libavfilter/vf_nnedi.c
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 /*
  * Copyright (C) 2010-2011 Kevin Stone
  * Copyright (C) 2016 Paul B Mahol
  *
  * This file is part of FFmpeg.
  *
  * FFmpeg is free software; you can redistribute it and/or modify
  * it under the terms of the GNU General Public License as published by
  * the Free Software Foundation; either version 2 of the License, or
  * (at your option) any later version.
  *
  * FFmpeg is distributed in the hope that it will be useful,
  * but WITHOUT ANY WARRANTY; without even the implied warranty of
  * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
  * GNU General Public License for more details.
  *
  * You should have received a copy of the GNU General Public License along
  * with FFmpeg; if not, write to the Free Software Foundation, Inc.,
  * 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
  */
 
 #include <float.h>
 
 #include "libavutil/common.h"
 #include "libavutil/float_dsp.h"
 #include "libavutil/imgutils.h"
 #include "libavutil/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 {
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             dstp[x] = 255;
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             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;
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     float scale;
     double tmp;
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     // 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;
     }
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     scale = 1.0f / (xdia * ydia);
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     mstd[0] = sum * scale;
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     tmp = (double)sumsq * scale - (double)mstd[0] * mstd[0];
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     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;
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     float scale;
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     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;
     }
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     scale = 1.0f / (float)(xdia * ydia);
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     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;
37afeabd
         const uint8_t *srcpp;
c1b23e15
 
         if (!(s->process_plane & (1 << plane)))
             continue;
79991b22
 
         srcp += (ystart + 6) * src_stride;
         dstp += ystart * dst_stride - 32;
c1b23e15
         srcpp = srcp - (ydia - 1) * src_stride - xdiad2m1;
79991b22
 
         for (y = ystart; y < ystop; y += 2) {
             for (x = 32; x < width - 32; x++) {
c1b23e15
                 float mstd[4];
 
ac3a275d
                 if (dstp[x] != 255)
79991b22
                     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++) {
c1b23e15
                 double scale, mval = 0.0;
79991b22
                 for (k = 0; k < 48; k++)
                     mval = FFMAX(mval, FFABS((bdata[j * 48 + k] - mean[j]) / 127.5));
c1b23e15
                 scale = 32767.0 / mval;
79991b22
                 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)
674cc26f
         ret = AVERROR(ENOMEM);
79991b22
 
 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);
44cf5b41
     av_freep(&s->fdsp);
79991b22
     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,
 };