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https://github.com/FFmpeg/FFmpeg.git
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37afeabd1b
Silences the following warning: libavfilter/vf_nnedi.c:611:15: warning: assignment discards ‘const’ qualifier from pointer target type
1212 lines
39 KiB
C
1212 lines
39 KiB
C
/*
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* Copyright (C) 2010-2011 Kevin Stone
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* Copyright (C) 2016 Paul B Mahol
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*
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* This file is part of FFmpeg.
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*
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* FFmpeg is free software; you can redistribute it and/or modify
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* it under the terms of the GNU General Public License as published by
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* the Free Software Foundation; either version 2 of the License, or
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* (at your option) any later version.
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*
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* FFmpeg is distributed in the hope that it will be useful,
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* but WITHOUT ANY WARRANTY; without even the implied warranty of
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* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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* GNU General Public License for more details.
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*
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* You should have received a copy of the GNU General Public License along
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* with FFmpeg; if not, write to the Free Software Foundation, Inc.,
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* 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
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*/
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#include <float.h>
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#include "libavutil/common.h"
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#include "libavutil/float_dsp.h"
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#include "libavutil/imgutils.h"
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#include "libavutil/opt.h"
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#include "libavutil/pixdesc.h"
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#include "avfilter.h"
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#include "formats.h"
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#include "internal.h"
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#include "video.h"
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typedef struct FrameData {
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uint8_t *paddedp[3];
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int padded_stride[3];
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int padded_width[3];
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int padded_height[3];
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uint8_t *dstp[3];
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int dst_stride[3];
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int field[3];
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int32_t *lcount[3];
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float *input;
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float *temp;
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} FrameData;
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typedef struct NNEDIContext {
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const AVClass *class;
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char *weights_file;
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AVFrame *src;
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AVFrame *second;
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AVFrame *dst;
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int eof;
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int64_t cur_pts;
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AVFloatDSPContext *fdsp;
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int nb_planes;
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int linesize[4];
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int planeheight[4];
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float *weights0;
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float *weights1[2];
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int asize;
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int nns;
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int xdia;
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int ydia;
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// Parameters
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int deint;
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int field;
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int process_plane;
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int nsize;
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int nnsparam;
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int qual;
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int etype;
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int pscrn;
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int fapprox;
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int max_value;
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void (*copy_pad)(const AVFrame *, FrameData *, struct NNEDIContext *, int);
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void (*evalfunc_0)(struct NNEDIContext *, FrameData *);
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void (*evalfunc_1)(struct NNEDIContext *, FrameData *);
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// Functions used in evalfunc_0
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void (*readpixels)(const uint8_t *, const int, float *);
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void (*compute_network0)(struct NNEDIContext *s, const float *, const float *, uint8_t *);
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int32_t (*process_line0)(const uint8_t *, int, uint8_t *, const uint8_t *, const int, const int, const int);
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// Functions used in evalfunc_1
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void (*extract)(const uint8_t *, const int, const int, const int, float *, float *);
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void (*dot_prod)(struct NNEDIContext *, const float *, const float *, float *, const int, const int, const float *);
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void (*expfunc)(float *, const int);
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void (*wae5)(const float *, const int, float *);
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FrameData frame_data;
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} NNEDIContext;
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#define OFFSET(x) offsetof(NNEDIContext, x)
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#define FLAGS AV_OPT_FLAG_VIDEO_PARAM|AV_OPT_FLAG_FILTERING_PARAM
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static const AVOption nnedi_options[] = {
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{"weights", "set weights file", OFFSET(weights_file), AV_OPT_TYPE_STRING, {.str="nnedi3_weights.bin"}, 0, 0, FLAGS },
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{"deint", "set which frames to deinterlace", OFFSET(deint), AV_OPT_TYPE_INT, {.i64=0}, 0, 1, FLAGS, "deint" },
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{"all", "deinterlace all frames", 0, AV_OPT_TYPE_CONST, {.i64=0}, 0, 0, FLAGS, "deint" },
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{"interlaced", "only deinterlace frames marked as interlaced", 0, AV_OPT_TYPE_CONST, {.i64=1}, 0, 0, FLAGS, "deint" },
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{"field", "set mode of operation", OFFSET(field), AV_OPT_TYPE_INT, {.i64=-1}, -2, 3, FLAGS, "field" },
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{"af", "use frame flags, both fields", 0, AV_OPT_TYPE_CONST, {.i64=-2}, 0, 0, FLAGS, "field" },
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{"a", "use frame flags, single field", 0, AV_OPT_TYPE_CONST, {.i64=-1}, 0, 0, FLAGS, "field" },
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{"t", "use top field only", 0, AV_OPT_TYPE_CONST, {.i64=0}, 0, 0, FLAGS, "field" },
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{"b", "use bottom field only", 0, AV_OPT_TYPE_CONST, {.i64=1}, 0, 0, FLAGS, "field" },
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{"tf", "use both fields, top first", 0, AV_OPT_TYPE_CONST, {.i64=2}, 0, 0, FLAGS, "field" },
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{"bf", "use both fields, bottom first", 0, AV_OPT_TYPE_CONST, {.i64=3}, 0, 0, FLAGS, "field" },
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{"planes", "set which planes to process", OFFSET(process_plane), AV_OPT_TYPE_INT, {.i64=7}, 0, 7, FLAGS },
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{"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" },
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{"s8x6", NULL, 0, AV_OPT_TYPE_CONST, {.i64=0}, 0, 0, FLAGS, "nsize" },
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{"s16x6", NULL, 0, AV_OPT_TYPE_CONST, {.i64=1}, 0, 0, FLAGS, "nsize" },
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{"s32x6", NULL, 0, AV_OPT_TYPE_CONST, {.i64=2}, 0, 0, FLAGS, "nsize" },
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{"s48x6", NULL, 0, AV_OPT_TYPE_CONST, {.i64=3}, 0, 0, FLAGS, "nsize" },
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{"s8x4", NULL, 0, AV_OPT_TYPE_CONST, {.i64=4}, 0, 0, FLAGS, "nsize" },
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{"s16x4", NULL, 0, AV_OPT_TYPE_CONST, {.i64=5}, 0, 0, FLAGS, "nsize" },
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{"s32x4", NULL, 0, AV_OPT_TYPE_CONST, {.i64=6}, 0, 0, FLAGS, "nsize" },
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{"nns", "set number of neurons in predictor neural network", OFFSET(nnsparam), AV_OPT_TYPE_INT, {.i64=1}, 0, 4, FLAGS, "nns" },
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{"n16", NULL, 0, AV_OPT_TYPE_CONST, {.i64=0}, 0, 0, FLAGS, "nns" },
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{"n32", NULL, 0, AV_OPT_TYPE_CONST, {.i64=1}, 0, 0, FLAGS, "nns" },
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{"n64", NULL, 0, AV_OPT_TYPE_CONST, {.i64=2}, 0, 0, FLAGS, "nns" },
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{"n128", NULL, 0, AV_OPT_TYPE_CONST, {.i64=3}, 0, 0, FLAGS, "nns" },
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{"n256", NULL, 0, AV_OPT_TYPE_CONST, {.i64=4}, 0, 0, FLAGS, "nns" },
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{"qual", "set quality", OFFSET(qual), AV_OPT_TYPE_INT, {.i64=1}, 1, 2, FLAGS, "qual" },
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{"fast", NULL, 0, AV_OPT_TYPE_CONST, {.i64=1}, 0, 0, FLAGS, "qual" },
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{"slow", NULL, 0, AV_OPT_TYPE_CONST, {.i64=2}, 0, 0, FLAGS, "qual" },
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{"etype", "set which set of weights to use in the predictor", OFFSET(etype), AV_OPT_TYPE_INT, {.i64=0}, 0, 1, FLAGS, "etype" },
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{"a", "weights trained to minimize absolute error", 0, AV_OPT_TYPE_CONST, {.i64=0}, 0, 0, FLAGS, "etype" },
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{"s", "weights trained to minimize squared error", 0, AV_OPT_TYPE_CONST, {.i64=1}, 0, 0, FLAGS, "etype" },
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{"pscrn", "set prescreening", OFFSET(pscrn), AV_OPT_TYPE_INT, {.i64=2}, 0, 2, FLAGS, "pscrn" },
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{"none", NULL, 0, AV_OPT_TYPE_CONST, {.i64=0}, 0, 0, FLAGS, "pscrn" },
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{"original", NULL, 0, AV_OPT_TYPE_CONST, {.i64=1}, 0, 0, FLAGS, "pscrn" },
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{"new", NULL, 0, AV_OPT_TYPE_CONST, {.i64=2}, 0, 0, FLAGS, "pscrn" },
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{"fapprox", NULL, OFFSET(fapprox), AV_OPT_TYPE_INT, {.i64=0}, 0, 3, FLAGS },
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{ NULL }
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};
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AVFILTER_DEFINE_CLASS(nnedi);
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static int config_input(AVFilterLink *inlink)
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{
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AVFilterContext *ctx = inlink->dst;
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NNEDIContext *s = ctx->priv;
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const AVPixFmtDescriptor *desc = av_pix_fmt_desc_get(inlink->format);
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int ret;
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s->nb_planes = av_pix_fmt_count_planes(inlink->format);
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if ((ret = av_image_fill_linesizes(s->linesize, inlink->format, inlink->w)) < 0)
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return ret;
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s->planeheight[1] = s->planeheight[2] = AV_CEIL_RSHIFT(inlink->h, desc->log2_chroma_h);
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s->planeheight[0] = s->planeheight[3] = inlink->h;
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return 0;
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}
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static int config_output(AVFilterLink *outlink)
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{
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AVFilterContext *ctx = outlink->src;
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NNEDIContext *s = ctx->priv;
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outlink->time_base.num = ctx->inputs[0]->time_base.num;
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outlink->time_base.den = ctx->inputs[0]->time_base.den * 2;
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outlink->w = ctx->inputs[0]->w;
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outlink->h = ctx->inputs[0]->h;
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if (s->field > 1 || s->field == -2)
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outlink->frame_rate = av_mul_q(ctx->inputs[0]->frame_rate,
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(AVRational){2, 1});
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return 0;
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}
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static int query_formats(AVFilterContext *ctx)
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{
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static const enum AVPixelFormat pix_fmts[] = {
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AV_PIX_FMT_YUV410P, AV_PIX_FMT_YUV411P,
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AV_PIX_FMT_YUV420P, AV_PIX_FMT_YUV422P,
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AV_PIX_FMT_YUV440P, AV_PIX_FMT_YUV444P,
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AV_PIX_FMT_YUVJ444P, AV_PIX_FMT_YUVJ440P,
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AV_PIX_FMT_YUVJ422P, AV_PIX_FMT_YUVJ420P,
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AV_PIX_FMT_YUVJ411P,
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AV_PIX_FMT_GBRP,
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AV_PIX_FMT_GRAY8,
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AV_PIX_FMT_NONE
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};
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AVFilterFormats *fmts_list = ff_make_format_list(pix_fmts);
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if (!fmts_list)
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return AVERROR(ENOMEM);
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return ff_set_common_formats(ctx, fmts_list);
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}
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static void copy_pad(const AVFrame *src, FrameData *frame_data, NNEDIContext *s, int fn)
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{
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const int off = 1 - fn;
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int plane, y, x;
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for (plane = 0; plane < s->nb_planes; plane++) {
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const uint8_t *srcp = (const uint8_t *)src->data[plane];
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uint8_t *dstp = (uint8_t *)frame_data->paddedp[plane];
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const int src_stride = src->linesize[plane];
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const int dst_stride = frame_data->padded_stride[plane];
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const int src_height = s->planeheight[plane];
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const int dst_height = frame_data->padded_height[plane];
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const int src_width = s->linesize[plane];
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const int dst_width = frame_data->padded_width[plane];
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int c = 4;
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if (!(s->process_plane & (1 << plane)))
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continue;
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// Copy.
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for (y = off; y < src_height; y += 2)
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memcpy(dstp + 32 + (6 + y) * dst_stride,
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srcp + y * src_stride,
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src_width * sizeof(uint8_t));
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// And pad.
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dstp += (6 + off) * dst_stride;
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for (y = 6 + off; y < dst_height - 6; y += 2) {
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int c = 2;
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for (x = 0; x < 32; x++)
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dstp[x] = dstp[64 - x];
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for (x = dst_width - 32; x < dst_width; x++, c += 2)
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dstp[x] = dstp[x - c];
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dstp += dst_stride * 2;
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}
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dstp = (uint8_t *)frame_data->paddedp[plane];
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for (y = off; y < 6; y += 2)
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memcpy(dstp + y * dst_stride,
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dstp + (12 + 2 * off - y) * dst_stride,
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dst_width * sizeof(uint8_t));
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for (y = dst_height - 6 + off; y < dst_height; y += 2, c += 4)
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memcpy(dstp + y * dst_stride,
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dstp + (y - c) * dst_stride,
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dst_width * sizeof(uint8_t));
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}
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}
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static void elliott(float *data, const int n)
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{
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int i;
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for (i = 0; i < n; i++)
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data[i] = data[i] / (1.0f + FFABS(data[i]));
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}
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static void dot_prod(NNEDIContext *s, const float *data, const float *weights, float *vals, const int n, const int len, const float *scale)
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{
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int i;
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for (i = 0; i < n; i++) {
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float sum;
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sum = s->fdsp->scalarproduct_float(data, &weights[i * len], len);
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vals[i] = sum * scale[0] + weights[n * len + i];
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}
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}
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static void dot_prods(NNEDIContext *s, const float *dataf, const float *weightsf, float *vals, const int n, const int len, const float *scale)
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{
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const int16_t *data = (int16_t *)dataf;
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const int16_t *weights = (int16_t *)weightsf;
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const float *wf = (float *)&weights[n * len];
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int i, j;
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for (i = 0; i < n; i++) {
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int sum = 0, off = ((i >> 2) << 3) + (i & 3);
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for (j = 0; j < len; j++)
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sum += data[j] * weights[i * len + j];
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vals[i] = sum * wf[off] * scale[0] + wf[off + 4];
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}
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}
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static void compute_network0(NNEDIContext *s, const float *input, const float *weights, uint8_t *d)
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{
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float t, temp[12], scale = 1.0f;
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dot_prod(s, input, weights, temp, 4, 48, &scale);
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t = temp[0];
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elliott(temp, 4);
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temp[0] = t;
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dot_prod(s, temp, weights + 4 * 49, temp + 4, 4, 4, &scale);
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elliott(temp + 4, 4);
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dot_prod(s, temp, weights + 4 * 49 + 4 * 5, temp + 8, 4, 8, &scale);
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if (FFMAX(temp[10], temp[11]) <= FFMAX(temp[8], temp[9]))
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d[0] = 1;
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else
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d[0] = 0;
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}
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static void compute_network0_i16(NNEDIContext *s, const float *inputf, const float *weightsf, uint8_t *d)
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{
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const float *wf = weightsf + 2 * 48;
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float t, temp[12], scale = 1.0f;
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dot_prods(s, inputf, weightsf, temp, 4, 48, &scale);
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t = temp[0];
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elliott(temp, 4);
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temp[0] = t;
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dot_prod(s, temp, wf + 8, temp + 4, 4, 4, &scale);
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elliott(temp + 4, 4);
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dot_prod(s, temp, wf + 8 + 4 * 5, temp + 8, 4, 8, &scale);
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if (FFMAX(temp[10], temp[11]) <= FFMAX(temp[8], temp[9]))
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d[0] = 1;
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else
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d[0] = 0;
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}
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static void pixel2float48(const uint8_t *t8, const int pitch, float *p)
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{
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const uint8_t *t = (const uint8_t *)t8;
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int y, x;
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for (y = 0; y < 4; y++)
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for (x = 0; x < 12; x++)
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p[y * 12 + x] = t[y * pitch * 2 + x];
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}
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static void byte2word48(const uint8_t *t, const int pitch, float *pf)
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{
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int16_t *p = (int16_t *)pf;
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int y, x;
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for (y = 0; y < 4; y++)
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for (x = 0; x < 12; x++)
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p[y * 12 + x] = t[y * pitch * 2 + x];
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}
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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)
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{
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uint8_t *dstp = (uint8_t *)dstp8;
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const uint8_t *src3p = (const uint8_t *)src3p8;
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int minimum = 0;
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int maximum = max_value - 1; // Technically the -1 is only needed for 8 and 16 bit input.
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int count = 0, x;
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for (x = 0; x < width; x++) {
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if (tempu[x]) {
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int tmp = 19 * (src3p[x + src_pitch * 2] + src3p[x + src_pitch * 4]) - 3 * (src3p[x] + src3p[x + src_pitch * 6]);
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tmp /= 32;
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dstp[x] = FFMAX(FFMIN(tmp, maximum), minimum);
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} else {
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dstp[x] = 255;
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count++;
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}
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}
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return count;
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}
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// new prescreener functions
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static void byte2word64(const uint8_t *t, const int pitch, float *p)
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{
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int16_t *ps = (int16_t *)p;
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int y, x;
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for (y = 0; y < 4; y++)
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for (x = 0; x < 16; x++)
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ps[y * 16 + x] = t[y * pitch * 2 + x];
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}
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static void compute_network0new(NNEDIContext *s, const float *datai, const float *weights, uint8_t *d)
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{
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int16_t *data = (int16_t *)datai;
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int16_t *ws = (int16_t *)weights;
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float *wf = (float *)&ws[4 * 64];
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float vals[8];
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int mask, i, j;
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for (i = 0; i < 4; i++) {
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int sum = 0;
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float t;
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for (j = 0; j < 64; j++)
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sum += data[j] * ws[(i << 3) + ((j >> 3) << 5) + (j & 7)];
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t = sum * wf[i] + wf[4 + i];
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vals[i] = t / (1.0f + FFABS(t));
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}
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for (i = 0; i < 4; i++) {
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float sum = 0.0f;
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for (j = 0; j < 4; j++)
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sum += vals[j] * wf[8 + i + (j << 2)];
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vals[4 + i] = sum + wf[8 + 16 + i];
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}
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mask = 0;
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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,
|
|
};
|