mirror of https://github.com/mpv-player/mpv
280 lines
8.0 KiB
C
280 lines
8.0 KiB
C
|
/*
|
||
|
* This file is part of mplayer2.
|
||
|
*
|
||
|
* Most code for computing the weights is taken from Anti-Grain Geometry (AGG)
|
||
|
* (licensed under GPL 2 or later), with modifications.
|
||
|
* Copyright (C) 2002-2006 Maxim Shemanarev
|
||
|
* http://vector-agg.cvs.sourceforge.net/viewvc/vector-agg/agg-2.5/include/agg_image_filters.h?view=markup
|
||
|
*
|
||
|
* Also see glumpy (BSD licensed), contains the same code in Python:
|
||
|
* http://code.google.com/p/glumpy/source/browse/glumpy/image/filter.py
|
||
|
*
|
||
|
* Also see: Paul Heckbert's "zoom"
|
||
|
*
|
||
|
* Also see XBMC: ConvolutionKernels.cpp etc.
|
||
|
*
|
||
|
* mplayer2 is free software; you can redistribute it and/or modify
|
||
|
* it under the terms of the GNU General Public License as published by
|
||
|
* the Free Software Foundation; either version 2 of the License, or
|
||
|
* (at your option) any later version.
|
||
|
*
|
||
|
* mplayer2 is distributed in the hope that it will be useful,
|
||
|
* but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||
|
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||
|
* GNU General Public License for more details.
|
||
|
*
|
||
|
* You should have received a copy of the GNU General Public License along
|
||
|
* with mplayer2; if not, write to the Free Software Foundation, Inc.,
|
||
|
* 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
|
||
|
*/
|
||
|
|
||
|
#include <stddef.h>
|
||
|
#include <string.h>
|
||
|
#include <math.h>
|
||
|
#include <assert.h>
|
||
|
|
||
|
#include "filter_kernels.h"
|
||
|
|
||
|
// NOTE: all filters are separable, symmetric, and are intended for use with
|
||
|
// a lookup table/texture.
|
||
|
|
||
|
const struct filter_kernel *mp_find_filter_kernel(const char *name)
|
||
|
{
|
||
|
for (const struct filter_kernel *k = mp_filter_kernels; k->name; k++) {
|
||
|
if (strcmp(k->name, name) == 0)
|
||
|
return k;
|
||
|
}
|
||
|
return NULL;
|
||
|
}
|
||
|
|
||
|
// sizes = sorted list of available filter sizes, terminated with size 0
|
||
|
// inv_scale = source_size / dest_size
|
||
|
bool mp_init_filter(struct filter_kernel *filter, const int *sizes,
|
||
|
double inv_scale)
|
||
|
{
|
||
|
// only downscaling requires widening the filter
|
||
|
filter->inv_scale = inv_scale >= 1.0 ? inv_scale : 1.0;
|
||
|
double support = filter->radius * filter->inv_scale;
|
||
|
int size = ceil(2.0 * support);
|
||
|
// round up to smallest available size that's still large enough
|
||
|
if (size < sizes[0])
|
||
|
size = sizes[0];
|
||
|
const int *cursize = sizes;
|
||
|
while (size > *cursize && *cursize)
|
||
|
cursize++;
|
||
|
if (*cursize) {
|
||
|
filter->size = *cursize;
|
||
|
return true;
|
||
|
} else {
|
||
|
// The filter doesn't fit - instead of failing completely, use the
|
||
|
// largest filter available. This is incorrect, but better than refusing
|
||
|
// to do anything.
|
||
|
filter->size = cursize[-1];
|
||
|
filter->inv_scale = filter->size / 2.0 / filter->radius;
|
||
|
return false;
|
||
|
}
|
||
|
}
|
||
|
|
||
|
// Calculate the 1D filtering kernel for N sample points.
|
||
|
// N = number of samples, which is filter->size
|
||
|
// The weights will be stored in out_w[0] to out_w[N - 1]
|
||
|
// f = x0 - abs(x0), subpixel position in the range [0,1) or [0,1].
|
||
|
void mp_compute_weights(struct filter_kernel *filter, double f, float *out_w)
|
||
|
{
|
||
|
assert(filter->size > 0);
|
||
|
double sum = 0;
|
||
|
for (int n = 0; n < filter->size; n++) {
|
||
|
double x = f - (n - filter->size / 2 + 1);
|
||
|
double w = filter->weight(filter, fabs(x) / filter->inv_scale);
|
||
|
out_w[n] = w;
|
||
|
sum += w;
|
||
|
}
|
||
|
//normalize
|
||
|
for (int n = 0; n < filter->size; n++)
|
||
|
out_w[n] /= sum;
|
||
|
}
|
||
|
|
||
|
// Fill the given array with weights for the range [0.0, 1.0]. The array is
|
||
|
// interpreted as rectangular array of count * filter->size items.
|
||
|
void mp_compute_lut(struct filter_kernel *filter, int count, float *out_array)
|
||
|
{
|
||
|
for (int n = 0; n < count; n++) {
|
||
|
mp_compute_weights(filter, n / (double)(count - 1),
|
||
|
out_array + filter->size * n);
|
||
|
}
|
||
|
}
|
||
|
|
||
|
typedef struct filter_kernel kernel;
|
||
|
|
||
|
static double bilinear(kernel *k, double x)
|
||
|
{
|
||
|
return 1.0 - x;
|
||
|
}
|
||
|
|
||
|
static double hanning(kernel *k, double x)
|
||
|
{
|
||
|
return 0.5 + 0.5 * cos(M_PI * x);
|
||
|
}
|
||
|
|
||
|
static double hamming(kernel *k, double x)
|
||
|
{
|
||
|
return 0.54 + 0.46 * cos(M_PI * x);
|
||
|
}
|
||
|
|
||
|
static double hermite(kernel *k, double x)
|
||
|
{
|
||
|
return (2.0 * x - 3.0) * x * x + 1.0;
|
||
|
}
|
||
|
|
||
|
static double quadric(kernel *k, double x)
|
||
|
{
|
||
|
// NOTE: glumpy uses 0.75, AGG uses 0.5
|
||
|
if (x < 0.5)
|
||
|
return 0.75 - x * x;
|
||
|
if (x < 1.5)
|
||
|
return 0.5 * (x - 1.5) * (x - 1.5);
|
||
|
return 0;
|
||
|
}
|
||
|
|
||
|
static double bc_pow3(double x)
|
||
|
{
|
||
|
return (x <= 0) ? 0 : x * x * x;
|
||
|
}
|
||
|
|
||
|
static double bicubic(kernel *k, double x)
|
||
|
{
|
||
|
return (1.0/6.0) * ( bc_pow3(x + 2)
|
||
|
- 4 * bc_pow3(x + 1)
|
||
|
+ 6 * bc_pow3(x)
|
||
|
- 4 * bc_pow3(x - 1));
|
||
|
}
|
||
|
|
||
|
static double bessel_i0(double epsilon, double x)
|
||
|
{
|
||
|
double sum = 1;
|
||
|
double y = x * x / 4;
|
||
|
double t = y;
|
||
|
for (int i = 2; t > epsilon; i++) {
|
||
|
sum += t;
|
||
|
t *= y / (i * i);
|
||
|
}
|
||
|
return sum;
|
||
|
}
|
||
|
|
||
|
static double kaiser(kernel *k, double x)
|
||
|
{
|
||
|
double a = k->params[0];
|
||
|
double b = k->params[1];
|
||
|
double epsilon = 1e-12;
|
||
|
double i0a = 1 / bessel_i0(epsilon, b);
|
||
|
return bessel_i0(epsilon, a * sqrt(1 - x * x)) * i0a;
|
||
|
}
|
||
|
|
||
|
static double catmull_rom(kernel *k, double x)
|
||
|
{
|
||
|
if (x < 1.0)
|
||
|
return 0.5 * (2.0 + x * x * (-5.0 + x * 3.0));
|
||
|
if (x < 2.0)
|
||
|
return 0.5 * (4.0 + x * (-8.0 + x * (5.0 - x)));
|
||
|
return 0;
|
||
|
}
|
||
|
|
||
|
// Mitchell-Netravali
|
||
|
static double mitchell(kernel *k, double x)
|
||
|
{
|
||
|
double b = k->params[0];
|
||
|
double c = k->params[1];
|
||
|
double
|
||
|
p0 = (6.0 - 2.0 * b) / 6.0,
|
||
|
p2 = (-18.0 + 12.0 * b + 6.0 * c) / 6.0,
|
||
|
p3 = (12.0 - 9.0 * b - 6.0 * c) / 6.0,
|
||
|
q0 = (8.0 * b + 24.0 * c) / 6.0,
|
||
|
q1 = (-12.0 * b - 48.0 * c) / 6.0,
|
||
|
q2 = (6.0 * b + 30.0 * c) / 6.0,
|
||
|
q3 = (-b - 6.0 * c) / 6.0;
|
||
|
if (x < 1.0)
|
||
|
return p0 + x * x * (p2 + x * p3);
|
||
|
if (x < 2.0)
|
||
|
return q0 + x * (q1 + x * (q2 + x * q3));
|
||
|
return 0;
|
||
|
}
|
||
|
|
||
|
static double spline16(kernel *k, double x)
|
||
|
{
|
||
|
if (x < 1.0)
|
||
|
return ((x - 9.0/5.0 ) * x - 1.0/5.0 ) * x + 1.0;
|
||
|
return ((-1.0/3.0 * (x-1) + 4.0/5.0) * (x-1) - 7.0/15.0 ) * (x-1);
|
||
|
}
|
||
|
|
||
|
static double spline36(kernel *k, double x)
|
||
|
{
|
||
|
if(x < 1.0)
|
||
|
return ((13.0/11.0 * x - 453.0/209.0) * x - 3.0/209.0) * x + 1.0;
|
||
|
if(x < 2.0)
|
||
|
return ((-6.0/11.0 * (x - 1) + 270.0/209.0) * (x - 1) - 156.0/209.0)
|
||
|
* (x - 1);
|
||
|
return ((1.0/11.0 * (x - 2) - 45.0/209.0) * (x - 2) + 26.0/209.0)
|
||
|
* (x - 2);
|
||
|
}
|
||
|
|
||
|
static double gaussian(kernel *k, double x)
|
||
|
{
|
||
|
return exp(-2.0 * x * x) * sqrt(2.0 / M_PI);
|
||
|
}
|
||
|
|
||
|
static double sinc(kernel *k, double x)
|
||
|
{
|
||
|
if (x == 0.0)
|
||
|
return 1.0;
|
||
|
double pix = M_PI * x;
|
||
|
return sin(pix) / pix;
|
||
|
}
|
||
|
|
||
|
static double lanczos(kernel *k, double x)
|
||
|
{
|
||
|
double radius = k->size / 2;
|
||
|
if (x < -radius || x > radius)
|
||
|
return 0;
|
||
|
if (x == 0)
|
||
|
return 1;
|
||
|
double pix = M_PI * x;
|
||
|
return radius * sin(pix) * sin(pix / radius) / (pix * pix);
|
||
|
}
|
||
|
|
||
|
static double blackman(kernel *k, double x)
|
||
|
{
|
||
|
double radius = k->size / 2;
|
||
|
if (x == 0.0)
|
||
|
return 1.0;
|
||
|
if (x > radius)
|
||
|
return 0.0;
|
||
|
x *= M_PI;
|
||
|
double xr = x / radius;
|
||
|
return (sin(x) / x) * (0.42 + 0.5 * cos(xr) + 0.08 * cos(2 * xr));
|
||
|
}
|
||
|
|
||
|
const struct filter_kernel mp_filter_kernels[] = {
|
||
|
{"bilinear_slow", 1, bilinear},
|
||
|
{"hanning", 1, hanning},
|
||
|
{"hamming", 1, hamming},
|
||
|
{"hermite", 1, hermite},
|
||
|
{"quadric", 1.5, quadric},
|
||
|
{"bicubic", 2, bicubic},
|
||
|
{"kaiser", 1, kaiser, .params = {6.33, 6.33} },
|
||
|
{"catmull_rom", 2, catmull_rom},
|
||
|
{"mitchell", 2, mitchell, .params = {1.0/3.0, 1.0/3.0} },
|
||
|
{"spline16", 2, spline16},
|
||
|
{"spline36", 3, spline36},
|
||
|
{"gaussian", 2, gaussian},
|
||
|
{"sinc2", 2, sinc},
|
||
|
{"sinc3", 3, sinc},
|
||
|
{"sinc4", 4, sinc},
|
||
|
{"lanczos2", 2, lanczos},
|
||
|
{"lanczos3", 3, lanczos},
|
||
|
{"lanczos4", 4, lanczos},
|
||
|
{"blackman2", 2, blackman},
|
||
|
{"blackman3", 3, blackman},
|
||
|
{"blackman4", 4, blackman},
|
||
|
{0}
|
||
|
};
|