mirror of https://github.com/mpv-player/mpv
381 lines
13 KiB
C
381 lines
13 KiB
C
/*
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* Most code for computing the weights is taken from Anti-Grain Geometry (AGG)
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* (licensed under GPL 2 or later), with modifications.
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*
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* Copyright (C) 2002-2006 Maxim Shemanarev
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*
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* http://vector-agg.cvs.sourceforge.net/viewvc/vector-agg/agg-2.5/include/agg_image_filters.h?view=markup
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*
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* Also see:
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* - glumpy (BSD licensed), contains the same code in Python:
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* http://code.google.com/p/glumpy/source/browse/glumpy/image/filter.py
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* - Vapoursynth plugin fmtconv (WTFPL Licensed), which is based on
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* dither plugin for avisynth from the same author:
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* https://github.com/vapoursynth/fmtconv/tree/master/src/fmtc
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* - Paul Heckbert's "zoom"
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* - XBMC: ConvolutionKernels.cpp etc.
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*
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* This file is part of mpv.
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*
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* mpv is free software; you can redistribute it and/or modify
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* it under the terms of the GNU General Public License as published by
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* the Free Software Foundation; either version 2 of the License, or
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* (at your option) any later version.
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*
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* mpv is distributed in the hope that it will be useful,
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* but WITHOUT ANY WARRANTY; without even the implied warranty of
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* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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* GNU General Public License for more details.
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*
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* You should have received a copy of the GNU General Public License along
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* with mpv. If not, see <http://www.gnu.org/licenses/>.
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*/
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#include <stddef.h>
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#include <string.h>
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#include <math.h>
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#include <assert.h>
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#include "filter_kernels.h"
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// NOTE: all filters are designed for discrete convolution
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const struct filter_window *mp_find_filter_window(const char *name)
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{
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if (!name)
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return NULL;
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for (const struct filter_window *w = mp_filter_windows; w->name; w++) {
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if (strcmp(w->name, name) == 0)
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return w;
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}
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return NULL;
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}
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const struct filter_kernel *mp_find_filter_kernel(const char *name)
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{
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if (!name)
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return NULL;
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for (const struct filter_kernel *k = mp_filter_kernels; k->f.name; k++) {
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if (strcmp(k->f.name, name) == 0)
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return k;
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}
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return NULL;
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}
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// sizes = sorted list of available filter sizes, terminated with size 0
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// inv_scale = source_size / dest_size
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bool mp_init_filter(struct filter_kernel *filter, const int *sizes,
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double inv_scale)
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{
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assert(filter->f.radius > 0);
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// Only downscaling requires widening the filter
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filter->inv_scale = inv_scale >= 1.0 ? inv_scale : 1.0;
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filter->f.radius *= filter->inv_scale;
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// Polar filters are dependent solely on the radius
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if (filter->polar) {
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filter->f.radius = fmin(filter->f.radius, 16.0);
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filter->size = 1;
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// Safety precaution to avoid generating a gigantic shader
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if (filter->f.radius > 16.0) {
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filter->f.radius = 16.0;
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return false;
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}
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return true;
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}
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int size = ceil(2.0 * filter->f.radius);
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// round up to smallest available size that's still large enough
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if (size < sizes[0])
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size = sizes[0];
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const int *cursize = sizes;
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while (size > *cursize && *cursize)
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cursize++;
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if (*cursize) {
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filter->size = *cursize;
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return true;
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} else {
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// The filter doesn't fit - instead of failing completely, use the
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// largest filter available. This is incorrect, but better than refusing
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// to do anything.
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filter->size = cursize[-1];
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filter->inv_scale *= (filter->size/2.0) / filter->f.radius;
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return false;
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}
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}
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// Sample from the blurred, windowed kernel. Note: The window is always
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// stretched to the true radius, regardless of the filter blur/scale.
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static double sample_filter(struct filter_kernel *filter,
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struct filter_window *window, double x)
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{
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double bk = filter->f.blur > 0.0 ? filter->f.blur : 1.0;
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double bw = window->blur > 0.0 ? window->blur : 1.0;
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double c = fabs(x) / (filter->inv_scale * bk);
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double w = window->weight ? window->weight(window, x/bw * window->radius
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/ filter->f.radius)
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: 1.0;
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double v = c < filter->f.radius ? w * filter->f.weight(&filter->f, c) : 0.0;
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return filter->clamp ? fmax(0.0, fmin(1.0, v)) : v;
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}
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// Calculate the 1D filtering kernel for N sample points.
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// N = number of samples, which is filter->size
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// The weights will be stored in out_w[0] to out_w[N - 1]
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// f = x0 - abs(x0), subpixel position in the range [0,1) or [0,1].
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static void mp_compute_weights(struct filter_kernel *filter,
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struct filter_window *window,
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double f, float *out_w)
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{
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assert(filter->size > 0);
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double sum = 0;
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for (int n = 0; n < filter->size; n++) {
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double x = f - (n - filter->size / 2 + 1);
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double w = sample_filter(filter, window, x);
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out_w[n] = w;
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sum += w;
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}
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// Normalize to preserve energy
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for (int n = 0; n < filter->size; n++)
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out_w[n] /= sum;
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}
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// Fill the given array with weights for the range [0.0, 1.0]. The array is
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// interpreted as rectangular array of count * filter->size items.
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void mp_compute_lut(struct filter_kernel *filter, int count, float *out_array)
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{
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struct filter_window *window = &filter->w;
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if (filter->polar) {
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// Compute a 1D array indexed by radius
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for (int x = 0; x < count; x++) {
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double r = x * filter->f.radius / (count - 1);
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out_array[x] = sample_filter(filter, window, r);
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}
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} else {
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// Compute a 2D array indexed by subpixel position
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for (int n = 0; n < count; n++) {
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mp_compute_weights(filter, window, n / (double)(count - 1),
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out_array + filter->size * n);
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}
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}
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}
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typedef struct filter_window params;
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static double box(params *p, double x)
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{
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// This is mathematically 1.0 everywhere, the clipping is done implicitly
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// based on the radius.
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return 1.0;
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}
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static double triangle(params *p, double x)
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{
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return fmax(0.0, 1.0 - fabs(x / p->radius));
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}
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static double hanning(params *p, double x)
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{
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return 0.5 + 0.5 * cos(M_PI * x);
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}
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static double hamming(params *p, double x)
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{
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return 0.54 + 0.46 * cos(M_PI * x);
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}
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static double quadric(params *p, double x)
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{
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// NOTE: glumpy uses 0.75, AGG uses 0.5
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if (x < 0.5)
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return 0.75 - x * x;
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if (x < 1.5)
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return 0.5 * (x - 1.5) * (x - 1.5);
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return 0;
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}
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static double bc_pow3(double x)
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{
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return (x <= 0) ? 0 : x * x * x;
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}
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static double bicubic(params *p, double x)
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{
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return (1.0/6.0) * ( bc_pow3(x + 2)
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- 4 * bc_pow3(x + 1)
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+ 6 * bc_pow3(x)
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- 4 * bc_pow3(x - 1));
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}
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static double bessel_i0(double epsilon, double x)
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{
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double sum = 1;
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double y = x * x / 4;
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double t = y;
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for (int i = 2; t > epsilon; i++) {
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sum += t;
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t *= y / (i * i);
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}
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return sum;
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}
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static double kaiser(params *p, double x)
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{
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double a = p->params[0];
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double epsilon = 1e-12;
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double i0a = 1 / bessel_i0(epsilon, a);
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return bessel_i0(epsilon, a * sqrt(1 - x * x)) * i0a;
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}
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static double blackman(params *p, double x)
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{
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double a = p->params[0];
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double a0 = (1-a)/2.0, a1 = 1/2.0, a2 = a/2.0;
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double pix = M_PI * x;
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return a0 + a1*cos(pix) + a2*cos(2 * pix);
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}
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static double welch(params *p, double x)
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{
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return 1.0 - x*x;
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}
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// Family of cubic B/C splines
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static double cubic_bc(params *p, double x)
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{
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double b = p->params[0];
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double c = p->params[1];
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double
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p0 = (6.0 - 2.0 * b) / 6.0,
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p2 = (-18.0 + 12.0 * b + 6.0 * c) / 6.0,
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p3 = (12.0 - 9.0 * b - 6.0 * c) / 6.0,
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q0 = (8.0 * b + 24.0 * c) / 6.0,
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q1 = (-12.0 * b - 48.0 * c) / 6.0,
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q2 = (6.0 * b + 30.0 * c) / 6.0,
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q3 = (-b - 6.0 * c) / 6.0;
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if (x < 1.0)
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return p0 + x * x * (p2 + x * p3);
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if (x < 2.0)
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return q0 + x * (q1 + x * (q2 + x * q3));
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return 0;
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}
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static double spline16(params *p, double x)
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{
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if (x < 1.0)
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return ((x - 9.0/5.0 ) * x - 1.0/5.0 ) * x + 1.0;
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return ((-1.0/3.0 * (x-1) + 4.0/5.0) * (x-1) - 7.0/15.0 ) * (x-1);
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}
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static double spline36(params *p, double x)
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{
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if(x < 1.0)
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return ((13.0/11.0 * x - 453.0/209.0) * x - 3.0/209.0) * x + 1.0;
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if(x < 2.0)
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return ((-6.0/11.0 * (x - 1) + 270.0/209.0) * (x - 1) - 156.0/209.0)
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* (x - 1);
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return ((1.0/11.0 * (x - 2) - 45.0/209.0) * (x - 2) + 26.0/209.0)
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* (x - 2);
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}
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static double spline64(params *p, double x)
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{
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if (x < 1.0)
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return ((49.0 / 41.0 * x - 6387.0 / 2911.0) * x - 3.0 / 2911.0) * x + 1.0;
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if (x < 2.0)
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return ((-24.0 / 41.0 * (x - 1) + 4032.0 / 2911.0) * (x - 1) - 2328.0 / 2911.0)
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* (x - 1);
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if (x < 3.0)
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return ((6.0 / 41.0 * (x - 2) - 1008.0 / 2911.0) * (x - 2) + 582.0 / 2911.0)
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* (x - 2);
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return ((-1.0 / 41.0 * (x - 3) + 168.0 / 2911.0) * (x - 3) - 97.0 / 2911.0)
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* (x - 3);
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}
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static double gaussian(params *p, double x)
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{
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return pow(2.0, -(M_E / p->params[0]) * x * x);
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}
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static double sinc(params *p, double x)
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{
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if (fabs(x) < 1e-8)
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return 1.0;
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double pix = M_PI * x;
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return sin(pix) / pix;
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}
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static double jinc(params *p, double x)
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{
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if (fabs(x) < 1e-8)
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return 1.0;
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double pix = M_PI * x;
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return 2.0 * j1(pix) / pix;
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}
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static double sphinx(params *p, double x)
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{
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if (fabs(x) < 1e-8)
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return 1.0;
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double pix = M_PI * x;
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return 3.0 * (sin(pix) - pix * cos(pix)) / (pix * pix * pix);
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}
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const struct filter_window mp_filter_windows[] = {
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{"box", 1, box},
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{"triangle", 1, triangle},
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{"bartlett", 1, triangle},
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{"hanning", 1, hanning},
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{"hamming", 1, hamming},
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{"quadric", 1.5, quadric},
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{"welch", 1, welch},
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{"kaiser", 1, kaiser, .params = {6.33, NAN} },
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{"blackman", 1, blackman, .params = {0.16, NAN} },
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{"gaussian", 2, gaussian, .params = {1.0, NAN} },
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{"sinc", 1, sinc},
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{"jinc", 1.2196698912665045, jinc},
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{"sphinx", 1.4302966531242027, sphinx},
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{0}
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};
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const struct filter_kernel mp_filter_kernels[] = {
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// Spline filters
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{{"spline16", 2, spline16}},
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{{"spline36", 3, spline36}},
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{{"spline64", 4, spline64}},
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// Sinc filters
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{{"sinc", 2, sinc, .resizable = true}},
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{{"lanczos", 3, sinc, .resizable = true}, .window = "sinc"},
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{{"ginseng", 3, sinc, .resizable = true}, .window = "jinc"},
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// Jinc filters
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{{"jinc", 3, jinc, .resizable = true}, .polar = true},
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{{"ewa_lanczos", 3, jinc, .resizable = true}, .polar = true, .window = "jinc"},
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{{"ewa_hanning", 3, jinc, .resizable = true}, .polar = true, .window = "hanning" },
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{{"ewa_ginseng", 3, jinc, .resizable = true}, .polar = true, .window = "sinc"},
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// Radius is based on the true jinc radius, slightly sharpened as per
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// calculations by Nicolas Robidoux. Source: Imagemagick's magick/resize.c
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{{"ewa_lanczossharp", 3.2383154841662362, jinc, .blur = 0.9812505644269356,
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.resizable = true}, .polar = true, .window = "jinc"},
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// Similar to the above, but softened instead. This one makes hash patterns
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// disappear completely. Blur determined by trial and error.
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{{"ewa_lanczossoft", 3.2383154841662362, jinc, .blur = 1.015,
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.resizable = true}, .polar = true, .window = "jinc"},
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// Very soft (blurred) hanning-windowed jinc; removes almost all aliasing.
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// Blur paramater picked to match orthogonal and diagonal contributions
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{{"haasnsoft", 3.2383154841662362, jinc, .blur = 1.11, .resizable = true},
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.polar = true, .window = "hanning"},
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// Cubic filters
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{{"bicubic", 2, bicubic}},
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{{"bcspline", 2, cubic_bc, .params = {0.5, 0.5} }},
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{{"catmull_rom", 2, cubic_bc, .params = {0.0, 0.5} }},
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{{"mitchell", 2, cubic_bc, .params = {1.0/3.0, 1.0/3.0} }},
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{{"robidoux", 2, cubic_bc, .params = {0.3782, 0.3109} }},
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{{"robidouxsharp", 2, cubic_bc, .params = {0.2620, 0.3690} }},
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{{"ewa_robidoux", 2, cubic_bc, .params = {0.3782, 0.3109}}, .polar = true},
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{{"ewa_robidouxsharp", 2, cubic_bc, .params = {0.2620, 0.3690}}, .polar = true},
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// Miscellaneous filters
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{{"box", 1, box, .resizable = true}},
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{{"nearest", 0.5, box}},
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{{"triangle", 1, triangle, .resizable = true}},
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{{"gaussian", 2, gaussian, .params = {1.0, NAN}, .resizable = true}},
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{{0}}
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};
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