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FFmpeg/libavfilter/vf_nnedi.c
Andreas Rheinhardt 8be701d9f7 avfilter/avfilter: Add numbers of (in|out)pads directly to AVFilter 
Up until now, an AVFilter's lists of input and output AVFilterPads
were terminated by a sentinel and the only way to get the length
of these lists was by using avfilter_pad_count(). This has two
drawbacks: first, sizeof(AVFilterPad) is not negligible
(i.e. 64B on 64bit systems); second, getting the size involves
a function call instead of just reading the data.

This commit therefore changes this. The sentinels are removed and new
private fields nb_inputs and nb_outputs are added to AVFilter that
contain the number of elements of the respective AVFilterPad array.

Given that AVFilter.(in|out)puts are the only arrays of zero-terminated
AVFilterPads an API user has access to (AVFilterContext.(in|out)put_pads
are not zero-terminated and they already have a size field) the argument
to avfilter_pad_count() is always one of these lists, so it just has to
find the filter the list belongs to and read said number. This is slower
than before, but a replacement function that just reads the internal numbers
that users are expected to switch to will be added soon; and furthermore,
avfilter_pad_count() is probably never called in hot loops anyway.

This saves about 49KiB from the binary; notice that these sentinels are
not in .bss despite being zeroed: they are in .data.rel.ro due to the
non-sentinels.

Reviewed-by: Nicolas George <george@nsup.org>
Signed-off-by: Andreas Rheinhardt <andreas.rheinhardt@outlook.com>
2021-08-20 12:53:58 +02:00

1168 lines
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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,
.query_formats = query_formats,
FILTER_INPUTS(inputs),
FILTER_OUTPUTS(outputs),
.flags = AVFILTER_FLAG_SUPPORT_TIMELINE_INTERNAL | AVFILTER_FLAG_SLICE_THREADS,
.process_command = ff_filter_process_command,
};
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