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This new vo is heavily based on vo_gl.c. It provides better scale filters, dithering, and optional color management with LittleCMS2. It requires OpenGL 3. Many features are enabled by default, so it will be slower than vo_gl. However, it can be tuned to behave almost as vo_gl.
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| /TAGS | ||
| /locale | ||
| /po | ||
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| libvo/vo_gl3_shaders.h | ||
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| /* | ||
| * 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. | ||
| */ | ||
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| #include <stddef.h> | ||
| #include <string.h> | ||
| #include <math.h> | ||
| #include <assert.h> | ||
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| #include "filter_kernels.h" | ||
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| // NOTE: all filters are separable, symmetric, and are intended for use with | ||
| // a lookup table/texture. | ||
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| 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; | ||
| } | ||
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| // 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; | ||
| } | ||
| } | ||
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| // 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; | ||
| } | ||
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| // 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); | ||
| } | ||
| } | ||
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| typedef struct filter_kernel kernel; | ||
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| static double bilinear(kernel *k, double x) | ||
| { | ||
| return 1.0 - x; | ||
| } | ||
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| static double hanning(kernel *k, double x) | ||
| { | ||
| return 0.5 + 0.5 * cos(M_PI * x); | ||
| } | ||
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| static double hamming(kernel *k, double x) | ||
| { | ||
| return 0.54 + 0.46 * cos(M_PI * x); | ||
| } | ||
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| static double hermite(kernel *k, double x) | ||
| { | ||
| return (2.0 * x - 3.0) * x * x + 1.0; | ||
| } | ||
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| 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; | ||
| } | ||
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| static double bc_pow3(double x) | ||
| { | ||
| return (x <= 0) ? 0 : x * x * x; | ||
| } | ||
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| 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)); | ||
| } | ||
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| 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; | ||
| } | ||
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| 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; | ||
| } | ||
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| 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; | ||
| } | ||
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| // 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; | ||
| } | ||
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| 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); | ||
| } | ||
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| 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); | ||
| } | ||
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| static double gaussian(kernel *k, double x) | ||
| { | ||
| return exp(-2.0 * x * x) * sqrt(2.0 / M_PI); | ||
| } | ||
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| static double sinc(kernel *k, double x) | ||
| { | ||
| if (x == 0.0) | ||
| return 1.0; | ||
| double pix = M_PI * x; | ||
| return sin(pix) / pix; | ||
| } | ||
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| 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); | ||
| } | ||
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| 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)); | ||
| } | ||
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| 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} | ||
| }; |
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