FFmpeg coverage

Line	Branch	Exec	Source
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/mem.h"
27			#include "libavutil/pixdesc.h"
28			#include "libavutil/opt.h"
29
30			#include "avfilter.h"
31			#include "filters.h"
32			#include "video.h"
33
34			typedef struct VagueDenoiserContext {
35			const AVClass *class;
36
37			float threshold;
38			float percent;
39			int method;
40			int type;
41			int nsteps;
42			int planes;
43
44			int depth;
45			int bpc;
46			int peak;
47			int nb_planes;
48			int planeheight[4];
49			int planewidth[4];
50
51			float *block;
52			float *in;
53			float *out;
54			float *tmp;
55
56			int hlowsize[4][32];
57			int hhighsize[4][32];
58			int vlowsize[4][32];
59			int vhighsize[4][32];
60
61			void (*thresholding)(float *block, const int width, const int height,
62			const int stride, const float threshold,
63			const float percent);
64			} VagueDenoiserContext;
65
66			#define OFFSET(x) offsetof(VagueDenoiserContext, x)
67			#define FLAGS AV_OPT_FLAG_VIDEO_PARAM | AV_OPT_FLAG_FILTERING_PARAM
68			static const AVOption vaguedenoiser_options[] = {
69			{ "threshold", "set filtering strength", OFFSET(threshold), AV_OPT_TYPE_FLOAT, {.dbl=2.}, 0,DBL_MAX, FLAGS },
70			{ "method", "set filtering method", OFFSET(method), AV_OPT_TYPE_INT, {.i64=2 }, 0, 2, FLAGS, .unit = "method" },
71			{ "hard", "hard thresholding", 0, AV_OPT_TYPE_CONST, {.i64=0}, 0, 0, FLAGS, .unit = "method" },
72			{ "soft", "soft thresholding", 0, AV_OPT_TYPE_CONST, {.i64=1}, 0, 0, FLAGS, .unit = "method" },
73			{ "garrote", "garrote thresholding", 0, AV_OPT_TYPE_CONST, {.i64=2}, 0, 0, FLAGS, .unit = "method" },
74			{ "nsteps", "set number of steps", OFFSET(nsteps), AV_OPT_TYPE_INT, {.i64=6 }, 1, 32, FLAGS },
75			{ "percent", "set percent of full denoising", OFFSET(percent),AV_OPT_TYPE_FLOAT, {.dbl=85}, 0,100, FLAGS },
76			{ "planes", "set planes to filter", OFFSET(planes), AV_OPT_TYPE_INT, {.i64=15 }, 0, 15, FLAGS },
77			{ "type", "set threshold type", OFFSET(type), AV_OPT_TYPE_INT, {.i64=0 }, 0, 1, FLAGS, .unit = "type" },
78			{ "universal", "universal (VisuShrink)", 0, AV_OPT_TYPE_CONST, {.i64=0}, 0, 0, FLAGS, .unit = "type" },
79			{ "bayes", "bayes (BayesShrink)", 0, AV_OPT_TYPE_CONST, {.i64=1}, 0, 0, FLAGS, .unit = "type" },
80			{ NULL }
81			};
82
83			AVFILTER_DEFINE_CLASS(vaguedenoiser);
84
85			#define NPAD 10
86
87			static const float analysis_low[9] = {
88			0.037828455506995f, -0.023849465019380f, -0.110624404418423f, 0.377402855612654f,
89			0.852698679009403f, 0.377402855612654f, -0.110624404418423f, -0.023849465019380f, 0.037828455506995f
90			};
91
92			static const float analysis_high[7] = {
93			-0.064538882628938f, 0.040689417609558f, 0.418092273222212f, -0.788485616405664f,
94			0.418092273222212f, 0.040689417609558f, -0.064538882628938f
95			};
96
97			static const float synthesis_low[7] = {
98			-0.064538882628938f, -0.040689417609558f, 0.418092273222212f, 0.788485616405664f,
99			0.418092273222212f, -0.040689417609558f, -0.064538882628938f
100			};
101
102			static const float synthesis_high[9] = {
103			-0.037828455506995f, -0.023849465019380f, 0.110624404418423f, 0.377402855612654f,
104			-0.852698679009403f, 0.377402855612654f, 0.110624404418423f, -0.023849465019380f, -0.037828455506995f
105			};
106
107			static const enum AVPixelFormat pix_fmts[] = {
108			AV_PIX_FMT_GRAY8, AV_PIX_FMT_GRAY9, AV_PIX_FMT_GRAY10,
109			AV_PIX_FMT_GRAY12, AV_PIX_FMT_GRAY14, AV_PIX_FMT_GRAY16,
110			AV_PIX_FMT_YUV410P, AV_PIX_FMT_YUV411P,
111			AV_PIX_FMT_YUV420P, AV_PIX_FMT_YUV422P,
112			AV_PIX_FMT_YUV440P, AV_PIX_FMT_YUV444P,
113			AV_PIX_FMT_YUVJ420P, AV_PIX_FMT_YUVJ422P,
114			AV_PIX_FMT_YUVJ440P, AV_PIX_FMT_YUVJ444P,
115			AV_PIX_FMT_YUVJ411P,
116			AV_PIX_FMT_YUV420P9, AV_PIX_FMT_YUV422P9, AV_PIX_FMT_YUV444P9,
117			AV_PIX_FMT_YUV420P10, AV_PIX_FMT_YUV422P10, AV_PIX_FMT_YUV444P10,
118			AV_PIX_FMT_YUV440P10,
119			AV_PIX_FMT_YUV444P12, AV_PIX_FMT_YUV422P12, AV_PIX_FMT_YUV420P12,
120			AV_PIX_FMT_YUV440P12,
121			AV_PIX_FMT_YUV444P14, AV_PIX_FMT_YUV422P14, AV_PIX_FMT_YUV420P14,
122			AV_PIX_FMT_YUV420P16, AV_PIX_FMT_YUV422P16, AV_PIX_FMT_YUV444P16,
123			AV_PIX_FMT_GBRP, AV_PIX_FMT_GBRP9, AV_PIX_FMT_GBRP10,
124			AV_PIX_FMT_GBRP12, AV_PIX_FMT_GBRP14, AV_PIX_FMT_GBRP16,
125			AV_PIX_FMT_YUVA420P, AV_PIX_FMT_YUVA422P, AV_PIX_FMT_YUVA444P,
126			AV_PIX_FMT_YUVA444P9, AV_PIX_FMT_YUVA444P10, AV_PIX_FMT_YUVA444P12, AV_PIX_FMT_YUVA444P16,
127			AV_PIX_FMT_YUVA422P9, AV_PIX_FMT_YUVA422P10, AV_PIX_FMT_YUVA422P12, AV_PIX_FMT_YUVA422P16,
128			AV_PIX_FMT_YUVA420P9, AV_PIX_FMT_YUVA420P10, AV_PIX_FMT_YUVA420P16,
129			AV_PIX_FMT_GBRAP, AV_PIX_FMT_GBRAP10, AV_PIX_FMT_GBRAP12, AV_PIX_FMT_GBRAP16,
130			AV_PIX_FMT_NONE
131			};
132
133		✗	static int config_input(AVFilterLink *inlink)
134			{
135		✗	VagueDenoiserContext *s = inlink->dst->priv;
136		✗	const AVPixFmtDescriptor *desc = av_pix_fmt_desc_get(inlink->format);
137			int p, i, nsteps_width, nsteps_height, nsteps_max;
138
139		✗	s->depth = desc->comp[0].depth;
140		✗	s->bpc = (s->depth + 7) / 8;
141		✗	s->nb_planes = desc->nb_components;
142
143		✗	s->planeheight[1] = s->planeheight[2] = AV_CEIL_RSHIFT(inlink->h, desc->log2_chroma_h);
144		✗	s->planeheight[0] = s->planeheight[3] = inlink->h;
145		✗	s->planewidth[1] = s->planewidth[2] = AV_CEIL_RSHIFT(inlink->w, desc->log2_chroma_w);
146		✗	s->planewidth[0] = s->planewidth[3] = inlink->w;
147
148		✗	s->block = av_malloc_array(inlink->w * inlink->h, sizeof(*s->block));
149		✗	s->in = av_malloc_array(32 + FFMAX(inlink->w, inlink->h), sizeof(*s->in));
150		✗	s->out = av_malloc_array(32 + FFMAX(inlink->w, inlink->h), sizeof(*s->out));
151		✗	s->tmp = av_malloc_array(32 + FFMAX(inlink->w, inlink->h), sizeof(*s->tmp));
152
153		✗	if (!s->block || !s->in || !s->out || !s->tmp)
154		✗	return AVERROR(ENOMEM);
155
156		✗	s->threshold *= 1 << (s->depth - 8);
157		✗	s->peak = (1 << s->depth) - 1;
158
159		✗	nsteps_width = ((s->planes & 2 || s->planes & 4) && s->nb_planes > 1) ? s->planewidth[1] : s->planewidth[0];
160		✗	nsteps_height = ((s->planes & 2 || s->planes & 4) && s->nb_planes > 1) ? s->planeheight[1] : s->planeheight[0];
161
162		✗	for (nsteps_max = 1; nsteps_max < 15; nsteps_max++) {
163		✗	if (pow(2, nsteps_max) >= nsteps_width || pow(2, nsteps_max) >= nsteps_height)
164			break;
165			}
166
167		✗	s->nsteps = FFMIN(s->nsteps, nsteps_max - 2);
168
169		✗	for (p = 0; p < 4; p++) {
170		✗	s->hlowsize[p][0] = (s->planewidth[p] + 1) >> 1;
171		✗	s->hhighsize[p][0] = s->planewidth[p] >> 1;
172		✗	s->vlowsize[p][0] = (s->planeheight[p] + 1) >> 1;
173		✗	s->vhighsize[p][0] = s->planeheight[p] >> 1;
174
175		✗	for (i = 1; i < s->nsteps; i++) {
176		✗	s->hlowsize[p][i] = (s->hlowsize[p][i - 1] + 1) >> 1;
177		✗	s->hhighsize[p][i] = s->hlowsize[p][i - 1] >> 1;
178		✗	s->vlowsize[p][i] = (s->vlowsize[p][i - 1] + 1) >> 1;
179		✗	s->vhighsize[p][i] = s->vlowsize[p][i - 1] >> 1;
180			}
181			}
182
183		✗	return 0;
184			}
185
186		✗	static inline void copy(const float *p1, float *p2, const int length)
187			{
188		✗	memcpy(p2, p1, length * sizeof(float));
189		✗	}
190
191		✗	static inline void copyv(const float *p1, const int stride1, float *p2, const int length)
192			{
193			int i;
194
195		✗	for (i = 0; i < length; i++) {
196		✗	p2[i] = *p1;
197		✗	p1 += stride1;
198			}
199		✗	}
200
201		✗	static inline void copyh(const float *p1, float *p2, const int stride2, const int length)
202			{
203			int i;
204
205		✗	for (i = 0; i < length; i++) {
206		✗	*p2 = p1[i];
207		✗	p2 += stride2;
208			}
209		✗	}
210
211			// Do symmetric extension of data using prescribed symmetries
212			// Original values are in output[npad] through output[npad+size-1]
213			// 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)
214			// extension at left bdry is ... 3 2 1 0 | 0 1 2 3 ...
215			// same for right boundary
216			// if right_ext=1 then ... 3 2 1 0 | 1 2 3
217		✗	static void symmetric_extension(float *output, const int size, const int left_ext, const int right_ext)
218			{
219		✗	int first = NPAD;
220		✗	int last = NPAD - 1 + size;
221		✗	const int originalLast = last;
222			int i, nextend, idx;
223
224		✗	if (left_ext == 2)
225		✗	output[--first] = output[NPAD];
226		✗	if (right_ext == 2)
227		✗	output[++last] = output[originalLast];
228
229			// extend left end
230		✗	nextend = first;
231		✗	for (i = 0; i < nextend; i++)
232		✗	output[--first] = output[NPAD + 1 + i];
233
234		✗	idx = NPAD + NPAD - 1 + size;
235
236			// extend right end
237		✗	nextend = idx - last;
238		✗	for (i = 0; i < nextend; i++)
239		✗	output[++last] = output[originalLast - 1 - i];
240		✗	}
241
242		✗	static void transform_step(float *input, float *output, const int size, const int low_size, VagueDenoiserContext *s)
243			{
244			int i;
245
246		✗	symmetric_extension(input, size, 1, 1);
247
248		✗	for (i = NPAD; i < NPAD + low_size; i++) {
249		✗	const float a = input[2 * i - 14] * analysis_low[0];
250		✗	const float b = input[2 * i - 13] * analysis_low[1];
251		✗	const float c = input[2 * i - 12] * analysis_low[2];
252		✗	const float d = input[2 * i - 11] * analysis_low[3];
253		✗	const float e = input[2 * i - 10] * analysis_low[4];
254		✗	const float f = input[2 * i - 9] * analysis_low[3];
255		✗	const float g = input[2 * i - 8] * analysis_low[2];
256		✗	const float h = input[2 * i - 7] * analysis_low[1];
257		✗	const float k = input[2 * i - 6] * analysis_low[0];
258
259		✗	output[i] = a + b + c + d + e + f + g + h + k;
260			}
261
262		✗	for (i = NPAD; i < NPAD + low_size; i++) {
263		✗	const float a = input[2 * i - 12] * analysis_high[0];
264		✗	const float b = input[2 * i - 11] * analysis_high[1];
265		✗	const float c = input[2 * i - 10] * analysis_high[2];
266		✗	const float d = input[2 * i - 9] * analysis_high[3];
267		✗	const float e = input[2 * i - 8] * analysis_high[2];
268		✗	const float f = input[2 * i - 7] * analysis_high[1];
269		✗	const float g = input[2 * i - 6] * analysis_high[0];
270
271		✗	output[i + low_size] = a + b + c + d + e + f + g;
272			}
273		✗	}
274
275		✗	static void invert_step(const float *input, float *output, float *temp, const int size, VagueDenoiserContext *s)
276			{
277		✗	const int low_size = (size + 1) >> 1;
278		✗	const int high_size = size >> 1;
279		✗	int left_ext = 1, right_ext, i;
280			int findex;
281
282		✗	memcpy(temp + NPAD, input + NPAD, low_size * sizeof(float));
283
284		✗	right_ext = (size % 2 == 0) ? 2 : 1;
285		✗	symmetric_extension(temp, low_size, left_ext, right_ext);
286
287		✗	memset(output, 0, (NPAD + NPAD + size) * sizeof(float));
288		✗	findex = (size + 2) >> 1;
289
290		✗	for (i = 9; i < findex + 11; i++) {
291		✗	const float a = temp[i] * synthesis_low[0];
292		✗	const float b = temp[i] * synthesis_low[1];
293		✗	const float c = temp[i] * synthesis_low[2];
294		✗	const float d = temp[i] * synthesis_low[3];
295
296		✗	output[2 * i - 13] += a;
297		✗	output[2 * i - 12] += b;
298		✗	output[2 * i - 11] += c;
299		✗	output[2 * i - 10] += d;
300		✗	output[2 * i - 9] += c;
301		✗	output[2 * i - 8] += b;
302		✗	output[2 * i - 7] += a;
303			}
304
305		✗	memcpy(temp + NPAD, input + NPAD + low_size, high_size * sizeof(float));
306
307		✗	left_ext = 2;
308		✗	right_ext = (size % 2 == 0) ? 1 : 2;
309		✗	symmetric_extension(temp, high_size, left_ext, right_ext);
310
311		✗	for (i = 8; i < findex + 11; i++) {
312		✗	const float a = temp[i] * synthesis_high[0];
313		✗	const float b = temp[i] * synthesis_high[1];
314		✗	const float c = temp[i] * synthesis_high[2];
315		✗	const float d = temp[i] * synthesis_high[3];
316		✗	const float e = temp[i] * synthesis_high[4];
317
318		✗	output[2 * i - 13] += a;
319		✗	output[2 * i - 12] += b;
320		✗	output[2 * i - 11] += c;
321		✗	output[2 * i - 10] += d;
322		✗	output[2 * i - 9] += e;
323		✗	output[2 * i - 8] += d;
324		✗	output[2 * i - 7] += c;
325		✗	output[2 * i - 6] += b;
326		✗	output[2 * i - 5] += a;
327			}
328		✗	}
329
330		✗	static void hard_thresholding(float *block, const int width, const int height,
331			const int stride, const float threshold,
332			const float percent)
333			{
334		✗	const float frac = 1.f - percent * 0.01f;
335			int y, x;
336
337		✗	for (y = 0; y < height; y++) {
338		✗	for (x = 0; x < width; x++) {
339		✗	if (FFABS(block[x]) <= threshold)
340		✗	block[x] *= frac;
341			}
342		✗	block += stride;
343			}
344		✗	}
345
346		✗	static void soft_thresholding(float *block, const int width, const int height, const int stride,
347			const float threshold, const float percent)
348			{
349		✗	const float frac = 1.f - percent * 0.01f;
350		✗	const float shift = threshold * 0.01f * percent;
351			int y, x;
352
353		✗	for (y = 0; y < height; y++) {
354		✗	for (x = 0; x < width; x++) {
355		✗	const float temp = FFABS(block[x]);
356		✗	if (temp <= threshold)
357		✗	block[x] *= frac;
358			else
359		✗	block[x] = (block[x] < 0.f ? -1.f : (block[x] > 0.f ? 1.f : 0.f)) * (temp - shift);
360			}
361		✗	block += stride;
362			}
363		✗	}
364
365		✗	static void qian_thresholding(float *block, const int width, const int height,
366			const int stride, const float threshold,
367			const float percent)
368			{
369		✗	const float percent01 = percent * 0.01f;
370		✗	const float tr2 = threshold * threshold * percent01;
371		✗	const float frac = 1.f - percent01;
372			int y, x;
373
374		✗	for (y = 0; y < height; y++) {
375		✗	for (x = 0; x < width; x++) {
376		✗	const float temp = FFABS(block[x]);
377		✗	if (temp <= threshold) {
378		✗	block[x] *= frac;
379			} else {
380		✗	const float tp2 = temp * temp;
381		✗	block[x] *= (tp2 - tr2) / tp2;
382			}
383			}
384		✗	block += stride;
385			}
386		✗	}
387
388		✗	static float bayes_threshold(float *block, const int width, const int height,
389			const int stride, const float threshold)
390			{
391		✗	float mean = 0.f;
392
393		✗	for (int y = 0; y < height; y++) {
394		✗	for (int x = 0; x < width; x++) {
395		✗	mean += block[x] * block[x];
396			}
397		✗	block += stride;
398			}
399
400		✗	mean /= width * height;
401
402		✗	return threshold * threshold / (FFMAX(sqrtf(mean - threshold), FLT_EPSILON));
403			}
404
405		✗	static void filter(VagueDenoiserContext *s, AVFrame *in, AVFrame *out)
406			{
407			int p, y, x, i, j;
408
409		✗	for (p = 0; p < s->nb_planes; p++) {
410		✗	const int height = s->planeheight[p];
411		✗	const int width = s->planewidth[p];
412		✗	const uint8_t *srcp8 = in->data[p];
413		✗	const uint16_t *srcp16 = (const uint16_t *)in->data[p];
414		✗	uint8_t *dstp8 = out->data[p];
415		✗	uint16_t *dstp16 = (uint16_t *)out->data[p];
416		✗	float *output = s->block;
417		✗	int h_low_size0 = width;
418		✗	int v_low_size0 = height;
419		✗	int nsteps_transform = s->nsteps;
420		✗	int nsteps_invert = s->nsteps;
421		✗	const float *input = s->block;
422
423		✗	if (!((1 << p) & s->planes)) {
424		✗	av_image_copy_plane(out->data[p], out->linesize[p], in->data[p], in->linesize[p],
425		✗	s->planewidth[p] * s->bpc, s->planeheight[p]);
426		✗	continue;
427			}
428
429		✗	if (s->depth <= 8) {
430		✗	for (y = 0; y < height; y++) {
431		✗	for (x = 0; x < width; x++)
432		✗	output[x] = srcp8[x];
433		✗	srcp8 += in->linesize[p];
434		✗	output += width;
435			}
436			} else {
437		✗	for (y = 0; y < height; y++) {
438		✗	for (x = 0; x < width; x++)
439		✗	output[x] = srcp16[x];
440		✗	srcp16 += in->linesize[p] / 2;
441		✗	output += width;
442			}
443			}
444
445		✗	while (nsteps_transform--) {
446		✗	int low_size = (h_low_size0 + 1) >> 1;
447		✗	float *input = s->block;
448		✗	for (j = 0; j < v_low_size0; j++) {
449		✗	copy(input, s->in + NPAD, h_low_size0);
450		✗	transform_step(s->in, s->out, h_low_size0, low_size, s);
451		✗	copy(s->out + NPAD, input, h_low_size0);
452		✗	input += width;
453			}
454
455		✗	low_size = (v_low_size0 + 1) >> 1;
456		✗	input = s->block;
457		✗	for (j = 0; j < h_low_size0; j++) {
458		✗	copyv(input, width, s->in + NPAD, v_low_size0);
459		✗	transform_step(s->in, s->out, v_low_size0, low_size, s);
460		✗	copyh(s->out + NPAD, input, width, v_low_size0);
461		✗	input++;
462			}
463
464		✗	h_low_size0 = (h_low_size0 + 1) >> 1;
465		✗	v_low_size0 = (v_low_size0 + 1) >> 1;
466			}
467
468		✗	if (s->type == 0) {
469		✗	s->thresholding(s->block, width, height, width, s->threshold, s->percent);
470			} else {
471		✗	for (int n = 0; n < s->nsteps; n++) {
472			float threshold;
473			float *block;
474
475		✗	if (n == s->nsteps - 1) {
476		✗	threshold = bayes_threshold(s->block, s->hlowsize[p][n], s->vlowsize[p][n], width, s->threshold);
477		✗	s->thresholding(s->block, s->hlowsize[p][n], s->vlowsize[p][n], width, threshold, s->percent);
478			}
479		✗	block = s->block + s->hlowsize[p][n];
480		✗	threshold = bayes_threshold(block, s->hhighsize[p][n], s->vlowsize[p][n], width, s->threshold);
481		✗	s->thresholding(block, s->hhighsize[p][n], s->vlowsize[p][n], width, threshold, s->percent);
482		✗	block = s->block + s->vlowsize[p][n] * width;
483		✗	threshold = bayes_threshold(block, s->hlowsize[p][n], s->vhighsize[p][n], width, s->threshold);
484		✗	s->thresholding(block, s->hlowsize[p][n], s->vhighsize[p][n], width, threshold, s->percent);
485		✗	block = s->block + s->hlowsize[p][n] + s->vlowsize[p][n] * width;
486		✗	threshold = bayes_threshold(block, s->hhighsize[p][n], s->vhighsize[p][n], width, s->threshold);
487		✗	s->thresholding(block, s->hhighsize[p][n], s->vhighsize[p][n], width, threshold, s->percent);
488			}
489			}
490
491		✗	while (nsteps_invert--) {
492		✗	const int idx = s->vlowsize[p][nsteps_invert] + s->vhighsize[p][nsteps_invert];
493		✗	const int idx2 = s->hlowsize[p][nsteps_invert] + s->hhighsize[p][nsteps_invert];
494		✗	float * idx3 = s->block;
495		✗	for (i = 0; i < idx2; i++) {
496		✗	copyv(idx3, width, s->in + NPAD, idx);
497		✗	invert_step(s->in, s->out, s->tmp, idx, s);
498		✗	copyh(s->out + NPAD, idx3, width, idx);
499		✗	idx3++;
500			}
501
502		✗	idx3 = s->block;
503		✗	for (i = 0; i < idx; i++) {
504		✗	copy(idx3, s->in + NPAD, idx2);
505		✗	invert_step(s->in, s->out, s->tmp, idx2, s);
506		✗	copy(s->out + NPAD, idx3, idx2);
507		✗	idx3 += width;
508			}
509			}
510
511		✗	if (s->depth <= 8) {
512		✗	for (y = 0; y < height; y++) {
513		✗	for (x = 0; x < width; x++)
514		✗	dstp8[x] = av_clip_uint8(input[x] + 0.5f);
515		✗	input += width;
516		✗	dstp8 += out->linesize[p];
517			}
518			} else {
519		✗	for (y = 0; y < height; y++) {
520		✗	for (x = 0; x < width; x++)
521		✗	dstp16[x] = av_clip(input[x] + 0.5f, 0, s->peak);
522		✗	input += width;
523		✗	dstp16 += out->linesize[p] / 2;
524			}
525			}
526			}
527		✗	}
528
529		✗	static int filter_frame(AVFilterLink *inlink, AVFrame *in)
530			{
531		✗	AVFilterContext *ctx = inlink->dst;
532		✗	VagueDenoiserContext *s = ctx->priv;
533		✗	AVFilterLink *outlink = ctx->outputs[0];
534			AVFrame *out;
535		✗	int direct = av_frame_is_writable(in);
536
537		✗	if (direct) {
538		✗	out = in;
539			} else {
540		✗	out = ff_get_video_buffer(outlink, outlink->w, outlink->h);
541		✗	if (!out) {
542		✗	av_frame_free(&in);
543		✗	return AVERROR(ENOMEM);
544			}
545
546		✗	av_frame_copy_props(out, in);
547			}
548
549		✗	filter(s, in, out);
550
551		✗	if (!direct)
552		✗	av_frame_free(&in);
553
554		✗	return ff_filter_frame(outlink, out);
555			}
556
557		✗	static av_cold int init(AVFilterContext *ctx)
558			{
559		✗	VagueDenoiserContext *s = ctx->priv;
560
561		✗	switch (s->method) {
562		✗	case 0:
563		✗	s->thresholding = hard_thresholding;
564		✗	break;
565		✗	case 1:
566		✗	s->thresholding = soft_thresholding;
567		✗	break;
568		✗	case 2:
569		✗	s->thresholding = qian_thresholding;
570		✗	break;
571			}
572
573		✗	return 0;
574			}
575
576		✗	static av_cold void uninit(AVFilterContext *ctx)
577			{
578		✗	VagueDenoiserContext *s = ctx->priv;
579
580		✗	av_freep(&s->block);
581		✗	av_freep(&s->in);
582		✗	av_freep(&s->out);
583		✗	av_freep(&s->tmp);
584		✗	}
585
586			static const AVFilterPad vaguedenoiser_inputs[] = {
587			{
588			.name = "default",
589			.type = AVMEDIA_TYPE_VIDEO,
590			.config_props = config_input,
591			.filter_frame = filter_frame,
592			},
593			};
594
595
596			const FFFilter ff_vf_vaguedenoiser = {
597			.p.name = "vaguedenoiser",
598			.p.description = NULL_IF_CONFIG_SMALL("Apply a Wavelet based Denoiser."),
599			.p.priv_class = &vaguedenoiser_class,
600			.p.flags = AVFILTER_FLAG_SUPPORT_TIMELINE_GENERIC,
601			.priv_size = sizeof(VagueDenoiserContext),
602			.init = init,
603			.uninit = uninit,
604			FILTER_INPUTS(vaguedenoiser_inputs),
605			FILTER_OUTPUTS(ff_video_default_filterpad),
606			FILTER_PIXFMTS_ARRAY(pix_fmts),
607			};
608