generic-library/ffmpeg
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity
f099cb2f8a
ffmpeg/libavfilter/vf_nnedi.c
Carl Eugen Hoyos 37afeabd1b lavfi/nnedi: Fix a compilation warning. 
Silences the following warning:
libavfilter/vf_nnedi.c:611:15: warning: assignment discards ‘const’ qualifier from pointer target type
2016-02-23 00:21:49 +01:00

1212 lines
39 KiB
C
Raw Blame History

/*
* 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/opt.h"
#include "libavutil/pixdesc.h"
#include "avfilter.h"
#include "formats.h"
#include "internal.h"
#include "video.h"
typedef struct FrameData {
uint8_t *paddedp[3];
int padded_stride[3];
int padded_width[3];
int padded_height[3];
uint8_t *dstp[3];
int dst_stride[3];
int field[3];
int32_t *lcount[3];
float *input;
float *temp;
} FrameData;
typedef struct NNEDIContext {
const AVClass *class;
char *weights_file;
AVFrame *src;
AVFrame *second;
AVFrame *dst;
int eof;
int64_t cur_pts;
AVFloatDSPContext *fdsp;
int nb_planes;
int linesize[4];
int planeheight[4];
float *weights0;
float *weights1[2];
int asize;
int nns;
int xdia;
int ydia;
// Parameters
int deint;
int field;
int process_plane;
int nsize;
int nnsparam;
int qual;
int etype;
int pscrn;
int fapprox;
int max_value;
void (*copy_pad)(const AVFrame *, FrameData *, struct NNEDIContext *, int);
void (*evalfunc_0)(struct NNEDIContext *, FrameData *);
void (*evalfunc_1)(struct NNEDIContext *, FrameData *);
// Functions used in evalfunc_0
void (*readpixels)(const uint8_t *, const int, float *);
void (*compute_network0)(struct NNEDIContext *s, const float *, const float *, uint8_t *);
int32_t (*process_line0)(const uint8_t *, int, uint8_t *, const uint8_t *, const int, const int, const int);
// Functions used in evalfunc_1
void (*extract)(const uint8_t *, const int, const int, const int, float *, float *);
void (*dot_prod)(struct NNEDIContext *, const float *, const float *, float *, const int, const int, const float *);
void (*expfunc)(float *, const int);
void (*wae5)(const float *, const int, float *);
FrameData frame_data;
} NNEDIContext;
#define OFFSET(x) offsetof(NNEDIContext, x)
#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, FLAGS, "deint" },
{"all", "deinterlace all frames", 0, AV_OPT_TYPE_CONST, {.i64=0}, 0, 0, FLAGS, "deint" },
{"interlaced", "only deinterlace frames marked as interlaced", 0, AV_OPT_TYPE_CONST, {.i64=1}, 0, 0, FLAGS, "deint" },
{"field", "set mode of operation", OFFSET(field), AV_OPT_TYPE_INT, {.i64=-1}, -2, 3, FLAGS, "field" },
{"af", "use frame flags, both fields", 0, AV_OPT_TYPE_CONST, {.i64=-2}, 0, 0, FLAGS, "field" },
{"a", "use frame flags, single field", 0, AV_OPT_TYPE_CONST, {.i64=-1}, 0, 0, FLAGS, "field" },
{"t", "use top field only", 0, AV_OPT_TYPE_CONST, {.i64=0}, 0, 0, FLAGS, "field" },
{"b", "use bottom field only", 0, AV_OPT_TYPE_CONST, {.i64=1}, 0, 0, FLAGS, "field" },
{"tf", "use both fields, top first", 0, AV_OPT_TYPE_CONST, {.i64=2}, 0, 0, FLAGS, "field" },
{"bf", "use both fields, bottom first", 0, AV_OPT_TYPE_CONST, {.i64=3}, 0, 0, FLAGS, "field" },
{"planes", "set which planes to process", OFFSET(process_plane), AV_OPT_TYPE_INT, {.i64=7}, 0, 7, FLAGS },
{"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, FLAGS, "nsize" },
{"s8x6", NULL, 0, AV_OPT_TYPE_CONST, {.i64=0}, 0, 0, FLAGS, "nsize" },
{"s16x6", NULL, 0, AV_OPT_TYPE_CONST, {.i64=1}, 0, 0, FLAGS, "nsize" },
{"s32x6", NULL, 0, AV_OPT_TYPE_CONST, {.i64=2}, 0, 0, FLAGS, "nsize" },
{"s48x6", NULL, 0, AV_OPT_TYPE_CONST, {.i64=3}, 0, 0, FLAGS, "nsize" },
{"s8x4", NULL, 0, AV_OPT_TYPE_CONST, {.i64=4}, 0, 0, FLAGS, "nsize" },
{"s16x4", NULL, 0, AV_OPT_TYPE_CONST, {.i64=5}, 0, 0, FLAGS, "nsize" },
{"s32x4", NULL, 0, AV_OPT_TYPE_CONST, {.i64=6}, 0, 0, FLAGS, "nsize" },
{"nns", "set number of neurons in predictor neural network", OFFSET(nnsparam), AV_OPT_TYPE_INT, {.i64=1}, 0, 4, FLAGS, "nns" },
{"n16", NULL, 0, AV_OPT_TYPE_CONST, {.i64=0}, 0, 0, FLAGS, "nns" },
{"n32", NULL, 0, AV_OPT_TYPE_CONST, {.i64=1}, 0, 0, FLAGS, "nns" },
{"n64", NULL, 0, AV_OPT_TYPE_CONST, {.i64=2}, 0, 0, FLAGS, "nns" },
{"n128", NULL, 0, AV_OPT_TYPE_CONST, {.i64=3}, 0, 0, FLAGS, "nns" },
{"n256", NULL, 0, AV_OPT_TYPE_CONST, {.i64=4}, 0, 0, FLAGS, "nns" },
{"qual", "set quality", OFFSET(qual), AV_OPT_TYPE_INT, {.i64=1}, 1, 2, FLAGS, "qual" },
{"fast", NULL, 0, AV_OPT_TYPE_CONST, {.i64=1}, 0, 0, FLAGS, "qual" },
{"slow", NULL, 0, AV_OPT_TYPE_CONST, {.i64=2}, 0, 0, FLAGS, "qual" },
{"etype", "set which set of weights to use in the predictor", OFFSET(etype), AV_OPT_TYPE_INT, {.i64=0}, 0, 1, FLAGS, "etype" },
{"a", "weights trained to minimize absolute error", 0, AV_OPT_TYPE_CONST, {.i64=0}, 0, 0, FLAGS, "etype" },
{"s", "weights trained to minimize squared error", 0, AV_OPT_TYPE_CONST, {.i64=1}, 0, 0, FLAGS, "etype" },
{"pscrn", "set prescreening", OFFSET(pscrn), AV_OPT_TYPE_INT, {.i64=2}, 0, 2, FLAGS, "pscrn" },
{"none", NULL, 0, AV_OPT_TYPE_CONST, {.i64=0}, 0, 0, FLAGS, "pscrn" },
{"original", NULL, 0, AV_OPT_TYPE_CONST, {.i64=1}, 0, 0, FLAGS, "pscrn" },
{"new", NULL, 0, AV_OPT_TYPE_CONST, {.i64=2}, 0, 0, FLAGS, "pscrn" },
{"fapprox", NULL, OFFSET(fapprox), AV_OPT_TYPE_INT, {.i64=0}, 0, 3, FLAGS },
{ NULL }
};
AVFILTER_DEFINE_CLASS(nnedi);
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->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->planeheight[1] = s->planeheight[2] = AV_CEIL_RSHIFT(inlink->h, desc->log2_chroma_h);
s->planeheight[0] = s->planeheight[3] = inlink->h;
return 0;
}
static int config_output(AVFilterLink *outlink)
{
AVFilterContext *ctx = outlink->src;
NNEDIContext *s = ctx->priv;
outlink->time_base.num = ctx->inputs[0]->time_base.num;
outlink->time_base.den = ctx->inputs[0]->time_base.den * 2;
outlink->w = ctx->inputs[0]->w;
outlink->h = ctx->inputs[0]->h;
if (s->field > 1 || s->field == -2)
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_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_GBRP,
AV_PIX_FMT_GRAY8,
AV_PIX_FMT_NONE
};
AVFilterFormats *fmts_list = ff_make_format_list(pix_fmts);
if (!fmts_list)
return AVERROR(ENOMEM);
return ff_set_common_formats(ctx, fmts_list);
}
static void copy_pad(const AVFrame *src, FrameData *frame_data, NNEDIContext *s, int fn)
{
const int off = 1 - fn;
int plane, y, x;
for (plane = 0; plane < s->nb_planes; plane++) {
const uint8_t *srcp = (const uint8_t *)src->data[plane];
uint8_t *dstp = (uint8_t *)frame_data->paddedp[plane];
const int src_stride = src->linesize[plane];
const int dst_stride = frame_data->padded_stride[plane];
const int src_height = s->planeheight[plane];
const int dst_height = frame_data->padded_height[plane];
const int src_width = s->linesize[plane];
const int dst_width = frame_data->padded_width[plane];
int c = 4;
if (!(s->process_plane & (1 << plane)))
continue;
// Copy.
for (y = off; y < src_height; y += 2)
memcpy(dstp + 32 + (6 + y) * dst_stride,
srcp + y * src_stride,
src_width * sizeof(uint8_t));
// And pad.
dstp += (6 + off) * dst_stride;
for (y = 6 + off; y < dst_height - 6; y += 2) {
int c = 2;
for (x = 0; x < 32; x++)
dstp[x] = dstp[64 - x];
for (x = dst_width - 32; x < dst_width; x++, c += 2)
dstp[x] = dstp[x - c];
dstp += dst_stride * 2;
}
dstp = (uint8_t *)frame_data->paddedp[plane];
for (y = off; y < 6; y += 2)
memcpy(dstp + y * dst_stride,
dstp + (12 + 2 * off - y) * dst_stride,
dst_width * sizeof(uint8_t));
for (y = dst_height - 6 + off; y < dst_height; y += 2, c += 4)
memcpy(dstp + y * dst_stride,
dstp + (y - c) * dst_stride,
dst_width * sizeof(uint8_t));
}
}
static void elliott(float *data, const int n)
{
int i;
for (i = 0; i < n; i++)
data[i] = data[i] / (1.0f + FFABS(data[i]));
}
static void dot_prod(NNEDIContext *s, const float *data, const float *weights, float *vals, const int n, const int len, const float *scale)
{
int i;
for (i = 0; i < n; i++) {
float sum;
sum = s->fdsp->scalarproduct_float(data, &weights[i * len], len);
vals[i] = sum * scale[0] + weights[n * len + i];
}
}
static void dot_prods(NNEDIContext *s, const float *dataf, const float *weightsf, float *vals, const int n, const int len, const float *scale)
{
const int16_t *data = (int16_t *)dataf;
const int16_t *weights = (int16_t *)weightsf;
const float *wf = (float *)&weights[n * len];
int i, j;
for (i = 0; i < n; i++) {
int sum = 0, off = ((i >> 2) << 3) + (i & 3);
for (j = 0; j < len; j++)
sum += data[j] * weights[i * len + j];
vals[i] = sum * wf[off] * scale[0] + wf[off + 4];
}
}
static void compute_network0(NNEDIContext *s, const float *input, const float *weights, uint8_t *d)
{
float t, temp[12], scale = 1.0f;
dot_prod(s, input, weights, temp, 4, 48, &scale);
t = temp[0];
elliott(temp, 4);
temp[0] = t;
dot_prod(s, temp, weights + 4 * 49, temp + 4, 4, 4, &scale);
elliott(temp + 4, 4);
dot_prod(s, temp, weights + 4 * 49 + 4 * 5, temp + 8, 4, 8, &scale);
if (FFMAX(temp[10], temp[11]) <= FFMAX(temp[8], temp[9]))
d[0] = 1;
else
d[0] = 0;
}
static void compute_network0_i16(NNEDIContext *s, const float *inputf, const float *weightsf, uint8_t *d)
{
const float *wf = weightsf + 2 * 48;
float t, temp[12], scale = 1.0f;
dot_prods(s, inputf, weightsf, temp, 4, 48, &scale);
t = temp[0];
elliott(temp, 4);
temp[0] = t;
dot_prod(s, temp, wf + 8, temp + 4, 4, 4, &scale);
elliott(temp + 4, 4);
dot_prod(s, temp, wf + 8 + 4 * 5, temp + 8, 4, 8, &scale);
if (FFMAX(temp[10], temp[11]) <= FFMAX(temp[8], temp[9]))
d[0] = 1;
else
d[0] = 0;
}
static void pixel2float48(const uint8_t *t8, const int pitch, float *p)
{
const uint8_t *t = (const uint8_t *)t8;
int y, x;
for (y = 0; y < 4; y++)
for (x = 0; x < 12; x++)
p[y * 12 + x] = t[y * pitch * 2 + x];
}
static void byte2word48(const uint8_t *t, const int pitch, float *pf)
{
int16_t *p = (int16_t *)pf;
int y, x;
for (y = 0; y < 4; y++)
for (x = 0; x < 12; x++)
p[y * 12 + x] = t[y * pitch * 2 + x];
}
static int32_t process_line0(const uint8_t *tempu, int width, uint8_t *dstp8, const uint8_t *src3p8, const int src_pitch, const int max_value, const int chroma)
{
uint8_t *dstp = (uint8_t *)dstp8;
const uint8_t *src3p = (const uint8_t *)src3p8;
int minimum = 0;
int maximum = max_value - 1; // Technically the -1 is only needed for 8 and 16 bit input.
int count = 0, x;
for (x = 0; x < width; x++) {
if (tempu[x]) {
int tmp = 19 * (src3p[x + src_pitch * 2] + src3p[x + src_pitch * 4]) - 3 * (src3p[x] + src3p[x + src_pitch * 6]);
tmp /= 32;
dstp[x] = FFMAX(FFMIN(tmp, maximum), minimum);
} else {
dstp[x] = 255;
count++;
}
}
return count;
}
// new prescreener functions
static void byte2word64(const uint8_t *t, const int pitch, float *p)
{
int16_t *ps = (int16_t *)p;
int y, x;
for (y = 0; y < 4; y++)
for (x = 0; x < 16; x++)
ps[y * 16 + x] = t[y * pitch * 2 + x];
}
static void compute_network0new(NNEDIContext *s, const float *datai, const float *weights, uint8_t *d)
{
int16_t *data = (int16_t *)datai;
int16_t *ws = (int16_t *)weights;
float *wf = (float *)&ws[4 * 64];
float vals[8];
int mask, i, j;
for (i = 0; i < 4; i++) {
int sum = 0;
float t;
for (j = 0; j < 64; j++)
sum += data[j] * ws[(i << 3) + ((j >> 3) << 5) + (j & 7)];
t = sum * wf[i] + wf[4 + i];
vals[i] = t / (1.0f + FFABS(t));
}
for (i = 0; i < 4; i++) {
float sum = 0.0f;
for (j = 0; j < 4; j++)
sum += vals[j] * wf[8 + i + (j << 2)];
vals[4 + i] = sum + wf[8 + 16 + i];
}
mask = 0;
for (i = 0; i < 4; i++) {
if (vals[4 + i] > 0.0f)
mask |= (0x1 << (i << 3));
}
((int *)d)[0] = mask;
}
static void evalfunc_0(NNEDIContext *s, FrameData *frame_data)
{
float *input = frame_data->input;
const float *weights0 = s->weights0;
float *temp = frame_data->temp;
uint8_t *tempu = (uint8_t *)temp;
int plane, x, y;
// And now the actual work.
for (plane = 0; plane < s->nb_planes; plane++) {
const uint8_t *srcp = (const uint8_t *)frame_data->paddedp[plane];
const int src_stride = frame_data->padded_stride[plane] / sizeof(uint8_t);
const int width = frame_data->padded_width[plane];
const int height = frame_data->padded_height[plane];
uint8_t *dstp = (uint8_t *)frame_data->dstp[plane];
const int dst_stride = frame_data->dst_stride[plane] / sizeof(uint8_t);
const uint8_t *src3p;
int ystart, ystop;
int32_t *lcount;
if (!(s->process_plane & (1 << plane)))
continue;
for (y = 1 - frame_data->field[plane]; y < height - 12; y += 2) {
memcpy(dstp + y * dst_stride,
srcp + 32 + (6 + y) * src_stride,
(width - 64) * sizeof(uint8_t));
}
ystart = 6 + frame_data->field[plane];
ystop = height - 6;
srcp += ystart * src_stride;
dstp += (ystart - 6) * dst_stride - 32;
src3p = srcp - src_stride * 3;
lcount = frame_data->lcount[plane] - 6;
if (s->pscrn == 1) { // original
for (y = ystart; y < ystop; y += 2) {
for (x = 32; x < width - 32; x++) {
s->readpixels((const uint8_t *)(src3p + x - 5), src_stride, input);
s->compute_network0(s, input, weights0, tempu+x);
}
lcount[y] += s->process_line0(tempu + 32, width - 64, (uint8_t *)(dstp + 32), (const uint8_t *)(src3p + 32), src_stride, s->max_value, plane);
src3p += src_stride * 2;
dstp += dst_stride * 2;
}
} else if (s->pscrn > 1) { // new
for (y = ystart; y < ystop; y += 2) {
for (x = 32; x < width - 32; x += 4) {
s->readpixels((const uint8_t *)(src3p + x - 6), src_stride, input);
s->compute_network0(s, input, weights0, tempu + x);
}
lcount[y] += s->process_line0(tempu + 32, width - 64, (uint8_t *)(dstp + 32), (const uint8_t *)(src3p + 32), src_stride, s->max_value, plane);
src3p += src_stride * 2;
dstp += dst_stride * 2;
}
} else { // no prescreening
for (y = ystart; y < ystop; y += 2) {
memset(dstp + 32, 255, (width - 64) * sizeof(uint8_t));
lcount[y] += width - 64;
dstp += dst_stride * 2;
}
}
}
}
static void extract_m8(const uint8_t *srcp8, const int stride, const int xdia, const int ydia, float *mstd, float *input)
{
// uint8_t or uint16_t or float
const uint8_t *srcp = (const uint8_t *)srcp8;
float scale;
double tmp;
// int32_t or int64_t or double
int64_t sum = 0, sumsq = 0;
int y, x;
for (y = 0; y < ydia; y++) {
const uint8_t *srcpT = srcp + y * stride * 2;
for (x = 0; x < xdia; x++) {
sum += srcpT[x];
sumsq += (uint32_t)srcpT[x] * (uint32_t)srcpT[x];
input[x] = srcpT[x];
}
input += xdia;
}
scale = 1.0f / (xdia * ydia);
mstd[0] = sum * scale;
tmp = (double)sumsq * scale - (double)mstd[0] * mstd[0];
mstd[3] = 0.0f;
if (tmp <= FLT_EPSILON)
mstd[1] = mstd[2] = 0.0f;
else {
mstd[1] = sqrt(tmp);
mstd[2] = 1.0f / mstd[1];
}
}
static void extract_m8_i16(const uint8_t *srcp, const int stride, const int xdia, const int ydia, float *mstd, float *inputf)
{
int16_t *input = (int16_t *)inputf;
float scale;
int sum = 0, sumsq = 0;
int y, x;
for (y = 0; y < ydia; y++) {
const uint8_t *srcpT = srcp + y * stride * 2;
for (x = 0; x < xdia; x++) {
sum += srcpT[x];
sumsq += srcpT[x] * srcpT[x];
input[x] = srcpT[x];
}
input += xdia;
}
scale = 1.0f / (float)(xdia * ydia);
mstd[0] = sum * scale;
mstd[1] = sumsq * scale - mstd[0] * mstd[0];
mstd[3] = 0.0f;
if (mstd[1] <= FLT_EPSILON)
mstd[1] = mstd[2] = 0.0f;
else {
mstd[1] = sqrt(mstd[1]);
mstd[2] = 1.0f / mstd[1];
}
}
static const float exp_lo = -80.0f;
static const float exp_hi = +80.0f;
static void e2_m16(float *s, const int n)
{
int i;
for (i = 0; i < n; i++)
s[i] = exp(av_clipf(s[i], exp_lo, exp_hi));
}
const float min_weight_sum = 1e-10f;
static void weighted_avg_elliott_mul5_m16(const float *w, const int n, float *mstd)
{
float vsum = 0.0f, wsum = 0.0f;
int i;
for (i = 0; i < n; i++) {
vsum += w[i] * (w[n + i] / (1.0f + FFABS(w[n + i])));
wsum += w[i];
}
if (wsum > min_weight_sum)
mstd[3] += ((5.0f * vsum) / wsum) * mstd[1] + mstd[0];
else
mstd[3] += mstd[0];
}
static void evalfunc_1(NNEDIContext *s, FrameData *frame_data)
{
float *input = frame_data->input;
float *temp = frame_data->temp;
float **weights1 = s->weights1;
const int qual = s->qual;
const int asize = s->asize;
const int nns = s->nns;
const int xdia = s->xdia;
const int xdiad2m1 = (xdia / 2) - 1;
const int ydia = s->ydia;
const float scale = 1.0f / (float)qual;
int plane, y, x, i;
for (plane = 0; plane < s->nb_planes; plane++) {
const uint8_t *srcp = (const uint8_t *)frame_data->paddedp[plane];
const int src_stride = frame_data->padded_stride[plane] / sizeof(uint8_t);
const int width = frame_data->padded_width[plane];
const int height = frame_data->padded_height[plane];
uint8_t *dstp = (uint8_t *)frame_data->dstp[plane];
const int dst_stride = frame_data->dst_stride[plane] / sizeof(uint8_t);
const int ystart = frame_data->field[plane];
const int ystop = height - 12;
const uint8_t *srcpp;
if (!(s->process_plane & (1 << plane)))
continue;
srcp += (ystart + 6) * src_stride;
dstp += ystart * dst_stride - 32;
srcpp = srcp - (ydia - 1) * src_stride - xdiad2m1;
for (y = ystart; y < ystop; y += 2) {
for (x = 32; x < width - 32; x++) {
float mstd[4];
if (dstp[x] != 255)
continue;
s->extract((const uint8_t *)(srcpp + x), src_stride, xdia, ydia, mstd, input);
for (i = 0; i < qual; i++) {
s->dot_prod(s, input, weights1[i], temp, nns * 2, asize, mstd + 2);
s->expfunc(temp, nns);
s->wae5(temp, nns, mstd);
}
dstp[x] = FFMIN(FFMAX((int)(mstd[3] * scale + 0.5f), 0), s->max_value);
}
srcpp += src_stride * 2;
dstp += dst_stride * 2;
}
}
}
#define NUM_NSIZE 7
#define NUM_NNS 5
static int roundds(const double f)
{
if (f - floor(f) >= 0.5)
return FFMIN((int)ceil(f), 32767);
return FFMAX((int)floor(f), -32768);
}
static void select_functions(NNEDIContext *s)
{
s->copy_pad = copy_pad;
s->evalfunc_0 = evalfunc_0;
s->evalfunc_1 = evalfunc_1;
// evalfunc_0
s->process_line0 = process_line0;
if (s->pscrn < 2) { // original prescreener
if (s->fapprox & 1) { // int16 dot products
s->readpixels = byte2word48;
s->compute_network0 = compute_network0_i16;
} else {
s->readpixels = pixel2float48;
s->compute_network0 = compute_network0;
}
} else { // new prescreener
// only int16 dot products
s->readpixels = byte2word64;
s->compute_network0 = compute_network0new;
}
// evalfunc_1
s->wae5 = weighted_avg_elliott_mul5_m16;
if (s->fapprox & 2) { // use int16 dot products
s->extract = extract_m8_i16;
s->dot_prod = dot_prods;
} else { // use float dot products
s->extract = extract_m8;
s->dot_prod = dot_prod;
}
s->expfunc = e2_m16;
}
static int modnpf(const int m, const int n)
{
if ((m % n) == 0)
return m;
return m + n - (m % n);
}
static int get_frame(AVFilterContext *ctx, int is_second)
{
NNEDIContext *s = ctx->priv;
AVFilterLink *outlink = ctx->outputs[0];
AVFrame *src = s->src;
FrameData *frame_data;
int effective_field = s->field;
size_t temp_size;
int field_n;
int plane;
if (effective_field > 1)
effective_field -= 2;
else if (effective_field < 0)
effective_field += 2;
if (s->field < 0 && src->interlaced_frame && src->top_field_first == 0)
effective_field = 0;
else if (s->field < 0 && src->interlaced_frame && src->top_field_first == 1)
effective_field = 1;
else
effective_field = !effective_field;
if (s->field > 1 || s->field == -2) {
if (is_second) {
field_n = (effective_field == 0);
} else {
field_n = (effective_field == 1);
}
} else {
field_n = effective_field;
}
s->dst = ff_get_video_buffer(outlink, outlink->w, outlink->h);
if (!s->dst)
return AVERROR(ENOMEM);
av_frame_copy_props(s->dst, src);
s->dst->interlaced_frame = 0;
frame_data = &s->frame_data;
for (plane = 0; plane < s->nb_planes; plane++) {
int dst_height = s->planeheight[plane];
int dst_width = s->linesize[plane];
const int min_alignment = 16;
const int min_pad = 10;
if (!(s->process_plane & (1 << plane))) {
av_image_copy_plane(s->dst->data[plane], s->dst->linesize[plane],
src->data[plane], src->linesize[plane],
s->linesize[plane],
s->planeheight[plane]);
continue;
}
frame_data->padded_width[plane] = dst_width + 64;
frame_data->padded_height[plane] = dst_height + 12;
frame_data->padded_stride[plane] = modnpf(frame_data->padded_width[plane] + min_pad, min_alignment); // TODO: maybe min_pad is in pixels too?
if (!frame_data->paddedp[plane]) {
frame_data->paddedp[plane] = av_malloc_array(frame_data->padded_stride[plane], frame_data->padded_height[plane]);
if (!frame_data->paddedp[plane])
return AVERROR(ENOMEM);
}
frame_data->dstp[plane] = s->dst->data[plane];
frame_data->dst_stride[plane] = s->dst->linesize[plane];
if (!frame_data->lcount[plane]) {
frame_data->lcount[plane] = av_calloc(dst_height, sizeof(int32_t) * 16);
if (!frame_data->lcount[plane])
return AVERROR(ENOMEM);
} else {
memset(frame_data->lcount[plane], 0, dst_height * sizeof(int32_t) * 16);
}
frame_data->field[plane] = field_n;
}
if (!frame_data->input) {
frame_data->input = av_malloc(512 * sizeof(float));
if (!frame_data->input)
return AVERROR(ENOMEM);
}
// evalfunc_0 requires at least padded_width[0] bytes.
// evalfunc_1 requires at least 512 floats.
if (!frame_data->temp) {
temp_size = FFMAX(frame_data->padded_width[0], 512 * sizeof(float));
frame_data->temp = av_malloc(temp_size);
if (!frame_data->temp)
return AVERROR(ENOMEM);
}
// Copy src to a padded "frame" in frame_data and mirror the edges.
s->copy_pad(src, frame_data, s, field_n);
// Handles prescreening and the cubic interpolation.
s->evalfunc_0(s, frame_data);
// The rest.
s->evalfunc_1(s, frame_data);
return 0;
}
static int filter_frame(AVFilterLink *inlink, AVFrame *src)
{
AVFilterContext *ctx = inlink->dst;
AVFilterLink *outlink = ctx->outputs[0];
NNEDIContext *s = ctx->priv;
int ret;
if ((s->field > 1 ||
s->field == -2) && !s->second) {
goto second;
} else if (s->field > 1 ||
s->field == -2) {
AVFrame *dst;
s->src = s->second;
ret = get_frame(ctx, 1);
if (ret < 0) {
av_frame_free(&s->dst);
av_frame_free(&s->src);
av_frame_free(&s->second);
return ret;
}
dst = s->dst;
if (src->pts != AV_NOPTS_VALUE &&
dst->pts != AV_NOPTS_VALUE)
dst->pts += src->pts;
else
dst->pts = AV_NOPTS_VALUE;
ret = ff_filter_frame(outlink, dst);
if (ret < 0)
return ret;
if (s->eof)
return 0;
s->cur_pts = s->second->pts;
av_frame_free(&s->second);
second:
if ((s->deint && src->interlaced_frame &&
!ctx->is_disabled) ||
(!s->deint && !ctx->is_disabled)) {
s->second = src;
}
}
if ((s->deint && !src->interlaced_frame) || ctx->is_disabled) {
AVFrame *dst = av_frame_clone(src);
if (!dst) {
av_frame_free(&src);
av_frame_free(&s->second);
return AVERROR(ENOMEM);
}
if (s->field > 1 || s->field == -2) {
av_frame_free(&s->second);
if ((s->deint && src->interlaced_frame) ||
(!s->deint))
s->second = src;
} else {
av_frame_free(&src);
}
if (dst->pts != AV_NOPTS_VALUE)
dst->pts *= 2;
return ff_filter_frame(outlink, dst);
}
s->src = src;
ret = get_frame(ctx, 0);
if (ret < 0) {
av_frame_free(&s->dst);
av_frame_free(&s->src);
av_frame_free(&s->second);
return ret;
}
if (src->pts != AV_NOPTS_VALUE)
s->dst->pts = src->pts * 2;
if (s->field <= 1 && s->field > -2) {
av_frame_free(&src);
s->src = NULL;
}
return ff_filter_frame(outlink, s->dst);
}
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->second) {
AVFrame *next = av_frame_clone(s->second);
if (!next)
return AVERROR(ENOMEM);
next->pts = s->second->pts * 2 - s->cur_pts;
s->eof = 1;
filter_frame(ctx->inputs[0], next);
} else if (ret < 0) {
return ret;
}
return 0;
}
static av_cold int init(AVFilterContext *ctx)
{
NNEDIContext *s = ctx->priv;
FILE *weights_file = NULL;
int64_t expected_size = 13574928;
int64_t weights_size;
float *bdata;
size_t bytes_read;
const int xdia_table[NUM_NSIZE] = { 8, 16, 32, 48, 8, 16, 32 };
const int ydia_table[NUM_NSIZE] = { 6, 6, 6, 6, 4, 4, 4 };
const int nns_table[NUM_NNS] = { 16, 32, 64, 128, 256 };
const int dims0 = 49 * 4 + 5 * 4 + 9 * 4;
const int dims0new = 4 * 65 + 4 * 5;
const int dims1 = nns_table[s->nnsparam] * 2 * (xdia_table[s->nsize] * ydia_table[s->nsize] + 1);
int dims1tsize = 0;
int dims1offset = 0;
int ret = 0, i, j, k;
weights_file = fopen(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 != expected_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 = (float *)av_malloc(expected_size);
if (!bdata) {
fclose(weights_file);
return AVERROR(ENOMEM);
}
bytes_read = fread(bdata, 1, expected_size, weights_file);
if (bytes_read != (size_t)expected_size) {
fclose(weights_file);
ret = AVERROR_INVALIDDATA;
av_log(ctx, AV_LOG_ERROR, "Couldn't read weights file.\n");
goto fail;
}
fclose(weights_file);
for (j = 0; j < NUM_NNS; j++) {
for (i = 0; i < NUM_NSIZE; i++) {
if (i == s->nsize && j == s->nnsparam)
dims1offset = dims1tsize;
dims1tsize += nns_table[j] * 2 * (xdia_table[i] * ydia_table[i] + 1) * 2;
}
}
s->weights0 = av_malloc_array(FFMAX(dims0, dims0new), sizeof(float));
if (!s->weights0) {
ret = AVERROR(ENOMEM);
goto fail;
}
for (i = 0; i < 2; i++) {
s->weights1[i] = av_malloc_array(dims1, sizeof(float));
if (!s->weights1[i]) {
ret = AVERROR(ENOMEM);
goto fail;
}
}
// Adjust prescreener weights
if (s->pscrn >= 2) {// using new prescreener
const float *bdw;
int16_t *ws;
float *wf;
double mean[4] = { 0.0, 0.0, 0.0, 0.0 };
int *offt = av_calloc(4 * 64, sizeof(int));
if (!offt) {
ret = AVERROR(ENOMEM);
goto fail;
}
for (j = 0; j < 4; j++)
for (k = 0; k < 64; k++)
offt[j * 64 + k] = ((k >> 3) << 5) + ((j & 3) << 3) + (k & 7);
bdw = bdata + dims0 + dims0new * (s->pscrn - 2);
ws = (int16_t *)s->weights0;
wf = (float *)&ws[4 * 64];
// Calculate mean weight of each first layer neuron
for (j = 0; j < 4; j++) {
double cmean = 0.0;
for (k = 0; k < 64; k++)
cmean += bdw[offt[j * 64 + k]];
mean[j] = cmean / 64.0;
}
// Factor mean removal and 1.0/127.5 scaling
// into first layer weights. scale to int16 range
for (j = 0; j < 4; j++) {
double scale, mval = 0.0;
for (k = 0; k < 64; k++)
mval = FFMAX(mval, FFABS((bdw[offt[j * 64 + k]] - mean[j]) / 127.5));
scale = 32767.0 / mval;
for (k = 0; k < 64; k++)
ws[offt[j * 64 + k]] = roundds(((bdw[offt[j * 64 + k]] - mean[j]) / 127.5) * scale);
wf[j] = (float)(mval / 32767.0);
}
memcpy(wf + 4, bdw + 4 * 64, (dims0new - 4 * 64) * sizeof(float));
av_free(offt);
} else { // using old prescreener
double mean[4] = { 0.0, 0.0, 0.0, 0.0 };
// Calculate mean weight of each first layer neuron
for (j = 0; j < 4; j++) {
double cmean = 0.0;
for (k = 0; k < 48; k++)
cmean += bdata[j * 48 + k];
mean[j] = cmean / 48.0;
}
if (s->fapprox & 1) {// use int16 dot products in first layer
int16_t *ws = (int16_t *)s->weights0;
float *wf = (float *)&ws[4 * 48];
// Factor mean removal and 1.0/127.5 scaling
// into first layer weights. scale to int16 range
for (j = 0; j < 4; j++) {
double scale, mval = 0.0;
for (k = 0; k < 48; k++)
mval = FFMAX(mval, FFABS((bdata[j * 48 + k] - mean[j]) / 127.5));
scale = 32767.0 / mval;
for (k = 0; k < 48; k++)
ws[j * 48 + k] = roundds(((bdata[j * 48 + k] - mean[j]) / 127.5) * scale);
wf[j] = (float)(mval / 32767.0);
}
memcpy(wf + 4, bdata + 4 * 48, (dims0 - 4 * 48) * sizeof(float));
} else {// use float dot products in first layer
double half = (1 << 8) - 1;
half /= 2;
// Factor mean removal and 1.0/half scaling
// into first layer weights.
for (j = 0; j < 4; j++)
for (k = 0; k < 48; k++)
s->weights0[j * 48 + k] = (float)((bdata[j * 48 + k] - mean[j]) / half);
memcpy(s->weights0 + 4 * 48, bdata + 4 * 48, (dims0 - 4 * 48) * sizeof(float));
}
}
// Adjust prediction weights
for (i = 0; i < 2; i++) {
const float *bdataT = bdata + dims0 + dims0new * 3 + dims1tsize * s->etype + dims1offset + i * dims1;
const int nnst = nns_table[s->nnsparam];
const int asize = xdia_table[s->nsize] * ydia_table[s->nsize];
const int boff = nnst * 2 * asize;
double *mean = (double *)av_calloc(asize + 1 + nnst * 2, sizeof(double));
if (!mean) {
ret = AVERROR(ENOMEM);
goto fail;
}
// Calculate mean weight of each neuron (ignore bias)
for (j = 0; j < nnst * 2; j++) {
double cmean = 0.0;
for (k = 0; k < asize; k++)
cmean += bdataT[j * asize + k];
mean[asize + 1 + j] = cmean / (double)asize;
}
// Calculate mean softmax neuron
for (j = 0; j < nnst; j++) {
for (k = 0; k < asize; k++)
mean[k] += bdataT[j * asize + k] - mean[asize + 1 + j];
mean[asize] += bdataT[boff + j];
}
for (j = 0; j < asize + 1; j++)
mean[j] /= (double)(nnst);
if (s->fapprox & 2) { // use int16 dot products
int16_t *ws = (int16_t *)s->weights1[i];
float *wf = (float *)&ws[nnst * 2 * asize];
// Factor mean removal into weights, remove global offset from
// softmax neurons, and scale weights to int16 range.
for (j = 0; j < nnst; j++) { // softmax neurons
double scale, mval = 0.0;
for (k = 0; k < asize; k++)
mval = FFMAX(mval, FFABS(bdataT[j * asize + k] - mean[asize + 1 + j] - mean[k]));
scale = 32767.0 / mval;
for (k = 0; k < asize; k++)
ws[j * asize + k] = roundds((bdataT[j * asize + k] - mean[asize + 1 + j] - mean[k]) * scale);
wf[(j >> 2) * 8 + (j & 3)] = (float)(mval / 32767.0);
wf[(j >> 2) * 8 + (j & 3) + 4] = (float)(bdataT[boff + j] - mean[asize]);
}
for (j = nnst; j < nnst * 2; j++) { // elliott neurons
double scale, mval = 0.0;
for (k = 0; k < asize; k++)
mval = FFMAX(mval, FFABS(bdataT[j * asize + k] - mean[asize + 1 + j]));
scale = 32767.0 / mval;
for (k = 0; k < asize; k++)
ws[j * asize + k] = roundds((bdataT[j * asize + k] - mean[asize + 1 + j]) * scale);
wf[(j >> 2) * 8 + (j & 3)] = (float)(mval / 32767.0);
wf[(j >> 2) * 8 + (j & 3) + 4] = bdataT[boff + j];
}
} else { // use float dot products
// Factor mean removal into weights, and remove global
// offset from softmax neurons.
for (j = 0; j < nnst * 2; j++) {
for (k = 0; k < asize; k++) {
const double q = j < nnst ? mean[k] : 0.0;
s->weights1[i][j * asize + k] = (float)(bdataT[j * asize + k] - mean[asize + 1 + j] - q);
}
s->weights1[i][boff + j] = (float)(bdataT[boff + j] - (j < nnst ? mean[asize] : 0.0));
}
}
av_free(mean);
}
s->nns = nns_table[s->nnsparam];
s->xdia = xdia_table[s->nsize];
s->ydia = ydia_table[s->nsize];
s->asize = xdia_table[s->nsize] * ydia_table[s->nsize];
s->max_value = 65535 >> 8;
select_functions(s);
s->fdsp = avpriv_float_dsp_alloc(0);
if (!s->fdsp)
ret = AVERROR(ENOMEM);
fail:
av_free(bdata);
return ret;
}
static av_cold void uninit(AVFilterContext *ctx)
{
NNEDIContext *s = ctx->priv;
int i;
av_freep(&s->weights0);
for (i = 0; i < 2; i++)
av_freep(&s->weights1[i]);
for (i = 0; i < s->nb_planes; i++) {
av_freep(&s->frame_data.paddedp[i]);
av_freep(&s->frame_data.lcount[i]);
}
av_freep(&s->frame_data.input);
av_freep(&s->frame_data.temp);
av_freep(&s->fdsp);
av_frame_free(&s->second);
}
static const AVFilterPad inputs[] = {
{
.name = "default",
.type = AVMEDIA_TYPE_VIDEO,
.filter_frame = filter_frame,
.config_props = config_input,
},
{ NULL }
};
static const AVFilterPad outputs[] = {
{
.name = "default",
.type = AVMEDIA_TYPE_VIDEO,
.config_props = config_output,
.request_frame = request_frame,
},
{ NULL }
};
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,
.inputs = inputs,
.outputs = outputs,
.flags = AVFILTER_FLAG_SUPPORT_TIMELINE_INTERNAL,
};
Reference in New Issue View Git Blame Copy Permalink