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v110.2 Added CRT-Royale [hunterk] Improved libretro target [rtretiakov]
2019-09-25 06:13:12 +00:00
#version 150
///////////////////////////////// MIT LICENSE ////////////////////////////////
// Copyright (C) 2014 TroggleMonkey
//
// 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.
#if __VERSION__ >= 130
#define COMPAT_TEXTURE texture
#else
#define COMPAT_TEXTURE texture2D
#endif
#ifdef GL_ES
#define COMPAT_PRECISION mediump
#else
#define COMPAT_PRECISION
#endif
in vec4 position;
in vec2 texCoord;
out Vertex {
vec2 vTexCoord;
vec2 blur_dxdy;
};
uniform vec4 targetSize;
uniform vec4 sourceSize[];
///////////////////////////// SETTINGS MANAGEMENT ////////////////////////////
// PASS SETTINGS:
// gamma-management.h needs to know what kind of pipeline we're using and
// what pass this is in that pipeline. This will become obsolete if/when we
// can #define things like this in the .cgp preset file.
//#define GAMMA_ENCODE_EVERY_FBO
//#define FIRST_PASS
//#define LAST_PASS
//#define SIMULATE_CRT_ON_LCD
//#define SIMULATE_GBA_ON_LCD
//#define SIMULATE_LCD_ON_CRT
//#define SIMULATE_GBA_ON_CRT
#ifndef GAMMA_MANAGEMENT_H
#define GAMMA_MANAGEMENT_H
/////////////////////////////// BASE CONSTANTS ///////////////////////////////
// Set standard gamma constants, but allow users to override them:
#ifndef OVERRIDE_STANDARD_GAMMA
// Standard encoding gammas:
float ntsc_gamma = 2.2; // Best to use NTSC for PAL too?
float pal_gamma = 2.8; // Never actually 2.8 in practice
// Typical device decoding gammas (only use for emulating devices):
// CRT/LCD reference gammas are higher than NTSC and Rec.709 video standard
// gammas: The standards purposely undercorrected for an analog CRT's
// assumed 2.5 reference display gamma to maintain contrast in assumed
// [dark] viewing conditions: http://www.poynton.com/PDFs/GammaFAQ.pdf
// These unstated assumptions about display gamma and perceptual rendering
// intent caused a lot of confusion, and more modern CRT's seemed to target
// NTSC 2.2 gamma with circuitry. LCD displays seem to have followed suit
// (they struggle near black with 2.5 gamma anyway), especially PC/laptop
// displays designed to view sRGB in bright environments. (Standards are
// also in flux again with BT.1886, but it's underspecified for displays.)
float crt_reference_gamma_high = 2.5; // In (2.35, 2.55)
float crt_reference_gamma_low = 2.35; // In (2.35, 2.55)
float lcd_reference_gamma = 2.5; // To match CRT
float crt_office_gamma = 2.2; // Circuitry-adjusted for NTSC
float lcd_office_gamma = 2.2; // Approximates sRGB
#endif // OVERRIDE_STANDARD_GAMMA
// Assuming alpha == 1.0 might make it easier for users to avoid some bugs,
// but only if they're aware of it.
#ifndef OVERRIDE_ALPHA_ASSUMPTIONS
bool assume_opaque_alpha = false;
#endif
/////////////////////// DERIVED CONSTANTS AS FUNCTIONS ///////////////////////
// gamma-management.h should be compatible with overriding gamma values with
// runtime user parameters, but we can only define other global constants in
// terms of static constants, not uniform user parameters. To get around this
// limitation, we need to define derived constants using functions.
// Set device gamma constants, but allow users to override them:
#ifdef OVERRIDE_DEVICE_GAMMA
// The user promises to globally define the appropriate constants:
float get_crt_gamma() { return crt_gamma; }
float get_gba_gamma() { return gba_gamma; }
float get_lcd_gamma() { return lcd_gamma; }
#else
float get_crt_gamma() { return crt_reference_gamma_high; }
float get_gba_gamma() { return 3.5; } // Game Boy Advance; in (3.0, 4.0)
float get_lcd_gamma() { return lcd_office_gamma; }
#endif // OVERRIDE_DEVICE_GAMMA
// Set decoding/encoding gammas for the first/lass passes, but allow overrides:
#ifdef OVERRIDE_FINAL_GAMMA
// The user promises to globally define the appropriate constants:
float get_intermediate_gamma() { return intermediate_gamma; }
float get_input_gamma() { return input_gamma; }
float get_output_gamma() { return output_gamma; }
#else
// If we gamma-correct every pass, always use ntsc_gamma between passes to
// ensure middle passes don't need to care if anything is being simulated:
float get_intermediate_gamma() { return ntsc_gamma; }
#ifdef SIMULATE_CRT_ON_LCD
float get_input_gamma() { return get_crt_gamma(); }
float get_output_gamma() { return get_lcd_gamma(); }
#else
#ifdef SIMULATE_GBA_ON_LCD
float get_input_gamma() { return get_gba_gamma(); }
float get_output_gamma() { return get_lcd_gamma(); }
#else
#ifdef SIMULATE_LCD_ON_CRT
float get_input_gamma() { return get_lcd_gamma(); }
float get_output_gamma() { return get_crt_gamma(); }
#else
#ifdef SIMULATE_GBA_ON_CRT
float get_input_gamma() { return get_gba_gamma(); }
float get_output_gamma() { return get_crt_gamma(); }
#else // Don't simulate anything:
float get_input_gamma() { return ntsc_gamma; }
float get_output_gamma() { return ntsc_gamma; }
#endif // SIMULATE_GBA_ON_CRT
#endif // SIMULATE_LCD_ON_CRT
#endif // SIMULATE_GBA_ON_LCD
#endif // SIMULATE_CRT_ON_LCD
#endif // OVERRIDE_FINAL_GAMMA
#ifndef GAMMA_ENCODE_EVERY_FBO
#ifdef FIRST_PASS
bool linearize_input = true;
float get_pass_input_gamma() { return get_input_gamma(); }
#else
bool linearize_input = false;
float get_pass_input_gamma() { return 1.0; }
#endif
#ifdef LAST_PASS
bool gamma_encode_output = true;
float get_pass_output_gamma() { return get_output_gamma(); }
#else
bool gamma_encode_output = false;
float get_pass_output_gamma() { return 1.0; }
#endif
#else
bool linearize_input = true;
bool gamma_encode_output = true;
#ifdef FIRST_PASS
float get_pass_input_gamma() { return get_input_gamma(); }
#else
float get_pass_input_gamma() { return get_intermediate_gamma(); }
#endif
#ifdef LAST_PASS
float get_pass_output_gamma() { return get_output_gamma(); }
#else
float get_pass_output_gamma() { return get_intermediate_gamma(); }
#endif
#endif
vec4 decode_input(vec4 color)
{
if(linearize_input = true)
{
if(assume_opaque_alpha = true)
{
return vec4(pow(color.rgb, vec3(get_pass_input_gamma())), 1.0);
}
else
{
return vec4(pow(color.rgb, vec3(get_pass_input_gamma())), color.a);
}
}
else
{
return color;
}
}
vec4 encode_output(vec4 color)
{
if(gamma_encode_output = true)
{
if(assume_opaque_alpha = true)
{
return vec4(pow(color.rgb, vec3(1.0/get_pass_output_gamma())), 1.0);
}
else
{
return vec4(pow(color.rgb, vec3(1.0/get_pass_output_gamma())), color.a);
}
}
else
{
return color;
}
}
#define tex2D_linearize(C, D) decode_input(vec4(COMPAT_TEXTURE(C, D)))
//vec4 tex2D_linearize(sampler2D tex, vec2 tex_coords)
//{ return decode_input(vec4(COMPAT_TEXTURE(tex, tex_coords))); }
//#define tex2D_linearize(C, D, E) decode_input(vec4(COMPAT_TEXTURE(C, D, E)))
//vec4 tex2D_linearize(sampler2D tex, vec2 tex_coords, int texel_off)
//{ return decode_input(vec4(COMPAT_TEXTURE(tex, tex_coords, texel_off))); }
#endif // GAMMA_MANAGEMENT_H
#ifndef BLUR_FUNCTIONS_H
#define BLUR_FUNCTIONS_H
///////////////////////////////// MIT LICENSE ////////////////////////////////
// Copyright (C) 2014 TroggleMonkey
//
// 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.
///////////////////////////////// DESCRIPTION ////////////////////////////////
// This file provides reusable one-pass and separable (two-pass) blurs.
// Requires: All blurs share these requirements (dxdy requirement is split):
// 1.) All requirements of gamma-management.h must be satisfied!
// 2.) filter_linearN must == "true" in your .cgp preset unless
// you're using tex2DblurNresize at 1x scale.
// 3.) mipmap_inputN must == "true" in your .cgp preset if
// IN.output_size < IN.video_size.
// 4.) IN.output_size == IN.video_size / pow(2, M), where M is some
// positive integer. tex2Dblur*resize can resize arbitrarily
// (and the blur will be done after resizing), but arbitrary
// resizes "fail" with other blurs due to the way they mix
// static weights with bilinear sample exploitation.
// 5.) In general, dxdy should contain the uv pixel spacing:
// dxdy = (IN.video_size/IN.output_size)/IN.texture_size
// 6.) For separable blurs (tex2DblurNresize and tex2DblurNfast),
// zero out the dxdy component in the unblurred dimension:
// dxdy = vec2(dxdy.x, 0.0) or vec2(0.0, dxdy.y)
// Many blurs share these requirements:
// 1.) One-pass blurs require scale_xN == scale_yN or scales > 1.0,
// or they will blur more in the lower-scaled dimension.
// 2.) One-pass shared sample blurs require ddx(), ddy(), and
// tex2Dlod() to be supported by the current Cg profile, and
// the drivers must support high-quality derivatives.
// 3.) One-pass shared sample blurs require:
// tex_uv.w == log2(IN.video_size/IN.output_size).y;
// Non-wrapper blurs share this requirement:
// 1.) sigma is the intended standard deviation of the blur
// Wrapper blurs share this requirement, which is automatically
// met (unless OVERRIDE_BLUR_STD_DEVS is #defined; see below):
// 1.) blurN_std_dev must be global static float values
// specifying standard deviations for Nx blurs in units
// of destination pixels
// Optional: 1.) The including file (or an earlier included file) may
// optionally #define USE_BINOMIAL_BLUR_STD_DEVS to replace
// default standard deviations with those matching a binomial
// distribution. (See below for details/properties.)
// 2.) The including file (or an earlier included file) may
// optionally #define OVERRIDE_BLUR_STD_DEVS and override:
// static float blur3_std_dev
// static float blur4_std_dev
// static float blur5_std_dev
// static float blur6_std_dev
// static float blur7_std_dev
// static float blur8_std_dev
// static float blur9_std_dev
// static float blur10_std_dev
// static float blur11_std_dev
// static float blur12_std_dev
// static float blur17_std_dev
// static float blur25_std_dev
// static float blur31_std_dev
// static float blur43_std_dev
// 3.) The including file (or an earlier included file) may
// optionally #define OVERRIDE_ERROR_BLURRING and override:
// static float error_blurring
// This tuning value helps mitigate weighting errors from one-
// pass shared-sample blurs sharing bilinear samples between
// fragments. Values closer to 0.0 have "correct" blurriness
// but allow more artifacts, and values closer to 1.0 blur away
// artifacts by sampling closer to halfway between texels.
// UPDATE 6/21/14: The above static constants may now be overridden
// by non-static uniform constants. This permits exposing blur
// standard deviations as runtime GUI shader parameters. However,
// using them keeps weights from being statically computed, and the
// speed hit depends on the blur: On my machine, uniforms kill over
// 53% of the framerate with tex2Dblur12x12shared, but they only
// drop the framerate by about 18% with tex2Dblur11fast.
// Quality and Performance Comparisons:
// For the purposes of the following discussion, "no sRGB" means
// GAMMA_ENCODE_EVERY_FBO is #defined, and "sRGB" means it isn't.
// 1.) tex2DblurNfast is always faster than tex2DblurNresize.
// 2.) tex2DblurNresize functions are the only ones that can arbitrarily resize
// well, because they're the only ones that don't exploit bilinear samples.
// This also means they're the only functions which can be truly gamma-
// correct without linear (or sRGB FBO) input, but only at 1x scale.
// 3.) One-pass shared sample blurs only have a speed advantage without sRGB.
// They also have some inaccuracies due to their shared-[bilinear-]sample
// design, which grow increasingly bothersome for smaller blurs and higher-
// frequency source images (relative to their resolution). I had high
// hopes for them, but their most realistic use case is limited to quickly
// reblurring an already blurred input at full resolution. Otherwise:
// a.) If you're blurring a low-resolution source, you want a better blur.
// b.) If you're blurring a lower mipmap, you want a better blur.
// c.) If you're blurring a high-resolution, high-frequency source, you
// want a better blur.
// 4.) The one-pass blurs without shared samples grow slower for larger blurs,
// but they're competitive with separable blurs at 5x5 and smaller, and
// even tex2Dblur7x7 isn't bad if you're wanting to conserve passes.
// Here are some framerates from a GeForce 8800GTS. The first pass resizes to
// viewport size (4x in this test) and linearizes for sRGB codepaths, and the
// remaining passes perform 6 full blurs. Mipmapped tests are performed at the
// same scale, so they just measure the cost of mipmapping each FBO (only every
// other FBO is mipmapped for separable blurs, to mimic realistic usage).
// Mipmap Neither sRGB+Mipmap sRGB Function
// 76.0 92.3 131.3 193.7 tex2Dblur3fast
// 63.2 74.4 122.4 175.5 tex2Dblur3resize
// 93.7 121.2 159.3 263.2 tex2Dblur3x3
// 59.7 68.7 115.4 162.1 tex2Dblur3x3resize
// 63.2 74.4 122.4 175.5 tex2Dblur5fast
// 49.3 54.8 100.0 132.7 tex2Dblur5resize
// 59.7 68.7 115.4 162.1 tex2Dblur5x5
// 64.9 77.2 99.1 137.2 tex2Dblur6x6shared
// 55.8 63.7 110.4 151.8 tex2Dblur7fast
// 39.8 43.9 83.9 105.8 tex2Dblur7resize
// 40.0 44.2 83.2 104.9 tex2Dblur7x7
// 56.4 65.5 71.9 87.9 tex2Dblur8x8shared
// 49.3 55.1 99.9 132.5 tex2Dblur9fast
// 33.3 36.2 72.4 88.0 tex2Dblur9resize
// 27.8 29.7 61.3 72.2 tex2Dblur9x9
// 37.2 41.1 52.6 60.2 tex2Dblur10x10shared
// 44.4 49.5 91.3 117.8 tex2Dblur11fast
// 28.8 30.8 63.6 75.4 tex2Dblur11resize
// 33.6 36.5 40.9 45.5 tex2Dblur12x12shared
// TODO: Fill in benchmarks for new untested blurs.
// tex2Dblur17fast
// tex2Dblur25fast
// tex2Dblur31fast
// tex2Dblur43fast
// tex2Dblur3x3resize
///////////////////////////// SETTINGS MANAGEMENT ////////////////////////////
// Set static standard deviations, but allow users to override them with their
// own constants (even non-static uniforms if they're okay with the speed hit):
#ifndef OVERRIDE_BLUR_STD_DEVS
// blurN_std_dev values are specified in terms of dxdy strides.
#ifdef USE_BINOMIAL_BLUR_STD_DEVS
// By request, we can define standard deviations corresponding to a
// binomial distribution with p = 0.5 (related to Pascal's triangle).
// This distribution works such that blurring multiple times should
// have the same result as a single larger blur. These values are
// larger than default for blurs up to 6x and smaller thereafter.
float blur3_std_dev = 0.84931640625;
float blur4_std_dev = 0.84931640625;
float blur5_std_dev = 1.0595703125;
float blur6_std_dev = 1.06591796875;
float blur7_std_dev = 1.17041015625;
float blur8_std_dev = 1.1720703125;
float blur9_std_dev = 1.2259765625;
float blur10_std_dev = 1.21982421875;
float blur11_std_dev = 1.25361328125;
float blur12_std_dev = 1.2423828125;
float blur17_std_dev = 1.27783203125;
float blur25_std_dev = 1.2810546875;
float blur31_std_dev = 1.28125;
float blur43_std_dev = 1.28125;
#else
// The defaults are the largest values that keep the largest unused
// blur term on each side <= 1.0/256.0. (We could get away with more
// or be more conservative, but this compromise is pretty reasonable.)
float blur3_std_dev = 0.62666015625;
float blur4_std_dev = 0.66171875;
float blur5_std_dev = 0.9845703125;
float blur6_std_dev = 1.02626953125;
float blur7_std_dev = 1.36103515625;
float blur8_std_dev = 1.4080078125;
float blur9_std_dev = 1.7533203125;
float blur10_std_dev = 1.80478515625;
float blur11_std_dev = 2.15986328125;
float blur12_std_dev = 2.215234375;
float blur17_std_dev = 3.45535583496;
float blur25_std_dev = 5.3409576416;
float blur31_std_dev = 6.86488037109;
float blur43_std_dev = 10.1852050781;
#endif // USE_BINOMIAL_BLUR_STD_DEVS
#endif // OVERRIDE_BLUR_STD_DEVS
#ifndef OVERRIDE_ERROR_BLURRING
// error_blurring should be in [0.0, 1.0]. Higher values reduce ringing
// in shared-sample blurs but increase blurring and feature shifting.
float error_blurring = 0.5;
#endif
// Make a length squared helper macro (for usage with static constants):
#define LENGTH_SQ(vec) (dot(vec, vec))
////////////////////////////////// INCLUDES //////////////////////////////////
// gamma-management.h relies on pass-specific settings to guide its behavior:
// FIRST_PASS, LAST_PASS, GAMMA_ENCODE_EVERY_FBO, etc. See it for details.
//#include "gamma-management.h"
//#include "quad-pixel-communication.h"
//#include "special-functions.h"
#ifndef SPECIAL_FUNCTIONS_H
#define SPECIAL_FUNCTIONS_H
///////////////////////////////// MIT LICENSE ////////////////////////////////
// Copyright (C) 2014 TroggleMonkey
//
// 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.
///////////////////////////////// DESCRIPTION ////////////////////////////////
// This file implements the following mathematical special functions:
// 1.) erf() = 2/sqrt(pi) * indefinite_integral(e**(-x**2))
// 2.) gamma(s), a real-numbered extension of the integer factorial function
// It also implements normalized_ligamma(s, z), a normalized lower incomplete
// gamma function for s < 0.5 only. Both gamma() and normalized_ligamma() can
// be called with an _impl suffix to use an implementation version with a few
// extra precomputed parameters (which may be useful for the caller to reuse).
// See below for details.
//
// Design Rationale:
// Pretty much every line of code in this file is duplicated four times for
// different input types (vec4/vec3/vec2/float). This is unfortunate,
// but Cg doesn't allow function templates. Macros would be far less verbose,
// but they would make the code harder to document and read. I don't expect
// these functions will require a whole lot of maintenance changes unless
// someone ever has need for more robust incomplete gamma functions, so code
// duplication seems to be the lesser evil in this case.
/////////////////////////// GAUSSIAN ERROR FUNCTION //////////////////////////
vec4 erf6(vec4 x)
{
// Requires: x is the standard parameter to erf().
// Returns: Return an Abramowitz/Stegun approximation of erf(), where:
// erf(x) = 2/sqrt(pi) * integral(e**(-x**2))
// This approximation has a max absolute error of 2.5*10**-5
// with solid numerical robustness and efficiency. See:
// https://en.wikipedia.org/wiki/Error_function#Approximation_with_elementary_functions
vec4 one = vec4(1.0);
vec4 sign_x = sign(x);
vec4 t = one/(one + 0.47047*abs(x));
vec4 result = one - t*(0.3480242 + t*(-0.0958798 + t*0.7478556))*
exp(-(x*x));
return result * sign_x;
}
vec3 erf6(vec3 x)
{
// vec3 version:
vec3 one = vec3(1.0);
vec3 sign_x = sign(x);
vec3 t = one/(one + 0.47047*abs(x));
vec3 result = one - t*(0.3480242 + t*(-0.0958798 + t*0.7478556))*
exp(-(x*x));
return result * sign_x;
}
vec2 erf6(vec2 x)
{
// vec2 version:
vec2 one = vec2(1.0);
vec2 sign_x = sign(x);
vec2 t = one/(one + 0.47047*abs(x));
vec2 result = one - t*(0.3480242 + t*(-0.0958798 + t*0.7478556))*
exp(-(x*x));
return result * sign_x;
}
float erf6(float x)
{
// Float version:
float sign_x = sign(x);
float t = 1.0/(1.0 + 0.47047*abs(x));
float result = 1.0 - t*(0.3480242 + t*(-0.0958798 + t*0.7478556))*
exp(-(x*x));
return result * sign_x;
}
vec4 erft(vec4 x)
{
// Requires: x is the standard parameter to erf().
// Returns: Approximate erf() with the hyperbolic tangent. The error is
// visually noticeable, but it's blazing fast and perceptually
// close...at least on ATI hardware. See:
// http://www.maplesoft.com/applications/view.aspx?SID=5525&view=html
// Warning: Only use this if your hardware drivers correctly implement
// tanh(): My nVidia 8800GTS returns garbage output.
return tanh(1.202760580 * x);
}
vec3 erft(vec3 x)
{
// vec3 version:
return tanh(1.202760580 * x);
}
vec2 erft(vec2 x)
{
// vec2 version:
return tanh(1.202760580 * x);
}
float erft(float x)
{
// Float version:
return tanh(1.202760580 * x);
}
vec4 erf(vec4 x)
{
// Requires: x is the standard parameter to erf().
// Returns: Some approximation of erf(x), depending on user settings.
#ifdef ERF_FAST_APPROXIMATION
return erft(x);
#else
return erf6(x);
#endif
}
vec3 erf(vec3 x)
{
// vec3 version:
#ifdef ERF_FAST_APPROXIMATION
return erft(x);
#else
return erf6(x);
#endif
}
vec2 erf(vec2 x)
{
// vec2 version:
#ifdef ERF_FAST_APPROXIMATION
return erft(x);
#else
return erf6(x);
#endif
}
float erf(float x)
{
// Float version:
#ifdef ERF_FAST_APPROXIMATION
return erft(x);
#else
return erf6(x);
#endif
}
/////////////////////////// COMPLETE GAMMA FUNCTION //////////////////////////
vec4 gamma_impl(vec4 s, vec4 s_inv)
{
// Requires: 1.) s is the standard parameter to the gamma function, and
// it should lie in the [0, 36] range.
// 2.) s_inv = 1.0/s. This implementation function requires
// the caller to precompute this value, giving users the
// opportunity to reuse it.
// Returns: Return approximate gamma function (real-numbered factorial)
// output using the Lanczos approximation with two coefficients
// calculated using Paul Godfrey's method here:
// http://my.fit.edu/~gabdo/gamma.txt
// An optimal g value for s in [0, 36] is ~1.12906830989, with
// a maximum relative error of 0.000463 for 2**16 equally
// evals. We could use three coeffs (0.0000346 error) without
// hurting latency, but this allows more parallelism with
// outside instructions.
vec4 g = vec4(1.12906830989);
vec4 c0 = vec4(0.8109119309638332633713423362694399653724431);
vec4 c1 = vec4(0.4808354605142681877121661197951496120000040);
vec4 e = vec4(2.71828182845904523536028747135266249775724709);
vec4 sph = s + vec4(0.5);
vec4 lanczos_sum = c0 + c1/(s + vec4(1.0));
vec4 base = (sph + g)/e; // or (s + g + vec4(0.5))/e
// gamma(s + 1) = base**sph * lanczos_sum; divide by s for gamma(s).
// This has less error for small s's than (s -= 1.0) at the beginning.
return (pow(base, sph) * lanczos_sum) * s_inv;
}
vec3 gamma_impl(vec3 s, vec3 s_inv)
{
// vec3 version:
vec3 g = vec3(1.12906830989);
vec3 c0 = vec3(0.8109119309638332633713423362694399653724431);
vec3 c1 = vec3(0.4808354605142681877121661197951496120000040);
vec3 e = vec3(2.71828182845904523536028747135266249775724709);
vec3 sph = s + vec3(0.5);
vec3 lanczos_sum = c0 + c1/(s + vec3(1.0));
vec3 base = (sph + g)/e;
return (pow(base, sph) * lanczos_sum) * s_inv;
}
vec2 gamma_impl(vec2 s, vec2 s_inv)
{
// vec2 version:
vec2 g = vec2(1.12906830989);
vec2 c0 = vec2(0.8109119309638332633713423362694399653724431);
vec2 c1 = vec2(0.4808354605142681877121661197951496120000040);
vec2 e = vec2(2.71828182845904523536028747135266249775724709);
vec2 sph = s + vec2(0.5);
vec2 lanczos_sum = c0 + c1/(s + vec2(1.0));
vec2 base = (sph + g)/e;
return (pow(base, sph) * lanczos_sum) * s_inv;
}
float gamma_impl(float s, float s_inv)
{
// Float version:
float g = 1.12906830989;
float c0 = 0.8109119309638332633713423362694399653724431;
float c1 = 0.4808354605142681877121661197951496120000040;
float e = 2.71828182845904523536028747135266249775724709;
float sph = s + 0.5;
float lanczos_sum = c0 + c1/(s + 1.0);
float base = (sph + g)/e;
return (pow(base, sph) * lanczos_sum) * s_inv;
}
vec4 gamma(vec4 s)
{
// Requires: s is the standard parameter to the gamma function, and it
// should lie in the [0, 36] range.
// Returns: Return approximate gamma function output with a maximum
// relative error of 0.000463. See gamma_impl for details.
return gamma_impl(s, vec4(1.0)/s);
}
vec3 gamma(vec3 s)
{
// vec3 version:
return gamma_impl(s, vec3(1.0)/s);
}
vec2 gamma(vec2 s)
{
// vec2 version:
return gamma_impl(s, vec2(1.0)/s);
}
float gamma(float s)
{
// Float version:
return gamma_impl(s, 1.0/s);
}
//////////////// INCOMPLETE GAMMA FUNCTIONS (RESTRICTED INPUT) ///////////////
// Lower incomplete gamma function for small s and z (implementation):
vec4 ligamma_small_z_impl(vec4 s, vec4 z, vec4 s_inv)
{
// Requires: 1.) s < ~0.5
// 2.) z <= ~0.775075
// 3.) s_inv = 1.0/s (precomputed for outside reuse)
// Returns: A series representation for the lower incomplete gamma
// function for small s and small z (4 terms).
// The actual "rolled up" summation looks like:
// last_sign = 1.0; last_pow = 1.0; last_factorial = 1.0;
// sum = last_sign * last_pow / ((s + k) * last_factorial)
// for(int i = 0; i < 4; ++i)
// {
// last_sign *= -1.0; last_pow *= z; last_factorial *= i;
// sum += last_sign * last_pow / ((s + k) * last_factorial);
// }
// Unrolled, constant-unfolded and arranged for madds and parallelism:
vec4 scale = pow(z, s);
vec4 sum = s_inv; // Summation iteration 0 result
// Summation iterations 1, 2, and 3:
vec4 z_sq = z*z;
vec4 denom1 = s + vec4(1.0);
vec4 denom2 = 2.0*s + vec4(4.0);
vec4 denom3 = 6.0*s + vec4(18.0);
//vec4 denom4 = 24.0*s + vec4(96.0);
sum -= z/denom1;
sum += z_sq/denom2;
sum -= z * z_sq/denom3;
//sum += z_sq * z_sq / denom4;
// Scale and return:
return scale * sum;
}
vec3 ligamma_small_z_impl(vec3 s, vec3 z, vec3 s_inv)
{
// vec3 version:
vec3 scale = pow(z, s);
vec3 sum = s_inv;
vec3 z_sq = z*z;
vec3 denom1 = s + vec3(1.0);
vec3 denom2 = 2.0*s + vec3(4.0);
vec3 denom3 = 6.0*s + vec3(18.0);
sum -= z/denom1;
sum += z_sq/denom2;
sum -= z * z_sq/denom3;
return scale * sum;
}
vec2 ligamma_small_z_impl(vec2 s, vec2 z, vec2 s_inv)
{
// vec2 version:
vec2 scale = pow(z, s);
vec2 sum = s_inv;
vec2 z_sq = z*z;
vec2 denom1 = s + vec2(1.0);
vec2 denom2 = 2.0*s + vec2(4.0);
vec2 denom3 = 6.0*s + vec2(18.0);
sum -= z/denom1;
sum += z_sq/denom2;
sum -= z * z_sq/denom3;
return scale * sum;
}
float ligamma_small_z_impl(float s, float z, float s_inv)
{
// Float version:
float scale = pow(z, s);
float sum = s_inv;
float z_sq = z*z;
float denom1 = s + 1.0;
float denom2 = 2.0*s + 4.0;
float denom3 = 6.0*s + 18.0;
sum -= z/denom1;
sum += z_sq/denom2;
sum -= z * z_sq/denom3;
return scale * sum;
}
// Upper incomplete gamma function for small s and large z (implementation):
vec4 uigamma_large_z_impl(vec4 s, vec4 z)
{
// Requires: 1.) s < ~0.5
// 2.) z > ~0.775075
// Returns: Gauss's continued fraction representation for the upper
// incomplete gamma function (4 terms).
// The "rolled up" continued fraction looks like this. The denominator
// is truncated, and it's calculated "from the bottom up:"
// denom = vec4('inf');
// vec4 one = vec4(1.0);
// for(int i = 4; i > 0; --i)
// {
// denom = ((i * 2.0) - one) + z - s + (i * (s - i))/denom;
// }
// Unrolled and constant-unfolded for madds and parallelism:
vec4 numerator = pow(z, s) * exp(-z);
vec4 denom = vec4(7.0) + z - s;
denom = vec4(5.0) + z - s + (3.0*s - vec4(9.0))/denom;
denom = vec4(3.0) + z - s + (2.0*s - vec4(4.0))/denom;
denom = vec4(1.0) + z - s + (s - vec4(1.0))/denom;
return numerator / denom;
}
vec3 uigamma_large_z_impl(vec3 s, vec3 z)
{
// vec3 version:
vec3 numerator = pow(z, s) * exp(-z);
vec3 denom = vec3(7.0) + z - s;
denom = vec3(5.0) + z - s + (3.0*s - vec3(9.0))/denom;
denom = vec3(3.0) + z - s + (2.0*s - vec3(4.0))/denom;
denom = vec3(1.0) + z - s + (s - vec3(1.0))/denom;
return numerator / denom;
}
vec2 uigamma_large_z_impl(vec2 s, vec2 z)
{
// vec2 version:
vec2 numerator = pow(z, s) * exp(-z);
vec2 denom = vec2(7.0) + z - s;
denom = vec2(5.0) + z - s + (3.0*s - vec2(9.0))/denom;
denom = vec2(3.0) + z - s + (2.0*s - vec2(4.0))/denom;
denom = vec2(1.0) + z - s + (s - vec2(1.0))/denom;
return numerator / denom;
}
float uigamma_large_z_impl(float s, float z)
{
// Float version:
float numerator = pow(z, s) * exp(-z);
float denom = 7.0 + z - s;
denom = 5.0 + z - s + (3.0*s - 9.0)/denom;
denom = 3.0 + z - s + (2.0*s - 4.0)/denom;
denom = 1.0 + z - s + (s - 1.0)/denom;
return numerator / denom;
}
// Normalized lower incomplete gamma function for small s (implementation):
vec4 normalized_ligamma_impl(vec4 s, vec4 z,
vec4 s_inv, vec4 gamma_s_inv)
{
// Requires: 1.) s < ~0.5
// 2.) s_inv = 1/s (precomputed for outside reuse)
// 3.) gamma_s_inv = 1/gamma(s) (precomputed for outside reuse)
// Returns: Approximate the normalized lower incomplete gamma function
// for s < 0.5. Since we only care about s < 0.5, we only need
// to evaluate two branches (not four) based on z. Each branch
// uses four terms, with a max relative error of ~0.00182. The
// branch threshold and specifics were adapted for fewer terms
// from Gil/Segura/Temme's paper here:
// http://oai.cwi.nl/oai/asset/20433/20433B.pdf
// Evaluate both branches: Real branches test slower even when available.
vec4 thresh = vec4(0.775075);
bvec4 z_is_large = greaterThan(z , thresh);
vec4 z_size_check = vec4(z_is_large.x ? 1.0 : 0.0, z_is_large.y ? 1.0 : 0.0, z_is_large.z ? 1.0 : 0.0, z_is_large.w ? 1.0 : 0.0);
vec4 large_z = vec4(1.0) - uigamma_large_z_impl(s, z) * gamma_s_inv;
vec4 small_z = ligamma_small_z_impl(s, z, s_inv) * gamma_s_inv;
// Combine the results from both branches:
return large_z * vec4(z_size_check) + small_z * vec4(z_size_check);
}
vec3 normalized_ligamma_impl(vec3 s, vec3 z,
vec3 s_inv, vec3 gamma_s_inv)
{
// vec3 version:
vec3 thresh = vec3(0.775075);
bvec3 z_is_large = greaterThan(z , thresh);
vec3 z_size_check = vec3(z_is_large.x ? 1.0 : 0.0, z_is_large.y ? 1.0 : 0.0, z_is_large.z ? 1.0 : 0.0);
vec3 large_z = vec3(1.0) - uigamma_large_z_impl(s, z) * gamma_s_inv;
vec3 small_z = ligamma_small_z_impl(s, z, s_inv) * gamma_s_inv;
return large_z * vec3(z_size_check) + small_z * vec3(z_size_check);
}
vec2 normalized_ligamma_impl(vec2 s, vec2 z,
vec2 s_inv, vec2 gamma_s_inv)
{
// vec2 version:
vec2 thresh = vec2(0.775075);
bvec2 z_is_large = greaterThan(z , thresh);
vec2 z_size_check = vec2(z_is_large.x ? 1.0 : 0.0, z_is_large.y ? 1.0 : 0.0);
vec2 large_z = vec2(1.0) - uigamma_large_z_impl(s, z) * gamma_s_inv;
vec2 small_z = ligamma_small_z_impl(s, z, s_inv) * gamma_s_inv;
return large_z * vec2(z_size_check) + small_z * vec2(z_size_check);
}
float normalized_ligamma_impl(float s, float z,
float s_inv, float gamma_s_inv)
{
// Float version:
float thresh = 0.775075;
float z_size_check = 0.0;
if (z > thresh) z_size_check = 1.0;
float large_z = 1.0 - uigamma_large_z_impl(s, z) * gamma_s_inv;
float small_z = ligamma_small_z_impl(s, z, s_inv) * gamma_s_inv;
return large_z * float(z_size_check) + small_z * float(z_size_check);
}
// Normalized lower incomplete gamma function for small s:
vec4 normalized_ligamma(vec4 s, vec4 z)
{
// Requires: s < ~0.5
// Returns: Approximate the normalized lower incomplete gamma function
// for s < 0.5. See normalized_ligamma_impl() for details.
vec4 s_inv = vec4(1.0)/s;
vec4 gamma_s_inv = vec4(1.0)/gamma_impl(s, s_inv);
return normalized_ligamma_impl(s, z, s_inv, gamma_s_inv);
}
vec3 normalized_ligamma(vec3 s, vec3 z)
{
// vec3 version:
vec3 s_inv = vec3(1.0)/s;
vec3 gamma_s_inv = vec3(1.0)/gamma_impl(s, s_inv);
return normalized_ligamma_impl(s, z, s_inv, gamma_s_inv);
}
vec2 normalized_ligamma(vec2 s, vec2 z)
{
// vec2 version:
vec2 s_inv = vec2(1.0)/s;
vec2 gamma_s_inv = vec2(1.0)/gamma_impl(s, s_inv);
return normalized_ligamma_impl(s, z, s_inv, gamma_s_inv);
}
float normalized_ligamma(float s, float z)
{
// Float version:
float s_inv = 1.0/s;
float gamma_s_inv = 1.0/gamma_impl(s, s_inv);
return normalized_ligamma_impl(s, z, s_inv, gamma_s_inv);
}
#endif // SPECIAL_FUNCTIONS_H
/////////////////////////////////// HELPERS //////////////////////////////////
vec4 uv2_to_uv4(vec2 tex_uv)
{
// Make a vec2 uv offset safe for adding to vec4 tex2Dlod coords:
return vec4(tex_uv, 0.0, 0.0);
}
// Make a length squared helper macro (for usage with static constants):
#define LENGTH_SQ(vec) (dot(vec, vec))
float get_fast_gaussian_weight_sum_inv(float sigma)
{
// We can use the Gaussian integral to calculate the asymptotic weight for
// the center pixel. Since the unnormalized center pixel weight is 1.0,
// the normalized weight is the same as the weight sum inverse. Given a
// large enough blur (9+), the asymptotic weight sum is close and faster:
// center_weight = 0.5 *
// (erf(0.5/(sigma*sqrt(2.0))) - erf(-0.5/(sigma*sqrt(2.0))))
// erf(-x) == -erf(x), so we get 0.5 * (2.0 * erf(blah blah)):
// However, we can get even faster results with curve-fitting. These are
// also closer than the asymptotic results, because they were constructed
// from 64 blurs sizes from [3, 131) and 255 equally-spaced sigmas from
// (0, blurN_std_dev), so the results for smaller sigmas are biased toward
// smaller blurs. The max error is 0.0031793913.
// Relative FPS: 134.3 with erf, 135.8 with curve-fitting.
//static float temp = 0.5/sqrt(2.0);
//return erf(temp/sigma);
return min(exp(exp(0.348348412457428/
(sigma - 0.0860587260734721))), 0.399334576340352/sigma);
}
//////////////////// ARBITRARILY RESIZABLE SEPARABLE BLURS ///////////////////
vec3 tex2Dblur11resize(sampler2D tex, vec2 tex_uv,
vec2 dxdy, float sigma)
{
// Requires: Global requirements must be met (see file description).
// Returns: A 1D 11x Gaussian blurred texture lookup using a 11-tap blur.
// It may be mipmapped depending on settings and dxdy.
// Calculate Gaussian blur kernel weights and a normalization factor for
// distances of 0-4, ignoring constant factors (since we're normalizing).
float denom_inv = 0.5/(sigma*sigma);
float w0 = 1.0;
float w1 = exp(-1.0 * denom_inv);
float w2 = exp(-4.0 * denom_inv);
float w3 = exp(-9.0 * denom_inv);
float w4 = exp(-16.0 * denom_inv);
float w5 = exp(-25.0 * denom_inv);
float weight_sum_inv = 1.0 /
(w0 + 2.0 * (w1 + w2 + w3 + w4 + w5));
// Statically normalize weights, sum weighted samples, and return. Blurs are
// currently optimized for dynamic weights.
vec3 sum = vec3(0.0);
sum += w5 * tex2D_linearize(tex, tex_uv - 5.0 * dxdy).rgb;
sum += w4 * tex2D_linearize(tex, tex_uv - 4.0 * dxdy).rgb;
sum += w3 * tex2D_linearize(tex, tex_uv - 3.0 * dxdy).rgb;
sum += w2 * tex2D_linearize(tex, tex_uv - 2.0 * dxdy).rgb;
sum += w1 * tex2D_linearize(tex, tex_uv - 1.0 * dxdy).rgb;
sum += w0 * tex2D_linearize(tex, tex_uv).rgb;
sum += w1 * tex2D_linearize(tex, tex_uv + 1.0 * dxdy).rgb;
sum += w2 * tex2D_linearize(tex, tex_uv + 2.0 * dxdy).rgb;
sum += w3 * tex2D_linearize(tex, tex_uv + 3.0 * dxdy).rgb;
sum += w4 * tex2D_linearize(tex, tex_uv + 4.0 * dxdy).rgb;
sum += w5 * tex2D_linearize(tex, tex_uv + 5.0 * dxdy).rgb;
return sum * weight_sum_inv;
}
vec3 tex2Dblur9resize(sampler2D tex, vec2 tex_uv,
vec2 dxdy, float sigma)
{
// Requires: Global requirements must be met (see file description).
// Returns: A 1D 9x Gaussian blurred texture lookup using a 9-tap blur.
// It may be mipmapped depending on settings and dxdy.
// First get the texel weights and normalization factor as above.
float denom_inv = 0.5/(sigma*sigma);
float w0 = 1.0;
float w1 = exp(-1.0 * denom_inv);
float w2 = exp(-4.0 * denom_inv);
float w3 = exp(-9.0 * denom_inv);
float w4 = exp(-16.0 * denom_inv);
float weight_sum_inv = 1.0 / (w0 + 2.0 * (w1 + w2 + w3 + w4));
// Statically normalize weights, sum weighted samples, and return:
vec3 sum = vec3(0.0);
sum += w4 * tex2D_linearize(tex, tex_uv - 4.0 * dxdy).rgb;
sum += w3 * tex2D_linearize(tex, tex_uv - 3.0 * dxdy).rgb;
sum += w2 * tex2D_linearize(tex, tex_uv - 2.0 * dxdy).rgb;
sum += w1 * tex2D_linearize(tex, tex_uv - 1.0 * dxdy).rgb;
sum += w0 * tex2D_linearize(tex, tex_uv).rgb;
sum += w1 * tex2D_linearize(tex, tex_uv + 1.0 * dxdy).rgb;
sum += w2 * tex2D_linearize(tex, tex_uv + 2.0 * dxdy).rgb;
sum += w3 * tex2D_linearize(tex, tex_uv + 3.0 * dxdy).rgb;
sum += w4 * tex2D_linearize(tex, tex_uv + 4.0 * dxdy).rgb;
return sum * weight_sum_inv;
}
vec3 tex2Dblur7resize(sampler2D tex, vec2 tex_uv,
vec2 dxdy, float sigma)
{
// Requires: Global requirements must be met (see file description).
// Returns: A 1D 7x Gaussian blurred texture lookup using a 7-tap blur.
// It may be mipmapped depending on settings and dxdy.
// First get the texel weights and normalization factor as above.
float denom_inv = 0.5/(sigma*sigma);
float w0 = 1.0;
float w1 = exp(-1.0 * denom_inv);
float w2 = exp(-4.0 * denom_inv);
float w3 = exp(-9.0 * denom_inv);
float weight_sum_inv = 1.0 / (w0 + 2.0 * (w1 + w2 + w3));
// Statically normalize weights, sum weighted samples, and return:
vec3 sum = vec3(0.0);
sum += w3 * tex2D_linearize(tex, tex_uv - 3.0 * dxdy).rgb;
sum += w2 * tex2D_linearize(tex, tex_uv - 2.0 * dxdy).rgb;
sum += w1 * tex2D_linearize(tex, tex_uv - 1.0 * dxdy).rgb;
sum += w0 * tex2D_linearize(tex, tex_uv).rgb;
sum += w1 * tex2D_linearize(tex, tex_uv + 1.0 * dxdy).rgb;
sum += w2 * tex2D_linearize(tex, tex_uv + 2.0 * dxdy).rgb;
sum += w3 * tex2D_linearize(tex, tex_uv + 3.0 * dxdy).rgb;
return sum * weight_sum_inv;
}
vec3 tex2Dblur5resize(sampler2D tex, vec2 tex_uv,
vec2 dxdy, float sigma)
{
// Requires: Global requirements must be met (see file description).
// Returns: A 1D 5x Gaussian blurred texture lookup using a 5-tap blur.
// It may be mipmapped depending on settings and dxdy.
// First get the texel weights and normalization factor as above.
float denom_inv = 0.5/(sigma*sigma);
float w0 = 1.0;
float w1 = exp(-1.0 * denom_inv);
float w2 = exp(-4.0 * denom_inv);
float weight_sum_inv = 1.0 / (w0 + 2.0 * (w1 + w2));
// Statically normalize weights, sum weighted samples, and return:
vec3 sum = vec3(0.0);
sum += w2 * tex2D_linearize(tex, tex_uv - 2.0 * dxdy).rgb;
sum += w1 * tex2D_linearize(tex, tex_uv - 1.0 * dxdy).rgb;
sum += w0 * tex2D_linearize(tex, tex_uv).rgb;
sum += w1 * tex2D_linearize(tex, tex_uv + 1.0 * dxdy).rgb;
sum += w2 * tex2D_linearize(tex, tex_uv + 2.0 * dxdy).rgb;
return sum * weight_sum_inv;
}
vec3 tex2Dblur3resize(sampler2D tex, vec2 tex_uv,
vec2 dxdy, float sigma)
{
// Requires: Global requirements must be met (see file description).
// Returns: A 1D 3x Gaussian blurred texture lookup using a 3-tap blur.
// It may be mipmapped depending on settings and dxdy.
// First get the texel weights and normalization factor as above.
float denom_inv = 0.5/(sigma*sigma);
float w0 = 1.0;
float w1 = exp(-1.0 * denom_inv);
float weight_sum_inv = 1.0 / (w0 + 2.0 * w1);
// Statically normalize weights, sum weighted samples, and return:
vec3 sum = vec3(0.0);
sum += w1 * tex2D_linearize(tex, tex_uv - 1.0 * dxdy).rgb;
sum += w0 * tex2D_linearize(tex, tex_uv).rgb;
sum += w1 * tex2D_linearize(tex, tex_uv + 1.0 * dxdy).rgb;
return sum * weight_sum_inv;
}
/////////////////////////// FAST SEPARABLE BLURS ///////////////////////////
vec3 tex2Dblur11fast(sampler2D tex, vec2 tex_uv,
vec2 dxdy, float sigma)
{
// Requires: 1.) Global requirements must be met (see file description).
// 2.) filter_linearN must = "true" in your .cgp file.
// 3.) For gamma-correct bilinear filtering, global
// gamma_aware_bilinear == true (from gamma-management.h)
// Returns: A 1D 11x Gaussian blurred texture lookup using 6 linear
// taps. It may be mipmapped depending on settings and dxdy.
// First get the texel weights and normalization factor as above.
float denom_inv = 0.5/(sigma*sigma);
float w0 = 1.0;
float w1 = exp(-1.0 * denom_inv);
float w2 = exp(-4.0 * denom_inv);
float w3 = exp(-9.0 * denom_inv);
float w4 = exp(-16.0 * denom_inv);
float w5 = exp(-25.0 * denom_inv);
float weight_sum_inv = 1.0 /
(w0 + 2.0 * (w1 + w2 + w3 + w4 + w5));
// Calculate combined weights and linear sample ratios between texel pairs.
// The center texel (with weight w0) is used twice, so halve its weight.
float w01 = w0 * 0.5 + w1;
float w23 = w2 + w3;
float w45 = w4 + w5;
float w01_ratio = w1/w01;
float w23_ratio = w3/w23;
float w45_ratio = w5/w45;
// Statically normalize weights, sum weighted samples, and return:
vec3 sum = vec3(0.0);
sum += w45 * tex2D_linearize(tex, tex_uv - (4.0 + w45_ratio) * dxdy).rgb;
sum += w23 * tex2D_linearize(tex, tex_uv - (2.0 + w23_ratio) * dxdy).rgb;
sum += w01 * tex2D_linearize(tex, tex_uv - w01_ratio * dxdy).rgb;
sum += w01 * tex2D_linearize(tex, tex_uv + w01_ratio * dxdy).rgb;
sum += w23 * tex2D_linearize(tex, tex_uv + (2.0 + w23_ratio) * dxdy).rgb;
sum += w45 * tex2D_linearize(tex, tex_uv + (4.0 + w45_ratio) * dxdy).rgb;
return sum * weight_sum_inv;
}
vec3 tex2Dblur17fast(sampler2D tex, vec2 tex_uv,
vec2 dxdy, float sigma)
{
// Requires: Same as tex2Dblur11()
// Returns: A 1D 17x Gaussian blurred texture lookup using 1 nearest
// neighbor and 8 linear taps. It may be mipmapped depending
// on settings and dxdy.
// First get the texel weights and normalization factor as above.
float denom_inv = 0.5/(sigma*sigma);
float w0 = 1.0;
float w1 = exp(-1.0 * denom_inv);
float w2 = exp(-4.0 * denom_inv);
float w3 = exp(-9.0 * denom_inv);
float w4 = exp(-16.0 * denom_inv);
float w5 = exp(-25.0 * denom_inv);
float w6 = exp(-36.0 * denom_inv);
float w7 = exp(-49.0 * denom_inv);
float w8 = exp(-64.0 * denom_inv);
//float weight_sum_inv = 1.0 / (w0 + 2.0 * (
// w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8));
float weight_sum_inv = get_fast_gaussian_weight_sum_inv(sigma);
// Calculate combined weights and linear sample ratios between texel pairs.
float w1_2 = w1 + w2;
float w3_4 = w3 + w4;
float w5_6 = w5 + w6;
float w7_8 = w7 + w8;
float w1_2_ratio = w2/w1_2;
float w3_4_ratio = w4/w3_4;
float w5_6_ratio = w6/w5_6;
float w7_8_ratio = w8/w7_8;
// Statically normalize weights, sum weighted samples, and return:
vec3 sum = vec3(0.0);
sum += w7_8 * tex2D_linearize(tex, tex_uv - (7.0 + w7_8_ratio) * dxdy).rgb;
sum += w5_6 * tex2D_linearize(tex, tex_uv - (5.0 + w5_6_ratio) * dxdy).rgb;
sum += w3_4 * tex2D_linearize(tex, tex_uv - (3.0 + w3_4_ratio) * dxdy).rgb;
sum += w1_2 * tex2D_linearize(tex, tex_uv - (1.0 + w1_2_ratio) * dxdy).rgb;
sum += w0 * tex2D_linearize(tex, tex_uv).rgb;
sum += w1_2 * tex2D_linearize(tex, tex_uv + (1.0 + w1_2_ratio) * dxdy).rgb;
sum += w3_4 * tex2D_linearize(tex, tex_uv + (3.0 + w3_4_ratio) * dxdy).rgb;
sum += w5_6 * tex2D_linearize(tex, tex_uv + (5.0 + w5_6_ratio) * dxdy).rgb;
sum += w7_8 * tex2D_linearize(tex, tex_uv + (7.0 + w7_8_ratio) * dxdy).rgb;
return sum * weight_sum_inv;
}
vec3 tex2Dblur25fast(sampler2D tex, vec2 tex_uv,
vec2 dxdy, float sigma)
{
// Requires: Same as tex2Dblur11()
// Returns: A 1D 25x Gaussian blurred texture lookup using 1 nearest
// neighbor and 12 linear taps. It may be mipmapped depending
// on settings and dxdy.
// First get the texel weights and normalization factor as above.
float denom_inv = 0.5/(sigma*sigma);
float w0 = 1.0;
float w1 = exp(-1.0 * denom_inv);
float w2 = exp(-4.0 * denom_inv);
float w3 = exp(-9.0 * denom_inv);
float w4 = exp(-16.0 * denom_inv);
float w5 = exp(-25.0 * denom_inv);
float w6 = exp(-36.0 * denom_inv);
float w7 = exp(-49.0 * denom_inv);
float w8 = exp(-64.0 * denom_inv);
float w9 = exp(-81.0 * denom_inv);
float w10 = exp(-100.0 * denom_inv);
float w11 = exp(-121.0 * denom_inv);
float w12 = exp(-144.0 * denom_inv);
//float weight_sum_inv = 1.0 / (w0 + 2.0 * (
// w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10 + w11 + w12));
float weight_sum_inv = get_fast_gaussian_weight_sum_inv(sigma);
// Calculate combined weights and linear sample ratios between texel pairs.
float w1_2 = w1 + w2;
float w3_4 = w3 + w4;
float w5_6 = w5 + w6;
float w7_8 = w7 + w8;
float w9_10 = w9 + w10;
float w11_12 = w11 + w12;
float w1_2_ratio = w2/w1_2;
float w3_4_ratio = w4/w3_4;
float w5_6_ratio = w6/w5_6;
float w7_8_ratio = w8/w7_8;
float w9_10_ratio = w10/w9_10;
float w11_12_ratio = w12/w11_12;
// Statically normalize weights, sum weighted samples, and return:
vec3 sum = vec3(0.0);
sum += w11_12 * tex2D_linearize(tex, tex_uv - (11.0 + w11_12_ratio) * dxdy).rgb;
sum += w9_10 * tex2D_linearize(tex, tex_uv - (9.0 + w9_10_ratio) * dxdy).rgb;
sum += w7_8 * tex2D_linearize(tex, tex_uv - (7.0 + w7_8_ratio) * dxdy).rgb;
sum += w5_6 * tex2D_linearize(tex, tex_uv - (5.0 + w5_6_ratio) * dxdy).rgb;
sum += w3_4 * tex2D_linearize(tex, tex_uv - (3.0 + w3_4_ratio) * dxdy).rgb;
sum += w1_2 * tex2D_linearize(tex, tex_uv - (1.0 + w1_2_ratio) * dxdy).rgb;
sum += w0 * tex2D_linearize(tex, tex_uv).rgb;
sum += w1_2 * tex2D_linearize(tex, tex_uv + (1.0 + w1_2_ratio) * dxdy).rgb;
sum += w3_4 * tex2D_linearize(tex, tex_uv + (3.0 + w3_4_ratio) * dxdy).rgb;
sum += w5_6 * tex2D_linearize(tex, tex_uv + (5.0 + w5_6_ratio) * dxdy).rgb;
sum += w7_8 * tex2D_linearize(tex, tex_uv + (7.0 + w7_8_ratio) * dxdy).rgb;
sum += w9_10 * tex2D_linearize(tex, tex_uv + (9.0 + w9_10_ratio) * dxdy).rgb;
sum += w11_12 * tex2D_linearize(tex, tex_uv + (11.0 + w11_12_ratio) * dxdy).rgb;
return sum * weight_sum_inv;
}
vec3 tex2Dblur31fast(sampler2D tex, vec2 tex_uv,
vec2 dxdy, float sigma)
{
// Requires: Same as tex2Dblur11()
// Returns: A 1D 31x Gaussian blurred texture lookup using 16 linear
// taps. It may be mipmapped depending on settings and dxdy.
// First get the texel weights and normalization factor as above.
float denom_inv = 0.5/(sigma*sigma);
float w0 = 1.0;
float w1 = exp(-1.0 * denom_inv);
float w2 = exp(-4.0 * denom_inv);
float w3 = exp(-9.0 * denom_inv);
float w4 = exp(-16.0 * denom_inv);
float w5 = exp(-25.0 * denom_inv);
float w6 = exp(-36.0 * denom_inv);
float w7 = exp(-49.0 * denom_inv);
float w8 = exp(-64.0 * denom_inv);
float w9 = exp(-81.0 * denom_inv);
float w10 = exp(-100.0 * denom_inv);
float w11 = exp(-121.0 * denom_inv);
float w12 = exp(-144.0 * denom_inv);
float w13 = exp(-169.0 * denom_inv);
float w14 = exp(-196.0 * denom_inv);
float w15 = exp(-225.0 * denom_inv);
//float weight_sum_inv = 1.0 /
// (w0 + 2.0 * (w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 +
// w9 + w10 + w11 + w12 + w13 + w14 + w15));
float weight_sum_inv = get_fast_gaussian_weight_sum_inv(sigma);
// Calculate combined weights and linear sample ratios between texel pairs.
// The center texel (with weight w0) is used twice, so halve its weight.
float w0_1 = w0 * 0.5 + w1;
float w2_3 = w2 + w3;
float w4_5 = w4 + w5;
float w6_7 = w6 + w7;
float w8_9 = w8 + w9;
float w10_11 = w10 + w11;
float w12_13 = w12 + w13;
float w14_15 = w14 + w15;
float w0_1_ratio = w1/w0_1;
float w2_3_ratio = w3/w2_3;
float w4_5_ratio = w5/w4_5;
float w6_7_ratio = w7/w6_7;
float w8_9_ratio = w9/w8_9;
float w10_11_ratio = w11/w10_11;
float w12_13_ratio = w13/w12_13;
float w14_15_ratio = w15/w14_15;
// Statically normalize weights, sum weighted samples, and return:
vec3 sum = vec3(0.0);
sum += w14_15 * tex2D_linearize(tex, tex_uv - (14.0 + w14_15_ratio) * dxdy).rgb;
sum += w12_13 * tex2D_linearize(tex, tex_uv - (12.0 + w12_13_ratio) * dxdy).rgb;
sum += w10_11 * tex2D_linearize(tex, tex_uv - (10.0 + w10_11_ratio) * dxdy).rgb;
sum += w8_9 * tex2D_linearize(tex, tex_uv - (8.0 + w8_9_ratio) * dxdy).rgb;
sum += w6_7 * tex2D_linearize(tex, tex_uv - (6.0 + w6_7_ratio) * dxdy).rgb;
sum += w4_5 * tex2D_linearize(tex, tex_uv - (4.0 + w4_5_ratio) * dxdy).rgb;
sum += w2_3 * tex2D_linearize(tex, tex_uv - (2.0 + w2_3_ratio) * dxdy).rgb;
sum += w0_1 * tex2D_linearize(tex, tex_uv - w0_1_ratio * dxdy).rgb;
sum += w0_1 * tex2D_linearize(tex, tex_uv + w0_1_ratio * dxdy).rgb;
sum += w2_3 * tex2D_linearize(tex, tex_uv + (2.0 + w2_3_ratio) * dxdy).rgb;
sum += w4_5 * tex2D_linearize(tex, tex_uv + (4.0 + w4_5_ratio) * dxdy).rgb;
sum += w6_7 * tex2D_linearize(tex, tex_uv + (6.0 + w6_7_ratio) * dxdy).rgb;
sum += w8_9 * tex2D_linearize(tex, tex_uv + (8.0 + w8_9_ratio) * dxdy).rgb;
sum += w10_11 * tex2D_linearize(tex, tex_uv + (10.0 + w10_11_ratio) * dxdy).rgb;
sum += w12_13 * tex2D_linearize(tex, tex_uv + (12.0 + w12_13_ratio) * dxdy).rgb;
sum += w14_15 * tex2D_linearize(tex, tex_uv + (14.0 + w14_15_ratio) * dxdy).rgb;
return sum * weight_sum_inv;
}
vec3 tex2Dblur43fast(sampler2D tex, vec2 tex_uv,
vec2 dxdy, float sigma)
{
// Requires: Same as tex2Dblur11()
// Returns: A 1D 43x Gaussian blurred texture lookup using 22 linear
// taps. It may be mipmapped depending on settings and dxdy.
// First get the texel weights and normalization factor as above.
float denom_inv = 0.5/(sigma*sigma);
float w0 = 1.0;
float w1 = exp(-1.0 * denom_inv);
float w2 = exp(-4.0 * denom_inv);
float w3 = exp(-9.0 * denom_inv);
float w4 = exp(-16.0 * denom_inv);
float w5 = exp(-25.0 * denom_inv);
float w6 = exp(-36.0 * denom_inv);
float w7 = exp(-49.0 * denom_inv);
float w8 = exp(-64.0 * denom_inv);
float w9 = exp(-81.0 * denom_inv);
float w10 = exp(-100.0 * denom_inv);
float w11 = exp(-121.0 * denom_inv);
float w12 = exp(-144.0 * denom_inv);
float w13 = exp(-169.0 * denom_inv);
float w14 = exp(-196.0 * denom_inv);
float w15 = exp(-225.0 * denom_inv);
float w16 = exp(-256.0 * denom_inv);
float w17 = exp(-289.0 * denom_inv);
float w18 = exp(-324.0 * denom_inv);
float w19 = exp(-361.0 * denom_inv);
float w20 = exp(-400.0 * denom_inv);
float w21 = exp(-441.0 * denom_inv);
//float weight_sum_inv = 1.0 /
// (w0 + 2.0 * (w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10 + w11 +
// w12 + w13 + w14 + w15 + w16 + w17 + w18 + w19 + w20 + w21));
float weight_sum_inv = get_fast_gaussian_weight_sum_inv(sigma);
// Calculate combined weights and linear sample ratios between texel pairs.
// The center texel (with weight w0) is used twice, so halve its weight.
float w0_1 = w0 * 0.5 + w1;
float w2_3 = w2 + w3;
float w4_5 = w4 + w5;
float w6_7 = w6 + w7;
float w8_9 = w8 + w9;
float w10_11 = w10 + w11;
float w12_13 = w12 + w13;
float w14_15 = w14 + w15;
float w16_17 = w16 + w17;
float w18_19 = w18 + w19;
float w20_21 = w20 + w21;
float w0_1_ratio = w1/w0_1;
float w2_3_ratio = w3/w2_3;
float w4_5_ratio = w5/w4_5;
float w6_7_ratio = w7/w6_7;
float w8_9_ratio = w9/w8_9;
float w10_11_ratio = w11/w10_11;
float w12_13_ratio = w13/w12_13;
float w14_15_ratio = w15/w14_15;
float w16_17_ratio = w17/w16_17;
float w18_19_ratio = w19/w18_19;
float w20_21_ratio = w21/w20_21;
// Statically normalize weights, sum weighted samples, and return:
vec3 sum = vec3(0.0);
sum += w20_21 * tex2D_linearize(tex, tex_uv - (20.0 + w20_21_ratio) * dxdy).rgb;
sum += w18_19 * tex2D_linearize(tex, tex_uv - (18.0 + w18_19_ratio) * dxdy).rgb;
sum += w16_17 * tex2D_linearize(tex, tex_uv - (16.0 + w16_17_ratio) * dxdy).rgb;
sum += w14_15 * tex2D_linearize(tex, tex_uv - (14.0 + w14_15_ratio) * dxdy).rgb;
sum += w12_13 * tex2D_linearize(tex, tex_uv - (12.0 + w12_13_ratio) * dxdy).rgb;
sum += w10_11 * tex2D_linearize(tex, tex_uv - (10.0 + w10_11_ratio) * dxdy).rgb;
sum += w8_9 * tex2D_linearize(tex, tex_uv - (8.0 + w8_9_ratio) * dxdy).rgb;
sum += w6_7 * tex2D_linearize(tex, tex_uv - (6.0 + w6_7_ratio) * dxdy).rgb;
sum += w4_5 * tex2D_linearize(tex, tex_uv - (4.0 + w4_5_ratio) * dxdy).rgb;
sum += w2_3 * tex2D_linearize(tex, tex_uv - (2.0 + w2_3_ratio) * dxdy).rgb;
sum += w0_1 * tex2D_linearize(tex, tex_uv - w0_1_ratio * dxdy).rgb;
sum += w0_1 * tex2D_linearize(tex, tex_uv + w0_1_ratio * dxdy).rgb;
sum += w2_3 * tex2D_linearize(tex, tex_uv + (2.0 + w2_3_ratio) * dxdy).rgb;
sum += w4_5 * tex2D_linearize(tex, tex_uv + (4.0 + w4_5_ratio) * dxdy).rgb;
sum += w6_7 * tex2D_linearize(tex, tex_uv + (6.0 + w6_7_ratio) * dxdy).rgb;
sum += w8_9 * tex2D_linearize(tex, tex_uv + (8.0 + w8_9_ratio) * dxdy).rgb;
sum += w10_11 * tex2D_linearize(tex, tex_uv + (10.0 + w10_11_ratio) * dxdy).rgb;
sum += w12_13 * tex2D_linearize(tex, tex_uv + (12.0 + w12_13_ratio) * dxdy).rgb;
sum += w14_15 * tex2D_linearize(tex, tex_uv + (14.0 + w14_15_ratio) * dxdy).rgb;
sum += w16_17 * tex2D_linearize(tex, tex_uv + (16.0 + w16_17_ratio) * dxdy).rgb;
sum += w18_19 * tex2D_linearize(tex, tex_uv + (18.0 + w18_19_ratio) * dxdy).rgb;
sum += w20_21 * tex2D_linearize(tex, tex_uv + (20.0 + w20_21_ratio) * dxdy).rgb;
return sum * weight_sum_inv;
}
vec3 tex2Dblur3fast(sampler2D tex, vec2 tex_uv,
vec2 dxdy, float sigma)
{
// Requires: Same as tex2Dblur11()
// Returns: A 1D 3x Gaussian blurred texture lookup using 2 linear
// taps. It may be mipmapped depending on settings and dxdy.
// First get the texel weights and normalization factor as above.
float denom_inv = 0.5/(sigma*sigma);
float w0 = 1.0;
float w1 = exp(-1.0 * denom_inv);
float weight_sum_inv = 1.0 / (w0 + 2.0 * w1);
// Calculate combined weights and linear sample ratios between texel pairs.
// The center texel (with weight w0) is used twice, so halve its weight.
float w01 = w0 * 0.5 + w1;
float w01_ratio = w1/w01;
// Weights for all samples are the same, so just average them:
return 0.5 * (
tex2D_linearize(tex, tex_uv - w01_ratio * dxdy).rgb +
tex2D_linearize(tex, tex_uv + w01_ratio * dxdy).rgb);
}
vec3 tex2Dblur5fast(sampler2D tex, vec2 tex_uv,
vec2 dxdy, float sigma)
{
// Requires: Same as tex2Dblur11()
// Returns: A 1D 5x Gaussian blurred texture lookup using 1 nearest
// neighbor and 2 linear taps. It may be mipmapped depending
// on settings and dxdy.
// First get the texel weights and normalization factor as above.
float denom_inv = 0.5/(sigma*sigma);
float w0 = 1.0;
float w1 = exp(-1.0 * denom_inv);
float w2 = exp(-4.0 * denom_inv);
float weight_sum_inv = 1.0 / (w0 + 2.0 * (w1 + w2));
// Calculate combined weights and linear sample ratios between texel pairs.
float w12 = w1 + w2;
float w12_ratio = w2/w12;
// Statically normalize weights, sum weighted samples, and return:
vec3 sum = vec3(0.0);
sum += w12 * tex2D_linearize(tex, tex_uv - (1.0 + w12_ratio) * dxdy).rgb;
sum += w0 * tex2D_linearize(tex, tex_uv).rgb;
sum += w12 * tex2D_linearize(tex, tex_uv + (1.0 + w12_ratio) * dxdy).rgb;
return sum * weight_sum_inv;
}
vec3 tex2Dblur7fast(sampler2D tex, vec2 tex_uv,
vec2 dxdy, float sigma)
{
// Requires: Same as tex2Dblur11()
// Returns: A 1D 7x Gaussian blurred texture lookup using 4 linear
// taps. It may be mipmapped depending on settings and dxdy.
// First get the texel weights and normalization factor as above.
float denom_inv = 0.5/(sigma*sigma);
float w0 = 1.0;
float w1 = exp(-1.0 * denom_inv);
float w2 = exp(-4.0 * denom_inv);
float w3 = exp(-9.0 * denom_inv);
float weight_sum_inv = 1.0 / (w0 + 2.0 * (w1 + w2 + w3));
// Calculate combined weights and linear sample ratios between texel pairs.
// The center texel (with weight w0) is used twice, so halve its weight.
float w01 = w0 * 0.5 + w1;
float w23 = w2 + w3;
float w01_ratio = w1/w01;
float w23_ratio = w3/w23;
// Statically normalize weights, sum weighted samples, and return:
vec3 sum = vec3(0.0);
sum += w23 * tex2D_linearize(tex, tex_uv - (2.0 + w23_ratio) * dxdy).rgb;
sum += w01 * tex2D_linearize(tex, tex_uv - w01_ratio * dxdy).rgb;
sum += w01 * tex2D_linearize(tex, tex_uv + w01_ratio * dxdy).rgb;
sum += w23 * tex2D_linearize(tex, tex_uv + (2.0 + w23_ratio) * dxdy).rgb;
return sum * weight_sum_inv;
}
//////////////////// ARBITRARILY RESIZABLE ONE-PASS BLURS ////////////////////
vec3 tex2Dblur3x3resize(sampler2D tex, vec2 tex_uv,
vec2 dxdy, float sigma)
{
// Requires: Global requirements must be met (see file description).
// Returns: A 3x3 Gaussian blurred mipmapped texture lookup of the
// resized input.
// Description:
// This is the only arbitrarily resizable one-pass blur; tex2Dblur5x5resize
// would perform like tex2Dblur9x9, MUCH slower than tex2Dblur5resize.
float denom_inv = 0.5/(sigma*sigma);
// Load each sample. We need all 3x3 samples. Quad-pixel communication
// won't help either: This should perform like tex2Dblur5x5, but sharing a
// 4x4 sample field would perform more like tex2Dblur8x8shared (worse).
vec2 sample4_uv = tex_uv;
vec2 dx = vec2(dxdy.x, 0.0);
vec2 dy = vec2(0.0, dxdy.y);
vec2 sample1_uv = sample4_uv - dy;
vec2 sample7_uv = sample4_uv + dy;
vec3 sample0 = tex2D_linearize(tex, sample1_uv - dx).rgb;
vec3 sample1 = tex2D_linearize(tex, sample1_uv).rgb;
vec3 sample2 = tex2D_linearize(tex, sample1_uv + dx).rgb;
vec3 sample3 = tex2D_linearize(tex, sample4_uv - dx).rgb;
vec3 sample4 = tex2D_linearize(tex, sample4_uv).rgb;
vec3 sample5 = tex2D_linearize(tex, sample4_uv + dx).rgb;
vec3 sample6 = tex2D_linearize(tex, sample7_uv - dx).rgb;
vec3 sample7 = tex2D_linearize(tex, sample7_uv).rgb;
vec3 sample8 = tex2D_linearize(tex, sample7_uv + dx).rgb;
// Statically compute Gaussian sample weights:
float w4 = 1.0;
float w1_3_5_7 = exp(-LENGTH_SQ(vec2(1.0, 0.0)) * denom_inv);
float w0_2_6_8 = exp(-LENGTH_SQ(vec2(1.0, 1.0)) * denom_inv);
float weight_sum_inv = 1.0/(w4 + 4.0 * (w1_3_5_7 + w0_2_6_8));
// Weight and sum the samples:
vec3 sum = w4 * sample4 +
w1_3_5_7 * (sample1 + sample3 + sample5 + sample7) +
w0_2_6_8 * (sample0 + sample2 + sample6 + sample8);
return sum * weight_sum_inv;
}
// Resizable one-pass blurs:
vec3 tex2Dblur3x3resize(sampler2D texture, vec2 tex_uv,
vec2 dxdy)
{
return tex2Dblur3x3resize(texture, tex_uv, dxdy, blur3_std_dev);
}
vec3 tex2Dblur9fast(sampler2D tex, vec2 tex_uv,
vec2 dxdy, float sigma)
{
// Requires: Same as tex2Dblur11()
// Returns: A 1D 9x Gaussian blurred texture lookup using 1 nearest
// neighbor and 4 linear taps. It may be mipmapped depending
// on settings and dxdy.
// First get the texel weights and normalization factor as above.
float denom_inv = 0.5/(sigma*sigma);
float w0 = 1.0;
float w1 = exp(-1.0 * denom_inv);
float w2 = exp(-4.0 * denom_inv);
float w3 = exp(-9.0 * denom_inv);
float w4 = exp(-16.0 * denom_inv);
float weight_sum_inv = 1.0 / (w0 + 2.0 * (w1 + w2 + w3 + w4));
// Calculate combined weights and linear sample ratios between texel pairs.
float w12 = w1 + w2;
float w34 = w3 + w4;
float w12_ratio = w2/w12;
float w34_ratio = w4/w34;
// Statically normalize weights, sum weighted samples, and return:
vec3 sum = vec3(0.0);
sum += w34 * tex2D_linearize(tex, tex_uv - (3.0 + w34_ratio) * dxdy).rgb;
sum += w12 * tex2D_linearize(tex, tex_uv - (1.0 + w12_ratio) * dxdy).rgb;
sum += w0 * tex2D_linearize(tex, tex_uv).rgb;
sum += w12 * tex2D_linearize(tex, tex_uv + (1.0 + w12_ratio) * dxdy).rgb;
sum += w34 * tex2D_linearize(tex, tex_uv + (3.0 + w34_ratio) * dxdy).rgb;
return sum * weight_sum_inv;
}
vec3 tex2Dblur9x9(sampler2D tex, vec2 tex_uv,
vec2 dxdy, float sigma)
{
// Perform a 1-pass 9x9 blur with 5x5 bilinear samples.
// Requires: Same as tex2Dblur9()
// Returns: A 9x9 Gaussian blurred mipmapped texture lookup composed of
// 5x5 carefully selected bilinear samples.
// Description:
// Perform a 1-pass 9x9 blur with 5x5 bilinear samples. Adjust the
// bilinear sample location to reflect the true Gaussian weights for each
// underlying texel. The following diagram illustrates the relative
// locations of bilinear samples. Each sample with the same number has the
// same weight (notice the symmetry). The letters a, b, c, d distinguish
// quadrants, and the letters U, D, L, R, C (up, down, left, right, center)
// distinguish 1D directions along the line containing the pixel center:
// 6a 5a 2U 5b 6b
// 4a 3a 1U 3b 4b
// 2L 1L 0C 1R 2R
// 4c 3c 1D 3d 4d
// 6c 5c 2D 5d 6d
// The following diagram illustrates the underlying equally spaced texels,
// named after the sample that accesses them and subnamed by their location
// within their 2x2, 2x1, 1x2, or 1x1 texel block:
// 6a4 6a3 5a4 5a3 2U2 5b3 5b4 6b3 6b4
// 6a2 6a1 5a2 5a1 2U1 5b1 5b2 6b1 6b2
// 4a4 4a3 3a4 3a3 1U2 3b3 3b4 4b3 4b4
// 4a2 4a1 3a2 3a1 1U1 3b1 3b2 4b1 4b2
// 2L2 2L1 1L2 1L1 0C1 1R1 1R2 2R1 2R2
// 4c2 4c1 3c2 3c1 1D1 3d1 3d2 4d1 4d2
// 4c4 4c3 3c4 3c3 1D2 3d3 3d4 4d3 4d4
// 6c2 6c1 5c2 5c1 2D1 5d1 5d2 6d1 6d2
// 6c4 6c3 5c4 5c3 2D2 5d3 5d4 6d3 6d4
// Note there is only one C texel and only two texels for each U, D, L, or
// R sample. The center sample is effectively a nearest neighbor sample,
// and the U/D/L/R samples use 1D linear filtering. All other texels are
// read with bilinear samples somewhere within their 2x2 texel blocks.
// COMPUTE TEXTURE COORDS:
// Statically compute sampling offsets within each 2x2 texel block, based
// on 1D sampling ratios between texels [1, 2] and [3, 4] texels away from
// the center, and reuse them independently for both dimensions. Compute
// these offsets based on the relative 1D Gaussian weights of the texels
// in question. (w1off means "Gaussian weight for the texel 1.0 texels
// away from the pixel center," etc.).
float denom_inv = 0.5/(sigma*sigma);
float w1off = exp(-1.0 * denom_inv);
float w2off = exp(-4.0 * denom_inv);
float w3off = exp(-9.0 * denom_inv);
float w4off = exp(-16.0 * denom_inv);
float texel1to2ratio = w2off/(w1off + w2off);
float texel3to4ratio = w4off/(w3off + w4off);
// Statically compute texel offsets from the fragment center to each
// bilinear sample in the bottom-right quadrant, including x-axis-aligned:
vec2 sample1R_texel_offset = vec2(1.0, 0.0) + vec2(texel1to2ratio, 0.0);
vec2 sample2R_texel_offset = vec2(3.0, 0.0) + vec2(texel3to4ratio, 0.0);
vec2 sample3d_texel_offset = vec2(1.0, 1.0) + vec2(texel1to2ratio, texel1to2ratio);
vec2 sample4d_texel_offset = vec2(3.0, 1.0) + vec2(texel3to4ratio, texel1to2ratio);
vec2 sample5d_texel_offset = vec2(1.0, 3.0) + vec2(texel1to2ratio, texel3to4ratio);
vec2 sample6d_texel_offset = vec2(3.0, 3.0) + vec2(texel3to4ratio, texel3to4ratio);
// CALCULATE KERNEL WEIGHTS FOR ALL SAMPLES:
// Statically compute Gaussian texel weights for the bottom-right quadrant.
// Read underscores as "and."
float w1R1 = w1off;
float w1R2 = w2off;
float w2R1 = w3off;
float w2R2 = w4off;
float w3d1 = exp(-LENGTH_SQ(vec2(1.0, 1.0)) * denom_inv);
float w3d2_3d3 = exp(-LENGTH_SQ(vec2(2.0, 1.0)) * denom_inv);
float w3d4 = exp(-LENGTH_SQ(vec2(2.0, 2.0)) * denom_inv);
float w4d1_5d1 = exp(-LENGTH_SQ(vec2(3.0, 1.0)) * denom_inv);
float w4d2_5d3 = exp(-LENGTH_SQ(vec2(4.0, 1.0)) * denom_inv);
float w4d3_5d2 = exp(-LENGTH_SQ(vec2(3.0, 2.0)) * denom_inv);
float w4d4_5d4 = exp(-LENGTH_SQ(vec2(4.0, 2.0)) * denom_inv);
float w6d1 = exp(-LENGTH_SQ(vec2(3.0, 3.0)) * denom_inv);
float w6d2_6d3 = exp(-LENGTH_SQ(vec2(4.0, 3.0)) * denom_inv);
float w6d4 = exp(-LENGTH_SQ(vec2(4.0, 4.0)) * denom_inv);
// Statically add texel weights in each sample to get sample weights:
float w0 = 1.0;
float w1 = w1R1 + w1R2;
float w2 = w2R1 + w2R2;
float w3 = w3d1 + 2.0 * w3d2_3d3 + w3d4;
float w4 = w4d1_5d1 + w4d2_5d3 + w4d3_5d2 + w4d4_5d4;
float w5 = w4;
float w6 = w6d1 + 2.0 * w6d2_6d3 + w6d4;
// Get the weight sum inverse (normalization factor):
float weight_sum_inv =
1.0/(w0 + 4.0 * (w1 + w2 + w3 + w4 + w5 + w6));
// LOAD TEXTURE SAMPLES:
// Load all 25 samples (1 nearest, 8 linear, 16 bilinear) using symmetry:
vec2 mirror_x = vec2(-1.0, 1.0);
vec2 mirror_y = vec2(1.0, -1.0);
vec2 mirror_xy = vec2(-1.0, -1.0);
vec2 dxdy_mirror_x = dxdy * mirror_x;
vec2 dxdy_mirror_y = dxdy * mirror_y;
vec2 dxdy_mirror_xy = dxdy * mirror_xy;
// Sampling order doesn't seem to affect performance, so just be clear:
vec3 sample0C = tex2D_linearize(tex, tex_uv).rgb;
vec3 sample1R = tex2D_linearize(tex, tex_uv + dxdy * sample1R_texel_offset).rgb;
vec3 sample1D = tex2D_linearize(tex, tex_uv + dxdy * sample1R_texel_offset.yx).rgb;
vec3 sample1L = tex2D_linearize(tex, tex_uv - dxdy * sample1R_texel_offset).rgb;
vec3 sample1U = tex2D_linearize(tex, tex_uv - dxdy * sample1R_texel_offset.yx).rgb;
vec3 sample2R = tex2D_linearize(tex, tex_uv + dxdy * sample2R_texel_offset).rgb;
vec3 sample2D = tex2D_linearize(tex, tex_uv + dxdy * sample2R_texel_offset.yx).rgb;
vec3 sample2L = tex2D_linearize(tex, tex_uv - dxdy * sample2R_texel_offset).rgb;
vec3 sample2U = tex2D_linearize(tex, tex_uv - dxdy * sample2R_texel_offset.yx).rgb;
vec3 sample3d = tex2D_linearize(tex, tex_uv + dxdy * sample3d_texel_offset).rgb;
vec3 sample3c = tex2D_linearize(tex, tex_uv + dxdy_mirror_x * sample3d_texel_offset).rgb;
vec3 sample3b = tex2D_linearize(tex, tex_uv + dxdy_mirror_y * sample3d_texel_offset).rgb;
vec3 sample3a = tex2D_linearize(tex, tex_uv + dxdy_mirror_xy * sample3d_texel_offset).rgb;
vec3 sample4d = tex2D_linearize(tex, tex_uv + dxdy * sample4d_texel_offset).rgb;
vec3 sample4c = tex2D_linearize(tex, tex_uv + dxdy_mirror_x * sample4d_texel_offset).rgb;
vec3 sample4b = tex2D_linearize(tex, tex_uv + dxdy_mirror_y * sample4d_texel_offset).rgb;
vec3 sample4a = tex2D_linearize(tex, tex_uv + dxdy_mirror_xy * sample4d_texel_offset).rgb;
vec3 sample5d = tex2D_linearize(tex, tex_uv + dxdy * sample5d_texel_offset).rgb;
vec3 sample5c = tex2D_linearize(tex, tex_uv + dxdy_mirror_x * sample5d_texel_offset).rgb;
vec3 sample5b = tex2D_linearize(tex, tex_uv + dxdy_mirror_y * sample5d_texel_offset).rgb;
vec3 sample5a = tex2D_linearize(tex, tex_uv + dxdy_mirror_xy * sample5d_texel_offset).rgb;
vec3 sample6d = tex2D_linearize(tex, tex_uv + dxdy * sample6d_texel_offset).rgb;
vec3 sample6c = tex2D_linearize(tex, tex_uv + dxdy_mirror_x * sample6d_texel_offset).rgb;
vec3 sample6b = tex2D_linearize(tex, tex_uv + dxdy_mirror_y * sample6d_texel_offset).rgb;
vec3 sample6a = tex2D_linearize(tex, tex_uv + dxdy_mirror_xy * sample6d_texel_offset).rgb;
// SUM WEIGHTED SAMPLES:
// Statically normalize weights (so total = 1.0), and sum weighted samples.
vec3 sum = w0 * sample0C;
sum += w1 * (sample1R + sample1D + sample1L + sample1U);
sum += w2 * (sample2R + sample2D + sample2L + sample2U);
sum += w3 * (sample3d + sample3c + sample3b + sample3a);
sum += w4 * (sample4d + sample4c + sample4b + sample4a);
sum += w5 * (sample5d + sample5c + sample5b + sample5a);
sum += w6 * (sample6d + sample6c + sample6b + sample6a);
return sum * weight_sum_inv;
}
vec3 tex2Dblur7x7(sampler2D tex, vec2 tex_uv,
vec2 dxdy, float sigma)
{
// Perform a 1-pass 7x7 blur with 5x5 bilinear samples.
// Requires: Same as tex2Dblur9()
// Returns: A 7x7 Gaussian blurred mipmapped texture lookup composed of
// 4x4 carefully selected bilinear samples.
// Description:
// First see the descriptions for tex2Dblur9x9() and tex2Dblur7(). This
// blur mixes concepts from both. The sample layout is as follows:
// 4a 3a 3b 4b
// 2a 1a 1b 2b
// 2c 1c 1d 2d
// 4c 3c 3d 4d
// The texel layout is as follows. Note that samples 3a/3b, 1a/1b, 1c/1d,
// and 3c/3d share a vertical column of texels, and samples 2a/2c, 1a/1c,
// 1b/1d, and 2b/2d share a horizontal row of texels (all sample1's share
// the center texel):
// 4a4 4a3 3a4 3ab3 3b4 4b3 4b4
// 4a2 4a1 3a2 3ab1 3b2 4b1 4b2
// 2a4 2a3 1a4 1ab3 1b4 2b3 2b4
// 2ac2 2ac1 1ac2 1* 1bd2 2bd1 2bd2
// 2c4 2c3 1c4 1cd3 1d4 2d3 2d4
// 4c2 4c1 3c2 3cd1 3d2 4d1 4d2
// 4c4 4c3 3c4 3cd3 3d4 4d3 4d4
// COMPUTE TEXTURE COORDS:
// Statically compute bilinear sampling offsets (details in tex2Dblur9x9).
float denom_inv = 0.5/(sigma*sigma);
float w0off = 1.0;
float w1off = exp(-1.0 * denom_inv);
float w2off = exp(-4.0 * denom_inv);
float w3off = exp(-9.0 * denom_inv);
float texel0to1ratio = w1off/(w0off * 0.5 + w1off);
float texel2to3ratio = w3off/(w2off + w3off);
// Statically compute texel offsets from the fragment center to each
// bilinear sample in the bottom-right quadrant, including axis-aligned:
vec2 sample1d_texel_offset = vec2(texel0to1ratio, texel0to1ratio);
vec2 sample2d_texel_offset = vec2(2.0, 0.0) + vec2(texel2to3ratio, texel0to1ratio);
vec2 sample3d_texel_offset = vec2(0.0, 2.0) + vec2(texel0to1ratio, texel2to3ratio);
vec2 sample4d_texel_offset = vec2(2.0, 2.0) + vec2(texel2to3ratio, texel2to3ratio);
// CALCULATE KERNEL WEIGHTS FOR ALL SAMPLES:
// Statically compute Gaussian texel weights for the bottom-right quadrant.
// Read underscores as "and."
float w1abcd = 1.0;
float w1bd2_1cd3 = exp(-LENGTH_SQ(vec2(1.0, 0.0)) * denom_inv);
float w2bd1_3cd1 = exp(-LENGTH_SQ(vec2(2.0, 0.0)) * denom_inv);
float w2bd2_3cd2 = exp(-LENGTH_SQ(vec2(3.0, 0.0)) * denom_inv);
float w1d4 = exp(-LENGTH_SQ(vec2(1.0, 1.0)) * denom_inv);
float w2d3_3d2 = exp(-LENGTH_SQ(vec2(2.0, 1.0)) * denom_inv);
float w2d4_3d4 = exp(-LENGTH_SQ(vec2(3.0, 1.0)) * denom_inv);
float w4d1 = exp(-LENGTH_SQ(vec2(2.0, 2.0)) * denom_inv);
float w4d2_4d3 = exp(-LENGTH_SQ(vec2(3.0, 2.0)) * denom_inv);
float w4d4 = exp(-LENGTH_SQ(vec2(3.0, 3.0)) * denom_inv);
// Statically add texel weights in each sample to get sample weights.
// Split weights for shared texels between samples sharing them:
float w1 = w1abcd * 0.25 + w1bd2_1cd3 + w1d4;
float w2_3 = (w2bd1_3cd1 + w2bd2_3cd2) * 0.5 + w2d3_3d2 + w2d4_3d4;
float w4 = w4d1 + 2.0 * w4d2_4d3 + w4d4;
// Get the weight sum inverse (normalization factor):
float weight_sum_inv =
1.0/(4.0 * (w1 + 2.0 * w2_3 + w4));
// LOAD TEXTURE SAMPLES:
// Load all 16 samples using symmetry:
vec2 mirror_x = vec2(-1.0, 1.0);
vec2 mirror_y = vec2(1.0, -1.0);
vec2 mirror_xy = vec2(-1.0, -1.0);
vec2 dxdy_mirror_x = dxdy * mirror_x;
vec2 dxdy_mirror_y = dxdy * mirror_y;
vec2 dxdy_mirror_xy = dxdy * mirror_xy;
vec3 sample1a = tex2D_linearize(tex, tex_uv + dxdy_mirror_xy * sample1d_texel_offset).rgb;
vec3 sample2a = tex2D_linearize(tex, tex_uv + dxdy_mirror_xy * sample2d_texel_offset).rgb;
vec3 sample3a = tex2D_linearize(tex, tex_uv + dxdy_mirror_xy * sample3d_texel_offset).rgb;
vec3 sample4a = tex2D_linearize(tex, tex_uv + dxdy_mirror_xy * sample4d_texel_offset).rgb;
vec3 sample1b = tex2D_linearize(tex, tex_uv + dxdy_mirror_y * sample1d_texel_offset).rgb;
vec3 sample2b = tex2D_linearize(tex, tex_uv + dxdy_mirror_y * sample2d_texel_offset).rgb;
vec3 sample3b = tex2D_linearize(tex, tex_uv + dxdy_mirror_y * sample3d_texel_offset).rgb;
vec3 sample4b = tex2D_linearize(tex, tex_uv + dxdy_mirror_y * sample4d_texel_offset).rgb;
vec3 sample1c = tex2D_linearize(tex, tex_uv + dxdy_mirror_x * sample1d_texel_offset).rgb;
vec3 sample2c = tex2D_linearize(tex, tex_uv + dxdy_mirror_x * sample2d_texel_offset).rgb;
vec3 sample3c = tex2D_linearize(tex, tex_uv + dxdy_mirror_x * sample3d_texel_offset).rgb;
vec3 sample4c = tex2D_linearize(tex, tex_uv + dxdy_mirror_x * sample4d_texel_offset).rgb;
vec3 sample1d = tex2D_linearize(tex, tex_uv + dxdy * sample1d_texel_offset).rgb;
vec3 sample2d = tex2D_linearize(tex, tex_uv + dxdy * sample2d_texel_offset).rgb;
vec3 sample3d = tex2D_linearize(tex, tex_uv + dxdy * sample3d_texel_offset).rgb;
vec3 sample4d = tex2D_linearize(tex, tex_uv + dxdy * sample4d_texel_offset).rgb;
// SUM WEIGHTED SAMPLES:
// Statically normalize weights (so total = 1.0), and sum weighted samples.
vec3 sum = vec3(0.0);
sum += w1 * (sample1a + sample1b + sample1c + sample1d);
sum += w2_3 * (sample2a + sample2b + sample2c + sample2d);
sum += w2_3 * (sample3a + sample3b + sample3c + sample3d);
sum += w4 * (sample4a + sample4b + sample4c + sample4d);
return sum * weight_sum_inv;
}
vec3 tex2Dblur5x5(sampler2D tex, vec2 tex_uv,
vec2 dxdy, float sigma)
{
// Perform a 1-pass 5x5 blur with 3x3 bilinear samples.
// Requires: Same as tex2Dblur9()
// Returns: A 5x5 Gaussian blurred mipmapped texture lookup composed of
// 3x3 carefully selected bilinear samples.
// Description:
// First see the description for tex2Dblur9x9(). This blur uses the same
// concept and sample/texel locations except on a smaller scale. Samples:
// 2a 1U 2b
// 1L 0C 1R
// 2c 1D 2d
// Texels:
// 2a4 2a3 1U2 2b3 2b4
// 2a2 2a1 1U1 2b1 2b2
// 1L2 1L1 0C1 1R1 1R2
// 2c2 2c1 1D1 2d1 2d2
// 2c4 2c3 1D2 2d3 2d4
// COMPUTE TEXTURE COORDS:
// Statically compute bilinear sampling offsets (details in tex2Dblur9x9).
float denom_inv = 0.5/(sigma*sigma);
float w1off = exp(-1.0 * denom_inv);
float w2off = exp(-4.0 * denom_inv);
float texel1to2ratio = w2off/(w1off + w2off);
// Statically compute texel offsets from the fragment center to each
// bilinear sample in the bottom-right quadrant, including x-axis-aligned:
vec2 sample1R_texel_offset = vec2(1.0, 0.0) + vec2(texel1to2ratio, 0.0);
vec2 sample2d_texel_offset = vec2(1.0, 1.0) + vec2(texel1to2ratio, texel1to2ratio);
// CALCULATE KERNEL WEIGHTS FOR ALL SAMPLES:
// Statically compute Gaussian texel weights for the bottom-right quadrant.
// Read underscores as "and."
float w1R1 = w1off;
float w1R2 = w2off;
float w2d1 = exp(-LENGTH_SQ(vec2(1.0, 1.0)) * denom_inv);
float w2d2_3 = exp(-LENGTH_SQ(vec2(2.0, 1.0)) * denom_inv);
float w2d4 = exp(-LENGTH_SQ(vec2(2.0, 2.0)) * denom_inv);
// Statically add texel weights in each sample to get sample weights:
float w0 = 1.0;
float w1 = w1R1 + w1R2;
float w2 = w2d1 + 2.0 * w2d2_3 + w2d4;
// Get the weight sum inverse (normalization factor):
float weight_sum_inv = 1.0/(w0 + 4.0 * (w1 + w2));
// LOAD TEXTURE SAMPLES:
// Load all 9 samples (1 nearest, 4 linear, 4 bilinear) using symmetry:
vec2 mirror_x = vec2(-1.0, 1.0);
vec2 mirror_y = vec2(1.0, -1.0);
vec2 mirror_xy = vec2(-1.0, -1.0);
vec2 dxdy_mirror_x = dxdy * mirror_x;
vec2 dxdy_mirror_y = dxdy * mirror_y;
vec2 dxdy_mirror_xy = dxdy * mirror_xy;
vec3 sample0C = tex2D_linearize(tex, tex_uv).rgb;
vec3 sample1R = tex2D_linearize(tex, tex_uv + dxdy * sample1R_texel_offset).rgb;
vec3 sample1D = tex2D_linearize(tex, tex_uv + dxdy * sample1R_texel_offset.yx).rgb;
vec3 sample1L = tex2D_linearize(tex, tex_uv - dxdy * sample1R_texel_offset).rgb;
vec3 sample1U = tex2D_linearize(tex, tex_uv - dxdy * sample1R_texel_offset.yx).rgb;
vec3 sample2d = tex2D_linearize(tex, tex_uv + dxdy * sample2d_texel_offset).rgb;
vec3 sample2c = tex2D_linearize(tex, tex_uv + dxdy_mirror_x * sample2d_texel_offset).rgb;
vec3 sample2b = tex2D_linearize(tex, tex_uv + dxdy_mirror_y * sample2d_texel_offset).rgb;
vec3 sample2a = tex2D_linearize(tex, tex_uv + dxdy_mirror_xy * sample2d_texel_offset).rgb;
// SUM WEIGHTED SAMPLES:
// Statically normalize weights (so total = 1.0), and sum weighted samples.
vec3 sum = w0 * sample0C;
sum += w1 * (sample1R + sample1D + sample1L + sample1U);
sum += w2 * (sample2a + sample2b + sample2c + sample2d);
return sum * weight_sum_inv;
}
vec3 tex2Dblur3x3(sampler2D tex, vec2 tex_uv,
vec2 dxdy, float sigma)
{
// Perform a 1-pass 3x3 blur with 5x5 bilinear samples.
// Requires: Same as tex2Dblur9()
// Returns: A 3x3 Gaussian blurred mipmapped texture lookup composed of
// 2x2 carefully selected bilinear samples.
// Description:
// First see the descriptions for tex2Dblur9x9() and tex2Dblur7(). This
// blur mixes concepts from both. The sample layout is as follows:
// 0a 0b
// 0c 0d
// The texel layout is as follows. Note that samples 0a/0b and 0c/0d share
// a vertical column of texels, and samples 0a/0c and 0b/0d share a
// horizontal row of texels (all samples share the center texel):
// 0a3 0ab2 0b3
// 0ac1 0*0 0bd1
// 0c3 0cd2 0d3
// COMPUTE TEXTURE COORDS:
// Statically compute bilinear sampling offsets (details in tex2Dblur9x9).
float denom_inv = 0.5/(sigma*sigma);
float w0off = 1.0;
float w1off = exp(-1.0 * denom_inv);
float texel0to1ratio = w1off/(w0off * 0.5 + w1off);
// Statically compute texel offsets from the fragment center to each
// bilinear sample in the bottom-right quadrant, including axis-aligned:
vec2 sample0d_texel_offset = vec2(texel0to1ratio, texel0to1ratio);
// LOAD TEXTURE SAMPLES:
// Load all 4 samples using symmetry:
vec2 mirror_x = vec2(-1.0, 1.0);
vec2 mirror_y = vec2(1.0, -1.0);
vec2 mirror_xy = vec2(-1.0, -1.0);
vec2 dxdy_mirror_x = dxdy * mirror_x;
vec2 dxdy_mirror_y = dxdy * mirror_y;
vec2 dxdy_mirror_xy = dxdy * mirror_xy;
vec3 sample0a = tex2D_linearize(tex, tex_uv + dxdy_mirror_xy * sample0d_texel_offset).rgb;
vec3 sample0b = tex2D_linearize(tex, tex_uv + dxdy_mirror_y * sample0d_texel_offset).rgb;
vec3 sample0c = tex2D_linearize(tex, tex_uv + dxdy_mirror_x * sample0d_texel_offset).rgb;
vec3 sample0d = tex2D_linearize(tex, tex_uv + dxdy * sample0d_texel_offset).rgb;
// SUM WEIGHTED SAMPLES:
// Weights for all samples are the same, so just average them:
return 0.25 * (sample0a + sample0b + sample0c + sample0d);
}
vec3 tex2Dblur9fast(sampler2D tex, vec2 tex_uv,
vec2 dxdy)
{
return tex2Dblur9fast(tex, tex_uv, dxdy, blur9_std_dev);
}
vec3 tex2Dblur17fast(sampler2D texture, vec2 tex_uv,
vec2 dxdy)
{
return tex2Dblur17fast(texture, tex_uv, dxdy, blur17_std_dev);
}
vec3 tex2Dblur25fast(sampler2D texture, vec2 tex_uv,
vec2 dxdy)
{
return tex2Dblur25fast(texture, tex_uv, dxdy, blur25_std_dev);
}
vec3 tex2Dblur43fast(sampler2D texture, vec2 tex_uv,
vec2 dxdy)
{
return tex2Dblur43fast(texture, tex_uv, dxdy, blur43_std_dev);
}
vec3 tex2Dblur31fast(sampler2D texture, vec2 tex_uv,
vec2 dxdy)
{
return tex2Dblur31fast(texture, tex_uv, dxdy, blur31_std_dev);
}
vec3 tex2Dblur3fast(sampler2D texture, vec2 tex_uv,
vec2 dxdy)
{
return tex2Dblur3fast(texture, tex_uv, dxdy, blur3_std_dev);
}
vec3 tex2Dblur3x3(sampler2D texture, vec2 tex_uv,
vec2 dxdy)
{
return tex2Dblur3x3(texture, tex_uv, dxdy, blur3_std_dev);
}
vec3 tex2Dblur5fast(sampler2D texture, vec2 tex_uv,
vec2 dxdy)
{
return tex2Dblur5fast(texture, tex_uv, dxdy, blur5_std_dev);
}
vec3 tex2Dblur5resize(sampler2D texture, vec2 tex_uv,
vec2 dxdy)
{
return tex2Dblur5resize(texture, tex_uv, dxdy, blur5_std_dev);
}
vec3 tex2Dblur3resize(sampler2D texture, vec2 tex_uv,
vec2 dxdy)
{
return tex2Dblur3resize(texture, tex_uv, dxdy, blur3_std_dev);
}
vec3 tex2Dblur5x5(sampler2D texture, vec2 tex_uv,
vec2 dxdy)
{
return tex2Dblur5x5(texture, tex_uv, dxdy, blur5_std_dev);
}
vec3 tex2Dblur7resize(sampler2D texture, vec2 tex_uv,
vec2 dxdy)
{
return tex2Dblur7resize(texture, tex_uv, dxdy, blur7_std_dev);
}
vec3 tex2Dblur7fast(sampler2D texture, vec2 tex_uv,
vec2 dxdy)
{
return tex2Dblur7fast(texture, tex_uv, dxdy, blur7_std_dev);
}
vec3 tex2Dblur7x7(sampler2D texture, vec2 tex_uv,
vec2 dxdy)
{
return tex2Dblur7x7(texture, tex_uv, dxdy, blur7_std_dev);
}
vec3 tex2Dblur9resize(sampler2D texture, vec2 tex_uv,
vec2 dxdy)
{
return tex2Dblur9resize(texture, tex_uv, dxdy, blur9_std_dev);
}
vec3 tex2Dblur9x9(sampler2D texture, vec2 tex_uv,
vec2 dxdy)
{
return tex2Dblur9x9(texture, tex_uv, dxdy, blur9_std_dev);
}
vec3 tex2Dblur11resize(sampler2D texture, vec2 tex_uv,
vec2 dxdy)
{
return tex2Dblur11resize(texture, tex_uv, dxdy, blur11_std_dev);
}
vec3 tex2Dblur11fast(sampler2D texture, vec2 tex_uv,
vec2 dxdy)
{
return tex2Dblur11fast(texture, tex_uv, dxdy, blur11_std_dev);
}
#endif // BLUR_FUNCTIONS_H
#define InputSize sourceSize[0].xy
#define TextureSize sourceSize[0].xy
#define OutputSize targetSize.xy
void main() {
gl_Position = position;
vTexCoord = texCoord;
// Get the uv sample distance between output pixels. Blurs are not generic
// Gaussian resizers, and correct blurs require:
// 1.) OutputSize == InputSize * 2^m, where m is an integer <= 0.
// 2.) mipmap_inputN = "true" for this pass in the preset if m != 0
// 3.) filter_linearN = "true" except for 1x scale nearest neighbor blurs
// Gaussian resizers would upsize using the distance between input texels
// (not output pixels), but we avoid this and consistently blur at the
// destination size. Otherwise, combining statically calculated weights
// with bilinear sample exploitation would result in terrible artifacts.
vec2 dxdy_scale = InputSize/OutputSize;
vec2 dxdy = dxdy_scale/TextureSize;
// This blur is vertical-only, so zero out the horizontal offset:
blur_dxdy = vec2(0.0, dxdy.y);
}