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FFmpeg/libavfilter/vf_vaguedenoiser.c
Andreas Rheinhardt 790f793844 avutil/common: Don't auto-include mem.h 
There are lots of files that don't need it: The number of object
files that actually need it went down from 2011 to 884 here.

Keep it for external users in order to not cause breakages.

Also improve the other headers a bit while just at it.

Signed-off-by: Andreas Rheinhardt <andreas.rheinhardt@outlook.com>
2024-03-31 00:08:43 +01:00

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