenLab.x, Hunt(greenLab.x, greenLab.y), Hunt(greenLab.x, greenLab.z));
color3 blueLabHunt = color3(blueLab.x, Hunt(blueLab.x, blueLab.y), Hunt(blueLab.x, blueLab.z));
return powf(HyAB(greenLabHunt, blueLabHunt), gqc);
}
std::string toString(void) const { return "(" + std::to_string(this->x) + ", " + std::to_string(this->y) + ", " + std::to_string(this->z) + ")"; }
friend std::ostream& operator<<(std::ostream& os, const color3& c) { os << c.toString(); return os; }
};
static const color3 MapMagma[256] =
{
{0.001462f, 0.000466f, 0.013866f}, {0.002258f, 0.001295f, 0.018331f}, {0.003279f, 0.002305f, 0.023708f}, {0.004512f, 0.003490f, 0.029965f}, {0.005950f, 0.004843f, 0.037130f}, {0.007588f, 0.006356f, 0.044973f}, {0.009426f, 0.008022f, 0.052844f}, {0.011465f, 0.009828f, 0.060750f},
{0.013708f, 0.011771f, 0.068667f}, {0.016156f, 0.013840f, 0.076603f}, {0.018815f, 0.016026f, 0.084584f}, {0.021692f, 0.018320f, 0.092610f}, {0.024792f, 0.020715f, 0.100676f}, {0.028123f, 0.023201f, 0.108787f}, {0.031696f, 0.025765f, 0.116965f}, {0.035520f, 0.028397f, 0.125209f},
{0.039608f, 0.031090f, 0.133515f}, {0.043830f, 0.033830f, 0.141886f}, {0.048062f, 0.036607f, 0.150327f}, {0.052320f, 0.039407f, 0.158841f}, {0.056615f, 0.042160f, 0.167446f}, {0.060949f, 0.044794f, 0.176129f}, {0.065330f, 0.047318f, 0.184892f}, {0.069764f, 0.049726f, 0.193735f},
{0.074257f, 0.052017f, 0.202660f}, {0.078815f, 0.054184f, 0.211667f}, {0.083446f, 0.056225f, 0.220755f}, {0.088155f, 0.058133f, 0.229922f}, {0.092949f, 0.059904f, 0.239164f}, {0.097833f, 0.061531f, 0.248477f}, {0.102815f, 0.063010f, 0.257854f}, {0.107899f, 0.064335f, 0.267289f},
{0.113094f, 0.065492f, 0.276784f}, {0.118405f, 0.066479f, 0.286321f}, {0.123833f, 0.067295f, 0.295879f}, {0.129380f, 0.067935f, 0.305443f}, {0.135053f, 0.068391f, 0.315000f}, {0.140858f, 0.068654f, 0.324538f}, {0.146785f, 0.068738f, 0.334011f}, {0.152839f, 0.068637f, 0.343404f},
{0.159018f, 0.068354f, 0.352688f}, {0.165308f, 0.067911f, 0.361816f}, {0.171713f, 0.067305f, 0.370771f}, {0.178212f, 0.066576f, 0.379497f}, {0.184801f, 0.065732f, 0.387973f}, {0.191460f, 0.064818f, 0.396152f}, {0.198177f, 0.063862f, 0.404009f}, {0.204935f, 0.062907f, 0.411514f},
{0.211718f, 0.061992f, 0.418647f}, {0.218512f, 0.061158f, 0.425392f}, {0.225302f, 0.060445f, 0.431742f}, {0.232077f, 0.059889f, 0.437695f}, {0.238826f, 0.059517f, 0.443256f}, {0.245543f, 0.059352f, 0.448436f}, {0.252220f, 0.059415f, 0.453248f}, {0.258857f, 0.059706f, 0.457710f},
{0.265447f, 0.060237f, 0.461840f}, {0.271994f, 0.060994f, 0.465660f}, {0.278493f, 0.061978f, 0.469190f}, {0.284951f, 0.063168f, 0.472451f}, {0.291366f, 0.064553f, 0.475462f}, {0.297740f, 0.066117f, 0.478243f}, {0.304081f, 0.067835f, 0.480812f}, {0.310382f, 0.069702f, 0.483186f},
{0.316654f, 0.071690f, 0.485380f}, {0.322899f, 0.073782f, 0.487408f}, {0.329114f, 0.075972f, 0.489287f}, {0.335308f, 0.078236f, 0.491024f}, {0.341482f, 0.080564f, 0.492631f}, {0.347636f, 0.082946f, 0.494121f}, {0.353773f, 0.085373f, 0.495501f}, {0.359898f, 0.087831f, 0.496778f},
{0.366012f, 0.090314f, 0.497960f}, {0.372116f, 0.092816f, 0.499053f}, {0.378211f, 0.095332f, 0.500067f}, {0.384299f, 0.097855f, 0.501002f}, {0.390384f, 0.100379f, 0.501864f}, {0.396467f, 0.102902f, 0.502658f}, {0.402548f, 0.105420f, 0.503386f}, {0.408629f, 0.107930f, 0.504052f},
{0.414709f, 0.110431f, 0.504662f}, {0.420791f, 0.112920f, 0.505215f}, {0.426877f, 0.115395f, 0.505714f}, {0.432967f, 0.117855f, 0.506160f}, {0.439062f, 0.120298f, 0.506555f}, {0.445163f, 0.122724f, 0.506901f}, {0.451271f, 0.125132f, 0.507198f}, {0.457386f, 0.127522f, 0.507448f},
{0.463508f, 0.129893f, 0.507652f}, {0.469640f, 0.132245f, 0.507809f}, {0.475780f, 0.134577f, 0.507921f}, {0.481929f, 0.136891f, 0.507989f}, {0.488088f, 0.139186f, 0.508011f}, {0.494258f, 0.141462f, 0.507988f}, {0.500438f, 0.143719f, 0.507920f}, {0.506629f, 0.145958f, 0.507806f},
{0.512831f, 0.148179f, 0.507648f}, {0.519045f, 0.150383f, 0.507443f}, {0.525270f, 0.152569f, 0.507192f}, {0.531507f, 0.154739f, 0.506895f}, {0.537755f, 0.156894f, 0.506551f}, {0.544015f, 0.159033f, 0.506159f}, {0.550287f, 0.161158f, 0.505719f}, {0.556571f, 0.163269f, 0.505230f},
{0.562866f, 0.165368f, 0.504692f}, {0.569172f, 0.167454f, 0.504105f}, {0.575490f, 0.169530f, 0.503466f}, {0.581819f, 0.171596f, 0.502777f}, {0.588158f, 0.173652f, 0.502035f}, {0.594508f, 0.175701f, 0.501241f}, {0.600868f, 0.177743f, 0.500394f}, {0.607238f, 0.179779f, 0.499492f},
{0.613617f, 0.181811f, 0.498536f}, {0.620005f, 0.183840f, 0.497524f}, {0.626401f, 0.185867f, 0.496456f}, {0.632805f, 0.187893f, 0.495332f}, {0.639216f, 0.189921f, 0.494150f}, {0.645633f, 0.191952f, 0.492910f}, {0.652056f, 0.193986f, 0.491611f}, {0.658483f, 0.196027f, 0.490253f},
{0.664915f, 0.198075f, 0.488836f}, {0.671349f, 0.200133f, 0.487358f}, {0.677786f, 0.202203f, 0.485819f}, {0.684224f, 0.204286f, 0.484219f}, {0.690661f, 0.206384f, 0.482558f}, {0.697098f, 0.208501f, 0.480835f}, {0.703532f, 0.210638f, 0.479049f}, {0.709962f, 0.212797f, 0.477201f},
{0.716387f, 0.214982f, 0.475290f}, {0.722805f, 0.217194f, 0.473316f}, {0.729216f, 0.219437f, 0.471279f}, {0.735616f, 0.221713f, 0.469180f}, {0.742004f, 0.224025f, 0.467018f}, {0.748378f, 0.226377f, 0.464794f}, {0.754737f, 0.228772f, 0.462509f}, {0.761077f, 0.231214f, 0.460162f},
{0.767398f, 0.233705f, 0.457755f}, {0.773695f, 0.236249f, 0.455289f}, {0.779968f, 0.238851f, 0.452765f}, {0.786212f, 0.241514f, 0.450184f}, {0.792427f, 0.244242f, 0.447543f}, {0.798608f, 0.247040f, 0.444848f}, {0.804752f, 0.249911f, 0.442102f}, {0.810855f, 0.252861f, 0.439305f},
{0.816914f, 0.255895f, 0.436461f}, {0.822926f, 0.259016f, 0.433573f}, {0.828886f, 0.262229f, 0.430644f}, {0.834791f, 0.265540f, 0.427671f}, {0.840636f, 0.268953f, 0.424666f}, {0.846416f, 0.272473f, 0.421631f}, {0.852126f, 0.276106f, 0.418573f}, {0.857763f, 0.279857f, 0.415496f},
{0.863320f, 0.283729f, 0.412403f}, {0.868793f, 0.287728f, 0.409303f}, {0.874176f, 0.291859f, 0.406205f}, {0.879464f, 0.296125f, 0.403118f}, {0.884651f, 0.300530f, 0.400047f}, {0.889731f, 0.305079f, 0.397002f}, {0.894700f, 0.309773f, 0.393995f}, {0.899552f, 0.314616f, 0.391037f},
{0.904281f, 0.319610f, 0.388137f}, {0.908884f, 0.324755f, 0.385308f}, {0.913354f, 0.330052f, 0.382563f}, {0.917689f, 0.335500f, 0.379915f}, {0.921884f, 0.341098f, 0.377376f}, {0.925937f, 0.346844f, 0.374959f}, {0.929845f, 0.352734f, 0.372677f}, {0.933606f, 0.358764f, 0.370541f},
{0.937221f, 0.364929f, 0.368567f}, {0.940687f, 0.371224f, 0.366762f}, {0.944006f, 0.377643f, 0.365136f}, {0.947180f, 0.384178f, 0.363701f}, {0.950210f, 0.390820f, 0.362468f}, {0.953099f, 0.397563f, 0.361438f}, {0.955849f, 0.404400f, 0.360619f}, {0.958464f, 0.411324f, 0.360014f},
{0.960949f, 0.418323f, 0.359630f}, {0.963310f, 0.425390f, 0.359469f}, {0.965549f, 0.432519f, 0.359529f}, {0.967671f, 0.439703f, 0.359810f}, {0.969680f, 0.446936f, 0.360311f}, {0.971582f, 0.454210f, 0.361030f}, {0.973381f, 0.461520f, 0.361965f}, {0.975082f, 0.468861f, 0.363111f},
{0.976690f, 0.476226f, 0.364466f}, {0.978210f, 0.483612f, 0.366025f}, {0.979645f, 0.491014f, 0.367783f}, {0.981000f, 0.498428f, 0.369734f}, {0.982279f, 0.505851f, 0.371874f}, {0.983485f, 0.513280f, 0.374198f}, {0.984622f, 0.520713f, 0.376698f}, {0.985693f, 0.528148f, 0.379371f},
{0.986700f, 0.535582f, 0.382210f}, {0.987646f, 0.543015f, 0.385210f}, {0.988533f, 0.550446f, 0.388365f}, {0.989363f, 0.557873f, 0.391671f}, {0.990138f, 0.565296f, 0.395122f}, {0.990871f, 0.572706f, 0.398714f}, {0.991558f, 0.580107f, 0.402441f}, {0.992196f, 0.587502f, 0.406299f},
{0.992785f, 0.594891f, 0.410283f}, {0.993326f, 0.602275f, 0.414390f}, {0.993834f, 0.609644f, 0.418613f}, {0.994309f, 0.616999f, 0.422950f}, {0.994738f, 0.624350f, 0.427397f}, {0.995122f, 0.631696f, 0.431951f}, {0.995480f, 0.639027f, 0.436607f}, {0.995810f, 0.646344f, 0.441361f},
{0.996096f, 0.653659f, 0.446213f}, {0.996341f, 0.660969f, 0.451160f}, {0.996580f, 0.668256f, 0.456192f}, {0.996775f, 0.675541f, 0.461314f}, {0.996925f, 0.682828f, 0.466526f}, {0.997077f, 0.690088f, 0.471811f}, {0.997186f, 0.697349f, 0.477182f}, {0.997254f, 0.704611f, 0.482635f},
{0.997325f, 0.711848f, 0.488154f}, {0.997351f, 0.719089f, 0.493755f}, {0.997351f, 0.726324f, 0.499428f}, {0.997341f, 0.733545f, 0.505167f}, {0.997285f, 0.740772f, 0.510983f}, {0.997228f, 0.747981f, 0.516859f}, {0.997138f, 0.755190f, 0.522806f}, {0.997019f, 0.762398f, 0.528821f},
{0.996898f, 0.769591f, 0.534892f}, {0.996727f, 0.776795f, 0.541039f}, {0.996571f, 0.783977f, 0.547233f}, {0.996369f, 0.791167f, 0.553499f}, {0.996162f, 0.798348f, 0.559820f}, {0.995932f, 0.805527f, 0.566202f}, {0.995680f, 0.812706f, 0.572645f}, {0.995424f, 0.819875f, 0.579140f},
{0.995131f, 0.827052f, 0.585701f}, {0.994851f, 0.834213f, 0.592307f}, {0.994524f, 0.841387f, 0.598983f}, {0.994222f, 0.848540f, 0.605696f}, {0.993866f, 0.855711f, 0.612482f}, {0.993545f, 0.862859f, 0.619299f}, {0.993170f, 0.870024f, 0.626189f}, {0.992831f, 0.877168f, 0.633109f},
{0.992440f, 0.884330f, 0.640099f}, {0.992089f, 0.891470f, 0.647116f}, {0.991688f, 0.898627f, 0.654202f}, {0.991332f, 0.905763f, 0.661309f}, {0.990930f, 0.912915f, 0.668481f}, {0.990570f, 0.920049f, 0.675675f}, {0.990175f, 0.927196f, 0.682926f}, {0.989815f, 0.934329f, 0.690198f},
{0.989434f, 0.941470f, 0.697519f}, {0.989077f, 0.948604f, 0.704863f}, {0.988717f, 0.955742f, 0.712242f}, {0.988367f, 0.962878f, 0.719649f}, {0.988033f, 0.970012f, 0.727077f}, {0.987691f, 0.977154f, 0.734536f}, {0.987387f, 0.984288f, 0.742002f}, {0.987053f, 0.991438f, 0.749504f}
};
static const color3 MapViridis[256] =
{
{0.267004f, 0.004874f, 0.329415f}, {0.268510f, 0.009605f, 0.335427f}, {0.269944f, 0.014625f, 0.341379f}, {0.271305f, 0.019942f, 0.347269f}, {0.272594f, 0.025563f, 0.353093f}, {0.273809f, 0.031497f, 0.358853f}, {0.274952f, 0.037752f, 0.364543f}, {0.276022f, 0.044167f, 0.370164f},
{0.277018f, 0.050344f, 0.375715f}, {0.277941f, 0.056324f, 0.381191f}, {0.278791f, 0.062145f, 0.386592f}, {0.279566f, 0.067836f, 0.391917f}, {0.280267f, 0.073417f, 0.397163f}, {0.280894f, 0.078907f, 0.402329f}, {0.281446f, 0.084320f, 0.407414f}, {0.281924f, 0.089666f, 0.412415f},
{0.282327f, 0.094955f, 0.417331f}, {0.282656f, 0.100196f, 0.422160f}, {0.282910f, 0.105393f, 0.426902f}, {0.283091f, 0.110553f, 0.431554f}, {0.283197f, 0.115680f, 0.436115f}, {0.283229f, 0.120777f, 0.440584f}, {0.283187f, 0.125848f, 0.444960f}, {0.283072f, 0.130895f, 0.449241f},
{0.282884f, 0.135920f, 0.453427f}, {0.282623f, 0.140926f, 0.457517f}, {0.282290f, 0.145912f, 0.461510f}, {0.281887f, 0.150881f, 0.465405f}, {0.281412f, 0.155834f, 0.469201f}, {0.280868f, 0.160771f, 0.472899f}, {0.280255f, 0.165693f, 0.476498f}, {0.279574f, 0.170599f, 0.479997f},
{0.278826f, 0.175490f, 0.483397f}, {0.278012f, 0.180367f, 0.486697f}, {0.277134f, 0.185228f, 0.489898f}, {0.276194f, 0.190074f, 0.493001f}, {0.275191f, 0.194905f, 0.496005f}, {0.274128f, 0.199721f, 0.498911f}, {0.273006f, 0.204520f, 0.501721f}, {0.271828f, 0.209303f, 0.504434f},
{0.270595f, 0.214069f, 0.507052f}, {0.269308f, 0.218818f, 0.509577f}, {0.267968f, 0.223549f, 0.512008f}, {0.266580f, 0.228262f, 0.514349f}, {0.265145f, 0.232956f, 0.516599f}, {0.263663f, 0.237631f, 0.518762f}, {0.262138f, 0.242286f, 0.520837f}, {0.260571f, 0.246922f, 0.522828f},
{0.258965f, 0.251537f, 0.524736f}, {0.257322f, 0.256130f, 0.526563f}, {0.255645f, 0.260703f, 0.528312f}, {0.253935f, 0.265254f, 0.529983f}, {0.252194f, 0.269783f, 0.531579f}, {0.250425f, 0.274290f, 0.533103f}, {0.248629f, 0.278775f, 0.534556f}, {0.246811f, 0.283237f, 0.535941f},
{0.244972f, 0.287675f, 0.537260f}, {0.243113f, 0.292092f, 0.538516f}, {0.241237f, 0.296485f, 0.539709f}, {0.239346f, 0.300855f, 0.540844f}, {0.237441f, 0.305202f, 0.541921f}, {0.235526f, 0.309527f, 0.542944f}, {0.233603f, 0.313828f, 0.543914f}, {0.231674f, 0.318106f, 0.544834f},
{0.229739f, 0.322361f, 0.545706f}, {0.227802f, 0.326594f, 0.546532f}, {0.225863f, 0.330805f, 0.547314f}, {0.223925f, 0.334994f, 0.548053f}, {0.221989f, 0.339161f, 0.548752f}, {0.220057f, 0.343307f, 0.549413f}, {0.218130f, 0.347432f, 0.550038f}, {0.216210f, 0.351535f, 0.550627f},
{0.214298f, 0.355619f, 0.551184f}, {0.212395f, 0.359683f, 0.551710f}, {0.210503f, 0.363727f, 0.552206f}, {0.208623f, 0.367752f, 0.552675f}, {0.206756f, 0.371758f, 0.553117f}, {0.204903f, 0.375746f, 0.553533f}, {0.203063f, 0.379716f, 0.553925f}, {0.201239f, 0.383670f, 0.554294f},
{0.199430f, 0.387607f, 0.554642f}, {0.197636f, 0.391528f, 0.554969f}, {0.195860f, 0.395433f, 0.555276f}, {0.194100f, 0.399323f, 0.555565f}, {0.192357f, 0.403199f, 0.555836f}, {0.190631f, 0.407061f, 0.556089f}, {0.188923f, 0.410910f, 0.556326f}, {0.187231f, 0.414746f, 0.556547f},
{0.185556f, 0.418570f, 0.556753f}, {0.183898f, 0.422383f, 0.556944f}, {0.182256f, 0.426184f, 0.557120f}, {0.180629f, 0.429975f, 0.557282f}, {0.179019f, 0.433756f, 0.557430f}, {0.177423f, 0.437527f, 0.557565f}, {0.175841f, 0.441290f, 0.557685f}, {0.174274f, 0.445044f, 0.557792f},
{0.172719f, 0.448791f, 0.557885f}, {0.171176f, 0.452530f, 0.557965f}, {0.169646f, 0.456262f, 0.558030f}, {0.168126f, 0.459988f, 0.558082f}, {0.166617f, 0.463708f, 0.558119f}, {0.165117f, 0.467423f, 0.558141f}, {0.163625f, 0.471133f, 0.558148f}, {0.162142f, 0.474838f, 0.558140f},
{0.160665f, 0.478540f, 0.558115f}, {0.159194f, 0.482237f, 0.558073f}, {0.157729f, 0.485932f, 0.558013f}, {0.156270f, 0.489624f, 0.557936f}, {0.154815f, 0.493313f, 0.557840f}, {0.153364f, 0.497000f, 0.557724f}, {0.151918f, 0.500685f, 0.557587f}, {0.150476f, 0.504369f, 0.557430f},
{0.149039f, 0.508051f, 0.557250f}, {0.147607f, 0.511733f, 0.557049f}, {0.146180f, 0.515413f, 0.556823f}, {0.144759f, 0.519093f, 0.556572f}, {0.143343f, 0.522773f, 0.556295f}, {0.141935f, 0.526453f, 0.555991f}, {0.140536f, 0.530132f, 0.555659f}, {0.139147f, 0.533812f, 0.555298f},
{0.137770f, 0.537492f, 0.554906f}, {0.136408f, 0.541173f, 0.554483f}, {0.135066f, 0.544853f, 0.554029f}, {0.133743f, 0.548535f, 0.553541f}, {0.132444f, 0.552216f, 0.553018f}, {0.131172f, 0.555899f, 0.552459f}, {0.129933f, 0.559582f, 0.551864f}, {0.128729f, 0.563265f, 0.551229f},
{0.127568f, 0.566949f, 0.550556f}, {0.126453f, 0.570633f, 0.549841f}, {0.125394f, 0.574318f, 0.549086f}, {0.124395f, 0.578002f, 0.548287f}, {0.123463f, 0.581687f, 0.547445f}, {0.122606f, 0.585371f, 0.546557f}, {0.121831f, 0.589055f, 0.545623f}, {0.121148f, 0.592739f, 0.544641f},
{0.120565f, 0.596422f, 0.543611f}, {0.120092f, 0.600104f, 0.542530f}, {0.119738f, 0.603785f, 0.541400f}, {0.119512f, 0.607464f, 0.540218f}, {0.119423f, 0.611141f, 0.538982f}, {0.119483f, 0.614817f, 0.537692f}, {0.119699f, 0.618490f, 0.536347f}, {0.120081f, 0.622161f, 0.534946f},
{0.120638f, 0.625828f, 0.533488f}, {0.121380f, 0.629492f, 0.531973f}, {0.122312f, 0.633153f, 0.530398f}, {0.123444f, 0.636809f, 0.528763f}, {0.124780f, 0.640461f, 0.527068f}, {0.126326f, 0.644107f, 0.525311f}, {0.128087f, 0.647749f, 0.523491f}, {0.130067f, 0.651384f, 0.521608f},
{0.132268f, 0.655014f, 0.519661f}, {0.134692f, 0.658636f, 0.517649f}, {0.137339f, 0.662252f, 0.515571f}, {0.140210f, 0.665859f, 0.513427f}, {0.143303f, 0.669459f, 0.511215f}, {0.146616f, 0.673050f, 0.508936f}, {0.150148f, 0.676631f, 0.506589f}, {0.153894f, 0.680203f, 0.504172f},
{0.157851f, 0.683765f, 0.501686f}, {0.162016f, 0.687316f, 0.499129f}, {0.166383f, 0.690856f, 0.496502f}, {0.170948f, 0.694384f, 0.493803f}, {0.175707f, 0.697900f, 0.491033f}, {0.180653f, 0.701402f, 0.488189f}, {0.185783f, 0.704891f, 0.485273f}, {0.191090f, 0.708366f, 0.482284f},
{0.196571f, 0.711827f, 0.479221f}, {0.202219f, 0.715272f, 0.476084f}, {0.208030f, 0.718701f, 0.472873f}, {0.214000f, 0.722114f, 0.469588f}, {0.220124f, 0.725509f, 0.466226f}, {0.226397f, 0.728888f, 0.462789f}, {0.232815f, 0.732247f, 0.459277f}, {0.239374f, 0.735588f, 0.455688f},
{0.246070f, 0.738910f, 0.452024f}, {0.252899f, 0.742211f, 0.448284f}, {0.259857f, 0.745492f, 0.444467f}, {0.266941f, 0.748751f, 0.440573f}, {0.274149f, 0.751988f, 0.436601f}, {0.281477f, 0.755203f, 0.432552f}, {0.288921f, 0.758394f, 0.428426f}, {0.296479f, 0.761561f, 0.424223f},
{0.304148f, 0.764704f, 0.419943f}, {0.311925f, 0.767822f, 0.415586f}, {0.319809f, 0.770914f, 0.411152f}, {0.327796f, 0.773980f, 0.406640f}, {0.335885f, 0.777018f, 0.402049f}, {0.344074f, 0.780029f, 0.397381f}, {0.352360f, 0.783011f, 0.392636f}, {0.360741f, 0.785964f, 0.387814f},
{0.369214f, 0.788888f, 0.382914f}, {0.377779f, 0.791781f, 0.377939f}, {0.386433f, 0.794644f, 0.372886f}, {0.395174f, 0.797475f, 0.367757f}, {0.404001f, 0.800275f, 0.362552f}, {0.412913f, 0.803041f, 0.357269f}, {0.421908f, 0.805774f, 0.351910f}, {0.430983f, 0.808473f, 0.346476f},
{0.440137f, 0.811138f, 0.340967f}, {0.449368f, 0.813768f, 0.335384f}, {0.458674f, 0.816363f, 0.329727f}, {0.468053f, 0.818921f, 0.323998f}, {0.477504f, 0.821444f, 0.318195f}, {0.487026f, 0.823929f, 0.312321f}, {0.496615f, 0.826376f, 0.306377f}, {0.506271f, 0.828786f, 0.300362f},
{0.515992f, 0.831158f, 0.294279f}, {0.525776f, 0.833491f, 0.288127f}, {0.535621f, 0.835785f, 0.281908f}, {0.545524f, 0.838039f, 0.275626f}, {0.555484f, 0.840254f, 0.269281f}, {0.565498f, 0.842430f, 0.262877f}, {0.575563f, 0.844566f, 0.256415f}, {0.585678f, 0.846661f, 0.249897f},
{0.595839f, 0.848717f, 0.243329f}, {0.606045f, 0.850733f, 0.236712f}, {0.616293f, 0.852709f, 0.230052f}, {0.626579f, 0.854645f, 0.223353f}, {0.636902f, 0.856542f, 0.216620f}, {0.647257f, 0.858400f, 0.209861f}, {0.657642f, 0.860219f, 0.203082f}, {0.668054f, 0.861999f, 0.196293f},
{0.678489f, 0.863742f, 0.189503f}, {0.688944f, 0.865448f, 0.182725f}, {0.699415f, 0.867117f, 0.175971f}, {0.709898f, 0.868751f, 0.169257f}, {0.720391f, 0.870350f, 0.162603f}, {0.730889f, 0.871916f, 0.156029f}, {0.741388f, 0.873449f, 0.149561f}, {0.751884f, 0.874951f, 0.143228f},
{0.762373f, 0.876424f, 0.137064f}, {0.772852f, 0.877868f, 0.131109f}, {0.783315f, 0.879285f, 0.125405f}, {0.793760f, 0.880678f, 0.120005f}, {0.804182f, 0.882046f, 0.114965f}, {0.814576f, 0.883393f, 0.110347f}, {0.824940f, 0.884720f, 0.106217f}, {0.835270f, 0.886029f, 0.102646f},
{0.845561f, 0.887322f, 0.099702f}, {0.855810f, 0.888601f, 0.097452f}, {0.866013f, 0.889868f, 0.095953f}, {0.876168f, 0.891125f, 0.095250f}, {0.886271f, 0.892374f, 0.095374f}, {0.896320f, 0.893616f, 0.096335f}, {0.906311f, 0.894855f, 0.098125f}, {0.916242f, 0.896091f, 0.100717f},
{0.926106f, 0.897330f, 0.104071f}, {0.935904f, 0.898570f, 0.108131f}, {0.945636f, 0.899815f, 0.112838f}, {0.955300f, 0.901065f, 0.118128f}, {0.964894f, 0.902323f, 0.123941f}, {0.974417f, 0.903590f, 0.130215f}, {0.983868f, 0.904867f, 0.136897f}, {0.993248f, 0.906157f, 0.143936f}
};
static const struct xGaussianConstants
{
xGaussianConstants() = default;
color3 a1 = { 1.0f, 1.0f, 34.1f };
color3 b1 = { 0.0047f, 0.0053f, 0.04f };
color3 a2 = { 0.0f, 0.0f, 13.5f };
color3 b2 = { 1.0e-5f, 1.0e-5f, 0.025f };
} GaussianConstants; // Constants for Gaussians -- see paper for details.
static inline float Gaussian(const float x, const float sigma) // 1D Gaussian (without normalization factor).
{
return std::exp(-(x * x) / (2.0f * sigma * sigma));
}
static inline float Gaussian(const float x2, const float a, const float b) // 1D Gaussian in alternative form (see FLIP paper).
{
const float pi = float(PI);
const float pi_sq = float(PI * PI);
return a * std::sqrt(pi / b) * std::exp(-pi_sq * x2 / b);
}
// This function is needed to separate sum of Gaussians filters See separatedConvolutions.pdf in the FLIP repository:
// https://github.com/NVlabs/flip/blob/main/misc/separatedConvolutions.pdf
static inline float GaussianSqrt(const float x2, const float a, const float b)
{
const float pi = float(PI);
const float pi_sq = float(PI * PI);
return std::sqrt(a * std::sqrt(pi / b)) * std::exp(-pi_sq * x2 / b);
}
static inline void solveSecondDegree(float& xMin, float& xMax, float a, float b, float c)
{
// a * x^2 + b * x + c = 0
if (a == 0.0f)
{
xMin = xMax = -c / b;
return;
}
float d1 = -0.5f * (b / a);
float d2 = sqrtf((d1 * d1) - (c / a));
xMin = d1 - d2;
xMax = d1 + d2;
}
static int calculateSpatialFilterRadius(const float ppd)
{
const float pi_sq = float(PI * PI);
float maxScaleParameter = std::max(std::max(std::max(GaussianConstants.b1.x, GaussianConstants.b1.y), std::max(GaussianConstants.b1.z, GaussianConstants.b2.x)), std::max(GaussianConstants.b2.y, GaussianConstants.b2.z));
int radius = int(std::ceil(3.0f * std::sqrt(maxScaleParameter / (2.0f * pi_sq)) * ppd)); // Set radius based on largest scale parameter.
return radius;
}
// CUDA kernels.
#ifdef FLIP_ENABLE_CUDA
__global__ static void kernelColorMap(color3* pDstImage, const float* pSrcImage, const color3* pColorMap, const int3 dim, const int mapSize)
{
int x = blockIdx.x * blockDim.x + threadIdx.x;
int y = blockIdx.y * blockDim.y + threadIdx.y;
int z = blockIdx.z * blockDim.z + threadIdx.z;
int i = (z * dim.y + y) * dim.x + x;
if (x >= dim.x || y >= dim.y || z >= dim.z) return;
pDstImage[i] = pColorMap[int(pSrcImage[i] * 255.0f + 0.5f) % mapSize];
}
__global__ static void kernelFloatToColor3(color3* pDstImage, float* pSrcImage, const int3 dim)
{
int x = blockIdx.x * blockDim.x + threadIdx.x;
int y = blockIdx.y * blockDim.y + threadIdx.y;
int z = blockIdx.z * blockDim.z + threadIdx.z;
int i = (z * dim.y + y) * dim.x + x;
if (x >= dim.x || y >= dim.y || z >= dim.z) return;
pDstImage[i] = color3(pSrcImage[i]);
}
__global__ static void kernelFinalError(float* pDstImage, color3* pColorFeatureDifference, const int3 dim)
{
int x = blockIdx.x * blockDim.x + threadIdx.x;
int y = blockIdx.y * blockDim.y + threadIdx.y;
int z = blockIdx.z * blockDim.z + threadIdx.z;
int i = (z * dim.y + y) * dim.x + x;
if (x >= dim.x || y >= dim.y || z >= dim.z) return;
const float cdiff = pColorFeatureDifference[i].x;
const float fdiff = pColorFeatureDifference[i].y;
const float errorFLIP = std::pow(cdiff, 1.0f - fdiff);
pDstImage[i] = errorFLIP;
}
__global__ static void kernelSetMaxExposure(float* pDstErrorMap, float* pSrcErrorMap, float* pExposureMap, const int3 dim, float exposure)
{
int x = blockIdx.x * blockDim.x + threadIdx.x;
int y = blockIdx.y * blockDim.y + threadIdx.y;
int z = blockIdx.z * blockDim.z + threadIdx.z;
int i = (z * dim.y + y) * dim.x + x;
if (x >= dim.x || y >= dim.y || z >= dim.z) return;
float srcValue = pSrcErrorMap[i];
float dstValue = pDstErrorMap[i];
if (srcValue > dstValue)
{
pExposureMap[i] = exposure;
pDstErrorMap[i] = srcValue;
}
}
__global__ static void kernelsRGBToYCxCz(color3* pImage, const int3 dim)
{
int x = blockIdx.x * blockDim.x + threadIdx.x;
int y = blockIdx.y * blockDim.y + threadIdx.y;
int z = blockIdx.z * blockDim.z + threadIdx.z;
int i = (z * dim.y + y) * dim.x + x;
if (x >= dim.x || y >= dim.y || z >= dim.z) return;
pImage[i] = color3::XYZToYCxCz(color3::LinearRGBToXYZ(color3::sRGBToLinearRGB(pImage[i])));
}
__global__ static void kernelsRGBToLinearRGB(color3* pImage, const int3 dim)
{
int x = blockIdx.x * blockDim.x + threadIdx.x;
int y = blockIdx.y * blockDim.y + threadIdx.y;
int z = blockIdx.z * blockDim.z + threadIdx.z;
int i = (z * dim.y + y) * dim.x + x;
if (x >= dim.x || y >= dim.y || z >= dim.z) return;
pImage[i] = color3::sRGBToLinearRGB(pImage[i]);
}
__global__ static void kernelLinearRGBToYCxCz(color3* pImage, const int3 dim)
{
int x = blockIdx.x * blockDim.x + threadIdx.x;
int y = blockIdx.y * blockDim.y + threadIdx.y;
int z = blockIdx.z * blockDim.z + threadIdx.z;
int i = (z * dim.y + y) * dim.x + x;
if (x >= dim.x || y >= dim.y || z >= dim.z) return;
pImage[i] = color3::XYZToYCxCz(color3::LinearRGBToXYZ(pImage[i]));
}
__global__ static void kernelLinearRGBTosRGB(color3* pImage, const int3 dim)
{
int x = blockIdx.x * blockDim.x + threadIdx.x;
int y = blockIdx.y * blockDim.y + threadIdx.y;
int z = blockIdx.z * blockDim.z + threadIdx.z;
int i = (z * dim.y + y) * dim.x + x;
if (x >= dim.x || y >= dim.y || z >= dim.z) return;
pImage[i] = color3::LinearRGBTosRGB(pImage[i]);
}
// General kernels.
__global__ static void kernelClear(color3* pImage, const int3 dim, const color3 color = { 0.0f, 0.0f, 0.0f })
{
int x = blockIdx.x * blockDim.x + threadIdx.x;
int y = blockIdx.y * blockDim.y + threadIdx.y;
int z = blockIdx.z * blockDim.z + threadIdx.z;
int i = (z * dim.y + y) * dim.x + x;
if (x >= dim.x || y >= dim.y || z >= dim.z) return;
pImage[i] = color;
}
__global__ static void kernelClear(float* pImage, const int3 dim, const float color = 0.0f)
{
int x = blockIdx.x * blockDim.x + threadIdx.x;
int y = blockIdx.y * blockDim.y + threadIdx.y;
int z = blockIdx.z * blockDim.z + threadIdx.z;
int i = (z * dim.y + y) * dim.x + x;
if (x >= dim.x || y >= dim.y || z >= dim.z) return;
pImage[i] = color;
}
__global__ static void kernelMultiplyAndAdd(color3* pImage, const int3 dim, color3 m, color3 a)
{
int x = blockIdx.x * blockDim.x + threadIdx.x;
int y = blockIdx.y * blockDim.y + threadIdx.y;
int z = blockIdx.z * blockDim.z + threadIdx.z;
int i = (z * dim.y + y) * dim.x + x;
if (x >= dim.x || y >= dim.y || z >= dim.z) return;
pImage[i] = pImage[i] * m + a;
}
__global__ static void kernelMultiplyAndAdd(color3* pImage, const int3 dim, float m, float a)
{
int x = blockIdx.x * blockDim.x + threadIdx.x;
int y = blockIdx.y * blockDim.y + threadIdx.y;
int z = blockIdx.z * blockDim.z + threadIdx.z;
int i = (z * dim.y + y) * dim.x + x;
if (x >= dim.x || y >= dim.y || z >= dim.z) return;
pImage[i] = pImage[i] * m + a;
}
__global__ static void kernelMultiplyAndAdd(float* pImage, const int3 dim, float m, float a)
{
int x = blockIdx.x * blockDim.x + threadIdx.x;
int y = blockIdx.y * blockDim.y + threadIdx.y;
int z = blockIdx.z * blockDim.z + threadIdx.z;
int i = (z * dim.y + y) * dim.x + x;
if (x >= dim.x || y >= dim.y || z >= dim.z) return;
pImage[i] = pImage[i] * m + a;
}
__global__ static void kernelToneMap(color3* pImage, const int3 dim, int toneMapper)
{
int x = blockIdx.x * blockDim.x + threadIdx.x;
int y = blockIdx.y * blockDim.y + threadIdx.y;
int z = blockIdx.z * blockDim.z + threadIdx.z;
int i = (z * dim.y + y) * dim.x + x;
if (x >= dim.x || y >= dim.y || z >= dim.z) return;
if (toneMapper == 0)
{
color3 color = pImage[i];
float luminance = color3::linearRGBToLuminance(color);
pImage[i] /= (1.0f + luminance);
return;
}
const float* tc = ToneMappingCoefficients[toneMapper];
color3 color = pImage[i];
pImage[i] = color3(((color * color) * tc[0] + color * tc[1] + tc[2]) / (color * color * tc[3] + color * tc[4] + tc[5]));
}
__global__ static void kernelClamp(color3* pImage, const int3 dim, float low, float high)
{
int x = blockIdx.x * blockDim.x + threadIdx.x;
int y = blockIdx.y * blockDim.y + threadIdx.y;
int z = blockIdx.z * blockDim.z + threadIdx.z;
int i = (z * dim.y + y) * dim.x + x;
if (x >= dim.x || y >= dim.y || z >= dim.z) return;
pImage[i] = color3::clamp(pImage[i], low, high);
}
// Convolve in x direction (1st and 2nd derivative for filter in x direction, Gaussian in y direction).
// For details on the convolution, see separatedConvolutions.pdf in the FLIP repository:
// https://github.com/NVlabs/flip/blob/main/misc/separatedConvolutions.pdf.
// We filter both reference and test image simultaneously (for better performance).
// referenceImage and testImage are expected to be in YCxCz space.
__global__ static void kernelFeatureFilterFirstDir(color3* intermediateFeaturesImageReference, color3* referenceImage, color3* intermediateFeaturesImageTest, color3* testImage, color3* pFilter, const int3 dim, const int3 filterDim)
{
int x = blockIdx.x * blockDim.x + threadIdx.x;
int y = blockIdx.y * blockDim.y + threadIdx.y;
int z = blockIdx.z * blockDim.z + threadIdx.z;
int dstIndex = (z * dim.y + y) * dim.x + x;
if (x >= dim.x || y >= dim.y) return;
const int halfFilterWidth = filterDim.x / 2;
float dxReference = 0.0f, dxTest = 0.0f, ddxReference = 0.0f, ddxTest = 0.0f;
float gaussianFilteredReference = 0.0f, gaussianFilteredTest = 0.0f;
const float oneOver116 = 1.0f / 116.0f;
const float sixteenOver116 = 16.0f / 116.0f;
for (int ix = -halfFilterWidth; ix <= halfFilterWidth; ix++)
{
int xx = Min(Max(0, x + ix), dim.x - 1);
int filterIndex = ix + halfFilterWidth;
int srcIndex = y * dim.x + xx;
const color3 featureWeights = pFilter[filterIndex];
float yReference = referenceImage[srcIndex].x;
float yTest = testImage[srcIndex].x;
// Normalize the Y values to [0,1].
float yReferenceNormalized = yReference * oneOver116 + sixteenOver116;
float yTestNormalized = yTest * oneOver116 + sixteenOver116;
// Image multiplied by 1st and 2nd x-derivatives of Gaussian.
dxReference += featureWeights.y * yReferenceNormalized;
dxTest += featureWeights.y * yTestNormalized;
ddxReference += featureWeights.z * yReferenceNormalized;
ddxTest += featureWeights.z * yTestNormalized;
// Image multiplied by Gaussian.
gaussianFilteredReference += featureWeights.x * yReferenceNormalized;
gaussianFilteredTest += featureWeights.x * yTestNormalized;
}
intermediateFeaturesImageReference[dstIndex] = color3(dxReference, ddxReference, gaussianFilteredReference);
intermediateFeaturesImageTest[dstIndex] = color3(dxTest, ddxTest, gaussianFilteredTest);
}
// Convolve in y direction (1st and 2nd derivative for filter in y direction, Gaussian in x direction), then compute difference.
// For details on the convolution, see separatedConvolutions.pdf in the FLIP repository:
// https://github.com/NVlabs/flip/blob/main/misc/separatedConvolutions.pdf.
// We filter both reference and test image simultaneously (for better performance).
__global__ static void kernelFeatureFilterSecondDirAndFeatureDifference(color3* featureDifferenceImage, color3* intermediateFeaturesImageReference, color3* intermediateFeaturesImageTest, color3* pFilter, const int3 dim, const int3 filterDim)
{
int x = blockIdx.x * blockDim.x + threadIdx.x;
int y = blockIdx.y * blockDim.y + threadIdx.y;
int z = blockIdx.z * blockDim.z + threadIdx.z;
int dstIndex = (z * dim.y + y) * dim.x + x;
if (x >= dim.x || y >= dim.y) return;
const float normalizationFactor = 1.0f / std::sqrt(2.0f);
const int halfFilterWidth = filterDim.x / 2;
float dxReference = 0.0f, dxTest = 0.0f, ddxReference = 0.0f, ddxTest = 0.0f;
float dyReference = 0.0f, dyTest = 0.0f, ddyReference = 0.0f, ddyTest = 0.0f;
for (int iy = -halfFilterWidth; iy <= halfFilterWidth; iy++)
{
int yy = Min(Max(0, y + iy), dim.y - 1);
int filterIndex = iy + halfFilterWidth;
int srcIndex = yy * dim.x + x;
const color3 featureWeights = pFilter[filterIndex];
const color3 intermediateFeaturesReference = intermediateFeaturesImageReference[srcIndex];
const color3 intermediateFeatureTest = intermediateFeaturesImageTest[srcIndex];
// Intermediate images (1st and 2nd derivative in x) multiplied by Gaussian.
dxReference += featureWeights.x * intermediateFeaturesReference.x;
dxTest += featureWeights.x * intermediateFeatureTest.x;
ddxReference += featureWeights.x * intermediateFeaturesReference.y;
ddxTest += featureWeights.x * intermediateFeatureTest.y;
// Intermediate image (Gaussian) multiplied by 1st and 2nd y-derivatives of Gaussian.
dyReference += featureWeights.y * intermediateFeaturesReference.z;
dyTest += featureWeights.y * intermediateFeatureTest.z;
ddyReference += featureWeights.z * intermediateFeaturesReference.z;
ddyTest += featureWeights.z * intermediateFeatureTest.z;
}
const float edgeValueRef = std::sqrt(dxReference * dxReference + dyReference * dyReference);
const float edgeValueTest = std::sqrt(dxTest * dxTest + dyTest * dyTest);
const float pointValueRef = std::sqrt(ddxReference * ddxReference + ddyReference * ddyReference);
const float pointValueTest = std::sqrt(ddxTest * ddxTest + ddyTest * ddyTest);
const float edgeDifference = std::abs(edgeValueRef - edgeValueTest);
const float pointDifference = std::abs(pointValueRef - pointValueTest);
const float featureDifference = std::pow(normalizationFactor * Max(edgeDifference, pointDifference), DeviceFLIPConstants.gqf);
featureDifferenceImage[dstIndex].y = featureDifference;
}
// Performs spatial filtering in the x direction on both the reference and test image at the same time (for better performance).
// Filtering has been changed to using separable filtering for better performance.
// For details on the convolution, see separatedConvolutions.pdf in the FLIP repository:
// https://github.com/NVlabs/flip/blob/main/misc/separatedConvolutions.pdf.
// referenceImage and testImage are expected to be in YCxCz space.
__global__ static void kernelSpatialFilterFirstDir(color3* intermediateYCxImageReference, color3* intermediateCzImageReference, color3* referenceImage, color3* intermediateYCxImageTest, color3* intermediateCzImageTest, color3* testImage, color3* pFilterYCx, color3* pFilterCz, const int3 dim, const int3 filterDim)
{
int x = blockIdx.x * blockDim.x + threadIdx.x;
int y = blockIdx.y * blockDim.y + threadIdx.y;
int z = blockIdx.z * blockDim.z + threadIdx.z;
int dstIndex = (z * dim.y + y) * dim.x + x;
if (x >= dim.x || y >= dim.y) return;
const int halfFilterWidth = filterDim.x / 2;
// Filter in x direction.
color3 intermediateYCxReference = { 0.0f, 0.0f, 0.0f };
color3 intermediateYCxTest = { 0.0f, 0.0f, 0.0f };
color3 intermediateCzReference = { 0.0f, 0.0f, 0.0f };
color3 intermediateCzTest = { 0.0f, 0.0f, 0.0f };
for (int ix = -halfFilterWidth; ix <= halfFilterWidth; ix++)
{
int xx = Min(Max(0, x + ix), dim.x - 1);
int filterIndex = ix + halfFilterWidth;
int srcIndex = y * dim.x + xx;
const color3 weightsYCx = pFilterYCx[filterIndex];
const color3 weightsCz = pFilterCz[filterIndex];
const color3 referenceColor = referenceImage[srcIndex];
const color3 testColor = testImage[srcIndex];
intermediateYCxReference += color3(weightsYCx.x * referenceColor.x, weightsYCx.y * referenceColor.y, 0.0f);
intermediateYCxTest += color3(weightsYCx.x * testColor.x, weightsYCx.y * testColor.y, 0.0f);
intermediateCzReference += color3(weightsCz.x * referenceColor.z, weightsCz.y * referenceColor.z, 0.0f);
intermediateCzTest += color3(weightsCz.x * testColor.z, weightsCz.y * testColor.z, 0.0f);
}
intermediateYCxImageReference[dstIndex] = color3(intermediateYCxReference.x, intermediateYCxReference.y, 0.0f);
intermediateYCxImageTest[dstIndex] = color3(intermediateYCxTest.x, intermediateYCxTest.y, 0.0f);
intermediateCzImageReference[dstIndex] = color3(intermediateCzReference.x, intermediateCzReference.y, 0.0f);
intermediateCzImageTest[dstIndex] = color3(intermediateCzTest.x, intermediateCzTest.y, 0.0f);
}
// Performs spatial filtering in the y direction (and clamps the results) on both the reference and test image at the same time (for better performance).
// Filtering has been changed to using separable filtering for better performance. For details on the convolution, see separatedConvolutions.pdf in the FLIP repository:
// https://github.com/NVlabs/flip/blob/main/misc/separatedConvolutions.pdf.
// After filtering, compute color differences.
__global__ static void kernelSpatialFilterSecondDirAndColorDifference(color3* colorDifferenceImage, color3* intermediateYCxImageReference, color3* intermediateCzImageReference, color3* intermediateYCxImageTest, color3* intermediateCzImageTest, color3* pFilterYCx, color3* pFilterCz, const int3 dim, const int3 filterDim, const float cmax, const float pccmax)
{
int x = blockIdx.x * blockDim.x + threadIdx.x;
int y = blockIdx.y * blockDim.y + threadIdx.y;
int z = blockIdx.z * blockDim.z + threadIdx.z;
int dstIndex = (z * dim.y + y) * dim.x + x;
if (x >= dim.x || y >= dim.y) return;
const int halfFilterWidth = filterDim.x / 2;
// Filter in y direction.
color3 filteredYCxReference = { 0.0f, 0.0f, 0.0f };
color3 filteredYCxTest = { 0.0f, 0.0f, 0.0f };
color3 filteredCzReference = { 0.0f, 0.0f, 0.0f };
color3 filteredCzTest = { 0.0f, 0.0f, 0.0f };
for (int iy = -halfFilterWidth; iy <= halfFilterWidth; iy++)
{
int yy = Min(Max(0, y + iy), dim.y - 1);
int filterIndex = iy + halfFilterWidth;
int srcIndex = yy * dim.x + x;
const color3 weightsYCx = pFilterYCx[filterIndex];
const color3 weightsCz = pFilterCz[filterIndex];
const color3 intermediateYCxReference = intermediateYCxImageReference[srcIndex];
const color3 intermediateYCxTest = intermediateYCxImageTest[srcIndex];
const color3 intermediateCzReference = intermediateCzImageReference[srcIndex];
const color3 intermediateCzTest = intermediateCzImageTest[srcIndex];
filteredYCxReference += color3(weightsYCx.x * intermediateYCxReference.x, weightsYCx.y * intermediateYCxReference.y, 0.0f);
filteredYCxTest += color3(weightsYCx.x * intermediateYCxTest.x, weightsYCx.y * intermediateYCxTest.y, 0.0f);
filteredCzReference += color3(weightsCz.x * intermediateCzReference.x, weightsCz.y * intermediateCzReference.y, 0.0f);
filteredCzTest += color3(weightsCz.x * intermediateCzTest.x, weightsCz.y * intermediateCzTest.y, 0.0f);
}
// Clamp to [0,1] in linear RGB.