FFmpeg coverage


Directory: ../../../ffmpeg/
File: src/libavfilter/vf_vaguedenoiser.c
Date: 2024-03-28 14:59:00
Exec Total Coverage
Lines: 0 291 0.0%
Functions: 0 15 0.0%
Branches: 0 136 0.0%

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1 /*
2 * Copyright (c) 2003 LeFunGus, lefungus@altern.org
3 *
4 * This file is part of FFmpeg
5 *
6 * FFmpeg is free software; you can redistribute it and/or modify
7 * it under the terms of the GNU General Public License as published by
8 * the Free Software Foundation; either version 2 of the License, or
9 * (at your option) any later version.
10 *
11 * FFmpeg is distributed in the hope that it will be useful,
12 * but WITHOUT ANY WARRANTY; without even the implied warranty of
13 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
14 * GNU General Public License for more details.
15 *
16 * You should have received a copy of the GNU General Public License along
17 * with FFmpeg; if not, write to the Free Software Foundation, Inc.,
18 * 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
19 */
20
21 #include <float.h>
22
23 #include "libavutil/imgutils.h"
24 #include "libavutil/attributes.h"
25 #include "libavutil/common.h"
26 #include "libavutil/pixdesc.h"
27 #include "libavutil/opt.h"
28
29 #include "avfilter.h"
30 #include "internal.h"
31 #include "video.h"
32
33 typedef struct VagueDenoiserContext {
34 const AVClass *class;
35
36 float threshold;
37 float percent;
38 int method;
39 int type;
40 int nsteps;
41 int planes;
42
43 int depth;
44 int bpc;
45 int peak;
46 int nb_planes;
47 int planeheight[4];
48 int planewidth[4];
49
50 float *block;
51 float *in;
52 float *out;
53 float *tmp;
54
55 int hlowsize[4][32];
56 int hhighsize[4][32];
57 int vlowsize[4][32];
58 int vhighsize[4][32];
59
60 void (*thresholding)(float *block, const int width, const int height,
61 const int stride, const float threshold,
62 const float percent);
63 } VagueDenoiserContext;
64
65 #define OFFSET(x) offsetof(VagueDenoiserContext, x)
66 #define FLAGS AV_OPT_FLAG_VIDEO_PARAM | AV_OPT_FLAG_FILTERING_PARAM
67 static const AVOption vaguedenoiser_options[] = {
68 { "threshold", "set filtering strength", OFFSET(threshold), AV_OPT_TYPE_FLOAT, {.dbl=2.}, 0,DBL_MAX, FLAGS },
69 { "method", "set filtering method", OFFSET(method), AV_OPT_TYPE_INT, {.i64=2 }, 0, 2, FLAGS, .unit = "method" },
70 { "hard", "hard thresholding", 0, AV_OPT_TYPE_CONST, {.i64=0}, 0, 0, FLAGS, .unit = "method" },
71 { "soft", "soft thresholding", 0, AV_OPT_TYPE_CONST, {.i64=1}, 0, 0, FLAGS, .unit = "method" },
72 { "garrote", "garrote thresholding", 0, AV_OPT_TYPE_CONST, {.i64=2}, 0, 0, FLAGS, .unit = "method" },
73 { "nsteps", "set number of steps", OFFSET(nsteps), AV_OPT_TYPE_INT, {.i64=6 }, 1, 32, FLAGS },
74 { "percent", "set percent of full denoising", OFFSET(percent),AV_OPT_TYPE_FLOAT, {.dbl=85}, 0,100, FLAGS },
75 { "planes", "set planes to filter", OFFSET(planes), AV_OPT_TYPE_INT, {.i64=15 }, 0, 15, FLAGS },
76 { "type", "set threshold type", OFFSET(type), AV_OPT_TYPE_INT, {.i64=0 }, 0, 1, FLAGS, .unit = "type" },
77 { "universal", "universal (VisuShrink)", 0, AV_OPT_TYPE_CONST, {.i64=0}, 0, 0, FLAGS, .unit = "type" },
78 { "bayes", "bayes (BayesShrink)", 0, AV_OPT_TYPE_CONST, {.i64=1}, 0, 0, FLAGS, .unit = "type" },
79 { NULL }
80 };
81
82 AVFILTER_DEFINE_CLASS(vaguedenoiser);
83
84 #define NPAD 10
85
86 static const float analysis_low[9] = {
87 0.037828455506995f, -0.023849465019380f, -0.110624404418423f, 0.377402855612654f,
88 0.852698679009403f, 0.377402855612654f, -0.110624404418423f, -0.023849465019380f, 0.037828455506995f
89 };
90
91 static const float analysis_high[7] = {
92 -0.064538882628938f, 0.040689417609558f, 0.418092273222212f, -0.788485616405664f,
93 0.418092273222212f, 0.040689417609558f, -0.064538882628938f
94 };
95
96 static const float synthesis_low[7] = {
97 -0.064538882628938f, -0.040689417609558f, 0.418092273222212f, 0.788485616405664f,
98 0.418092273222212f, -0.040689417609558f, -0.064538882628938f
99 };
100
101 static const float synthesis_high[9] = {
102 -0.037828455506995f, -0.023849465019380f, 0.110624404418423f, 0.377402855612654f,
103 -0.852698679009403f, 0.377402855612654f, 0.110624404418423f, -0.023849465019380f, -0.037828455506995f
104 };
105
106 static const enum AVPixelFormat pix_fmts[] = {
107 AV_PIX_FMT_GRAY8, AV_PIX_FMT_GRAY9, AV_PIX_FMT_GRAY10,
108 AV_PIX_FMT_GRAY12, AV_PIX_FMT_GRAY14, AV_PIX_FMT_GRAY16,
109 AV_PIX_FMT_YUV410P, AV_PIX_FMT_YUV411P,
110 AV_PIX_FMT_YUV420P, AV_PIX_FMT_YUV422P,
111 AV_PIX_FMT_YUV440P, AV_PIX_FMT_YUV444P,
112 AV_PIX_FMT_YUVJ420P, AV_PIX_FMT_YUVJ422P,
113 AV_PIX_FMT_YUVJ440P, AV_PIX_FMT_YUVJ444P,
114 AV_PIX_FMT_YUVJ411P,
115 AV_PIX_FMT_YUV420P9, AV_PIX_FMT_YUV422P9, AV_PIX_FMT_YUV444P9,
116 AV_PIX_FMT_YUV420P10, AV_PIX_FMT_YUV422P10, AV_PIX_FMT_YUV444P10,
117 AV_PIX_FMT_YUV440P10,
118 AV_PIX_FMT_YUV444P12, AV_PIX_FMT_YUV422P12, AV_PIX_FMT_YUV420P12,
119 AV_PIX_FMT_YUV440P12,
120 AV_PIX_FMT_YUV444P14, AV_PIX_FMT_YUV422P14, AV_PIX_FMT_YUV420P14,
121 AV_PIX_FMT_YUV420P16, AV_PIX_FMT_YUV422P16, AV_PIX_FMT_YUV444P16,
122 AV_PIX_FMT_GBRP, AV_PIX_FMT_GBRP9, AV_PIX_FMT_GBRP10,
123 AV_PIX_FMT_GBRP12, AV_PIX_FMT_GBRP14, AV_PIX_FMT_GBRP16,
124 AV_PIX_FMT_YUVA420P, AV_PIX_FMT_YUVA422P, AV_PIX_FMT_YUVA444P,
125 AV_PIX_FMT_YUVA444P9, AV_PIX_FMT_YUVA444P10, AV_PIX_FMT_YUVA444P12, AV_PIX_FMT_YUVA444P16,
126 AV_PIX_FMT_YUVA422P9, AV_PIX_FMT_YUVA422P10, AV_PIX_FMT_YUVA422P12, AV_PIX_FMT_YUVA422P16,
127 AV_PIX_FMT_YUVA420P9, AV_PIX_FMT_YUVA420P10, AV_PIX_FMT_YUVA420P16,
128 AV_PIX_FMT_GBRAP, AV_PIX_FMT_GBRAP10, AV_PIX_FMT_GBRAP12, AV_PIX_FMT_GBRAP16,
129 AV_PIX_FMT_NONE
130 };
131
132 static int config_input(AVFilterLink *inlink)
133 {
134 VagueDenoiserContext *s = inlink->dst->priv;
135 const AVPixFmtDescriptor *desc = av_pix_fmt_desc_get(inlink->format);
136 int p, i, nsteps_width, nsteps_height, nsteps_max;
137
138 s->depth = desc->comp[0].depth;
139 s->bpc = (s->depth + 7) / 8;
140 s->nb_planes = desc->nb_components;
141
142 s->planeheight[1] = s->planeheight[2] = AV_CEIL_RSHIFT(inlink->h, desc->log2_chroma_h);
143 s->planeheight[0] = s->planeheight[3] = inlink->h;
144 s->planewidth[1] = s->planewidth[2] = AV_CEIL_RSHIFT(inlink->w, desc->log2_chroma_w);
145 s->planewidth[0] = s->planewidth[3] = inlink->w;
146
147 s->block = av_malloc_array(inlink->w * inlink->h, sizeof(*s->block));
148 s->in = av_malloc_array(32 + FFMAX(inlink->w, inlink->h), sizeof(*s->in));
149 s->out = av_malloc_array(32 + FFMAX(inlink->w, inlink->h), sizeof(*s->out));
150 s->tmp = av_malloc_array(32 + FFMAX(inlink->w, inlink->h), sizeof(*s->tmp));
151
152 if (!s->block || !s->in || !s->out || !s->tmp)
153 return AVERROR(ENOMEM);
154
155 s->threshold *= 1 << (s->depth - 8);
156 s->peak = (1 << s->depth) - 1;
157
158 nsteps_width = ((s->planes & 2 || s->planes & 4) && s->nb_planes > 1) ? s->planewidth[1] : s->planewidth[0];
159 nsteps_height = ((s->planes & 2 || s->planes & 4) && s->nb_planes > 1) ? s->planeheight[1] : s->planeheight[0];
160
161 for (nsteps_max = 1; nsteps_max < 15; nsteps_max++) {
162 if (pow(2, nsteps_max) >= nsteps_width || pow(2, nsteps_max) >= nsteps_height)
163 break;
164 }
165
166 s->nsteps = FFMIN(s->nsteps, nsteps_max - 2);
167
168 for (p = 0; p < 4; p++) {
169 s->hlowsize[p][0] = (s->planewidth[p] + 1) >> 1;
170 s->hhighsize[p][0] = s->planewidth[p] >> 1;
171 s->vlowsize[p][0] = (s->planeheight[p] + 1) >> 1;
172 s->vhighsize[p][0] = s->planeheight[p] >> 1;
173
174 for (i = 1; i < s->nsteps; i++) {
175 s->hlowsize[p][i] = (s->hlowsize[p][i - 1] + 1) >> 1;
176 s->hhighsize[p][i] = s->hlowsize[p][i - 1] >> 1;
177 s->vlowsize[p][i] = (s->vlowsize[p][i - 1] + 1) >> 1;
178 s->vhighsize[p][i] = s->vlowsize[p][i - 1] >> 1;
179 }
180 }
181
182 return 0;
183 }
184
185 static inline void copy(const float *p1, float *p2, const int length)
186 {
187 memcpy(p2, p1, length * sizeof(float));
188 }
189
190 static inline void copyv(const float *p1, const int stride1, float *p2, const int length)
191 {
192 int i;
193
194 for (i = 0; i < length; i++) {
195 p2[i] = *p1;
196 p1 += stride1;
197 }
198 }
199
200 static inline void copyh(const float *p1, float *p2, const int stride2, const int length)
201 {
202 int i;
203
204 for (i = 0; i < length; i++) {
205 *p2 = p1[i];
206 p2 += stride2;
207 }
208 }
209
210 // Do symmetric extension of data using prescribed symmetries
211 // Original values are in output[npad] through output[npad+size-1]
212 // New values will be placed in output[0] through output[npad] and in output[npad+size] through output[2*npad+size-1] (note: end values may not be filled in)
213 // extension at left bdry is ... 3 2 1 0 | 0 1 2 3 ...
214 // same for right boundary
215 // if right_ext=1 then ... 3 2 1 0 | 1 2 3
216 static void symmetric_extension(float *output, const int size, const int left_ext, const int right_ext)
217 {
218 int first = NPAD;
219 int last = NPAD - 1 + size;
220 const int originalLast = last;
221 int i, nextend, idx;
222
223 if (left_ext == 2)
224 output[--first] = output[NPAD];
225 if (right_ext == 2)
226 output[++last] = output[originalLast];
227
228 // extend left end
229 nextend = first;
230 for (i = 0; i < nextend; i++)
231 output[--first] = output[NPAD + 1 + i];
232
233 idx = NPAD + NPAD - 1 + size;
234
235 // extend right end
236 nextend = idx - last;
237 for (i = 0; i < nextend; i++)
238 output[++last] = output[originalLast - 1 - i];
239 }
240
241 static void transform_step(float *input, float *output, const int size, const int low_size, VagueDenoiserContext *s)
242 {
243 int i;
244
245 symmetric_extension(input, size, 1, 1);
246
247 for (i = NPAD; i < NPAD + low_size; i++) {
248 const float a = input[2 * i - 14] * analysis_low[0];
249 const float b = input[2 * i - 13] * analysis_low[1];
250 const float c = input[2 * i - 12] * analysis_low[2];
251 const float d = input[2 * i - 11] * analysis_low[3];
252 const float e = input[2 * i - 10] * analysis_low[4];
253 const float f = input[2 * i - 9] * analysis_low[3];
254 const float g = input[2 * i - 8] * analysis_low[2];
255 const float h = input[2 * i - 7] * analysis_low[1];
256 const float k = input[2 * i - 6] * analysis_low[0];
257
258 output[i] = a + b + c + d + e + f + g + h + k;
259 }
260
261 for (i = NPAD; i < NPAD + low_size; i++) {
262 const float a = input[2 * i - 12] * analysis_high[0];
263 const float b = input[2 * i - 11] * analysis_high[1];
264 const float c = input[2 * i - 10] * analysis_high[2];
265 const float d = input[2 * i - 9] * analysis_high[3];
266 const float e = input[2 * i - 8] * analysis_high[2];
267 const float f = input[2 * i - 7] * analysis_high[1];
268 const float g = input[2 * i - 6] * analysis_high[0];
269
270 output[i + low_size] = a + b + c + d + e + f + g;
271 }
272 }
273
274 static void invert_step(const float *input, float *output, float *temp, const int size, VagueDenoiserContext *s)
275 {
276 const int low_size = (size + 1) >> 1;
277 const int high_size = size >> 1;
278 int left_ext = 1, right_ext, i;
279 int findex;
280
281 memcpy(temp + NPAD, input + NPAD, low_size * sizeof(float));
282
283 right_ext = (size % 2 == 0) ? 2 : 1;
284 symmetric_extension(temp, low_size, left_ext, right_ext);
285
286 memset(output, 0, (NPAD + NPAD + size) * sizeof(float));
287 findex = (size + 2) >> 1;
288
289 for (i = 9; i < findex + 11; i++) {
290 const float a = temp[i] * synthesis_low[0];
291 const float b = temp[i] * synthesis_low[1];
292 const float c = temp[i] * synthesis_low[2];
293 const float d = temp[i] * synthesis_low[3];
294
295 output[2 * i - 13] += a;
296 output[2 * i - 12] += b;
297 output[2 * i - 11] += c;
298 output[2 * i - 10] += d;
299 output[2 * i - 9] += c;
300 output[2 * i - 8] += b;
301 output[2 * i - 7] += a;
302 }
303
304 memcpy(temp + NPAD, input + NPAD + low_size, high_size * sizeof(float));
305
306 left_ext = 2;
307 right_ext = (size % 2 == 0) ? 1 : 2;
308 symmetric_extension(temp, high_size, left_ext, right_ext);
309
310 for (i = 8; i < findex + 11; i++) {
311 const float a = temp[i] * synthesis_high[0];
312 const float b = temp[i] * synthesis_high[1];
313 const float c = temp[i] * synthesis_high[2];
314 const float d = temp[i] * synthesis_high[3];
315 const float e = temp[i] * synthesis_high[4];
316
317 output[2 * i - 13] += a;
318 output[2 * i - 12] += b;
319 output[2 * i - 11] += c;
320 output[2 * i - 10] += d;
321 output[2 * i - 9] += e;
322 output[2 * i - 8] += d;
323 output[2 * i - 7] += c;
324 output[2 * i - 6] += b;
325 output[2 * i - 5] += a;
326 }
327 }
328
329 static void hard_thresholding(float *block, const int width, const int height,
330 const int stride, const float threshold,
331 const float percent)
332 {
333 const float frac = 1.f - percent * 0.01f;
334 int y, x;
335
336 for (y = 0; y < height; y++) {
337 for (x = 0; x < width; x++) {
338 if (FFABS(block[x]) <= threshold)
339 block[x] *= frac;
340 }
341 block += stride;
342 }
343 }
344
345 static void soft_thresholding(float *block, const int width, const int height, const int stride,
346 const float threshold, const float percent)
347 {
348 const float frac = 1.f - percent * 0.01f;
349 const float shift = threshold * 0.01f * percent;
350 int y, x;
351
352 for (y = 0; y < height; y++) {
353 for (x = 0; x < width; x++) {
354 const float temp = FFABS(block[x]);
355 if (temp <= threshold)
356 block[x] *= frac;
357 else
358 block[x] = (block[x] < 0.f ? -1.f : (block[x] > 0.f ? 1.f : 0.f)) * (temp - shift);
359 }
360 block += stride;
361 }
362 }
363
364 static void qian_thresholding(float *block, const int width, const int height,
365 const int stride, const float threshold,
366 const float percent)
367 {
368 const float percent01 = percent * 0.01f;
369 const float tr2 = threshold * threshold * percent01;
370 const float frac = 1.f - percent01;
371 int y, x;
372
373 for (y = 0; y < height; y++) {
374 for (x = 0; x < width; x++) {
375 const float temp = FFABS(block[x]);
376 if (temp <= threshold) {
377 block[x] *= frac;
378 } else {
379 const float tp2 = temp * temp;
380 block[x] *= (tp2 - tr2) / tp2;
381 }
382 }
383 block += stride;
384 }
385 }
386
387 static float bayes_threshold(float *block, const int width, const int height,
388 const int stride, const float threshold)
389 {
390 float mean = 0.f;
391
392 for (int y = 0; y < height; y++) {
393 for (int x = 0; x < width; x++) {
394 mean += block[x] * block[x];
395 }
396 block += stride;
397 }
398
399 mean /= width * height;
400
401 return threshold * threshold / (FFMAX(sqrtf(mean - threshold), FLT_EPSILON));
402 }
403
404 static void filter(VagueDenoiserContext *s, AVFrame *in, AVFrame *out)
405 {
406 int p, y, x, i, j;
407
408 for (p = 0; p < s->nb_planes; p++) {
409 const int height = s->planeheight[p];
410 const int width = s->planewidth[p];
411 const uint8_t *srcp8 = in->data[p];
412 const uint16_t *srcp16 = (const uint16_t *)in->data[p];
413 uint8_t *dstp8 = out->data[p];
414 uint16_t *dstp16 = (uint16_t *)out->data[p];
415 float *output = s->block;
416 int h_low_size0 = width;
417 int v_low_size0 = height;
418 int nsteps_transform = s->nsteps;
419 int nsteps_invert = s->nsteps;
420 const float *input = s->block;
421
422 if (!((1 << p) & s->planes)) {
423 av_image_copy_plane(out->data[p], out->linesize[p], in->data[p], in->linesize[p],
424 s->planewidth[p] * s->bpc, s->planeheight[p]);
425 continue;
426 }
427
428 if (s->depth <= 8) {
429 for (y = 0; y < height; y++) {
430 for (x = 0; x < width; x++)
431 output[x] = srcp8[x];
432 srcp8 += in->linesize[p];
433 output += width;
434 }
435 } else {
436 for (y = 0; y < height; y++) {
437 for (x = 0; x < width; x++)
438 output[x] = srcp16[x];
439 srcp16 += in->linesize[p] / 2;
440 output += width;
441 }
442 }
443
444 while (nsteps_transform--) {
445 int low_size = (h_low_size0 + 1) >> 1;
446 float *input = s->block;
447 for (j = 0; j < v_low_size0; j++) {
448 copy(input, s->in + NPAD, h_low_size0);
449 transform_step(s->in, s->out, h_low_size0, low_size, s);
450 copy(s->out + NPAD, input, h_low_size0);
451 input += width;
452 }
453
454 low_size = (v_low_size0 + 1) >> 1;
455 input = s->block;
456 for (j = 0; j < h_low_size0; j++) {
457 copyv(input, width, s->in + NPAD, v_low_size0);
458 transform_step(s->in, s->out, v_low_size0, low_size, s);
459 copyh(s->out + NPAD, input, width, v_low_size0);
460 input++;
461 }
462
463 h_low_size0 = (h_low_size0 + 1) >> 1;
464 v_low_size0 = (v_low_size0 + 1) >> 1;
465 }
466
467 if (s->type == 0) {
468 s->thresholding(s->block, width, height, width, s->threshold, s->percent);
469 } else {
470 for (int n = 0; n < s->nsteps; n++) {
471 float threshold;
472 float *block;
473
474 if (n == s->nsteps - 1) {
475 threshold = bayes_threshold(s->block, s->hlowsize[p][n], s->vlowsize[p][n], width, s->threshold);
476 s->thresholding(s->block, s->hlowsize[p][n], s->vlowsize[p][n], width, threshold, s->percent);
477 }
478 block = s->block + s->hlowsize[p][n];
479 threshold = bayes_threshold(block, s->hhighsize[p][n], s->vlowsize[p][n], width, s->threshold);
480 s->thresholding(block, s->hhighsize[p][n], s->vlowsize[p][n], width, threshold, s->percent);
481 block = s->block + s->vlowsize[p][n] * width;
482 threshold = bayes_threshold(block, s->hlowsize[p][n], s->vhighsize[p][n], width, s->threshold);
483 s->thresholding(block, s->hlowsize[p][n], s->vhighsize[p][n], width, threshold, s->percent);
484 block = s->block + s->hlowsize[p][n] + s->vlowsize[p][n] * width;
485 threshold = bayes_threshold(block, s->hhighsize[p][n], s->vhighsize[p][n], width, s->threshold);
486 s->thresholding(block, s->hhighsize[p][n], s->vhighsize[p][n], width, threshold, s->percent);
487 }
488 }
489
490 while (nsteps_invert--) {
491 const int idx = s->vlowsize[p][nsteps_invert] + s->vhighsize[p][nsteps_invert];
492 const int idx2 = s->hlowsize[p][nsteps_invert] + s->hhighsize[p][nsteps_invert];
493 float * idx3 = s->block;
494 for (i = 0; i < idx2; i++) {
495 copyv(idx3, width, s->in + NPAD, idx);
496 invert_step(s->in, s->out, s->tmp, idx, s);
497 copyh(s->out + NPAD, idx3, width, idx);
498 idx3++;
499 }
500
501 idx3 = s->block;
502 for (i = 0; i < idx; i++) {
503 copy(idx3, s->in + NPAD, idx2);
504 invert_step(s->in, s->out, s->tmp, idx2, s);
505 copy(s->out + NPAD, idx3, idx2);
506 idx3 += width;
507 }
508 }
509
510 if (s->depth <= 8) {
511 for (y = 0; y < height; y++) {
512 for (x = 0; x < width; x++)
513 dstp8[x] = av_clip_uint8(input[x] + 0.5f);
514 input += width;
515 dstp8 += out->linesize[p];
516 }
517 } else {
518 for (y = 0; y < height; y++) {
519 for (x = 0; x < width; x++)
520 dstp16[x] = av_clip(input[x] + 0.5f, 0, s->peak);
521 input += width;
522 dstp16 += out->linesize[p] / 2;
523 }
524 }
525 }
526 }
527
528 static int filter_frame(AVFilterLink *inlink, AVFrame *in)
529 {
530 AVFilterContext *ctx = inlink->dst;
531 VagueDenoiserContext *s = ctx->priv;
532 AVFilterLink *outlink = ctx->outputs[0];
533 AVFrame *out;
534 int direct = av_frame_is_writable(in);
535
536 if (direct) {
537 out = in;
538 } else {
539 out = ff_get_video_buffer(outlink, outlink->w, outlink->h);
540 if (!out) {
541 av_frame_free(&in);
542 return AVERROR(ENOMEM);
543 }
544
545 av_frame_copy_props(out, in);
546 }
547
548 filter(s, in, out);
549
550 if (!direct)
551 av_frame_free(&in);
552
553 return ff_filter_frame(outlink, out);
554 }
555
556 static av_cold int init(AVFilterContext *ctx)
557 {
558 VagueDenoiserContext *s = ctx->priv;
559
560 switch (s->method) {
561 case 0:
562 s->thresholding = hard_thresholding;
563 break;
564 case 1:
565 s->thresholding = soft_thresholding;
566 break;
567 case 2:
568 s->thresholding = qian_thresholding;
569 break;
570 }
571
572 return 0;
573 }
574
575 static av_cold void uninit(AVFilterContext *ctx)
576 {
577 VagueDenoiserContext *s = ctx->priv;
578
579 av_freep(&s->block);
580 av_freep(&s->in);
581 av_freep(&s->out);
582 av_freep(&s->tmp);
583 }
584
585 static const AVFilterPad vaguedenoiser_inputs[] = {
586 {
587 .name = "default",
588 .type = AVMEDIA_TYPE_VIDEO,
589 .config_props = config_input,
590 .filter_frame = filter_frame,
591 },
592 };
593
594
595 const AVFilter ff_vf_vaguedenoiser = {
596 .name = "vaguedenoiser",
597 .description = NULL_IF_CONFIG_SMALL("Apply a Wavelet based Denoiser."),
598 .priv_size = sizeof(VagueDenoiserContext),
599 .priv_class = &vaguedenoiser_class,
600 .init = init,
601 .uninit = uninit,
602 FILTER_INPUTS(vaguedenoiser_inputs),
603 FILTER_OUTPUTS(ff_video_default_filterpad),
604 FILTER_PIXFMTS_ARRAY(pix_fmts),
605 .flags = AVFILTER_FLAG_SUPPORT_TIMELINE_GENERIC,
606 };
607