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FFmpeg/libavfilter/vf_nnedi.c
Andreas Rheinhardt b4f5201967 avfilter: Replace query_formats callback with union of list and callback 
If one looks at the many query_formats callbacks in existence,
one will immediately recognize that there is one type of default
callback for video and a slightly different default callback for
audio: It is "return ff_set_common_formats_from_list(ctx, pix_fmts);"
for video with a filter-specific pix_fmts list. For audio, it is
the same with a filter-specific sample_fmts list together with
ff_set_common_all_samplerates() and ff_set_common_all_channel_counts().

This commit allows to remove the boilerplate query_formats callbacks
by replacing said callback with a union consisting the old callback
and pointers for pixel and sample format arrays. For the not uncommon
case in which these lists only contain a single entry (besides the
sentinel) enum AVPixelFormat and enum AVSampleFormat fields are also
added to the union to store them directly in the AVFilter,
thereby avoiding a relocation.

The state of said union will be contained in a new, dedicated AVFilter
field (the nb_inputs and nb_outputs fields have been shrunk to uint8_t
in order to create a hole for this new field; this is no problem, as
the maximum of all the nb_inputs is four; for nb_outputs it is only
two).

The state's default value coincides with the earlier default of
query_formats being unset, namely that the filter accepts all formats
(and also sample rates and channel counts/layouts for audio)
provided that these properties agree coincide for all inputs and
outputs.

By using different union members for audio and video filters
the type-unsafety of using the same functions for audio and video
lists will furthermore be more confined to formats.c than before.

When the new fields are used, they will also avoid allocations:
Currently something nearly equivalent to ff_default_query_formats()
is called after every successful call to a query_formats callback;
yet in the common case that the newly allocated AVFilterFormats
are not used at all (namely if there are no free links) these newly
allocated AVFilterFormats are freed again without ever being used.
Filters no longer using the callback will not exhibit this any more.

Reviewed-by: Paul B Mahol <onemda@gmail.com>
Reviewed-by: Nicolas George <george@nsup.org>
Signed-off-by: Andreas Rheinhardt <andreas.rheinhardt@outlook.com>
2021-10-05 17:48:25 +02:00

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/*
* Copyright (C) 2010-2011 Kevin Stone
* Copyright (C) 2016 Paul B Mahol
*
* 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/common.h"
#include "libavutil/float_dsp.h"
#include "libavutil/imgutils.h"
#include "libavutil/mem_internal.h"
#include "libavutil/opt.h"
#include "libavutil/pixdesc.h"
#include "avfilter.h"
#include "formats.h"
#include "internal.h"
#include "video.h"
static const size_t NNEDI_WEIGHTS_SIZE = 13574928;
static const uint8_t NNEDI_XDIM[] = { 8, 16, 32, 48, 8, 16, 32 };
static const uint8_t NNEDI_YDIM[] = { 6, 6, 6, 6, 4, 4, 4 };
static const uint16_t NNEDI_NNS[] = { 16, 32, 64, 128, 256 };
typedef struct PrescreenerCoefficients {
DECLARE_ALIGNED(32, float, kernel_l0)[4][16 * 4];
DECLARE_ALIGNED(32, float, bias_l0)[4];
DECLARE_ALIGNED(32, float, kernel_l1)[4][4];
DECLARE_ALIGNED(32, float, bias_l1)[4];
DECLARE_ALIGNED(32, float, kernel_l2)[4][8];
DECLARE_ALIGNED(32, float, bias_l2)[4];
} PrescreenerCoefficients;
typedef struct PredictorCoefficients {
int xdim, ydim, nns, nsize;
float *data;
float *softmax_q1;
float *elliott_q1;
float *softmax_bias_q1;
float *elliott_bias_q1;
float *softmax_q2;
float *elliott_q2;
float *softmax_bias_q2;
float *elliott_bias_q2;
} PredictorCoefficients;
typedef struct NNEDIContext {
const AVClass *class;
char *weights_file;
AVFrame *prev;
int eof;
int64_t pts;
AVFloatDSPContext *fdsp;
int depth;
int nb_planes;
int nb_threads;
int linesize[4];
int planewidth[4];
int planeheight[4];
int field_n;
PrescreenerCoefficients prescreener[4];
PredictorCoefficients coeffs[2][5][7];
float half;
float in_scale;
float out_scale;
// Parameters
int deint;
int field;
int process_plane;
int nsize;
int nnsparam;
int qual;
int etype;
int pscrn;
int input_size;
uint8_t **prescreen_buf;
float **input_buf;
float **output_buf;
void (*read)(const uint8_t *src, float *dst,
int src_stride, int dst_stride,
int width, int height, float scale);
void (*write)(const float *src, uint8_t *dst,
int src_stride, int dst_stride,
int width, int height, int depth, float scale);
void (*prescreen[2])(AVFilterContext *ctx,
const void *src, ptrdiff_t src_stride,
uint8_t *prescreen, int N,
const PrescreenerCoefficients *const coeffs);
} NNEDIContext;
#define OFFSET(x) offsetof(NNEDIContext, x)
#define RFLAGS AV_OPT_FLAG_VIDEO_PARAM|AV_OPT_FLAG_FILTERING_PARAM|AV_OPT_FLAG_RUNTIME_PARAM
#define FLAGS AV_OPT_FLAG_VIDEO_PARAM|AV_OPT_FLAG_FILTERING_PARAM
static const AVOption nnedi_options[] = {
{"weights", "set weights file", OFFSET(weights_file), AV_OPT_TYPE_STRING, {.str="nnedi3_weights.bin"}, 0, 0, FLAGS },
{"deint", "set which frames to deinterlace", OFFSET(deint), AV_OPT_TYPE_INT, {.i64=0}, 0, 1, RFLAGS, "deint" },
{"all", "deinterlace all frames", 0, AV_OPT_TYPE_CONST, {.i64=0}, 0, 0, RFLAGS, "deint" },
{"interlaced", "only deinterlace frames marked as interlaced", 0, AV_OPT_TYPE_CONST, {.i64=1}, 0, 0, RFLAGS, "deint" },
{"field", "set mode of operation", OFFSET(field), AV_OPT_TYPE_INT, {.i64=-1}, -2, 3, RFLAGS, "field" },
{"af", "use frame flags, both fields", 0, AV_OPT_TYPE_CONST, {.i64=-2}, 0, 0, RFLAGS, "field" },
{"a", "use frame flags, single field", 0, AV_OPT_TYPE_CONST, {.i64=-1}, 0, 0, RFLAGS, "field" },
{"t", "use top field only", 0, AV_OPT_TYPE_CONST, {.i64=0}, 0, 0, RFLAGS, "field" },
{"b", "use bottom field only", 0, AV_OPT_TYPE_CONST, {.i64=1}, 0, 0, RFLAGS, "field" },
{"tf", "use both fields, top first", 0, AV_OPT_TYPE_CONST, {.i64=2}, 0, 0, RFLAGS, "field" },
{"bf", "use both fields, bottom first", 0, AV_OPT_TYPE_CONST, {.i64=3}, 0, 0, RFLAGS, "field" },
{"planes", "set which planes to process", OFFSET(process_plane), AV_OPT_TYPE_INT, {.i64=7}, 0, 15, RFLAGS },
{"nsize", "set size of local neighborhood around each pixel, used by the predictor neural network", OFFSET(nsize), AV_OPT_TYPE_INT, {.i64=6}, 0, 6, RFLAGS, "nsize" },
{"s8x6", NULL, 0, AV_OPT_TYPE_CONST, {.i64=0}, 0, 0, RFLAGS, "nsize" },
{"s16x6", NULL, 0, AV_OPT_TYPE_CONST, {.i64=1}, 0, 0, RFLAGS, "nsize" },
{"s32x6", NULL, 0, AV_OPT_TYPE_CONST, {.i64=2}, 0, 0, RFLAGS, "nsize" },
{"s48x6", NULL, 0, AV_OPT_TYPE_CONST, {.i64=3}, 0, 0, RFLAGS, "nsize" },
{"s8x4", NULL, 0, AV_OPT_TYPE_CONST, {.i64=4}, 0, 0, RFLAGS, "nsize" },
{"s16x4", NULL, 0, AV_OPT_TYPE_CONST, {.i64=5}, 0, 0, RFLAGS, "nsize" },
{"s32x4", NULL, 0, AV_OPT_TYPE_CONST, {.i64=6}, 0, 0, RFLAGS, "nsize" },
{"nns", "set number of neurons in predictor neural network", OFFSET(nnsparam), AV_OPT_TYPE_INT, {.i64=1}, 0, 4, RFLAGS, "nns" },
{"n16", NULL, 0, AV_OPT_TYPE_CONST, {.i64=0}, 0, 0, RFLAGS, "nns" },
{"n32", NULL, 0, AV_OPT_TYPE_CONST, {.i64=1}, 0, 0, RFLAGS, "nns" },
{"n64", NULL, 0, AV_OPT_TYPE_CONST, {.i64=2}, 0, 0, RFLAGS, "nns" },
{"n128", NULL, 0, AV_OPT_TYPE_CONST, {.i64=3}, 0, 0, RFLAGS, "nns" },
{"n256", NULL, 0, AV_OPT_TYPE_CONST, {.i64=4}, 0, 0, RFLAGS, "nns" },
{"qual", "set quality", OFFSET(qual), AV_OPT_TYPE_INT, {.i64=1}, 1, 2, RFLAGS, "qual" },
{"fast", NULL, 0, AV_OPT_TYPE_CONST, {.i64=1}, 0, 0, RFLAGS, "qual" },
{"slow", NULL, 0, AV_OPT_TYPE_CONST, {.i64=2}, 0, 0, RFLAGS, "qual" },
{"etype", "set which set of weights to use in the predictor", OFFSET(etype), AV_OPT_TYPE_INT, {.i64=0}, 0, 1, RFLAGS, "etype" },
{"a", "weights trained to minimize absolute error", 0, AV_OPT_TYPE_CONST, {.i64=0}, 0, 0, RFLAGS, "etype" },
{"abs","weights trained to minimize absolute error", 0, AV_OPT_TYPE_CONST, {.i64=0}, 0, 0, RFLAGS, "etype" },
{"s", "weights trained to minimize squared error", 0, AV_OPT_TYPE_CONST, {.i64=1}, 0, 0, RFLAGS, "etype" },
{"mse","weights trained to minimize squared error", 0, AV_OPT_TYPE_CONST, {.i64=1}, 0, 0, RFLAGS, "etype" },
{"pscrn", "set prescreening", OFFSET(pscrn), AV_OPT_TYPE_INT, {.i64=2}, 0, 4, RFLAGS, "pscrn" },
{"none", NULL, 0, AV_OPT_TYPE_CONST, {.i64=0}, 0, 0, RFLAGS, "pscrn" },
{"original", NULL, 0, AV_OPT_TYPE_CONST, {.i64=1}, 0, 0, RFLAGS, "pscrn" },
{"new", NULL, 0, AV_OPT_TYPE_CONST, {.i64=2}, 0, 0, RFLAGS, "pscrn" },
{"new2", NULL, 0, AV_OPT_TYPE_CONST, {.i64=3}, 0, 0, RFLAGS, "pscrn" },
{"new3", NULL, 0, AV_OPT_TYPE_CONST, {.i64=4}, 0, 0, RFLAGS, "pscrn" },
{ NULL }
};
AVFILTER_DEFINE_CLASS(nnedi);
static int config_output(AVFilterLink *outlink)
{
AVFilterContext *ctx = outlink->src;
outlink->time_base = av_mul_q(ctx->inputs[0]->time_base, (AVRational){1, 2});
outlink->w = ctx->inputs[0]->w;
outlink->h = ctx->inputs[0]->h;
outlink->frame_rate = av_mul_q(ctx->inputs[0]->frame_rate,
(AVRational){2, 1});
return 0;
}
static int query_formats(AVFilterContext *ctx)
{
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_YUVJ444P, AV_PIX_FMT_YUVJ440P,
AV_PIX_FMT_YUVJ422P, AV_PIX_FMT_YUVJ420P,
AV_PIX_FMT_YUVJ411P,
AV_PIX_FMT_YUVA420P, AV_PIX_FMT_YUVA422P, AV_PIX_FMT_YUVA444P,
AV_PIX_FMT_GBRP, AV_PIX_FMT_GBRAP,
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_YUV420P12, AV_PIX_FMT_YUV422P12, AV_PIX_FMT_YUV444P12,
AV_PIX_FMT_YUV440P12,
AV_PIX_FMT_YUV420P14, AV_PIX_FMT_YUV422P14, AV_PIX_FMT_YUV444P14,
AV_PIX_FMT_YUV420P16, AV_PIX_FMT_YUV422P16, AV_PIX_FMT_YUV444P16,
AV_PIX_FMT_GBRP9, AV_PIX_FMT_GBRP10, AV_PIX_FMT_GBRP12, AV_PIX_FMT_GBRP14, AV_PIX_FMT_GBRP16,
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_GBRAP10, AV_PIX_FMT_GBRAP12, AV_PIX_FMT_GBRAP16,
AV_PIX_FMT_NONE
};
return ff_set_common_formats_from_list(ctx, pix_fmts);
}
static float dot_dsp(const NNEDIContext *const s, const float *kernel, const float *input,
int n, float scale, float bias)
{
float sum, y;
sum = s->fdsp->scalarproduct_float(kernel, input, n);
y = sum * scale + bias + 1e-20f;
return y;
}
static float elliott(float x)
{
return x / (1.0f + fabsf(x));
}
static void transform_elliott(float *input, int size)
{
for (int i = 0; i < size; i++)
input[i] = elliott(input[i]);
}
static void process_old(AVFilterContext *ctx,
const void *src, ptrdiff_t src_stride,
uint8_t *prescreen, int N,
const PrescreenerCoefficients *const m_data)
{
NNEDIContext *s = ctx->priv;
const float *src_p = src;
// Adjust source pointer to point to top-left of filter window.
const float *window = src_p - 2 * src_stride - 5;
for (int j = 0; j < N; j++) {
LOCAL_ALIGNED_32(float, input, [48]);
float state[12];
for (int i = 0; i < 4; i++)
memcpy(input + i * 12, window + i * src_stride + j, 12 * sizeof(float));
// Layer 0.
for (int n = 0; n < 4; n++)
state[n] = dot_dsp(s, m_data->kernel_l0[n], input, 48, 1.0f, m_data->bias_l0[n]);
transform_elliott(state + 1, 3);
// Layer 1.
for (int n = 0; n < 4; n++)
state[n + 4] = dot_dsp(s, m_data->kernel_l1[n], state, 4, 1.0f, m_data->bias_l1[n]);
transform_elliott(state + 4, 3);
// Layer 2.
for (int n = 0; n < 4; n++)
state[n + 8] = dot_dsp(s, m_data->kernel_l2[n], state, 8, 1.0f, m_data->bias_l2[n]);
prescreen[j] = FFMAX(state[10], state[11]) <= FFMAX(state[8], state[9]) ? 255 : 0;
}
}
static void process_new(AVFilterContext *ctx,
const void *src, ptrdiff_t src_stride,
uint8_t *prescreen, int N,
const PrescreenerCoefficients *const m_data)
{
NNEDIContext *s = ctx->priv;
const float *src_p = src;
// Adjust source pointer to point to top-left of filter window.
const float *window = src_p - 2 * src_stride - 6;
for (int j = 0; j < N; j += 4) {
LOCAL_ALIGNED_32(float, input, [64]);
float state[8];
for (int i = 0; i < 4; i++)
memcpy(input + i * 16, window + i * src_stride + j, 16 * sizeof(float));
for (int n = 0; n < 4; n++)
state[n] = dot_dsp(s, m_data->kernel_l0[n], input, 64, 1.0f, m_data->bias_l0[n]);
transform_elliott(state, 4);
for (int n = 0; n < 4; n++)
state[n + 4] = dot_dsp(s, m_data->kernel_l1[n], state, 4, 1.0f, m_data->bias_l1[n]);
for (int n = 0; n < 4; n++)
prescreen[j + n] = state[n + 4] > 0.f;
}
}
static int filter_offset(int nn, const PredictorCoefficients *const model)
{
return nn * model->nsize;
}
static const float *softmax_q1_filter(int nn,
const PredictorCoefficients *const model)
{
return model->softmax_q1 + filter_offset(nn, model);
}
static const float *elliott_q1_filter(int nn,
const PredictorCoefficients *const model)
{
return model->elliott_q1 + filter_offset(nn, model);
}
static const float *softmax_q2_filter(int nn,
const PredictorCoefficients *const model)
{
return model->softmax_q2 + filter_offset(nn, model);
}
static const float *elliott_q2_filter(int nn,
const PredictorCoefficients *const model)
{
return model->elliott_q2 + filter_offset(nn, model);
}
static void gather_input(const float *src, ptrdiff_t src_stride,
float *buf, float mstd[4],
const PredictorCoefficients *const model)
{
const float scale = 1.f / model->nsize;
float sum = 0.f;
float sum_sq = 0.f;
float tmp;
for (int i = 0; i < model->ydim; i++) {
memcpy(buf, src, model->xdim * sizeof(float));
for (int j = 0; j < model->xdim; j++) {
const float val = src[j];
sum += val;
sum_sq += val * val;
}
src += src_stride;
buf += model->xdim;
}
mstd[0] = sum * scale;
mstd[3] = 0.f;
tmp = sum_sq * scale - mstd[0] * mstd[0];
if (tmp < FLT_EPSILON) {
mstd[1] = 0.0f;
mstd[2] = 0.0f;
} else {
mstd[1] = sqrtf(tmp);
mstd[2] = 1.0f / mstd[1];
}
}
static float softmax_exp(float x)
{
return expf(av_clipf(x, -80.f, 80.f));
}
static void transform_softmax_exp(float *input, int size)
{
for (int i = 0; i < size; i++)
input[i] = softmax_exp(input[i]);
}
static void wae5(const float *softmax, const float *el,
int n, float mstd[4])
{
float vsum = 0.0f, wsum = 0.0f;
for (int i = 0; i < n; i++) {
vsum += softmax[i] * elliott(el[i]);
wsum += softmax[i];
}
if (wsum > 1e-10f)
mstd[3] += (5.0f * vsum) / wsum * mstd[1] + mstd[0];
else
mstd[3] += mstd[0];
}
static void predictor(AVFilterContext *ctx,
const void *src, ptrdiff_t src_stride, void *dst,
const uint8_t *prescreen, int N,
const PredictorCoefficients *const model, int use_q2)
{
const NNEDIContext *const s = ctx->priv;
const float *src_p = src;
float *dst_p = dst;
// Adjust source pointer to point to top-left of filter window.
const float *window = src_p - (model->ydim / 2) * src_stride - (model->xdim / 2 - 1);
const int filter_size = model->nsize;
const int nns = model->nns;
for (int i = 0; i < N; i++) {
LOCAL_ALIGNED_32(float, input, [48 * 6]);
float activation[256 * 2];
float mstd[4];
float scale;
if (prescreen[i])
continue;
gather_input(window + i, src_stride, input, mstd, model);
scale = mstd[2];
for (int nn = 0; nn < nns; nn++)
activation[nn] = dot_dsp(s, softmax_q1_filter(nn, model), input, filter_size, scale, model->softmax_bias_q1[nn]);
for (int nn = 0; nn < nns; nn++)
activation[nns + nn] = dot_dsp(s, elliott_q1_filter(nn, model), input, filter_size, scale, model->elliott_bias_q1[nn]);
transform_softmax_exp(activation, nns);
wae5(activation, activation + nns, nns, mstd);
if (use_q2) {
for (int nn = 0; nn < nns; nn++)
activation[nn] = dot_dsp(s, softmax_q2_filter(nn, model), input, filter_size, scale, model->softmax_bias_q2[nn]);
for (int nn = 0; nn < nns; nn++)
activation[nns + nn] = dot_dsp(s, elliott_q2_filter(nn, model), input, filter_size, scale, model->elliott_bias_q2[nn]);
transform_softmax_exp(activation, nns);
wae5(activation, activation + nns, nns, mstd);
}
dst_p[i] = mstd[3] * (use_q2 ? 0.5f : 1.f);
}
}
static void read_bytes(const uint8_t *src, float *dst,
int src_stride, int dst_stride,
int width, int height, float scale)
{
for (int y = 0; y < height; y++) {
for (int x = 0; x < 32; x++)
dst[-x - 1] = src[x];
for (int x = 0; x < width; x++)
dst[x] = src[x];
for (int x = 0; x < 32; x++)
dst[width + x] = src[width - x - 1];
dst += dst_stride;
src += src_stride;
}
}
static void read_words(const uint8_t *srcp, float *dst,
int src_stride, int dst_stride,
int width, int height, float scale)
{
const uint16_t *src = (const uint16_t *)srcp;
src_stride /= 2;
for (int y = 0; y < height; y++) {
for (int x = 0; x < 32; x++)
dst[-x - 1] = src[x] * scale;
for (int x = 0; x < width; x++)
dst[x] = src[x] * scale;
for (int x = 0; x < 32; x++)
dst[width + x] = src[width - x - 1] * scale;
dst += dst_stride;
src += src_stride;
}
}
static void write_bytes(const float *src, uint8_t *dst,
int src_stride, int dst_stride,
int width, int height, int depth,
float scale)
{
for (int y = 0; y < height; y++) {
for (int x = 0; x < width; x++)
dst[x] = av_clip_uint8(src[x]);
dst += dst_stride;
src += src_stride;
}
}
static void write_words(const float *src, uint8_t *dstp,
int src_stride, int dst_stride,
int width, int height, int depth,
float scale)
{
uint16_t *dst = (uint16_t *)dstp;
dst_stride /= 2;
for (int y = 0; y < height; y++) {
for (int x = 0; x < width; x++)
dst[x] = av_clip_uintp2_c(src[x] * scale, depth);
dst += dst_stride;
src += src_stride;
}
}
static void interpolation(const void *src, ptrdiff_t src_stride,
void *dst, const uint8_t *prescreen, int n)
{
const float *src_p = src;
float *dst_p = dst;
const float *window = src_p - 2 * src_stride;
for (int i = 0; i < n; i++) {
float accum = 0.0f;
if (!prescreen[i])
continue;
accum += (-3.0f / 32.0f) * window[0 * src_stride + i];
accum += (19.0f / 32.0f) * window[1 * src_stride + i];
accum += (19.0f / 32.0f) * window[2 * src_stride + i];
accum += (-3.0f / 32.0f) * window[3 * src_stride + i];
dst_p[i] = accum;
}
}
static int filter_slice(AVFilterContext *ctx, void *arg, int jobnr, int nb_jobs)
{
const NNEDIContext *const s = ctx->priv;
AVFrame *out = arg;
AVFrame *in = s->prev;
const float in_scale = s->in_scale;
const float out_scale = s->out_scale;
const int depth = s->depth;
const int interlaced = in->interlaced_frame;
const int tff = s->field_n == (s->field < 0 ? interlaced ? in->top_field_first : 1 :
(s->field & 1) ^ 1);
for (int p = 0; p < s->nb_planes; p++) {
const int height = s->planeheight[p];
const int width = s->planewidth[p];
const int slice_start = 2 * ((height / 2 * jobnr) / nb_jobs);
const int slice_end = 2 * ((height / 2 * (jobnr+1)) / nb_jobs);
const uint8_t *src_data = in->data[p];
uint8_t *dst_data = out->data[p];
uint8_t *dst = out->data[p] + slice_start * out->linesize[p];
const int src_linesize = in->linesize[p];
const int dst_linesize = out->linesize[p];
uint8_t *prescreen_buf = s->prescreen_buf[jobnr];
float *srcbuf = s->input_buf[jobnr];
const int srcbuf_stride = width + 64;
float *dstbuf = s->output_buf[jobnr];
const int dstbuf_stride = width;
const int slice_height = (slice_end - slice_start) / 2;
const int last_slice = slice_end == height;
const uint8_t *in_line;
uint8_t *out_line;
int y_out;
if (!(s->process_plane & (1 << p))) {
av_image_copy_plane(dst, out->linesize[p],
in->data[p] + slice_start * in->linesize[p],
in->linesize[p],
s->linesize[p], slice_end - slice_start);
continue;
}
y_out = slice_start + (tff ^ (slice_start & 1));
in_line = src_data + (y_out * src_linesize);
out_line = dst_data + (y_out * dst_linesize);
while (y_out < slice_end) {
memcpy(out_line, in_line, s->linesize[p]);
y_out += 2;
in_line += src_linesize * 2;
out_line += dst_linesize * 2;
}
y_out = slice_start + ((!tff) ^ (slice_start & 1));
s->read(src_data + FFMAX(y_out - 5, tff) * src_linesize,
srcbuf + 32,
src_linesize * 2, srcbuf_stride,
width, 1, in_scale);
srcbuf += srcbuf_stride;
s->read(src_data + FFMAX(y_out - 3, tff) * src_linesize,
srcbuf + 32,
src_linesize * 2, srcbuf_stride,
width, 1, in_scale);
srcbuf += srcbuf_stride;
s->read(src_data + FFMAX(y_out - 1, tff) * src_linesize,
srcbuf + 32,
src_linesize * 2, srcbuf_stride,
width, 1, in_scale);
srcbuf += srcbuf_stride;
in_line = src_data + FFMIN(y_out + 1, height - 1 - !tff) * src_linesize;
out_line = dst_data + (y_out * dst_linesize);
s->read(in_line, srcbuf + 32, src_linesize * 2, srcbuf_stride,
width, slice_height - last_slice, in_scale);
y_out += (slice_height - last_slice) * 2;
s->read(src_data + FFMIN(y_out + 1, height - 1 - !tff) * src_linesize,
srcbuf + 32 + srcbuf_stride * (slice_height - last_slice),
src_linesize * 2, srcbuf_stride,
width, 1, in_scale);
s->read(src_data + FFMIN(y_out + 3, height - 1 - !tff) * src_linesize,
srcbuf + 32 + srcbuf_stride * (slice_height + 1 - last_slice),
src_linesize * 2, srcbuf_stride,
width, 1, in_scale);
s->read(src_data + FFMIN(y_out + 5, height - 1 - !tff) * src_linesize,
srcbuf + 32 + srcbuf_stride * (slice_height + 2 - last_slice),
src_linesize * 2, srcbuf_stride,
width, 1, in_scale);
for (int y = 0; y < slice_end - slice_start; y += 2) {
if (s->pscrn > 0)
s->prescreen[s->pscrn > 1](ctx, srcbuf + (y / 2) * srcbuf_stride + 32,
srcbuf_stride, prescreen_buf, width,
&s->prescreener[s->pscrn - 1]);
predictor(ctx,
srcbuf + (y / 2) * srcbuf_stride + 32,
srcbuf_stride,
dstbuf + (y / 2) * dstbuf_stride,
prescreen_buf, width,
&s->coeffs[s->etype][s->nnsparam][s->nsize], s->qual == 2);
if (s->pscrn > 0)
interpolation(srcbuf + (y / 2) * srcbuf_stride + 32,
srcbuf_stride,
dstbuf + (y / 2) * dstbuf_stride,
prescreen_buf, width);
}
s->write(dstbuf, out_line, dstbuf_stride, dst_linesize * 2,
width, slice_height, depth, out_scale);
}
return 0;
}
static int get_frame(AVFilterContext *ctx, int is_second)
{
NNEDIContext *s = ctx->priv;
AVFilterLink *outlink = ctx->outputs[0];
AVFrame *dst;
dst = ff_get_video_buffer(outlink, outlink->w, outlink->h);
if (!dst)
return AVERROR(ENOMEM);
av_frame_copy_props(dst, s->prev);
dst->interlaced_frame = 0;
dst->pts = s->pts;
ff_filter_execute(ctx, filter_slice, dst, NULL,
FFMIN(s->planeheight[1] / 2, s->nb_threads));
if (s->field == -2 || s->field > 1)
s->field_n = !s->field_n;
return ff_filter_frame(outlink, dst);
}
static int filter_frame(AVFilterLink *inlink, AVFrame *in)
{
AVFilterContext *ctx = inlink->dst;
NNEDIContext *s = ctx->priv;
int ret;
if (!s->prev) {
s->prev = in;
return 0;
}
if ((s->deint && !s->prev->interlaced_frame) || ctx->is_disabled) {
s->prev->pts *= 2;
ret = ff_filter_frame(ctx->outputs[0], s->prev);
s->prev = in;
return ret;
}
s->pts = s->prev->pts * 2;
ret = get_frame(ctx, 0);
if (ret < 0 || (s->field > -2 && s->field < 2)) {
av_frame_free(&s->prev);
s->prev = in;
return ret;
}
s->pts = s->prev->pts + in->pts;
ret = get_frame(ctx, 1);
av_frame_free(&s->prev);
s->prev = in;
return ret;
}
static int request_frame(AVFilterLink *link)
{
AVFilterContext *ctx = link->src;
NNEDIContext *s = ctx->priv;
int ret;
if (s->eof)
return AVERROR_EOF;
ret = ff_request_frame(ctx->inputs[0]);
if (ret == AVERROR_EOF && s->prev) {
AVFrame *next = av_frame_clone(s->prev);
if (!next)
return AVERROR(ENOMEM);
next->pts = s->prev->pts + av_rescale_q(1, av_inv_q(ctx->outputs[0]->frame_rate),
ctx->outputs[0]->time_base);
s->eof = 1;
ret = filter_frame(ctx->inputs[0], next);
} else if (ret < 0) {
return ret;
}
return ret;
}
static void copy_weights(float *dst, int n, const float **data)
{
memcpy(dst, *data, n * sizeof(float));
*data += n;
}
static float *allocate(float **ptr, int size)
{
float *ret = *ptr;
*ptr += size;
return ret;
}
static int allocate_model(PredictorCoefficients *coeffs, int xdim, int ydim, int nns)
{
int filter_size = nns * xdim * ydim;
int bias_size = nns;
float *data;
data = av_calloc(filter_size + bias_size, 4 * sizeof(float));
if (!data)
return AVERROR(ENOMEM);
coeffs->data = data;
coeffs->xdim = xdim;
coeffs->ydim = ydim;
coeffs->nsize = xdim * ydim;
coeffs->nns = nns;
coeffs->softmax_q1 = allocate(&data, filter_size);
coeffs->elliott_q1 = allocate(&data, filter_size);
coeffs->softmax_bias_q1 = allocate(&data, bias_size);
coeffs->elliott_bias_q1 = allocate(&data, bias_size);
coeffs->softmax_q2 = allocate(&data, filter_size);
coeffs->elliott_q2 = allocate(&data, filter_size);
coeffs->softmax_bias_q2 = allocate(&data, bias_size);
coeffs->elliott_bias_q2 = allocate(&data, bias_size);
return 0;
}
static int read_weights(AVFilterContext *ctx, const float *bdata)
{
NNEDIContext *s = ctx->priv;
int ret;
copy_weights(&s->prescreener[0].kernel_l0[0][0], 4 * 48, &bdata);
copy_weights(s->prescreener[0].bias_l0, 4, &bdata);
copy_weights(&s->prescreener[0].kernel_l1[0][0], 4 * 4, &bdata);
copy_weights(s->prescreener[0].bias_l1, 4, &bdata);
copy_weights(&s->prescreener[0].kernel_l2[0][0], 4 * 8, &bdata);
copy_weights(s->prescreener[0].bias_l2, 4, &bdata);
for (int i = 0; i < 3; i++) {
PrescreenerCoefficients *data = &s->prescreener[i + 1];
float kernel_l0_shuffled[4 * 64];
float kernel_l1_shuffled[4 * 4];
copy_weights(kernel_l0_shuffled, 4 * 64, &bdata);
copy_weights(data->bias_l0, 4, &bdata);
copy_weights(kernel_l1_shuffled, 4 * 4, &bdata);
copy_weights(data->bias_l1, 4, &bdata);
for (int n = 0; n < 4; n++) {
for (int k = 0; k < 64; k++)
data->kernel_l0[n][k] = kernel_l0_shuffled[(k / 8) * 32 + n * 8 + k % 8];
for (int k = 0; k < 4; k++)
data->kernel_l1[n][k] = kernel_l1_shuffled[k * 4 + n];
}
}
for (int m = 0; m < 2; m++) {
// Grouping by neuron count.
for (int i = 0; i < 5; i++) {
const int nns = NNEDI_NNS[i];
// Grouping by window size.
for (int j = 0; j < 7; j++) {
PredictorCoefficients *model = &s->coeffs[m][i][j];
const int xdim = NNEDI_XDIM[j];
const int ydim = NNEDI_YDIM[j];
const int filter_size = xdim * ydim;
ret = allocate_model(model, xdim, ydim, nns);
if (ret < 0)
return ret;
// Quality 1 model. NNS[i] * (XDIM[j] * YDIM[j]) * 2 coefficients.
copy_weights(model->softmax_q1, nns * filter_size, &bdata);
copy_weights(model->elliott_q1, nns * filter_size, &bdata);
// Quality 1 model bias. NNS[i] * 2 coefficients.
copy_weights(model->softmax_bias_q1, nns, &bdata);
copy_weights(model->elliott_bias_q1, nns, &bdata);
// Quality 2 model. NNS[i] * (XDIM[j] * YDIM[j]) * 2 coefficients.
copy_weights(model->softmax_q2, nns * filter_size, &bdata);
copy_weights(model->elliott_q2, nns * filter_size, &bdata);
// Quality 2 model bias. NNS[i] * 2 coefficients.
copy_weights(model->softmax_bias_q2, nns, &bdata);
copy_weights(model->elliott_bias_q2, nns, &bdata);
}
}
}
return 0;
}
static float mean(const float *input, int size)
{
float sum = 0.f;
for (int i = 0; i < size; i++)
sum += input[i];
return sum / size;
}
static void transform(float *input, int size, float mean, float half)
{
for (int i = 0; i < size; i++)
input[i] = (input[i] - mean) / half;
}
static void subtract_mean_old(PrescreenerCoefficients *coeffs, float half)
{
for (int n = 0; n < 4; n++) {
float m = mean(coeffs->kernel_l0[n], 48);
transform(coeffs->kernel_l0[n], 48, m, half);
}
}
static void subtract_mean_new(PrescreenerCoefficients *coeffs, float half)
{
for (int n = 0; n < 4; n++) {
float m = mean(coeffs->kernel_l0[n], 64);
transform(coeffs->kernel_l0[n], 64, m, half);
}
}
static void subtract_mean_predictor(PredictorCoefficients *model)
{
const int filter_size = model->nsize;
const int nns = model->nns;
const float scale = 1.f / nns;
double softmax_means[256]; // Average of individual softmax filters.
double elliott_means[256]; // Average of individual elliott filters.
double mean_filter[48 * 6] = { 0 }; // Pointwise average of all softmax filters.
double mean_bias;
// Quality 1.
for (int nn = 0; nn < nns; nn++) {
softmax_means[nn] = mean(model->softmax_q1 + nn * filter_size, filter_size);
elliott_means[nn] = mean(model->elliott_q1 + nn * filter_size, filter_size);
for (int k = 0; k < filter_size; k++)
mean_filter[k] += model->softmax_q1[nn * filter_size + k] - softmax_means[nn];
}
for (int k = 0; k < filter_size; k++)
mean_filter[k] *= scale;
mean_bias = mean(model->softmax_bias_q1, nns);
for (int nn = 0; nn < nns; nn++) {
for (int k = 0; k < filter_size; k++) {
model->softmax_q1[nn * filter_size + k] -= softmax_means[nn] + mean_filter[k];
model->elliott_q1[nn * filter_size + k] -= elliott_means[nn];
}
model->softmax_bias_q1[nn] -= mean_bias;
}
// Quality 2.
memset(mean_filter, 0, sizeof(mean_filter));
for (int nn = 0; nn < nns; nn++) {
softmax_means[nn] = mean(model->softmax_q2 + nn * filter_size, filter_size);
elliott_means[nn] = mean(model->elliott_q2 + nn * filter_size, filter_size);
for (int k = 0; k < filter_size; k++) {
mean_filter[k] += model->softmax_q2[nn * filter_size + k] - softmax_means[nn];
}
}
for (int k = 0; k < filter_size; k++)
mean_filter[k] *= scale;
mean_bias = mean(model->softmax_bias_q2, nns);
for (int nn = 0; nn < nns; nn++) {
for (int k = 0; k < filter_size; k++) {
model->softmax_q2[nn * filter_size + k] -= softmax_means[nn] + mean_filter[k];
model->elliott_q2[nn * filter_size + k] -= elliott_means[nn];
}
model->softmax_bias_q2[nn] -= mean_bias;
}
}
static av_cold int init(AVFilterContext *ctx)
{
NNEDIContext *s = ctx->priv;
FILE *weights_file = NULL;
int64_t weights_size;
float *bdata;
size_t bytes_read;
int ret = 0;
weights_file = av_fopen_utf8(s->weights_file, "rb");
if (!weights_file) {
av_log(ctx, AV_LOG_ERROR, "No weights file provided, aborting!\n");
return AVERROR(EINVAL);
}
if (fseek(weights_file, 0, SEEK_END)) {
av_log(ctx, AV_LOG_ERROR, "Couldn't seek to the end of weights file.\n");
fclose(weights_file);
return AVERROR(EINVAL);
}
weights_size = ftell(weights_file);
if (weights_size == -1) {
fclose(weights_file);
av_log(ctx, AV_LOG_ERROR, "Couldn't get size of weights file.\n");
return AVERROR(EINVAL);
} else if (weights_size != NNEDI_WEIGHTS_SIZE) {
fclose(weights_file);
av_log(ctx, AV_LOG_ERROR, "Unexpected weights file size.\n");
return AVERROR(EINVAL);
}
if (fseek(weights_file, 0, SEEK_SET)) {
fclose(weights_file);
av_log(ctx, AV_LOG_ERROR, "Couldn't seek to the start of weights file.\n");
return AVERROR(EINVAL);
}
bdata = av_malloc(NNEDI_WEIGHTS_SIZE);
if (!bdata) {
fclose(weights_file);
return AVERROR(ENOMEM);
}
bytes_read = fread(bdata, 1, NNEDI_WEIGHTS_SIZE, weights_file);
if (bytes_read != NNEDI_WEIGHTS_SIZE) {
fclose(weights_file);
ret = AVERROR_INVALIDDATA;
av_log(ctx, AV_LOG_ERROR, "Couldn't read weights file.\n");
goto fail;
}
fclose(weights_file);
s->fdsp = avpriv_float_dsp_alloc(0);
if (!s->fdsp) {
ret = AVERROR(ENOMEM);
goto fail;
}
ret = read_weights(ctx, bdata);
if (ret < 0)
goto fail;
fail:
av_free(bdata);
return ret;
}
static int config_input(AVFilterLink *inlink)
{
AVFilterContext *ctx = inlink->dst;
NNEDIContext *s = ctx->priv;
const AVPixFmtDescriptor *desc = av_pix_fmt_desc_get(inlink->format);
int ret;
s->depth = desc->comp[0].depth;
s->nb_threads = ff_filter_get_nb_threads(ctx);
s->nb_planes = av_pix_fmt_count_planes(inlink->format);
if ((ret = av_image_fill_linesizes(s->linesize, inlink->format, inlink->w)) < 0)
return ret;
s->planewidth[1] = s->planewidth[2] = AV_CEIL_RSHIFT(inlink->w, desc->log2_chroma_w);
s->planewidth[0] = s->planewidth[3] = inlink->w;
s->planeheight[1] = s->planeheight[2] = AV_CEIL_RSHIFT(inlink->h, desc->log2_chroma_h);
s->planeheight[0] = s->planeheight[3] = inlink->h;
s->half = ((1 << 8) - 1) / 2.f;
s->out_scale = 1 << (s->depth - 8);
s->in_scale = 1.f / s->out_scale;
switch (s->depth) {
case 8:
s->read = read_bytes;
s->write = write_bytes;
break;
default:
s->read = read_words;
s->write = write_words;
break;
}
subtract_mean_old(&s->prescreener[0], s->half);
subtract_mean_new(&s->prescreener[1], s->half);
subtract_mean_new(&s->prescreener[2], s->half);
subtract_mean_new(&s->prescreener[3], s->half);
s->prescreen[0] = process_old;
s->prescreen[1] = process_new;
for (int i = 0; i < 2; i++) {
for (int j = 0; j < 5; j++) {
for (int k = 0; k < 7; k++)
subtract_mean_predictor(&s->coeffs[i][j][k]);
}
}
s->input_size = (s->planewidth[0] + 64) * (s->planeheight[0] + 6);
s->input_buf = av_calloc(s->nb_threads, sizeof(*s->input_buf));
if (!s->input_buf)
return AVERROR(ENOMEM);
for (int i = 0; i < s->nb_threads; i++) {
s->input_buf[i] = av_calloc(s->input_size, sizeof(**s->input_buf));
if (!s->input_buf[i])
return AVERROR(ENOMEM);
}
s->output_buf = av_calloc(s->nb_threads, sizeof(*s->output_buf));
if (!s->output_buf)
return AVERROR(ENOMEM);
for (int i = 0; i < s->nb_threads; i++) {
s->output_buf[i] = av_calloc(s->input_size, sizeof(**s->output_buf));
if (!s->output_buf[i])
return AVERROR(ENOMEM);
}
s->prescreen_buf = av_calloc(s->nb_threads, sizeof(*s->prescreen_buf));
if (!s->prescreen_buf)
return AVERROR(ENOMEM);
for (int i = 0; i < s->nb_threads; i++) {
s->prescreen_buf[i] = av_calloc(s->planewidth[0], sizeof(**s->prescreen_buf));
if (!s->prescreen_buf[i])
return AVERROR(ENOMEM);
}
return 0;
}
static av_cold void uninit(AVFilterContext *ctx)
{
NNEDIContext *s = ctx->priv;
for (int i = 0; i < s->nb_threads && s->prescreen_buf; i++)
av_freep(&s->prescreen_buf[i]);
av_freep(&s->prescreen_buf);
for (int i = 0; i < s->nb_threads && s->input_buf; i++)
av_freep(&s->input_buf[i]);
av_freep(&s->input_buf);
for (int i = 0; i < s->nb_threads && s->output_buf; i++)
av_freep(&s->output_buf[i]);
av_freep(&s->output_buf);
av_freep(&s->fdsp);
for (int i = 0; i < 2; i++) {
for (int j = 0; j < 5; j++) {
for (int k = 0; k < 7; k++) {
av_freep(&s->coeffs[i][j][k].data);
}
}
}
av_frame_free(&s->prev);
}
static const AVFilterPad inputs[] = {
{
.name = "default",
.type = AVMEDIA_TYPE_VIDEO,
.filter_frame = filter_frame,
.config_props = config_input,
},
};
static const AVFilterPad outputs[] = {
{
.name = "default",
.type = AVMEDIA_TYPE_VIDEO,
.config_props = config_output,
.request_frame = request_frame,
},
};
const AVFilter ff_vf_nnedi = {
.name = "nnedi",
.description = NULL_IF_CONFIG_SMALL("Apply neural network edge directed interpolation intra-only deinterlacer."),
.priv_size = sizeof(NNEDIContext),
.priv_class = &nnedi_class,
.init = init,
.uninit = uninit,
FILTER_INPUTS(inputs),
FILTER_OUTPUTS(outputs),
FILTER_QUERY_FUNC(query_formats),
.flags = AVFILTER_FLAG_SUPPORT_TIMELINE_INTERNAL | AVFILTER_FLAG_SLICE_THREADS,
.process_command = ff_filter_process_command,
};
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