diff --git a/Changelog b/Changelog index 04e044e421..2f2ca3e630 100644 --- a/Changelog +++ b/Changelog @@ -63,6 +63,7 @@ version : - Cineform HD decoder - new DCA decoder with full support for DTS-HD extensions - significant performance improvements in Windows Television (WTV) demuxer +- nnedi deinterlacer version 2.8: diff --git a/configure b/configure index c17224ca7f..c415d5ab76 100755 --- a/configure +++ b/configure @@ -2873,6 +2873,7 @@ mpdecimate_filter_deps="gpl" mpdecimate_filter_select="pixelutils" mptestsrc_filter_deps="gpl" negate_filter_deps="lut_filter" +nnedi_filter_deps="gpl" ocr_filter_deps="libtesseract" ocv_filter_deps="libopencv" owdenoise_filter_deps="gpl" diff --git a/doc/filters.texi b/doc/filters.texi index 1169498433..664ebe8ca6 100644 --- a/doc/filters.texi +++ b/doc/filters.texi @@ -8490,6 +8490,115 @@ Negate input video. It accepts an integer in input; if non-zero it negates the alpha component (if available). The default value in input is 0. +@section nnedi + +Deinterlace video using neural network edge directed interpolation. + +This filter accepts the following options: + +@table @option +@item weights +Mandatory option, without binary file filter can not work. +Currently file can be found here: +https://github.com/dubhater/vapoursynth-nnedi3/blob/master/src/nnedi3_weights.bin + +@item deint +Set which frames to deinterlace, by default it is @code{all}. +Can be @code{all} or @code{interlaced}. + +@item field +Set mode of operation. + +Can be one of the following: + +@table @samp +@item af +Use frame flags, both fields. +@item a +Use frame flags, single field. +@item t +Use top field only. +@item b +Use bottom field only. +@item ft +Use both fields, top first. +@item fb +Use both fields, bottom first. +@end table + +@item planes +Set which planes to process, by default filter process all frames. + +@item nsize +Set size of local neighborhood around each pixel, used by the predictor neural +network. + +Can be one of the following: + +@table @samp +@item s8x6 +@item s16x6 +@item s32x6 +@item s48x6 +@item s8x4 +@item s16x4 +@item s32x4 +@end table + +@item nns +Set the number of neurons in predicctor neural network. +Can be one of the following: + +@table @samp +@item n16 +@item n32 +@item n64 +@item n128 +@item n256 +@end table + +@item qual +Controls the number of different neural network predictions that are blended +together to compute the final output value. Can be @code{fast}, default or +@code{slow}. + +@item etype +Set which set of weights to use in the predictor. +Can be one of the following: + +@table @samp +@item a +weights trained to minimize absolute error +@item s +weights trained to minimize squared error +@end table + +@item pscrn +Controls whether or not the prescreener neural network is used to decide +which pixels should be processed by the predictor neural network and which +can be handled by simple cubic interpolation. +The prescreener is trained to know whether cubic interpolation will be +sufficient for a pixel or whether it should be predicted by the predictor nn. +The computational complexity of the prescreener nn is much less than that of +the predictor nn. Since most pixels can be handled by cubic interpolation, +using the prescreener generally results in much faster processing. +The prescreener is pretty accurate, so the difference between using it and not +using it is almost always unnoticeable. + +Can be one of the following: + +@table @samp +@item none +@item original +@item new +@end table + +Default is @code{new}. + +@item fapprox +Set various debugging flags. +@end table + @section noformat Force libavfilter not to use any of the specified pixel formats for the diff --git a/libavfilter/Makefile b/libavfilter/Makefile index b93e5f26ee..e76d18e89d 100644 --- a/libavfilter/Makefile +++ b/libavfilter/Makefile @@ -187,6 +187,7 @@ OBJS-$(CONFIG_MCDEINT_FILTER) += vf_mcdeint.o OBJS-$(CONFIG_MERGEPLANES_FILTER) += vf_mergeplanes.o framesync.o OBJS-$(CONFIG_MPDECIMATE_FILTER) += vf_mpdecimate.o OBJS-$(CONFIG_NEGATE_FILTER) += vf_lut.o +OBJS-$(CONFIG_NNEDI_FILTER) += vf_nnedi.o OBJS-$(CONFIG_NOFORMAT_FILTER) += vf_format.o OBJS-$(CONFIG_NOISE_FILTER) += vf_noise.o OBJS-$(CONFIG_NULL_FILTER) += vf_null.o diff --git a/libavfilter/allfilters.c b/libavfilter/allfilters.c index 1d48970300..27d54bcec7 100644 --- a/libavfilter/allfilters.c +++ b/libavfilter/allfilters.c @@ -208,6 +208,7 @@ void avfilter_register_all(void) REGISTER_FILTER(MERGEPLANES, mergeplanes, vf); REGISTER_FILTER(MPDECIMATE, mpdecimate, vf); REGISTER_FILTER(NEGATE, negate, vf); + REGISTER_FILTER(NNEDI, nnedi, vf); REGISTER_FILTER(NOFORMAT, noformat, vf); REGISTER_FILTER(NOISE, noise, vf); REGISTER_FILTER(NULL, null, vf); diff --git a/libavfilter/version.h b/libavfilter/version.h index 71e2cc5511..55ba68b7bd 100644 --- a/libavfilter/version.h +++ b/libavfilter/version.h @@ -30,7 +30,7 @@ #include "libavutil/version.h" #define LIBAVFILTER_VERSION_MAJOR 6 -#define LIBAVFILTER_VERSION_MINOR 27 +#define LIBAVFILTER_VERSION_MINOR 28 #define LIBAVFILTER_VERSION_MICRO 100 #define LIBAVFILTER_VERSION_INT AV_VERSION_INT(LIBAVFILTER_VERSION_MAJOR, \ diff --git a/libavfilter/vf_nnedi.c b/libavfilter/vf_nnedi.c new file mode 100644 index 0000000000..6880d30663 --- /dev/null +++ b/libavfilter/vf_nnedi.c @@ -0,0 +1,1211 @@ +/* + * 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 + +#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 { + memset(dstp + x, 255, sizeof(uint8_t)); + 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; + + // 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; + } + const float scale = 1.0f / (xdia * ydia); + mstd[0] = sum * scale; + const double 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; + 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; + } + const float 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++) { + if (!(s->process_plane & (1 << plane))) + continue; + + 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; + + srcp += (ystart + 6) * src_stride; + dstp += ystart * dst_stride - 32; + const uint8_t *srcpp = srcp - (ydia - 1) * src_stride - xdiad2m1; + + for (y = ystart; y < ystop; y += 2) { + for (x = 32; x < width - 32; x++) { + uint32_t pixel = 0; + memcpy(&pixel, dstp + x, sizeof(uint8_t)); + + uint32_t all_ones = 0; + memset(&all_ones, 255, sizeof(uint8_t)); + + if (pixel != all_ones) + continue; + + float mstd[4]; + 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 mval = 0.0; + for (k = 0; k < 48; k++) + mval = FFMAX(mval, FFABS((bdata[j * 48 + k] - mean[j]) / 127.5)); + const double 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) + return 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_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, +};