mirror of
https://github.com/nyanmisaka/ffmpeg-rockchip.git
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616 lines
21 KiB
C
616 lines
21 KiB
C
/*
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* Copyright (c) 2003 LeFunGus, lefungus@altern.org
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*
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* This file is part of FFmpeg
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*
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* FFmpeg 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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* FFmpeg 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 FFmpeg; if not, write to the Free Software Foundation, Inc.,
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* 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
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*/
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#include <float.h>
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#include "libavutil/imgutils.h"
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#include "libavutil/attributes.h"
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#include "libavutil/common.h"
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#include "libavutil/pixdesc.h"
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#include "libavutil/intreadwrite.h"
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#include "libavutil/opt.h"
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#include "avfilter.h"
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#include "formats.h"
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#include "internal.h"
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#include "video.h"
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typedef struct VagueDenoiserContext {
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const AVClass *class;
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float threshold;
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float percent;
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int method;
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int type;
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int nsteps;
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int planes;
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int depth;
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int bpc;
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int peak;
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int nb_planes;
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int planeheight[4];
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int planewidth[4];
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float *block;
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float *in;
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float *out;
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float *tmp;
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int hlowsize[4][32];
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int hhighsize[4][32];
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int vlowsize[4][32];
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int vhighsize[4][32];
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void (*thresholding)(float *block, const int width, const int height,
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const int stride, const float threshold,
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const float percent);
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} VagueDenoiserContext;
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#define OFFSET(x) offsetof(VagueDenoiserContext, x)
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#define FLAGS AV_OPT_FLAG_VIDEO_PARAM | AV_OPT_FLAG_FILTERING_PARAM
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static const AVOption vaguedenoiser_options[] = {
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{ "threshold", "set filtering strength", OFFSET(threshold), AV_OPT_TYPE_FLOAT, {.dbl=2.}, 0,DBL_MAX, FLAGS },
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{ "method", "set filtering method", OFFSET(method), AV_OPT_TYPE_INT, {.i64=2 }, 0, 2, FLAGS, "method" },
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{ "hard", "hard thresholding", 0, AV_OPT_TYPE_CONST, {.i64=0}, 0, 0, FLAGS, "method" },
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{ "soft", "soft thresholding", 0, AV_OPT_TYPE_CONST, {.i64=1}, 0, 0, FLAGS, "method" },
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{ "garrote", "garrote thresholding", 0, AV_OPT_TYPE_CONST, {.i64=2}, 0, 0, FLAGS, "method" },
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{ "nsteps", "set number of steps", OFFSET(nsteps), AV_OPT_TYPE_INT, {.i64=6 }, 1, 32, FLAGS },
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{ "percent", "set percent of full denoising", OFFSET(percent),AV_OPT_TYPE_FLOAT, {.dbl=85}, 0,100, FLAGS },
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{ "planes", "set planes to filter", OFFSET(planes), AV_OPT_TYPE_INT, {.i64=15 }, 0, 15, FLAGS },
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{ "type", "set threshold type", OFFSET(type), AV_OPT_TYPE_INT, {.i64=0 }, 0, 1, FLAGS, "type" },
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{ "universal", "universal (VisuShrink)", 0, AV_OPT_TYPE_CONST, {.i64=0}, 0, 0, FLAGS, "type" },
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{ "bayes", "bayes (BayesShrink)", 0, AV_OPT_TYPE_CONST, {.i64=1}, 0, 0, FLAGS, "type" },
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{ NULL }
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};
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AVFILTER_DEFINE_CLASS(vaguedenoiser);
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#define NPAD 10
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static const float analysis_low[9] = {
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0.037828455506995f, -0.023849465019380f, -0.110624404418423f, 0.377402855612654f,
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0.852698679009403f, 0.377402855612654f, -0.110624404418423f, -0.023849465019380f, 0.037828455506995f
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};
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static const float analysis_high[7] = {
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-0.064538882628938f, 0.040689417609558f, 0.418092273222212f, -0.788485616405664f,
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0.418092273222212f, 0.040689417609558f, -0.064538882628938f
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};
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static const float synthesis_low[7] = {
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-0.064538882628938f, -0.040689417609558f, 0.418092273222212f, 0.788485616405664f,
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0.418092273222212f, -0.040689417609558f, -0.064538882628938f
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};
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static const float synthesis_high[9] = {
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-0.037828455506995f, -0.023849465019380f, 0.110624404418423f, 0.377402855612654f,
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-0.852698679009403f, 0.377402855612654f, 0.110624404418423f, -0.023849465019380f, -0.037828455506995f
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};
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static const enum AVPixelFormat pix_fmts[] = {
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AV_PIX_FMT_GRAY8, AV_PIX_FMT_GRAY9, AV_PIX_FMT_GRAY10,
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AV_PIX_FMT_GRAY12, AV_PIX_FMT_GRAY14, AV_PIX_FMT_GRAY16,
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AV_PIX_FMT_YUV410P, AV_PIX_FMT_YUV411P,
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AV_PIX_FMT_YUV420P, AV_PIX_FMT_YUV422P,
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AV_PIX_FMT_YUV440P, AV_PIX_FMT_YUV444P,
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AV_PIX_FMT_YUVJ420P, AV_PIX_FMT_YUVJ422P,
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AV_PIX_FMT_YUVJ440P, AV_PIX_FMT_YUVJ444P,
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AV_PIX_FMT_YUVJ411P,
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AV_PIX_FMT_YUV420P9, AV_PIX_FMT_YUV422P9, AV_PIX_FMT_YUV444P9,
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AV_PIX_FMT_YUV420P10, AV_PIX_FMT_YUV422P10, AV_PIX_FMT_YUV444P10,
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AV_PIX_FMT_YUV440P10,
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AV_PIX_FMT_YUV444P12, AV_PIX_FMT_YUV422P12, AV_PIX_FMT_YUV420P12,
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AV_PIX_FMT_YUV440P12,
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AV_PIX_FMT_YUV444P14, AV_PIX_FMT_YUV422P14, AV_PIX_FMT_YUV420P14,
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AV_PIX_FMT_YUV420P16, AV_PIX_FMT_YUV422P16, AV_PIX_FMT_YUV444P16,
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AV_PIX_FMT_GBRP, AV_PIX_FMT_GBRP9, AV_PIX_FMT_GBRP10,
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AV_PIX_FMT_GBRP12, AV_PIX_FMT_GBRP14, AV_PIX_FMT_GBRP16,
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AV_PIX_FMT_YUVA420P, AV_PIX_FMT_YUVA422P, AV_PIX_FMT_YUVA444P,
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AV_PIX_FMT_YUVA444P9, AV_PIX_FMT_YUVA444P10, AV_PIX_FMT_YUVA444P12, AV_PIX_FMT_YUVA444P16,
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AV_PIX_FMT_YUVA422P9, AV_PIX_FMT_YUVA422P10, AV_PIX_FMT_YUVA422P12, AV_PIX_FMT_YUVA422P16,
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AV_PIX_FMT_YUVA420P9, AV_PIX_FMT_YUVA420P10, AV_PIX_FMT_YUVA420P16,
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AV_PIX_FMT_GBRAP, AV_PIX_FMT_GBRAP10, AV_PIX_FMT_GBRAP12, AV_PIX_FMT_GBRAP16,
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AV_PIX_FMT_NONE
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};
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static int config_input(AVFilterLink *inlink)
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{
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VagueDenoiserContext *s = inlink->dst->priv;
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const AVPixFmtDescriptor *desc = av_pix_fmt_desc_get(inlink->format);
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int p, i, nsteps_width, nsteps_height, nsteps_max;
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s->depth = desc->comp[0].depth;
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s->bpc = (s->depth + 7) / 8;
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s->nb_planes = desc->nb_components;
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s->planeheight[1] = s->planeheight[2] = AV_CEIL_RSHIFT(inlink->h, desc->log2_chroma_h);
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s->planeheight[0] = s->planeheight[3] = inlink->h;
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s->planewidth[1] = s->planewidth[2] = AV_CEIL_RSHIFT(inlink->w, desc->log2_chroma_w);
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s->planewidth[0] = s->planewidth[3] = inlink->w;
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s->block = av_malloc_array(inlink->w * inlink->h, sizeof(*s->block));
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s->in = av_malloc_array(32 + FFMAX(inlink->w, inlink->h), sizeof(*s->in));
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s->out = av_malloc_array(32 + FFMAX(inlink->w, inlink->h), sizeof(*s->out));
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s->tmp = av_malloc_array(32 + FFMAX(inlink->w, inlink->h), sizeof(*s->tmp));
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if (!s->block || !s->in || !s->out || !s->tmp)
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return AVERROR(ENOMEM);
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s->threshold *= 1 << (s->depth - 8);
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s->peak = (1 << s->depth) - 1;
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nsteps_width = ((s->planes & 2 || s->planes & 4) && s->nb_planes > 1) ? s->planewidth[1] : s->planewidth[0];
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nsteps_height = ((s->planes & 2 || s->planes & 4) && s->nb_planes > 1) ? s->planeheight[1] : s->planeheight[0];
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for (nsteps_max = 1; nsteps_max < 15; nsteps_max++) {
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if (pow(2, nsteps_max) >= nsteps_width || pow(2, nsteps_max) >= nsteps_height)
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break;
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}
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s->nsteps = FFMIN(s->nsteps, nsteps_max - 2);
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for (p = 0; p < 4; p++) {
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s->hlowsize[p][0] = (s->planewidth[p] + 1) >> 1;
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s->hhighsize[p][0] = s->planewidth[p] >> 1;
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s->vlowsize[p][0] = (s->planeheight[p] + 1) >> 1;
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s->vhighsize[p][0] = s->planeheight[p] >> 1;
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for (i = 1; i < s->nsteps; i++) {
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s->hlowsize[p][i] = (s->hlowsize[p][i - 1] + 1) >> 1;
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s->hhighsize[p][i] = s->hlowsize[p][i - 1] >> 1;
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s->vlowsize[p][i] = (s->vlowsize[p][i - 1] + 1) >> 1;
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s->vhighsize[p][i] = s->vlowsize[p][i - 1] >> 1;
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}
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}
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return 0;
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}
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static inline void copy(const float *p1, float *p2, const int length)
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{
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memcpy(p2, p1, length * sizeof(float));
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}
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static inline void copyv(const float *p1, const int stride1, float *p2, const int length)
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{
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int i;
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for (i = 0; i < length; i++) {
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p2[i] = *p1;
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p1 += stride1;
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}
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}
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static inline void copyh(const float *p1, float *p2, const int stride2, const int length)
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{
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int i;
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for (i = 0; i < length; i++) {
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*p2 = p1[i];
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p2 += stride2;
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}
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}
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// Do symmetric extension of data using prescribed symmetries
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// Original values are in output[npad] through output[npad+size-1]
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// New values will be placed in output[0] through output[npad] and in output[npad+size] through output[2*npad+size-1] (note: end values may not be filled in)
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// extension at left bdry is ... 3 2 1 0 | 0 1 2 3 ...
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// same for right boundary
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// if right_ext=1 then ... 3 2 1 0 | 1 2 3
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static void symmetric_extension(float *output, const int size, const int left_ext, const int right_ext)
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{
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int first = NPAD;
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int last = NPAD - 1 + size;
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const int originalLast = last;
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int i, nextend, idx;
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if (left_ext == 2)
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output[--first] = output[NPAD];
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if (right_ext == 2)
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output[++last] = output[originalLast];
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// extend left end
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nextend = first;
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for (i = 0; i < nextend; i++)
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output[--first] = output[NPAD + 1 + i];
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idx = NPAD + NPAD - 1 + size;
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// extend right end
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nextend = idx - last;
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for (i = 0; i < nextend; i++)
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output[++last] = output[originalLast - 1 - i];
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}
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static void transform_step(float *input, float *output, const int size, const int low_size, VagueDenoiserContext *s)
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{
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int i;
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symmetric_extension(input, size, 1, 1);
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for (i = NPAD; i < NPAD + low_size; i++) {
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const float a = input[2 * i - 14] * analysis_low[0];
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const float b = input[2 * i - 13] * analysis_low[1];
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const float c = input[2 * i - 12] * analysis_low[2];
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const float d = input[2 * i - 11] * analysis_low[3];
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const float e = input[2 * i - 10] * analysis_low[4];
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const float f = input[2 * i - 9] * analysis_low[3];
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const float g = input[2 * i - 8] * analysis_low[2];
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const float h = input[2 * i - 7] * analysis_low[1];
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const float k = input[2 * i - 6] * analysis_low[0];
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output[i] = a + b + c + d + e + f + g + h + k;
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}
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for (i = NPAD; i < NPAD + low_size; i++) {
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const float a = input[2 * i - 12] * analysis_high[0];
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const float b = input[2 * i - 11] * analysis_high[1];
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const float c = input[2 * i - 10] * analysis_high[2];
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const float d = input[2 * i - 9] * analysis_high[3];
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const float e = input[2 * i - 8] * analysis_high[2];
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const float f = input[2 * i - 7] * analysis_high[1];
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const float g = input[2 * i - 6] * analysis_high[0];
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output[i + low_size] = a + b + c + d + e + f + g;
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}
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}
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static void invert_step(const float *input, float *output, float *temp, const int size, VagueDenoiserContext *s)
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{
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const int low_size = (size + 1) >> 1;
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const int high_size = size >> 1;
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int left_ext = 1, right_ext, i;
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int findex;
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memcpy(temp + NPAD, input + NPAD, low_size * sizeof(float));
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right_ext = (size % 2 == 0) ? 2 : 1;
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symmetric_extension(temp, low_size, left_ext, right_ext);
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memset(output, 0, (NPAD + NPAD + size) * sizeof(float));
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findex = (size + 2) >> 1;
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for (i = 9; i < findex + 11; i++) {
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const float a = temp[i] * synthesis_low[0];
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const float b = temp[i] * synthesis_low[1];
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const float c = temp[i] * synthesis_low[2];
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const float d = temp[i] * synthesis_low[3];
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output[2 * i - 13] += a;
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output[2 * i - 12] += b;
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output[2 * i - 11] += c;
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output[2 * i - 10] += d;
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output[2 * i - 9] += c;
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output[2 * i - 8] += b;
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output[2 * i - 7] += a;
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}
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memcpy(temp + NPAD, input + NPAD + low_size, high_size * sizeof(float));
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left_ext = 2;
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right_ext = (size % 2 == 0) ? 1 : 2;
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symmetric_extension(temp, high_size, left_ext, right_ext);
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for (i = 8; i < findex + 11; i++) {
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const float a = temp[i] * synthesis_high[0];
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const float b = temp[i] * synthesis_high[1];
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const float c = temp[i] * synthesis_high[2];
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const float d = temp[i] * synthesis_high[3];
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const float e = temp[i] * synthesis_high[4];
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output[2 * i - 13] += a;
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output[2 * i - 12] += b;
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output[2 * i - 11] += c;
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output[2 * i - 10] += d;
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output[2 * i - 9] += e;
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output[2 * i - 8] += d;
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output[2 * i - 7] += c;
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output[2 * i - 6] += b;
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output[2 * i - 5] += a;
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}
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}
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static void hard_thresholding(float *block, const int width, const int height,
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const int stride, const float threshold,
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const float percent)
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{
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const float frac = 1.f - percent * 0.01f;
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int y, x;
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for (y = 0; y < height; y++) {
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for (x = 0; x < width; x++) {
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if (FFABS(block[x]) <= threshold)
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block[x] *= frac;
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}
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block += stride;
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}
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}
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static void soft_thresholding(float *block, const int width, const int height, const int stride,
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const float threshold, const float percent)
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{
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const float frac = 1.f - percent * 0.01f;
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const float shift = threshold * 0.01f * percent;
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int y, x;
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for (y = 0; y < height; y++) {
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for (x = 0; x < width; x++) {
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const float temp = FFABS(block[x]);
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if (temp <= threshold)
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block[x] *= frac;
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else
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block[x] = (block[x] < 0.f ? -1.f : (block[x] > 0.f ? 1.f : 0.f)) * (temp - shift);
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}
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block += stride;
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}
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}
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static void qian_thresholding(float *block, const int width, const int height,
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const int stride, const float threshold,
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const float percent)
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{
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const float percent01 = percent * 0.01f;
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const float tr2 = threshold * threshold * percent01;
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const float frac = 1.f - percent01;
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int y, x;
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for (y = 0; y < height; y++) {
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for (x = 0; x < width; x++) {
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const float temp = FFABS(block[x]);
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if (temp <= threshold) {
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block[x] *= frac;
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} else {
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const float tp2 = temp * temp;
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block[x] *= (tp2 - tr2) / tp2;
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}
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}
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block += stride;
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}
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}
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static float bayes_threshold(float *block, const int width, const int height,
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const int stride, const float threshold)
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{
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float mean = 0.f;
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for (int y = 0; y < height; y++) {
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for (int x = 0; x < width; x++) {
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mean += block[x] * block[x];
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}
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block += stride;
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}
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mean /= width * height;
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return threshold * threshold / (FFMAX(sqrtf(mean - threshold), FLT_EPSILON));
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}
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static void filter(VagueDenoiserContext *s, AVFrame *in, AVFrame *out)
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{
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int p, y, x, i, j;
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for (p = 0; p < s->nb_planes; p++) {
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const int height = s->planeheight[p];
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const int width = s->planewidth[p];
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const uint8_t *srcp8 = in->data[p];
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const uint16_t *srcp16 = (const uint16_t *)in->data[p];
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uint8_t *dstp8 = out->data[p];
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uint16_t *dstp16 = (uint16_t *)out->data[p];
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float *output = s->block;
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int h_low_size0 = width;
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int v_low_size0 = height;
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int nsteps_transform = s->nsteps;
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int nsteps_invert = s->nsteps;
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const float *input = s->block;
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if (!((1 << p) & s->planes)) {
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av_image_copy_plane(out->data[p], out->linesize[p], in->data[p], in->linesize[p],
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s->planewidth[p] * s->bpc, s->planeheight[p]);
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continue;
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}
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if (s->depth <= 8) {
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for (y = 0; y < height; y++) {
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for (x = 0; x < width; x++)
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output[x] = srcp8[x];
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srcp8 += in->linesize[p];
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output += width;
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}
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} else {
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for (y = 0; y < height; y++) {
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for (x = 0; x < width; x++)
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output[x] = srcp16[x];
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srcp16 += in->linesize[p] / 2;
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output += width;
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}
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}
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while (nsteps_transform--) {
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int low_size = (h_low_size0 + 1) >> 1;
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float *input = s->block;
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for (j = 0; j < v_low_size0; j++) {
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copy(input, s->in + NPAD, h_low_size0);
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transform_step(s->in, s->out, h_low_size0, low_size, s);
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copy(s->out + NPAD, input, h_low_size0);
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input += width;
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}
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low_size = (v_low_size0 + 1) >> 1;
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input = s->block;
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for (j = 0; j < h_low_size0; j++) {
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copyv(input, width, s->in + NPAD, v_low_size0);
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transform_step(s->in, s->out, v_low_size0, low_size, s);
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copyh(s->out + NPAD, input, width, v_low_size0);
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input++;
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}
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|
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h_low_size0 = (h_low_size0 + 1) >> 1;
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v_low_size0 = (v_low_size0 + 1) >> 1;
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}
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if (s->type == 0) {
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s->thresholding(s->block, width, height, width, s->threshold, s->percent);
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} else {
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for (int n = 0; n < s->nsteps; n++) {
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float threshold;
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float *block;
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if (n == s->nsteps - 1) {
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threshold = bayes_threshold(s->block, s->hlowsize[p][n], s->vlowsize[p][n], width, s->threshold);
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s->thresholding(s->block, s->hlowsize[p][n], s->vlowsize[p][n], width, threshold, s->percent);
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}
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block = s->block + s->hlowsize[p][n];
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threshold = bayes_threshold(block, s->hhighsize[p][n], s->vlowsize[p][n], width, s->threshold);
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s->thresholding(block, s->hhighsize[p][n], s->vlowsize[p][n], width, threshold, s->percent);
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block = s->block + s->vlowsize[p][n] * width;
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threshold = bayes_threshold(block, s->hlowsize[p][n], s->vhighsize[p][n], width, s->threshold);
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s->thresholding(block, s->hlowsize[p][n], s->vhighsize[p][n], width, threshold, s->percent);
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block = s->block + s->hlowsize[p][n] + s->vlowsize[p][n] * width;
|
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threshold = bayes_threshold(block, s->hhighsize[p][n], s->vhighsize[p][n], width, s->threshold);
|
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s->thresholding(block, s->hhighsize[p][n], s->vhighsize[p][n], width, threshold, s->percent);
|
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}
|
|
}
|
|
|
|
while (nsteps_invert--) {
|
|
const int idx = s->vlowsize[p][nsteps_invert] + s->vhighsize[p][nsteps_invert];
|
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const int idx2 = s->hlowsize[p][nsteps_invert] + s->hhighsize[p][nsteps_invert];
|
|
float * idx3 = s->block;
|
|
for (i = 0; i < idx2; i++) {
|
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copyv(idx3, width, s->in + NPAD, idx);
|
|
invert_step(s->in, s->out, s->tmp, idx, s);
|
|
copyh(s->out + NPAD, idx3, width, idx);
|
|
idx3++;
|
|
}
|
|
|
|
idx3 = s->block;
|
|
for (i = 0; i < idx; i++) {
|
|
copy(idx3, s->in + NPAD, idx2);
|
|
invert_step(s->in, s->out, s->tmp, idx2, s);
|
|
copy(s->out + NPAD, idx3, idx2);
|
|
idx3 += width;
|
|
}
|
|
}
|
|
|
|
if (s->depth <= 8) {
|
|
for (y = 0; y < height; y++) {
|
|
for (x = 0; x < width; x++)
|
|
dstp8[x] = av_clip_uint8(input[x] + 0.5f);
|
|
input += width;
|
|
dstp8 += out->linesize[p];
|
|
}
|
|
} else {
|
|
for (y = 0; y < height; y++) {
|
|
for (x = 0; x < width; x++)
|
|
dstp16[x] = av_clip(input[x] + 0.5f, 0, s->peak);
|
|
input += width;
|
|
dstp16 += out->linesize[p] / 2;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
static int filter_frame(AVFilterLink *inlink, AVFrame *in)
|
|
{
|
|
AVFilterContext *ctx = inlink->dst;
|
|
VagueDenoiserContext *s = ctx->priv;
|
|
AVFilterLink *outlink = ctx->outputs[0];
|
|
AVFrame *out;
|
|
int direct = av_frame_is_writable(in);
|
|
|
|
if (direct) {
|
|
out = in;
|
|
} else {
|
|
out = ff_get_video_buffer(outlink, outlink->w, outlink->h);
|
|
if (!out) {
|
|
av_frame_free(&in);
|
|
return AVERROR(ENOMEM);
|
|
}
|
|
|
|
av_frame_copy_props(out, in);
|
|
}
|
|
|
|
filter(s, in, out);
|
|
|
|
if (!direct)
|
|
av_frame_free(&in);
|
|
|
|
return ff_filter_frame(outlink, out);
|
|
}
|
|
|
|
static av_cold int init(AVFilterContext *ctx)
|
|
{
|
|
VagueDenoiserContext *s = ctx->priv;
|
|
|
|
switch (s->method) {
|
|
case 0:
|
|
s->thresholding = hard_thresholding;
|
|
break;
|
|
case 1:
|
|
s->thresholding = soft_thresholding;
|
|
break;
|
|
case 2:
|
|
s->thresholding = qian_thresholding;
|
|
break;
|
|
}
|
|
|
|
return 0;
|
|
}
|
|
|
|
static av_cold void uninit(AVFilterContext *ctx)
|
|
{
|
|
VagueDenoiserContext *s = ctx->priv;
|
|
|
|
av_freep(&s->block);
|
|
av_freep(&s->in);
|
|
av_freep(&s->out);
|
|
av_freep(&s->tmp);
|
|
}
|
|
|
|
static const AVFilterPad vaguedenoiser_inputs[] = {
|
|
{
|
|
.name = "default",
|
|
.type = AVMEDIA_TYPE_VIDEO,
|
|
.config_props = config_input,
|
|
.filter_frame = filter_frame,
|
|
},
|
|
};
|
|
|
|
|
|
static const AVFilterPad vaguedenoiser_outputs[] = {
|
|
{
|
|
.name = "default",
|
|
.type = AVMEDIA_TYPE_VIDEO
|
|
},
|
|
};
|
|
|
|
const AVFilter ff_vf_vaguedenoiser = {
|
|
.name = "vaguedenoiser",
|
|
.description = NULL_IF_CONFIG_SMALL("Apply a Wavelet based Denoiser."),
|
|
.priv_size = sizeof(VagueDenoiserContext),
|
|
.priv_class = &vaguedenoiser_class,
|
|
.init = init,
|
|
.uninit = uninit,
|
|
FILTER_INPUTS(vaguedenoiser_inputs),
|
|
FILTER_OUTPUTS(vaguedenoiser_outputs),
|
|
FILTER_PIXFMTS_ARRAY(pix_fmts),
|
|
.flags = AVFILTER_FLAG_SUPPORT_TIMELINE_GENERIC,
|
|
};
|