/* * Copyright (C) 2010-2011 Kevin Stone * Copyright (C) 2016 Paul B Mahol * * This file is part of FFmpeg. * * FFmpeg is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * FFmpeg is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License along * with FFmpeg; if not, write to the Free Software Foundation, Inc., * 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. */ #include <float.h> #include "libavutil/common.h" #include "libavutil/float_dsp.h" #include "libavutil/imgutils.h" #include "libavutil/opt.h" #include "libavutil/pixdesc.h" #include "avfilter.h" #include "formats.h" #include "internal.h" #include "video.h" typedef struct FrameData { uint8_t *paddedp[3]; int padded_stride[3]; int padded_width[3]; int padded_height[3]; uint8_t *dstp[3]; int dst_stride[3]; int field[3]; int32_t *lcount[3]; float *input; float *temp; } FrameData; typedef struct NNEDIContext { const AVClass *class; char *weights_file; AVFrame *src; AVFrame *second; AVFrame *dst; int eof; int64_t cur_pts; AVFloatDSPContext *fdsp; int nb_planes; int linesize[4]; int planeheight[4]; float *weights0; float *weights1[2]; int asize; int nns; int xdia; int ydia; // Parameters int deint; int field; int process_plane; int nsize; int nnsparam; int qual; int etype; int pscrn; int fapprox; int max_value; void (*copy_pad)(const AVFrame *, FrameData *, struct NNEDIContext *, int); void (*evalfunc_0)(struct NNEDIContext *, FrameData *); void (*evalfunc_1)(struct NNEDIContext *, FrameData *); // Functions used in evalfunc_0 void (*readpixels)(const uint8_t *, const int, float *); void (*compute_network0)(struct NNEDIContext *s, const float *, const float *, uint8_t *); int32_t (*process_line0)(const uint8_t *, int, uint8_t *, const uint8_t *, const int, const int, const int); // Functions used in evalfunc_1 void (*extract)(const uint8_t *, const int, const int, const int, float *, float *); void (*dot_prod)(struct NNEDIContext *, const float *, const float *, float *, const int, const int, const float *); void (*expfunc)(float *, const int); void (*wae5)(const float *, const int, float *); FrameData frame_data; } NNEDIContext; #define OFFSET(x) offsetof(NNEDIContext, x) #define FLAGS AV_OPT_FLAG_VIDEO_PARAM|AV_OPT_FLAG_FILTERING_PARAM static const AVOption nnedi_options[] = { {"weights", "set weights file", OFFSET(weights_file), AV_OPT_TYPE_STRING, {.str="nnedi3_weights.bin"}, 0, 0, FLAGS }, {"deint", "set which frames to deinterlace", OFFSET(deint), AV_OPT_TYPE_INT, {.i64=0}, 0, 1, FLAGS, "deint" }, {"all", "deinterlace all frames", 0, AV_OPT_TYPE_CONST, {.i64=0}, 0, 0, FLAGS, "deint" }, {"interlaced", "only deinterlace frames marked as interlaced", 0, AV_OPT_TYPE_CONST, {.i64=1}, 0, 0, FLAGS, "deint" }, {"field", "set mode of operation", OFFSET(field), AV_OPT_TYPE_INT, {.i64=-1}, -2, 3, FLAGS, "field" }, {"af", "use frame flags, both fields", 0, AV_OPT_TYPE_CONST, {.i64=-2}, 0, 0, FLAGS, "field" }, {"a", "use frame flags, single field", 0, AV_OPT_TYPE_CONST, {.i64=-1}, 0, 0, FLAGS, "field" }, {"t", "use top field only", 0, AV_OPT_TYPE_CONST, {.i64=0}, 0, 0, FLAGS, "field" }, {"b", "use bottom field only", 0, AV_OPT_TYPE_CONST, {.i64=1}, 0, 0, FLAGS, "field" }, {"tf", "use both fields, top first", 0, AV_OPT_TYPE_CONST, {.i64=2}, 0, 0, FLAGS, "field" }, {"bf", "use both fields, bottom first", 0, AV_OPT_TYPE_CONST, {.i64=3}, 0, 0, FLAGS, "field" }, {"planes", "set which planes to process", OFFSET(process_plane), AV_OPT_TYPE_INT, {.i64=7}, 0, 7, FLAGS }, {"nsize", "set size of local neighborhood around each pixel, used by the predictor neural network", OFFSET(nsize), AV_OPT_TYPE_INT, {.i64=6}, 0, 6, FLAGS, "nsize" }, {"s8x6", NULL, 0, AV_OPT_TYPE_CONST, {.i64=0}, 0, 0, FLAGS, "nsize" }, {"s16x6", NULL, 0, AV_OPT_TYPE_CONST, {.i64=1}, 0, 0, FLAGS, "nsize" }, {"s32x6", NULL, 0, AV_OPT_TYPE_CONST, {.i64=2}, 0, 0, FLAGS, "nsize" }, {"s48x6", NULL, 0, AV_OPT_TYPE_CONST, {.i64=3}, 0, 0, FLAGS, "nsize" }, {"s8x4", NULL, 0, AV_OPT_TYPE_CONST, {.i64=4}, 0, 0, FLAGS, "nsize" }, {"s16x4", NULL, 0, AV_OPT_TYPE_CONST, {.i64=5}, 0, 0, FLAGS, "nsize" }, {"s32x4", NULL, 0, AV_OPT_TYPE_CONST, {.i64=6}, 0, 0, FLAGS, "nsize" }, {"nns", "set number of neurons in predictor neural network", OFFSET(nnsparam), AV_OPT_TYPE_INT, {.i64=1}, 0, 4, FLAGS, "nns" }, {"n16", NULL, 0, AV_OPT_TYPE_CONST, {.i64=0}, 0, 0, FLAGS, "nns" }, {"n32", NULL, 0, AV_OPT_TYPE_CONST, {.i64=1}, 0, 0, FLAGS, "nns" }, {"n64", NULL, 0, AV_OPT_TYPE_CONST, {.i64=2}, 0, 0, FLAGS, "nns" }, {"n128", NULL, 0, AV_OPT_TYPE_CONST, {.i64=3}, 0, 0, FLAGS, "nns" }, {"n256", NULL, 0, AV_OPT_TYPE_CONST, {.i64=4}, 0, 0, FLAGS, "nns" }, {"qual", "set quality", OFFSET(qual), AV_OPT_TYPE_INT, {.i64=1}, 1, 2, FLAGS, "qual" }, {"fast", NULL, 0, AV_OPT_TYPE_CONST, {.i64=1}, 0, 0, FLAGS, "qual" }, {"slow", NULL, 0, AV_OPT_TYPE_CONST, {.i64=2}, 0, 0, FLAGS, "qual" }, {"etype", "set which set of weights to use in the predictor", OFFSET(etype), AV_OPT_TYPE_INT, {.i64=0}, 0, 1, FLAGS, "etype" }, {"a", "weights trained to minimize absolute error", 0, AV_OPT_TYPE_CONST, {.i64=0}, 0, 0, FLAGS, "etype" }, {"s", "weights trained to minimize squared error", 0, AV_OPT_TYPE_CONST, {.i64=1}, 0, 0, FLAGS, "etype" }, {"pscrn", "set prescreening", OFFSET(pscrn), AV_OPT_TYPE_INT, {.i64=2}, 0, 2, FLAGS, "pscrn" }, {"none", NULL, 0, AV_OPT_TYPE_CONST, {.i64=0}, 0, 0, FLAGS, "pscrn" }, {"original", NULL, 0, AV_OPT_TYPE_CONST, {.i64=1}, 0, 0, FLAGS, "pscrn" }, {"new", NULL, 0, AV_OPT_TYPE_CONST, {.i64=2}, 0, 0, FLAGS, "pscrn" }, {"fapprox", NULL, OFFSET(fapprox), AV_OPT_TYPE_INT, {.i64=0}, 0, 3, FLAGS }, { NULL } }; AVFILTER_DEFINE_CLASS(nnedi); static int config_input(AVFilterLink *inlink) { AVFilterContext *ctx = inlink->dst; NNEDIContext *s = ctx->priv; const AVPixFmtDescriptor *desc = av_pix_fmt_desc_get(inlink->format); int ret; s->nb_planes = av_pix_fmt_count_planes(inlink->format); if ((ret = av_image_fill_linesizes(s->linesize, inlink->format, inlink->w)) < 0) return ret; s->planeheight[1] = s->planeheight[2] = AV_CEIL_RSHIFT(inlink->h, desc->log2_chroma_h); s->planeheight[0] = s->planeheight[3] = inlink->h; return 0; } static int config_output(AVFilterLink *outlink) { AVFilterContext *ctx = outlink->src; NNEDIContext *s = ctx->priv; outlink->time_base.num = ctx->inputs[0]->time_base.num; outlink->time_base.den = ctx->inputs[0]->time_base.den * 2; outlink->w = ctx->inputs[0]->w; outlink->h = ctx->inputs[0]->h; if (s->field > 1 || s->field == -2) outlink->frame_rate = av_mul_q(ctx->inputs[0]->frame_rate, (AVRational){2, 1}); return 0; } static int query_formats(AVFilterContext *ctx) { static const enum AVPixelFormat pix_fmts[] = { AV_PIX_FMT_YUV410P, AV_PIX_FMT_YUV411P, AV_PIX_FMT_YUV420P, AV_PIX_FMT_YUV422P, AV_PIX_FMT_YUV440P, AV_PIX_FMT_YUV444P, AV_PIX_FMT_YUVJ444P, AV_PIX_FMT_YUVJ440P, AV_PIX_FMT_YUVJ422P, AV_PIX_FMT_YUVJ420P, AV_PIX_FMT_YUVJ411P, AV_PIX_FMT_GBRP, AV_PIX_FMT_GRAY8, AV_PIX_FMT_NONE }; AVFilterFormats *fmts_list = ff_make_format_list(pix_fmts); if (!fmts_list) return AVERROR(ENOMEM); return ff_set_common_formats(ctx, fmts_list); } static void copy_pad(const AVFrame *src, FrameData *frame_data, NNEDIContext *s, int fn) { const int off = 1 - fn; int plane, y, x; for (plane = 0; plane < s->nb_planes; plane++) { const uint8_t *srcp = (const uint8_t *)src->data[plane]; uint8_t *dstp = (uint8_t *)frame_data->paddedp[plane]; const int src_stride = src->linesize[plane]; const int dst_stride = frame_data->padded_stride[plane]; const int src_height = s->planeheight[plane]; const int dst_height = frame_data->padded_height[plane]; const int src_width = s->linesize[plane]; const int dst_width = frame_data->padded_width[plane]; int c = 4; if (!(s->process_plane & (1 << plane))) continue; // Copy. for (y = off; y < src_height; y += 2) memcpy(dstp + 32 + (6 + y) * dst_stride, srcp + y * src_stride, src_width * sizeof(uint8_t)); // And pad. dstp += (6 + off) * dst_stride; for (y = 6 + off; y < dst_height - 6; y += 2) { int c = 2; for (x = 0; x < 32; x++) dstp[x] = dstp[64 - x]; for (x = dst_width - 32; x < dst_width; x++, c += 2) dstp[x] = dstp[x - c]; dstp += dst_stride * 2; } dstp = (uint8_t *)frame_data->paddedp[plane]; for (y = off; y < 6; y += 2) memcpy(dstp + y * dst_stride, dstp + (12 + 2 * off - y) * dst_stride, dst_width * sizeof(uint8_t)); for (y = dst_height - 6 + off; y < dst_height; y += 2, c += 4) memcpy(dstp + y * dst_stride, dstp + (y - c) * dst_stride, dst_width * sizeof(uint8_t)); } } static void elliott(float *data, const int n) { int i; for (i = 0; i < n; i++) data[i] = data[i] / (1.0f + FFABS(data[i])); } static void dot_prod(NNEDIContext *s, const float *data, const float *weights, float *vals, const int n, const int len, const float *scale) { int i; for (i = 0; i < n; i++) { float sum; sum = s->fdsp->scalarproduct_float(data, &weights[i * len], len); vals[i] = sum * scale[0] + weights[n * len + i]; } } static void dot_prods(NNEDIContext *s, const float *dataf, const float *weightsf, float *vals, const int n, const int len, const float *scale) { const int16_t *data = (int16_t *)dataf; const int16_t *weights = (int16_t *)weightsf; const float *wf = (float *)&weights[n * len]; int i, j; for (i = 0; i < n; i++) { int sum = 0, off = ((i >> 2) << 3) + (i & 3); for (j = 0; j < len; j++) sum += data[j] * weights[i * len + j]; vals[i] = sum * wf[off] * scale[0] + wf[off + 4]; } } static void compute_network0(NNEDIContext *s, const float *input, const float *weights, uint8_t *d) { float t, temp[12], scale = 1.0f; dot_prod(s, input, weights, temp, 4, 48, &scale); t = temp[0]; elliott(temp, 4); temp[0] = t; dot_prod(s, temp, weights + 4 * 49, temp + 4, 4, 4, &scale); elliott(temp + 4, 4); dot_prod(s, temp, weights + 4 * 49 + 4 * 5, temp + 8, 4, 8, &scale); if (FFMAX(temp[10], temp[11]) <= FFMAX(temp[8], temp[9])) d[0] = 1; else d[0] = 0; } static void compute_network0_i16(NNEDIContext *s, const float *inputf, const float *weightsf, uint8_t *d) { const float *wf = weightsf + 2 * 48; float t, temp[12], scale = 1.0f; dot_prods(s, inputf, weightsf, temp, 4, 48, &scale); t = temp[0]; elliott(temp, 4); temp[0] = t; dot_prod(s, temp, wf + 8, temp + 4, 4, 4, &scale); elliott(temp + 4, 4); dot_prod(s, temp, wf + 8 + 4 * 5, temp + 8, 4, 8, &scale); if (FFMAX(temp[10], temp[11]) <= FFMAX(temp[8], temp[9])) d[0] = 1; else d[0] = 0; } static void pixel2float48(const uint8_t *t8, const int pitch, float *p) { const uint8_t *t = (const uint8_t *)t8; int y, x; for (y = 0; y < 4; y++) for (x = 0; x < 12; x++) p[y * 12 + x] = t[y * pitch * 2 + x]; } static void byte2word48(const uint8_t *t, const int pitch, float *pf) { int16_t *p = (int16_t *)pf; int y, x; for (y = 0; y < 4; y++) for (x = 0; x < 12; x++) p[y * 12 + x] = t[y * pitch * 2 + x]; } static int32_t process_line0(const uint8_t *tempu, int width, uint8_t *dstp8, const uint8_t *src3p8, const int src_pitch, const int max_value, const int chroma) { uint8_t *dstp = (uint8_t *)dstp8; const uint8_t *src3p = (const uint8_t *)src3p8; int minimum = 0; int maximum = max_value - 1; // Technically the -1 is only needed for 8 and 16 bit input. int count = 0, x; for (x = 0; x < width; x++) { if (tempu[x]) { int tmp = 19 * (src3p[x + src_pitch * 2] + src3p[x + src_pitch * 4]) - 3 * (src3p[x] + src3p[x + src_pitch * 6]); tmp /= 32; dstp[x] = FFMAX(FFMIN(tmp, maximum), minimum); } else { dstp[x] = 255; count++; } } return count; } // new prescreener functions static void byte2word64(const uint8_t *t, const int pitch, float *p) { int16_t *ps = (int16_t *)p; int y, x; for (y = 0; y < 4; y++) for (x = 0; x < 16; x++) ps[y * 16 + x] = t[y * pitch * 2 + x]; } static void compute_network0new(NNEDIContext *s, const float *datai, const float *weights, uint8_t *d) { int16_t *data = (int16_t *)datai; int16_t *ws = (int16_t *)weights; float *wf = (float *)&ws[4 * 64]; float vals[8]; int mask, i, j; for (i = 0; i < 4; i++) { int sum = 0; float t; for (j = 0; j < 64; j++) sum += data[j] * ws[(i << 3) + ((j >> 3) << 5) + (j & 7)]; t = sum * wf[i] + wf[4 + i]; vals[i] = t / (1.0f + FFABS(t)); } for (i = 0; i < 4; i++) { float sum = 0.0f; for (j = 0; j < 4; j++) sum += vals[j] * wf[8 + i + (j << 2)]; vals[4 + i] = sum + wf[8 + 16 + i]; } mask = 0; for (i = 0; i < 4; i++) { if (vals[4 + i] > 0.0f) mask |= (0x1 << (i << 3)); } ((int *)d)[0] = mask; } static void evalfunc_0(NNEDIContext *s, FrameData *frame_data) { float *input = frame_data->input; const float *weights0 = s->weights0; float *temp = frame_data->temp; uint8_t *tempu = (uint8_t *)temp; int plane, x, y; // And now the actual work. for (plane = 0; plane < s->nb_planes; plane++) { const uint8_t *srcp = (const uint8_t *)frame_data->paddedp[plane]; const int src_stride = frame_data->padded_stride[plane] / sizeof(uint8_t); const int width = frame_data->padded_width[plane]; const int height = frame_data->padded_height[plane]; uint8_t *dstp = (uint8_t *)frame_data->dstp[plane]; const int dst_stride = frame_data->dst_stride[plane] / sizeof(uint8_t); const uint8_t *src3p; int ystart, ystop; int32_t *lcount; if (!(s->process_plane & (1 << plane))) continue; for (y = 1 - frame_data->field[plane]; y < height - 12; y += 2) { memcpy(dstp + y * dst_stride, srcp + 32 + (6 + y) * src_stride, (width - 64) * sizeof(uint8_t)); } ystart = 6 + frame_data->field[plane]; ystop = height - 6; srcp += ystart * src_stride; dstp += (ystart - 6) * dst_stride - 32; src3p = srcp - src_stride * 3; lcount = frame_data->lcount[plane] - 6; if (s->pscrn == 1) { // original for (y = ystart; y < ystop; y += 2) { for (x = 32; x < width - 32; x++) { s->readpixels((const uint8_t *)(src3p + x - 5), src_stride, input); s->compute_network0(s, input, weights0, tempu+x); } lcount[y] += s->process_line0(tempu + 32, width - 64, (uint8_t *)(dstp + 32), (const uint8_t *)(src3p + 32), src_stride, s->max_value, plane); src3p += src_stride * 2; dstp += dst_stride * 2; } } else if (s->pscrn > 1) { // new for (y = ystart; y < ystop; y += 2) { for (x = 32; x < width - 32; x += 4) { s->readpixels((const uint8_t *)(src3p + x - 6), src_stride, input); s->compute_network0(s, input, weights0, tempu + x); } lcount[y] += s->process_line0(tempu + 32, width - 64, (uint8_t *)(dstp + 32), (const uint8_t *)(src3p + 32), src_stride, s->max_value, plane); src3p += src_stride * 2; dstp += dst_stride * 2; } } else { // no prescreening for (y = ystart; y < ystop; y += 2) { memset(dstp + 32, 255, (width - 64) * sizeof(uint8_t)); lcount[y] += width - 64; dstp += dst_stride * 2; } } } } static void extract_m8(const uint8_t *srcp8, const int stride, const int xdia, const int ydia, float *mstd, float *input) { // uint8_t or uint16_t or float const uint8_t *srcp = (const uint8_t *)srcp8; float scale; double tmp; // int32_t or int64_t or double int64_t sum = 0, sumsq = 0; int y, x; for (y = 0; y < ydia; y++) { const uint8_t *srcpT = srcp + y * stride * 2; for (x = 0; x < xdia; x++) { sum += srcpT[x]; sumsq += (uint32_t)srcpT[x] * (uint32_t)srcpT[x]; input[x] = srcpT[x]; } input += xdia; } scale = 1.0f / (xdia * ydia); mstd[0] = sum * scale; tmp = (double)sumsq * scale - (double)mstd[0] * mstd[0]; mstd[3] = 0.0f; if (tmp <= FLT_EPSILON) mstd[1] = mstd[2] = 0.0f; else { mstd[1] = sqrt(tmp); mstd[2] = 1.0f / mstd[1]; } } static void extract_m8_i16(const uint8_t *srcp, const int stride, const int xdia, const int ydia, float *mstd, float *inputf) { int16_t *input = (int16_t *)inputf; float scale; int sum = 0, sumsq = 0; int y, x; for (y = 0; y < ydia; y++) { const uint8_t *srcpT = srcp + y * stride * 2; for (x = 0; x < xdia; x++) { sum += srcpT[x]; sumsq += srcpT[x] * srcpT[x]; input[x] = srcpT[x]; } input += xdia; } scale = 1.0f / (float)(xdia * ydia); mstd[0] = sum * scale; mstd[1] = sumsq * scale - mstd[0] * mstd[0]; mstd[3] = 0.0f; if (mstd[1] <= FLT_EPSILON) mstd[1] = mstd[2] = 0.0f; else { mstd[1] = sqrt(mstd[1]); mstd[2] = 1.0f / mstd[1]; } } static const float exp_lo = -80.0f; static const float exp_hi = +80.0f; static void e2_m16(float *s, const int n) { int i; for (i = 0; i < n; i++) s[i] = exp(av_clipf(s[i], exp_lo, exp_hi)); } const float min_weight_sum = 1e-10f; static void weighted_avg_elliott_mul5_m16(const float *w, const int n, float *mstd) { float vsum = 0.0f, wsum = 0.0f; int i; for (i = 0; i < n; i++) { vsum += w[i] * (w[n + i] / (1.0f + FFABS(w[n + i]))); wsum += w[i]; } if (wsum > min_weight_sum) mstd[3] += ((5.0f * vsum) / wsum) * mstd[1] + mstd[0]; else mstd[3] += mstd[0]; } static void evalfunc_1(NNEDIContext *s, FrameData *frame_data) { float *input = frame_data->input; float *temp = frame_data->temp; float **weights1 = s->weights1; const int qual = s->qual; const int asize = s->asize; const int nns = s->nns; const int xdia = s->xdia; const int xdiad2m1 = (xdia / 2) - 1; const int ydia = s->ydia; const float scale = 1.0f / (float)qual; int plane, y, x, i; for (plane = 0; plane < s->nb_planes; plane++) { const uint8_t *srcp = (const uint8_t *)frame_data->paddedp[plane]; const int src_stride = frame_data->padded_stride[plane] / sizeof(uint8_t); const int width = frame_data->padded_width[plane]; const int height = frame_data->padded_height[plane]; uint8_t *dstp = (uint8_t *)frame_data->dstp[plane]; const int dst_stride = frame_data->dst_stride[plane] / sizeof(uint8_t); const int ystart = frame_data->field[plane]; const int ystop = height - 12; const uint8_t *srcpp; if (!(s->process_plane & (1 << plane))) continue; srcp += (ystart + 6) * src_stride; dstp += ystart * dst_stride - 32; srcpp = srcp - (ydia - 1) * src_stride - xdiad2m1; for (y = ystart; y < ystop; y += 2) { for (x = 32; x < width - 32; x++) { float mstd[4]; if (dstp[x] != 255) continue; s->extract((const uint8_t *)(srcpp + x), src_stride, xdia, ydia, mstd, input); for (i = 0; i < qual; i++) { s->dot_prod(s, input, weights1[i], temp, nns * 2, asize, mstd + 2); s->expfunc(temp, nns); s->wae5(temp, nns, mstd); } dstp[x] = FFMIN(FFMAX((int)(mstd[3] * scale + 0.5f), 0), s->max_value); } srcpp += src_stride * 2; dstp += dst_stride * 2; } } } #define NUM_NSIZE 7 #define NUM_NNS 5 static int roundds(const double f) { if (f - floor(f) >= 0.5) return FFMIN((int)ceil(f), 32767); return FFMAX((int)floor(f), -32768); } static void select_functions(NNEDIContext *s) { s->copy_pad = copy_pad; s->evalfunc_0 = evalfunc_0; s->evalfunc_1 = evalfunc_1; // evalfunc_0 s->process_line0 = process_line0; if (s->pscrn < 2) { // original prescreener if (s->fapprox & 1) { // int16 dot products s->readpixels = byte2word48; s->compute_network0 = compute_network0_i16; } else { s->readpixels = pixel2float48; s->compute_network0 = compute_network0; } } else { // new prescreener // only int16 dot products s->readpixels = byte2word64; s->compute_network0 = compute_network0new; } // evalfunc_1 s->wae5 = weighted_avg_elliott_mul5_m16; if (s->fapprox & 2) { // use int16 dot products s->extract = extract_m8_i16; s->dot_prod = dot_prods; } else { // use float dot products s->extract = extract_m8; s->dot_prod = dot_prod; } s->expfunc = e2_m16; } static int modnpf(const int m, const int n) { if ((m % n) == 0) return m; return m + n - (m % n); } static int get_frame(AVFilterContext *ctx, int is_second) { NNEDIContext *s = ctx->priv; AVFilterLink *outlink = ctx->outputs[0]; AVFrame *src = s->src; FrameData *frame_data; int effective_field = s->field; size_t temp_size; int field_n; int plane; if (effective_field > 1) effective_field -= 2; else if (effective_field < 0) effective_field += 2; if (s->field < 0 && src->interlaced_frame && src->top_field_first == 0) effective_field = 0; else if (s->field < 0 && src->interlaced_frame && src->top_field_first == 1) effective_field = 1; else effective_field = !effective_field; if (s->field > 1 || s->field == -2) { if (is_second) { field_n = (effective_field == 0); } else { field_n = (effective_field == 1); } } else { field_n = effective_field; } s->dst = ff_get_video_buffer(outlink, outlink->w, outlink->h); if (!s->dst) return AVERROR(ENOMEM); av_frame_copy_props(s->dst, src); s->dst->interlaced_frame = 0; frame_data = &s->frame_data; for (plane = 0; plane < s->nb_planes; plane++) { int dst_height = s->planeheight[plane]; int dst_width = s->linesize[plane]; const int min_alignment = 16; const int min_pad = 10; if (!(s->process_plane & (1 << plane))) { av_image_copy_plane(s->dst->data[plane], s->dst->linesize[plane], src->data[plane], src->linesize[plane], s->linesize[plane], s->planeheight[plane]); continue; } frame_data->padded_width[plane] = dst_width + 64; frame_data->padded_height[plane] = dst_height + 12; frame_data->padded_stride[plane] = modnpf(frame_data->padded_width[plane] + min_pad, min_alignment); // TODO: maybe min_pad is in pixels too? if (!frame_data->paddedp[plane]) { frame_data->paddedp[plane] = av_malloc_array(frame_data->padded_stride[plane], frame_data->padded_height[plane]); if (!frame_data->paddedp[plane]) return AVERROR(ENOMEM); } frame_data->dstp[plane] = s->dst->data[plane]; frame_data->dst_stride[plane] = s->dst->linesize[plane]; if (!frame_data->lcount[plane]) { frame_data->lcount[plane] = av_calloc(dst_height, sizeof(int32_t) * 16); if (!frame_data->lcount[plane]) return AVERROR(ENOMEM); } else { memset(frame_data->lcount[plane], 0, dst_height * sizeof(int32_t) * 16); } frame_data->field[plane] = field_n; } if (!frame_data->input) { frame_data->input = av_malloc(512 * sizeof(float)); if (!frame_data->input) return AVERROR(ENOMEM); } // evalfunc_0 requires at least padded_width[0] bytes. // evalfunc_1 requires at least 512 floats. if (!frame_data->temp) { temp_size = FFMAX(frame_data->padded_width[0], 512 * sizeof(float)); frame_data->temp = av_malloc(temp_size); if (!frame_data->temp) return AVERROR(ENOMEM); } // Copy src to a padded "frame" in frame_data and mirror the edges. s->copy_pad(src, frame_data, s, field_n); // Handles prescreening and the cubic interpolation. s->evalfunc_0(s, frame_data); // The rest. s->evalfunc_1(s, frame_data); return 0; } static int filter_frame(AVFilterLink *inlink, AVFrame *src) { AVFilterContext *ctx = inlink->dst; AVFilterLink *outlink = ctx->outputs[0]; NNEDIContext *s = ctx->priv; int ret; if ((s->field > 1 || s->field == -2) && !s->second) { goto second; } else if (s->field > 1 || s->field == -2) { AVFrame *dst; s->src = s->second; ret = get_frame(ctx, 1); if (ret < 0) { av_frame_free(&s->dst); av_frame_free(&s->src); av_frame_free(&s->second); return ret; } dst = s->dst; if (src->pts != AV_NOPTS_VALUE && dst->pts != AV_NOPTS_VALUE) dst->pts += src->pts; else dst->pts = AV_NOPTS_VALUE; ret = ff_filter_frame(outlink, dst); if (ret < 0) return ret; if (s->eof) return 0; s->cur_pts = s->second->pts; av_frame_free(&s->second); second: if ((s->deint && src->interlaced_frame && !ctx->is_disabled) || (!s->deint && !ctx->is_disabled)) { s->second = src; } } if ((s->deint && !src->interlaced_frame) || ctx->is_disabled) { AVFrame *dst = av_frame_clone(src); if (!dst) { av_frame_free(&src); av_frame_free(&s->second); return AVERROR(ENOMEM); } if (s->field > 1 || s->field == -2) { av_frame_free(&s->second); if ((s->deint && src->interlaced_frame) || (!s->deint)) s->second = src; } else { av_frame_free(&src); } if (dst->pts != AV_NOPTS_VALUE) dst->pts *= 2; return ff_filter_frame(outlink, dst); } s->src = src; ret = get_frame(ctx, 0); if (ret < 0) { av_frame_free(&s->dst); av_frame_free(&s->src); av_frame_free(&s->second); return ret; } if (src->pts != AV_NOPTS_VALUE) s->dst->pts = src->pts * 2; if (s->field <= 1 && s->field > -2) { av_frame_free(&src); s->src = NULL; } return ff_filter_frame(outlink, s->dst); } static int request_frame(AVFilterLink *link) { AVFilterContext *ctx = link->src; NNEDIContext *s = ctx->priv; int ret; if (s->eof) return AVERROR_EOF; ret = ff_request_frame(ctx->inputs[0]); if (ret == AVERROR_EOF && s->second) { AVFrame *next = av_frame_clone(s->second); if (!next) return AVERROR(ENOMEM); next->pts = s->second->pts * 2 - s->cur_pts; s->eof = 1; filter_frame(ctx->inputs[0], next); } else if (ret < 0) { return ret; } return 0; } static av_cold int init(AVFilterContext *ctx) { NNEDIContext *s = ctx->priv; FILE *weights_file = NULL; int64_t expected_size = 13574928; int64_t weights_size; float *bdata; size_t bytes_read; const int xdia_table[NUM_NSIZE] = { 8, 16, 32, 48, 8, 16, 32 }; const int ydia_table[NUM_NSIZE] = { 6, 6, 6, 6, 4, 4, 4 }; const int nns_table[NUM_NNS] = { 16, 32, 64, 128, 256 }; const int dims0 = 49 * 4 + 5 * 4 + 9 * 4; const int dims0new = 4 * 65 + 4 * 5; const int dims1 = nns_table[s->nnsparam] * 2 * (xdia_table[s->nsize] * ydia_table[s->nsize] + 1); int dims1tsize = 0; int dims1offset = 0; int ret = 0, i, j, k; weights_file = fopen(s->weights_file, "rb"); if (!weights_file) { av_log(ctx, AV_LOG_ERROR, "No weights file provided, aborting!\n"); return AVERROR(EINVAL); } if (fseek(weights_file, 0, SEEK_END)) { av_log(ctx, AV_LOG_ERROR, "Couldn't seek to the end of weights file.\n"); fclose(weights_file); return AVERROR(EINVAL); } weights_size = ftell(weights_file); if (weights_size == -1) { fclose(weights_file); av_log(ctx, AV_LOG_ERROR, "Couldn't get size of weights file.\n"); return AVERROR(EINVAL); } else if (weights_size != expected_size) { fclose(weights_file); av_log(ctx, AV_LOG_ERROR, "Unexpected weights file size.\n"); return AVERROR(EINVAL); } if (fseek(weights_file, 0, SEEK_SET)) { fclose(weights_file); av_log(ctx, AV_LOG_ERROR, "Couldn't seek to the start of weights file.\n"); return AVERROR(EINVAL); } bdata = (float *)av_malloc(expected_size); if (!bdata) { fclose(weights_file); return AVERROR(ENOMEM); } bytes_read = fread(bdata, 1, expected_size, weights_file); if (bytes_read != (size_t)expected_size) { fclose(weights_file); ret = AVERROR_INVALIDDATA; av_log(ctx, AV_LOG_ERROR, "Couldn't read weights file.\n"); goto fail; } fclose(weights_file); for (j = 0; j < NUM_NNS; j++) { for (i = 0; i < NUM_NSIZE; i++) { if (i == s->nsize && j == s->nnsparam) dims1offset = dims1tsize; dims1tsize += nns_table[j] * 2 * (xdia_table[i] * ydia_table[i] + 1) * 2; } } s->weights0 = av_malloc_array(FFMAX(dims0, dims0new), sizeof(float)); if (!s->weights0) { ret = AVERROR(ENOMEM); goto fail; } for (i = 0; i < 2; i++) { s->weights1[i] = av_malloc_array(dims1, sizeof(float)); if (!s->weights1[i]) { ret = AVERROR(ENOMEM); goto fail; } } // Adjust prescreener weights if (s->pscrn >= 2) {// using new prescreener const float *bdw; int16_t *ws; float *wf; double mean[4] = { 0.0, 0.0, 0.0, 0.0 }; int *offt = av_calloc(4 * 64, sizeof(int)); if (!offt) { ret = AVERROR(ENOMEM); goto fail; } for (j = 0; j < 4; j++) for (k = 0; k < 64; k++) offt[j * 64 + k] = ((k >> 3) << 5) + ((j & 3) << 3) + (k & 7); bdw = bdata + dims0 + dims0new * (s->pscrn - 2); ws = (int16_t *)s->weights0; wf = (float *)&ws[4 * 64]; // Calculate mean weight of each first layer neuron for (j = 0; j < 4; j++) { double cmean = 0.0; for (k = 0; k < 64; k++) cmean += bdw[offt[j * 64 + k]]; mean[j] = cmean / 64.0; } // Factor mean removal and 1.0/127.5 scaling // into first layer weights. scale to int16 range for (j = 0; j < 4; j++) { double scale, mval = 0.0; for (k = 0; k < 64; k++) mval = FFMAX(mval, FFABS((bdw[offt[j * 64 + k]] - mean[j]) / 127.5)); scale = 32767.0 / mval; for (k = 0; k < 64; k++) ws[offt[j * 64 + k]] = roundds(((bdw[offt[j * 64 + k]] - mean[j]) / 127.5) * scale); wf[j] = (float)(mval / 32767.0); } memcpy(wf + 4, bdw + 4 * 64, (dims0new - 4 * 64) * sizeof(float)); av_free(offt); } else { // using old prescreener double mean[4] = { 0.0, 0.0, 0.0, 0.0 }; // Calculate mean weight of each first layer neuron for (j = 0; j < 4; j++) { double cmean = 0.0; for (k = 0; k < 48; k++) cmean += bdata[j * 48 + k]; mean[j] = cmean / 48.0; } if (s->fapprox & 1) {// use int16 dot products in first layer int16_t *ws = (int16_t *)s->weights0; float *wf = (float *)&ws[4 * 48]; // Factor mean removal and 1.0/127.5 scaling // into first layer weights. scale to int16 range for (j = 0; j < 4; j++) { double scale, mval = 0.0; for (k = 0; k < 48; k++) mval = FFMAX(mval, FFABS((bdata[j * 48 + k] - mean[j]) / 127.5)); scale = 32767.0 / mval; for (k = 0; k < 48; k++) ws[j * 48 + k] = roundds(((bdata[j * 48 + k] - mean[j]) / 127.5) * scale); wf[j] = (float)(mval / 32767.0); } memcpy(wf + 4, bdata + 4 * 48, (dims0 - 4 * 48) * sizeof(float)); } else {// use float dot products in first layer double half = (1 << 8) - 1; half /= 2; // Factor mean removal and 1.0/half scaling // into first layer weights. for (j = 0; j < 4; j++) for (k = 0; k < 48; k++) s->weights0[j * 48 + k] = (float)((bdata[j * 48 + k] - mean[j]) / half); memcpy(s->weights0 + 4 * 48, bdata + 4 * 48, (dims0 - 4 * 48) * sizeof(float)); } } // Adjust prediction weights for (i = 0; i < 2; i++) { const float *bdataT = bdata + dims0 + dims0new * 3 + dims1tsize * s->etype + dims1offset + i * dims1; const int nnst = nns_table[s->nnsparam]; const int asize = xdia_table[s->nsize] * ydia_table[s->nsize]; const int boff = nnst * 2 * asize; double *mean = (double *)av_calloc(asize + 1 + nnst * 2, sizeof(double)); if (!mean) { ret = AVERROR(ENOMEM); goto fail; } // Calculate mean weight of each neuron (ignore bias) for (j = 0; j < nnst * 2; j++) { double cmean = 0.0; for (k = 0; k < asize; k++) cmean += bdataT[j * asize + k]; mean[asize + 1 + j] = cmean / (double)asize; } // Calculate mean softmax neuron for (j = 0; j < nnst; j++) { for (k = 0; k < asize; k++) mean[k] += bdataT[j * asize + k] - mean[asize + 1 + j]; mean[asize] += bdataT[boff + j]; } for (j = 0; j < asize + 1; j++) mean[j] /= (double)(nnst); if (s->fapprox & 2) { // use int16 dot products int16_t *ws = (int16_t *)s->weights1[i]; float *wf = (float *)&ws[nnst * 2 * asize]; // Factor mean removal into weights, remove global offset from // softmax neurons, and scale weights to int16 range. for (j = 0; j < nnst; j++) { // softmax neurons double scale, mval = 0.0; for (k = 0; k < asize; k++) mval = FFMAX(mval, FFABS(bdataT[j * asize + k] - mean[asize + 1 + j] - mean[k])); scale = 32767.0 / mval; for (k = 0; k < asize; k++) ws[j * asize + k] = roundds((bdataT[j * asize + k] - mean[asize + 1 + j] - mean[k]) * scale); wf[(j >> 2) * 8 + (j & 3)] = (float)(mval / 32767.0); wf[(j >> 2) * 8 + (j & 3) + 4] = (float)(bdataT[boff + j] - mean[asize]); } for (j = nnst; j < nnst * 2; j++) { // elliott neurons double scale, mval = 0.0; for (k = 0; k < asize; k++) mval = FFMAX(mval, FFABS(bdataT[j * asize + k] - mean[asize + 1 + j])); scale = 32767.0 / mval; for (k = 0; k < asize; k++) ws[j * asize + k] = roundds((bdataT[j * asize + k] - mean[asize + 1 + j]) * scale); wf[(j >> 2) * 8 + (j & 3)] = (float)(mval / 32767.0); wf[(j >> 2) * 8 + (j & 3) + 4] = bdataT[boff + j]; } } else { // use float dot products // Factor mean removal into weights, and remove global // offset from softmax neurons. for (j = 0; j < nnst * 2; j++) { for (k = 0; k < asize; k++) { const double q = j < nnst ? mean[k] : 0.0; s->weights1[i][j * asize + k] = (float)(bdataT[j * asize + k] - mean[asize + 1 + j] - q); } s->weights1[i][boff + j] = (float)(bdataT[boff + j] - (j < nnst ? mean[asize] : 0.0)); } } av_free(mean); } s->nns = nns_table[s->nnsparam]; s->xdia = xdia_table[s->nsize]; s->ydia = ydia_table[s->nsize]; s->asize = xdia_table[s->nsize] * ydia_table[s->nsize]; s->max_value = 65535 >> 8; select_functions(s); s->fdsp = avpriv_float_dsp_alloc(0); if (!s->fdsp) ret = AVERROR(ENOMEM); fail: av_free(bdata); return ret; } static av_cold void uninit(AVFilterContext *ctx) { NNEDIContext *s = ctx->priv; int i; av_freep(&s->weights0); for (i = 0; i < 2; i++) av_freep(&s->weights1[i]); for (i = 0; i < s->nb_planes; i++) { av_freep(&s->frame_data.paddedp[i]); av_freep(&s->frame_data.lcount[i]); } av_freep(&s->frame_data.input); av_freep(&s->frame_data.temp); av_freep(&s->fdsp); av_frame_free(&s->second); } static const AVFilterPad inputs[] = { { .name = "default", .type = AVMEDIA_TYPE_VIDEO, .filter_frame = filter_frame, .config_props = config_input, }, { NULL } }; static const AVFilterPad outputs[] = { { .name = "default", .type = AVMEDIA_TYPE_VIDEO, .config_props = config_output, .request_frame = request_frame, }, { NULL } }; AVFilter ff_vf_nnedi = { .name = "nnedi", .description = NULL_IF_CONFIG_SMALL("Apply neural network edge directed interpolation intra-only deinterlacer."), .priv_size = sizeof(NNEDIContext), .priv_class = &nnedi_class, .init = init, .uninit = uninit, .query_formats = query_formats, .inputs = inputs, .outputs = outputs, .flags = AVFILTER_FLAG_SUPPORT_TIMELINE_INTERNAL, };