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FFmpeg/libavfilter/vf_nnedi.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) 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/file_open.h"
#include "libavutil/float_dsp.h"
#include "libavutil/imgutils.h"
#include "libavutil/mem.h"
#include "libavutil/mem_internal.h"
#include "libavutil/opt.h"
#include "libavutil/pixdesc.h"
#include "avfilter.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, .unit = "deint" },
{"all", "deinterlace all frames", 0, AV_OPT_TYPE_CONST, {.i64=0}, 0, 0, RFLAGS, .unit = "deint" },
{"interlaced", "only deinterlace frames marked as interlaced", 0, AV_OPT_TYPE_CONST, {.i64=1}, 0, 0, RFLAGS, .unit = "deint" },
{"field", "set mode of operation", OFFSET(field), AV_OPT_TYPE_INT, {.i64=-1}, -2, 3, RFLAGS, .unit = "field" },
{"af", "use frame flags, both fields", 0, AV_OPT_TYPE_CONST, {.i64=-2}, 0, 0, RFLAGS, .unit = "field" },
{"a", "use frame flags, single field", 0, AV_OPT_TYPE_CONST, {.i64=-1}, 0, 0, RFLAGS, .unit = "field" },
{"t", "use top field only", 0, AV_OPT_TYPE_CONST, {.i64=0}, 0, 0, RFLAGS, .unit = "field" },
{"b", "use bottom field only", 0, AV_OPT_TYPE_CONST, {.i64=1}, 0, 0, RFLAGS, .unit = "field" },
{"tf", "use both fields, top first", 0, AV_OPT_TYPE_CONST, {.i64=2}, 0, 0, RFLAGS, .unit = "field" },
{"bf", "use both fields, bottom first", 0, AV_OPT_TYPE_CONST, {.i64=3}, 0, 0, RFLAGS, .unit = "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, .unit = "nsize" },
{"s8x6", NULL, 0, AV_OPT_TYPE_CONST, {.i64=0}, 0, 0, RFLAGS, .unit = "nsize" },
{"s16x6", NULL, 0, AV_OPT_TYPE_CONST, {.i64=1}, 0, 0, RFLAGS, .unit = "nsize" },
{"s32x6", NULL, 0, AV_OPT_TYPE_CONST, {.i64=2}, 0, 0, RFLAGS, .unit = "nsize" },
{"s48x6", NULL, 0, AV_OPT_TYPE_CONST, {.i64=3}, 0, 0, RFLAGS, .unit = "nsize" },
{"s8x4", NULL, 0, AV_OPT_TYPE_CONST, {.i64=4}, 0, 0, RFLAGS, .unit = "nsize" },
{"s16x4", NULL, 0, AV_OPT_TYPE_CONST, {.i64=5}, 0, 0, RFLAGS, .unit = "nsize" },
{"s32x4", NULL, 0, AV_OPT_TYPE_CONST, {.i64=6}, 0, 0, RFLAGS, .unit = "nsize" },
{"nns", "set number of neurons in predictor neural network", OFFSET(nnsparam), AV_OPT_TYPE_INT, {.i64=1}, 0, 4, RFLAGS, .unit = "nns" },
{"n16", NULL, 0, AV_OPT_TYPE_CONST, {.i64=0}, 0, 0, RFLAGS, .unit = "nns" },
{"n32", NULL, 0, AV_OPT_TYPE_CONST, {.i64=1}, 0, 0, RFLAGS, .unit = "nns" },
{"n64", NULL, 0, AV_OPT_TYPE_CONST, {.i64=2}, 0, 0, RFLAGS, .unit = "nns" },
{"n128", NULL, 0, AV_OPT_TYPE_CONST, {.i64=3}, 0, 0, RFLAGS, .unit = "nns" },
{"n256", NULL, 0, AV_OPT_TYPE_CONST, {.i64=4}, 0, 0, RFLAGS, .unit = "nns" },
{"qual", "set quality", OFFSET(qual), AV_OPT_TYPE_INT, {.i64=1}, 1, 2, RFLAGS, .unit = "qual" },
{"fast", NULL, 0, AV_OPT_TYPE_CONST, {.i64=1}, 0, 0, RFLAGS, .unit = "qual" },
{"slow", NULL, 0, AV_OPT_TYPE_CONST, {.i64=2}, 0, 0, RFLAGS, .unit = "qual" },
{"etype", "set which set of weights to use in the predictor", OFFSET(etype), AV_OPT_TYPE_INT, {.i64=0}, 0, 1, RFLAGS, .unit = "etype" },
{"a", "weights trained to minimize absolute error", 0, AV_OPT_TYPE_CONST, {.i64=0}, 0, 0, RFLAGS, .unit = "etype" },
{"abs","weights trained to minimize absolute error", 0, AV_OPT_TYPE_CONST, {.i64=0}, 0, 0, RFLAGS, .unit = "etype" },
{"s", "weights trained to minimize squared error", 0, AV_OPT_TYPE_CONST, {.i64=1}, 0, 0, RFLAGS, .unit = "etype" },
{"mse","weights trained to minimize squared error", 0, AV_OPT_TYPE_CONST, {.i64=1}, 0, 0, RFLAGS, .unit = "etype" },
{"pscrn", "set prescreening", OFFSET(pscrn), AV_OPT_TYPE_INT, {.i64=2}, 0, 4, RFLAGS, .unit = "pscrn" },
{"none", NULL, 0, AV_OPT_TYPE_CONST, {.i64=0}, 0, 0, RFLAGS, .unit = "pscrn" },
{"original", NULL, 0, AV_OPT_TYPE_CONST, {.i64=1}, 0, 0, RFLAGS, .unit = "pscrn" },
{"new", NULL, 0, AV_OPT_TYPE_CONST, {.i64=2}, 0, 0, RFLAGS, .unit = "pscrn" },
{"new2", NULL, 0, AV_OPT_TYPE_CONST, {.i64=3}, 0, 0, RFLAGS, .unit = "pscrn" },
{"new3", NULL, 0, AV_OPT_TYPE_CONST, {.i64=4}, 0, 0, RFLAGS, .unit = "pscrn" },
{ NULL }
};
AVFILTER_DEFINE_CLASS(nnedi);
static int config_output(AVFilterLink *outlink)
{
AVFilterContext *ctx = outlink->src;
const NNEDIContext *const s = ctx->priv;
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;
if (s->field == -2 || s->field > 1)
outlink->frame_rate = av_mul_q(ctx->inputs[0]->frame_rate,
(AVRational){2, 1});
return 0;
}
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
};
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->flags & AV_FRAME_FLAG_INTERLACED);
const int tff = s->field_n == (s->field < 0 ? interlaced ? (in->flags & AV_FRAME_FLAG_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);
#if FF_API_INTERLACED_FRAME
FF_DISABLE_DEPRECATION_WARNINGS
dst->interlaced_frame = 0;
FF_ENABLE_DEPRECATION_WARNINGS
#endif
dst->flags &= ~AV_FRAME_FLAG_INTERLACED;
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->flags & AV_FRAME_FLAG_INTERLACED)) || 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 = avpriv_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_PIXFMTS_ARRAY(pix_fmts),
.flags = AVFILTER_FLAG_SUPPORT_TIMELINE_INTERNAL | AVFILTER_FLAG_SLICE_THREADS,
.process_command = ff_filter_process_command,
};
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