1 /*
2 * Copyright (c) 2012-2013 Oka Motofumi (chikuzen.mo at gmail dot com)
3 * Copyright (c) 2015 Paul B Mahol
4 *
5 * This file is part of FFmpeg.
6 *
7 * FFmpeg is free software; you can redistribute it and/or
8 * modify it under the terms of the GNU Lesser General Public
9 * License as published by the Free Software Foundation; either
10 * version 2.1 of the License, or (at your option) any later version.
11 *
12 * FFmpeg is distributed in the hope that it will be useful,
13 * but WITHOUT ANY WARRANTY; without even the implied warranty of
14 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
15 * Lesser General Public License for more details.
16 *
17 * You should have received a copy of the GNU Lesser General Public
18 * License along with FFmpeg; if not, write to the Free Software
19 * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
20 */
21
22 #include "config_components.h"
23
24 #include "libavutil/avstring.h"
25 #include "libavutil/imgutils.h"
26 #include "libavutil/intreadwrite.h"
27 #include "libavutil/mem_internal.h"
28 #include "libavutil/opt.h"
29 #include "libavutil/pixdesc.h"
30 #include "avfilter.h"
31 #include "convolution.h"
32 #include "formats.h"
33 #include "internal.h"
34 #include "video.h"
35
36 #define OFFSET(x) offsetof(ConvolutionContext, x)
37 #define FLAGS AV_OPT_FLAG_VIDEO_PARAM|AV_OPT_FLAG_FILTERING_PARAM|AV_OPT_FLAG_RUNTIME_PARAM
38
39 static const AVOption convolution_options[] = {
40 { "0m", "set matrix for 1st plane", OFFSET(matrix_str[0]), AV_OPT_TYPE_STRING, {.str="0 0 0 0 1 0 0 0 0"}, 0, 0, FLAGS },
41 { "1m", "set matrix for 2nd plane", OFFSET(matrix_str[1]), AV_OPT_TYPE_STRING, {.str="0 0 0 0 1 0 0 0 0"}, 0, 0, FLAGS },
42 { "2m", "set matrix for 3rd plane", OFFSET(matrix_str[2]), AV_OPT_TYPE_STRING, {.str="0 0 0 0 1 0 0 0 0"}, 0, 0, FLAGS },
43 { "3m", "set matrix for 4th plane", OFFSET(matrix_str[3]), AV_OPT_TYPE_STRING, {.str="0 0 0 0 1 0 0 0 0"}, 0, 0, FLAGS },
44 { "0rdiv", "set rdiv for 1st plane", OFFSET(rdiv[0]), AV_OPT_TYPE_FLOAT, {.dbl=0.0}, 0.0, INT_MAX, FLAGS},
45 { "1rdiv", "set rdiv for 2nd plane", OFFSET(rdiv[1]), AV_OPT_TYPE_FLOAT, {.dbl=0.0}, 0.0, INT_MAX, FLAGS},
46 { "2rdiv", "set rdiv for 3rd plane", OFFSET(rdiv[2]), AV_OPT_TYPE_FLOAT, {.dbl=0.0}, 0.0, INT_MAX, FLAGS},
47 { "3rdiv", "set rdiv for 4th plane", OFFSET(rdiv[3]), AV_OPT_TYPE_FLOAT, {.dbl=0.0}, 0.0, INT_MAX, FLAGS},
48 { "0bias", "set bias for 1st plane", OFFSET(bias[0]), AV_OPT_TYPE_FLOAT, {.dbl=0.0}, 0.0, INT_MAX, FLAGS},
49 { "1bias", "set bias for 2nd plane", OFFSET(bias[1]), AV_OPT_TYPE_FLOAT, {.dbl=0.0}, 0.0, INT_MAX, FLAGS},
50 { "2bias", "set bias for 3rd plane", OFFSET(bias[2]), AV_OPT_TYPE_FLOAT, {.dbl=0.0}, 0.0, INT_MAX, FLAGS},
51 { "3bias", "set bias for 4th plane", OFFSET(bias[3]), AV_OPT_TYPE_FLOAT, {.dbl=0.0}, 0.0, INT_MAX, FLAGS},
52 { "0mode", "set matrix mode for 1st plane", OFFSET(mode[0]), AV_OPT_TYPE_INT, {.i64=MATRIX_SQUARE}, 0, MATRIX_NBMODES-1, FLAGS, "mode" },
53 { "1mode", "set matrix mode for 2nd plane", OFFSET(mode[1]), AV_OPT_TYPE_INT, {.i64=MATRIX_SQUARE}, 0, MATRIX_NBMODES-1, FLAGS, "mode" },
54 { "2mode", "set matrix mode for 3rd plane", OFFSET(mode[2]), AV_OPT_TYPE_INT, {.i64=MATRIX_SQUARE}, 0, MATRIX_NBMODES-1, FLAGS, "mode" },
55 { "3mode", "set matrix mode for 4th plane", OFFSET(mode[3]), AV_OPT_TYPE_INT, {.i64=MATRIX_SQUARE}, 0, MATRIX_NBMODES-1, FLAGS, "mode" },
56 { "square", "square matrix", 0, AV_OPT_TYPE_CONST, {.i64=MATRIX_SQUARE}, 0, 0, FLAGS, "mode" },
57 { "row", "single row matrix", 0, AV_OPT_TYPE_CONST, {.i64=MATRIX_ROW} , 0, 0, FLAGS, "mode" },
58 { "column", "single column matrix", 0, AV_OPT_TYPE_CONST, {.i64=MATRIX_COLUMN}, 0, 0, FLAGS, "mode" },
59 { NULL }
60 };
61
62 AVFILTER_DEFINE_CLASS(convolution);
63
64 static const int same3x3[9] = {0, 0, 0,
65 0, 1, 0,
66 0, 0, 0};
67
68 static const int same5x5[25] = {0, 0, 0, 0, 0,
69 0, 0, 0, 0, 0,
70 0, 0, 1, 0, 0,
71 0, 0, 0, 0, 0,
72 0, 0, 0, 0, 0};
73
74 static const int same7x7[49] = {0, 0, 0, 0, 0, 0, 0,
75 0, 0, 0, 0, 0, 0, 0,
76 0, 0, 0, 0, 0, 0, 0,
77 0, 0, 0, 1, 0, 0, 0,
78 0, 0, 0, 0, 0, 0, 0,
79 0, 0, 0, 0, 0, 0, 0,
80 0, 0, 0, 0, 0, 0, 0};
81
82 static const enum AVPixelFormat pix_fmts[] = {
83 AV_PIX_FMT_YUVA444P, AV_PIX_FMT_YUV444P, AV_PIX_FMT_YUV440P,
84 AV_PIX_FMT_YUVJ444P, AV_PIX_FMT_YUVJ440P,
85 AV_PIX_FMT_YUVA422P, AV_PIX_FMT_YUV422P, AV_PIX_FMT_YUVA420P, AV_PIX_FMT_YUV420P,
86 AV_PIX_FMT_YUVJ422P, AV_PIX_FMT_YUVJ420P,
87 AV_PIX_FMT_YUVJ411P, AV_PIX_FMT_YUV411P, AV_PIX_FMT_YUV410P,
88 AV_PIX_FMT_YUV420P9, AV_PIX_FMT_YUV422P9, AV_PIX_FMT_YUV444P9,
89 AV_PIX_FMT_YUV420P10, AV_PIX_FMT_YUV422P10, AV_PIX_FMT_YUV444P10,
90 AV_PIX_FMT_YUV420P12, AV_PIX_FMT_YUV422P12, AV_PIX_FMT_YUV444P12, AV_PIX_FMT_YUV440P12,
91 AV_PIX_FMT_YUV420P14, AV_PIX_FMT_YUV422P14, AV_PIX_FMT_YUV444P14,
92 AV_PIX_FMT_YUV420P16, AV_PIX_FMT_YUV422P16, AV_PIX_FMT_YUV444P16,
93 AV_PIX_FMT_YUVA420P9, AV_PIX_FMT_YUVA422P9, AV_PIX_FMT_YUVA444P9,
94 AV_PIX_FMT_YUVA420P10, AV_PIX_FMT_YUVA422P10, AV_PIX_FMT_YUVA444P10,
95 AV_PIX_FMT_YUVA422P12, AV_PIX_FMT_YUVA444P12,
96 AV_PIX_FMT_YUVA420P16, AV_PIX_FMT_YUVA422P16, AV_PIX_FMT_YUVA444P16,
97 AV_PIX_FMT_GBRP, AV_PIX_FMT_GBRP9, AV_PIX_FMT_GBRP10,
98 AV_PIX_FMT_GBRP12, AV_PIX_FMT_GBRP14, AV_PIX_FMT_GBRP16,
99 AV_PIX_FMT_GBRAP, AV_PIX_FMT_GBRAP10, AV_PIX_FMT_GBRAP12, AV_PIX_FMT_GBRAP16,
100 AV_PIX_FMT_GRAY8, AV_PIX_FMT_GRAY9, AV_PIX_FMT_GRAY10, AV_PIX_FMT_GRAY12, AV_PIX_FMT_GRAY14, AV_PIX_FMT_GRAY16,
101 AV_PIX_FMT_NONE
102 };
103
104 typedef struct ThreadData {
105 AVFrame *in, *out;
106 } ThreadData;
107
filter16_prewitt(uint8_t * dstp,int width,float scale,float delta,const int * const matrix,const uint8_t * c[],int peak,int radius,int dstride,int stride,int size)108 static void filter16_prewitt(uint8_t *dstp, int width,
109 float scale, float delta, const int *const matrix,
110 const uint8_t *c[], int peak, int radius,
111 int dstride, int stride, int size)
112 {
113 uint16_t *dst = (uint16_t *)dstp;
114 int x;
115
116 for (x = 0; x < width; x++) {
117 float suma = AV_RN16A(&c[0][2 * x]) * -1 + AV_RN16A(&c[1][2 * x]) * -1 + AV_RN16A(&c[2][2 * x]) * -1 +
118 AV_RN16A(&c[6][2 * x]) * 1 + AV_RN16A(&c[7][2 * x]) * 1 + AV_RN16A(&c[8][2 * x]) * 1;
119 float sumb = AV_RN16A(&c[0][2 * x]) * -1 + AV_RN16A(&c[2][2 * x]) * 1 + AV_RN16A(&c[3][2 * x]) * -1 +
120 AV_RN16A(&c[5][2 * x]) * 1 + AV_RN16A(&c[6][2 * x]) * -1 + AV_RN16A(&c[8][2 * x]) * 1;
121
122 dst[x] = av_clip(sqrtf(suma*suma + sumb*sumb) * scale + delta, 0, peak);
123 }
124 }
125
filter16_roberts(uint8_t * dstp,int width,float scale,float delta,const int * const matrix,const uint8_t * c[],int peak,int radius,int dstride,int stride,int size)126 static void filter16_roberts(uint8_t *dstp, int width,
127 float scale, float delta, const int *const matrix,
128 const uint8_t *c[], int peak, int radius,
129 int dstride, int stride, int size)
130 {
131 uint16_t *dst = (uint16_t *)dstp;
132 int x;
133
134 for (x = 0; x < width; x++) {
135 float suma = AV_RN16A(&c[0][2 * x]) * 1 + AV_RN16A(&c[1][2 * x]) * -1;
136 float sumb = AV_RN16A(&c[4][2 * x]) * 1 + AV_RN16A(&c[3][2 * x]) * -1;
137
138 dst[x] = av_clip(sqrtf(suma*suma + sumb*sumb) * scale + delta, 0, peak);
139 }
140 }
141
filter16_sobel(uint8_t * dstp,int width,float scale,float delta,const int * const matrix,const uint8_t * c[],int peak,int radius,int dstride,int stride,int size)142 static void filter16_sobel(uint8_t *dstp, int width,
143 float scale, float delta, const int *const matrix,
144 const uint8_t *c[], int peak, int radius,
145 int dstride, int stride, int size)
146 {
147 uint16_t *dst = (uint16_t *)dstp;
148 int x;
149
150 for (x = 0; x < width; x++) {
151 float suma = AV_RN16A(&c[0][2 * x]) * -1 + AV_RN16A(&c[1][2 * x]) * -2 + AV_RN16A(&c[2][2 * x]) * -1 +
152 AV_RN16A(&c[6][2 * x]) * 1 + AV_RN16A(&c[7][2 * x]) * 2 + AV_RN16A(&c[8][2 * x]) * 1;
153 float sumb = AV_RN16A(&c[0][2 * x]) * -1 + AV_RN16A(&c[2][2 * x]) * 1 + AV_RN16A(&c[3][2 * x]) * -2 +
154 AV_RN16A(&c[5][2 * x]) * 2 + AV_RN16A(&c[6][2 * x]) * -1 + AV_RN16A(&c[8][2 * x]) * 1;
155
156 dst[x] = av_clip(sqrtf(suma*suma + sumb*sumb) * scale + delta, 0, peak);
157 }
158 }
159
filter16_scharr(uint8_t * dstp,int width,float scale,float delta,const int * const matrix,const uint8_t * c[],int peak,int radius,int dstride,int stride,int size)160 static void filter16_scharr(uint8_t *dstp, int width,
161 float scale, float delta, const int *const matrix,
162 const uint8_t *c[], int peak, int radius,
163 int dstride, int stride, int size)
164 {
165 uint16_t *dst = (uint16_t *)dstp;
166 int x;
167
168 for (x = 0; x < width; x++) {
169 float suma = AV_RN16A(&c[0][2 * x]) * -47 + AV_RN16A(&c[1][2 * x]) * -162 + AV_RN16A(&c[2][2 * x]) * -47 +
170 AV_RN16A(&c[6][2 * x]) * 47 + AV_RN16A(&c[7][2 * x]) * 162 + AV_RN16A(&c[8][2 * x]) * 47;
171 float sumb = AV_RN16A(&c[0][2 * x]) * -47 + AV_RN16A(&c[2][2 * x]) * 47 + AV_RN16A(&c[3][2 * x]) * -162 +
172 AV_RN16A(&c[5][2 * x]) * 162 + AV_RN16A(&c[6][2 * x]) * -47 + AV_RN16A(&c[8][2 * x]) * 47;
173
174 suma /= 256.f;
175 sumb /= 256.f;
176 dst[x] = av_clip(sqrtf(suma*suma + sumb*sumb) * scale + delta, 0, peak);
177 }
178 }
179
filter16_kirsch(uint8_t * dstp,int width,float scale,float delta,const int * const matrix,const uint8_t * c[],int peak,int radius,int dstride,int stride,int size)180 static void filter16_kirsch(uint8_t *dstp, int width,
181 float scale, float delta, const int *const matrix,
182 const uint8_t *c[], int peak, int radius,
183 int dstride, int stride, int size)
184 {
185 uint16_t *dst = (uint16_t *)dstp;
186 const uint16_t *c0 = (const uint16_t *)c[0], *c1 = (const uint16_t *)c[1], *c2 = (const uint16_t *)c[2];
187 const uint16_t *c3 = (const uint16_t *)c[3], *c5 = (const uint16_t *)c[5];
188 const uint16_t *c6 = (const uint16_t *)c[6], *c7 = (const uint16_t *)c[7], *c8 = (const uint16_t *)c[8];
189 int x;
190
191 for (x = 0; x < width; x++) {
192 int sum0 = c0[x] * 5 + c1[x] * 5 + c2[x] * 5 +
193 c3[x] * -3 + c5[x] * -3 +
194 c6[x] * -3 + c7[x] * -3 + c8[x] * -3;
195 int sum1 = c0[x] * -3 + c1[x] * 5 + c2[x] * 5 +
196 c3[x] * 5 + c5[x] * -3 +
197 c6[x] * -3 + c7[x] * -3 + c8[x] * -3;
198 int sum2 = c0[x] * -3 + c1[x] * -3 + c2[x] * 5 +
199 c3[x] * 5 + c5[x] * 5 +
200 c6[x] * -3 + c7[x] * -3 + c8[x] * -3;
201 int sum3 = c0[x] * -3 + c1[x] * -3 + c2[x] * -3 +
202 c3[x] * 5 + c5[x] * 5 +
203 c6[x] * 5 + c7[x] * -3 + c8[x] * -3;
204 int sum4 = c0[x] * -3 + c1[x] * -3 + c2[x] * -3 +
205 c3[x] * -3 + c5[x] * 5 +
206 c6[x] * 5 + c7[x] * 5 + c8[x] * -3;
207 int sum5 = c0[x] * -3 + c1[x] * -3 + c2[x] * -3 +
208 c3[x] * -3 + c5[x] * -3 +
209 c6[x] * 5 + c7[x] * 5 + c8[x] * 5;
210 int sum6 = c0[x] * 5 + c1[x] * -3 + c2[x] * -3 +
211 c3[x] * -3 + c5[x] * -3 +
212 c6[x] * -3 + c7[x] * 5 + c8[x] * 5;
213 int sum7 = c0[x] * 5 + c1[x] * 5 + c2[x] * -3 +
214 c3[x] * -3 + c5[x] * -3 +
215 c6[x] * -3 + c7[x] * -3 + c8[x] * 5;
216
217 sum0 = FFMAX(sum0, sum1);
218 sum2 = FFMAX(sum2, sum3);
219 sum4 = FFMAX(sum4, sum5);
220 sum6 = FFMAX(sum6, sum7);
221 sum0 = FFMAX(sum0, sum2);
222 sum4 = FFMAX(sum4, sum6);
223 sum0 = FFMAX(sum0, sum4);
224
225 dst[x] = av_clip(FFABS(sum0) * scale + delta, 0, peak);
226 }
227 }
228
filter_prewitt(uint8_t * dst,int width,float scale,float delta,const int * const matrix,const uint8_t * c[],int peak,int radius,int dstride,int stride,int size)229 static void filter_prewitt(uint8_t *dst, int width,
230 float scale, float delta, const int *const matrix,
231 const uint8_t *c[], int peak, int radius,
232 int dstride, int stride, int size)
233 {
234 const uint8_t *c0 = c[0], *c1 = c[1], *c2 = c[2];
235 const uint8_t *c3 = c[3], *c5 = c[5];
236 const uint8_t *c6 = c[6], *c7 = c[7], *c8 = c[8];
237 int x;
238
239 for (x = 0; x < width; x++) {
240 float suma = c0[x] * -1 + c1[x] * -1 + c2[x] * -1 +
241 c6[x] * 1 + c7[x] * 1 + c8[x] * 1;
242 float sumb = c0[x] * -1 + c2[x] * 1 + c3[x] * -1 +
243 c5[x] * 1 + c6[x] * -1 + c8[x] * 1;
244
245 dst[x] = av_clip_uint8(sqrtf(suma*suma + sumb*sumb) * scale + delta);
246 }
247 }
248
filter_roberts(uint8_t * dst,int width,float scale,float delta,const int * const matrix,const uint8_t * c[],int peak,int radius,int dstride,int stride,int size)249 static void filter_roberts(uint8_t *dst, int width,
250 float scale, float delta, const int *const matrix,
251 const uint8_t *c[], int peak, int radius,
252 int dstride, int stride, int size)
253 {
254 int x;
255
256 for (x = 0; x < width; x++) {
257 float suma = c[0][x] * 1 + c[1][x] * -1;
258 float sumb = c[4][x] * 1 + c[3][x] * -1;
259
260 dst[x] = av_clip_uint8(sqrtf(suma*suma + sumb*sumb) * scale + delta);
261 }
262 }
263
filter_sobel(uint8_t * dst,int width,float scale,float delta,const int * const matrix,const uint8_t * c[],int peak,int radius,int dstride,int stride,int size)264 static void filter_sobel(uint8_t *dst, int width,
265 float scale, float delta, const int *const matrix,
266 const uint8_t *c[], int peak, int radius,
267 int dstride, int stride, int size)
268 {
269 const uint8_t *c0 = c[0], *c1 = c[1], *c2 = c[2];
270 const uint8_t *c3 = c[3], *c5 = c[5];
271 const uint8_t *c6 = c[6], *c7 = c[7], *c8 = c[8];
272 int x;
273
274 for (x = 0; x < width; x++) {
275 float suma = c0[x] * -1 + c1[x] * -2 + c2[x] * -1 +
276 c6[x] * 1 + c7[x] * 2 + c8[x] * 1;
277 float sumb = c0[x] * -1 + c2[x] * 1 + c3[x] * -2 +
278 c5[x] * 2 + c6[x] * -1 + c8[x] * 1;
279
280 dst[x] = av_clip_uint8(sqrtf(suma*suma + sumb*sumb) * scale + delta);
281 }
282 }
283
filter_scharr(uint8_t * dst,int width,float scale,float delta,const int * const matrix,const uint8_t * c[],int peak,int radius,int dstride,int stride,int size)284 static void filter_scharr(uint8_t *dst, int width,
285 float scale, float delta, const int *const matrix,
286 const uint8_t *c[], int peak, int radius,
287 int dstride, int stride, int size)
288 {
289 const uint8_t *c0 = c[0], *c1 = c[1], *c2 = c[2];
290 const uint8_t *c3 = c[3], *c5 = c[5];
291 const uint8_t *c6 = c[6], *c7 = c[7], *c8 = c[8];
292 int x;
293
294 for (x = 0; x < width; x++) {
295 float suma = c0[x] * -47 + c1[x] * -162 + c2[x] * -47 +
296 c6[x] * 47 + c7[x] * 162 + c8[x] * 47;
297 float sumb = c0[x] * -47 + c2[x] * 47 + c3[x] * -162 +
298 c5[x] * 162 + c6[x] * -47 + c8[x] * 47;
299
300 suma /= 256.f;
301 sumb /= 256.f;
302 dst[x] = av_clip_uint8(sqrtf(suma*suma + sumb*sumb) * scale + delta);
303 }
304 }
305
filter_kirsch(uint8_t * dst,int width,float scale,float delta,const int * const matrix,const uint8_t * c[],int peak,int radius,int dstride,int stride,int size)306 static void filter_kirsch(uint8_t *dst, int width,
307 float scale, float delta, const int *const matrix,
308 const uint8_t *c[], int peak, int radius,
309 int dstride, int stride, int size)
310 {
311 const uint8_t *c0 = c[0], *c1 = c[1], *c2 = c[2];
312 const uint8_t *c3 = c[3], *c5 = c[5];
313 const uint8_t *c6 = c[6], *c7 = c[7], *c8 = c[8];
314 int x;
315
316 for (x = 0; x < width; x++) {
317 int sum0 = c0[x] * 5 + c1[x] * 5 + c2[x] * 5 +
318 c3[x] * -3 + c5[x] * -3 +
319 c6[x] * -3 + c7[x] * -3 + c8[x] * -3;
320 int sum1 = c0[x] * -3 + c1[x] * 5 + c2[x] * 5 +
321 c3[x] * 5 + c5[x] * -3 +
322 c6[x] * -3 + c7[x] * -3 + c8[x] * -3;
323 int sum2 = c0[x] * -3 + c1[x] * -3 + c2[x] * 5 +
324 c3[x] * 5 + c5[x] * 5 +
325 c6[x] * -3 + c7[x] * -3 + c8[x] * -3;
326 int sum3 = c0[x] * -3 + c1[x] * -3 + c2[x] * -3 +
327 c3[x] * 5 + c5[x] * 5 +
328 c6[x] * 5 + c7[x] * -3 + c8[x] * -3;
329 int sum4 = c0[x] * -3 + c1[x] * -3 + c2[x] * -3 +
330 c3[x] * -3 + c5[x] * 5 +
331 c6[x] * 5 + c7[x] * 5 + c8[x] * -3;
332 int sum5 = c0[x] * -3 + c1[x] * -3 + c2[x] * -3 +
333 c3[x] * -3 + c5[x] * -3 +
334 c6[x] * 5 + c7[x] * 5 + c8[x] * 5;
335 int sum6 = c0[x] * 5 + c1[x] * -3 + c2[x] * -3 +
336 c3[x] * -3 + c5[x] * -3 +
337 c6[x] * -3 + c7[x] * 5 + c8[x] * 5;
338 int sum7 = c0[x] * 5 + c1[x] * 5 + c2[x] * -3 +
339 c3[x] * -3 + c5[x] * -3 +
340 c6[x] * -3 + c7[x] * -3 + c8[x] * 5;
341
342 sum0 = FFMAX(sum0, sum1);
343 sum2 = FFMAX(sum2, sum3);
344 sum4 = FFMAX(sum4, sum5);
345 sum6 = FFMAX(sum6, sum7);
346 sum0 = FFMAX(sum0, sum2);
347 sum4 = FFMAX(sum4, sum6);
348 sum0 = FFMAX(sum0, sum4);
349
350 dst[x] = av_clip_uint8(FFABS(sum0) * scale + delta);
351 }
352 }
353
filter16_3x3(uint8_t * dstp,int width,float rdiv,float bias,const int * const matrix,const uint8_t * c[],int peak,int radius,int dstride,int stride,int size)354 static void filter16_3x3(uint8_t *dstp, int width,
355 float rdiv, float bias, const int *const matrix,
356 const uint8_t *c[], int peak, int radius,
357 int dstride, int stride, int size)
358 {
359 uint16_t *dst = (uint16_t *)dstp;
360 int x;
361
362 for (x = 0; x < width; x++) {
363 int sum = AV_RN16A(&c[0][2 * x]) * matrix[0] +
364 AV_RN16A(&c[1][2 * x]) * matrix[1] +
365 AV_RN16A(&c[2][2 * x]) * matrix[2] +
366 AV_RN16A(&c[3][2 * x]) * matrix[3] +
367 AV_RN16A(&c[4][2 * x]) * matrix[4] +
368 AV_RN16A(&c[5][2 * x]) * matrix[5] +
369 AV_RN16A(&c[6][2 * x]) * matrix[6] +
370 AV_RN16A(&c[7][2 * x]) * matrix[7] +
371 AV_RN16A(&c[8][2 * x]) * matrix[8];
372 sum = (int)(sum * rdiv + bias + 0.5f);
373 dst[x] = av_clip(sum, 0, peak);
374 }
375 }
376
filter16_5x5(uint8_t * dstp,int width,float rdiv,float bias,const int * const matrix,const uint8_t * c[],int peak,int radius,int dstride,int stride,int size)377 static void filter16_5x5(uint8_t *dstp, int width,
378 float rdiv, float bias, const int *const matrix,
379 const uint8_t *c[], int peak, int radius,
380 int dstride, int stride, int size)
381 {
382 uint16_t *dst = (uint16_t *)dstp;
383 int x;
384
385 for (x = 0; x < width; x++) {
386 int i, sum = 0;
387
388 for (i = 0; i < 25; i++)
389 sum += AV_RN16A(&c[i][2 * x]) * matrix[i];
390
391 sum = (int)(sum * rdiv + bias + 0.5f);
392 dst[x] = av_clip(sum, 0, peak);
393 }
394 }
395
filter16_7x7(uint8_t * dstp,int width,float rdiv,float bias,const int * const matrix,const uint8_t * c[],int peak,int radius,int dstride,int stride,int size)396 static void filter16_7x7(uint8_t *dstp, int width,
397 float rdiv, float bias, const int *const matrix,
398 const uint8_t *c[], int peak, int radius,
399 int dstride, int stride, int size)
400 {
401 uint16_t *dst = (uint16_t *)dstp;
402 int x;
403
404 for (x = 0; x < width; x++) {
405 int i, sum = 0;
406
407 for (i = 0; i < 49; i++)
408 sum += AV_RN16A(&c[i][2 * x]) * matrix[i];
409
410 sum = (int)(sum * rdiv + bias + 0.5f);
411 dst[x] = av_clip(sum, 0, peak);
412 }
413 }
414
filter16_row(uint8_t * dstp,int width,float rdiv,float bias,const int * const matrix,const uint8_t * c[],int peak,int radius,int dstride,int stride,int size)415 static void filter16_row(uint8_t *dstp, int width,
416 float rdiv, float bias, const int *const matrix,
417 const uint8_t *c[], int peak, int radius,
418 int dstride, int stride, int size)
419 {
420 uint16_t *dst = (uint16_t *)dstp;
421 int x;
422
423 for (x = 0; x < width; x++) {
424 int i, sum = 0;
425
426 for (i = 0; i < 2 * radius + 1; i++)
427 sum += AV_RN16A(&c[i][2 * x]) * matrix[i];
428
429 sum = (int)(sum * rdiv + bias + 0.5f);
430 dst[x] = av_clip(sum, 0, peak);
431 }
432 }
433
filter16_column(uint8_t * dstp,int height,float rdiv,float bias,const int * const matrix,const uint8_t * c[],int peak,int radius,int dstride,int stride,int size)434 static void filter16_column(uint8_t *dstp, int height,
435 float rdiv, float bias, const int *const matrix,
436 const uint8_t *c[], int peak, int radius,
437 int dstride, int stride, int size)
438 {
439 DECLARE_ALIGNED(64, int, sum)[16];
440 uint16_t *dst = (uint16_t *)dstp;
441 const int width = FFMIN(16, size);
442
443 for (int y = 0; y < height; y++) {
444
445 memset(sum, 0, sizeof(sum));
446 for (int i = 0; i < 2 * radius + 1; i++) {
447 for (int off16 = 0; off16 < width; off16++)
448 sum[off16] += AV_RN16A(&c[i][0 + y * stride + off16 * 2]) * matrix[i];
449 }
450
451 for (int off16 = 0; off16 < width; off16++) {
452 sum[off16] = (int)(sum[off16] * rdiv + bias + 0.5f);
453 dst[off16] = av_clip(sum[off16], 0, peak);
454 }
455 dst += dstride / 2;
456 }
457 }
458
filter_7x7(uint8_t * dst,int width,float rdiv,float bias,const int * const matrix,const uint8_t * c[],int peak,int radius,int dstride,int stride,int size)459 static void filter_7x7(uint8_t *dst, int width,
460 float rdiv, float bias, const int *const matrix,
461 const uint8_t *c[], int peak, int radius,
462 int dstride, int stride, int size)
463 {
464 int x;
465
466 for (x = 0; x < width; x++) {
467 int i, sum = 0;
468
469 for (i = 0; i < 49; i++)
470 sum += c[i][x] * matrix[i];
471
472 sum = (int)(sum * rdiv + bias + 0.5f);
473 dst[x] = av_clip_uint8(sum);
474 }
475 }
476
filter_5x5(uint8_t * dst,int width,float rdiv,float bias,const int * const matrix,const uint8_t * c[],int peak,int radius,int dstride,int stride,int size)477 static void filter_5x5(uint8_t *dst, int width,
478 float rdiv, float bias, const int *const matrix,
479 const uint8_t *c[], int peak, int radius,
480 int dstride, int stride, int size)
481 {
482 int x;
483
484 for (x = 0; x < width; x++) {
485 int i, sum = 0;
486
487 for (i = 0; i < 25; i++)
488 sum += c[i][x] * matrix[i];
489
490 sum = (int)(sum * rdiv + bias + 0.5f);
491 dst[x] = av_clip_uint8(sum);
492 }
493 }
494
filter_3x3(uint8_t * dst,int width,float rdiv,float bias,const int * const matrix,const uint8_t * c[],int peak,int radius,int dstride,int stride,int size)495 static void filter_3x3(uint8_t *dst, int width,
496 float rdiv, float bias, const int *const matrix,
497 const uint8_t *c[], int peak, int radius,
498 int dstride, int stride, int size)
499 {
500 const uint8_t *c0 = c[0], *c1 = c[1], *c2 = c[2];
501 const uint8_t *c3 = c[3], *c4 = c[4], *c5 = c[5];
502 const uint8_t *c6 = c[6], *c7 = c[7], *c8 = c[8];
503 int x;
504
505 for (x = 0; x < width; x++) {
506 int sum = c0[x] * matrix[0] + c1[x] * matrix[1] + c2[x] * matrix[2] +
507 c3[x] * matrix[3] + c4[x] * matrix[4] + c5[x] * matrix[5] +
508 c6[x] * matrix[6] + c7[x] * matrix[7] + c8[x] * matrix[8];
509 sum = (int)(sum * rdiv + bias + 0.5f);
510 dst[x] = av_clip_uint8(sum);
511 }
512 }
513
filter_row(uint8_t * dst,int width,float rdiv,float bias,const int * const matrix,const uint8_t * c[],int peak,int radius,int dstride,int stride,int size)514 static void filter_row(uint8_t *dst, int width,
515 float rdiv, float bias, const int *const matrix,
516 const uint8_t *c[], int peak, int radius,
517 int dstride, int stride, int size)
518 {
519 int x;
520
521 for (x = 0; x < width; x++) {
522 int i, sum = 0;
523
524 for (i = 0; i < 2 * radius + 1; i++)
525 sum += c[i][x] * matrix[i];
526
527 sum = (int)(sum * rdiv + bias + 0.5f);
528 dst[x] = av_clip_uint8(sum);
529 }
530 }
531
filter_column(uint8_t * dst,int height,float rdiv,float bias,const int * const matrix,const uint8_t * c[],int peak,int radius,int dstride,int stride,int size)532 static void filter_column(uint8_t *dst, int height,
533 float rdiv, float bias, const int *const matrix,
534 const uint8_t *c[], int peak, int radius,
535 int dstride, int stride, int size)
536 {
537 DECLARE_ALIGNED(64, int, sum)[16];
538
539 for (int y = 0; y < height; y++) {
540 memset(sum, 0, sizeof(sum));
541
542 for (int i = 0; i < 2 * radius + 1; i++) {
543 for (int off16 = 0; off16 < 16; off16++)
544 sum[off16] += c[i][0 + y * stride + off16] * matrix[i];
545 }
546
547 for (int off16 = 0; off16 < 16; off16++) {
548 sum[off16] = (int)(sum[off16] * rdiv + bias + 0.5f);
549 dst[off16] = av_clip_uint8(sum[off16]);
550 }
551 dst += dstride;
552 }
553 }
554
setup_3x3(int radius,const uint8_t * c[],const uint8_t * src,int stride,int x,int w,int y,int h,int bpc)555 static void setup_3x3(int radius, const uint8_t *c[], const uint8_t *src, int stride,
556 int x, int w, int y, int h, int bpc)
557 {
558 int i;
559
560 for (i = 0; i < 9; i++) {
561 int xoff = FFABS(x + ((i % 3) - 1));
562 int yoff = FFABS(y + (i / 3) - 1);
563
564 xoff = xoff >= w ? 2 * w - 1 - xoff : xoff;
565 yoff = yoff >= h ? 2 * h - 1 - yoff : yoff;
566
567 c[i] = src + xoff * bpc + yoff * stride;
568 }
569 }
570
setup_5x5(int radius,const uint8_t * c[],const uint8_t * src,int stride,int x,int w,int y,int h,int bpc)571 static void setup_5x5(int radius, const uint8_t *c[], const uint8_t *src, int stride,
572 int x, int w, int y, int h, int bpc)
573 {
574 int i;
575
576 for (i = 0; i < 25; i++) {
577 int xoff = FFABS(x + ((i % 5) - 2));
578 int yoff = FFABS(y + (i / 5) - 2);
579
580 xoff = xoff >= w ? 2 * w - 1 - xoff : xoff;
581 yoff = yoff >= h ? 2 * h - 1 - yoff : yoff;
582
583 c[i] = src + xoff * bpc + yoff * stride;
584 }
585 }
586
setup_7x7(int radius,const uint8_t * c[],const uint8_t * src,int stride,int x,int w,int y,int h,int bpc)587 static void setup_7x7(int radius, const uint8_t *c[], const uint8_t *src, int stride,
588 int x, int w, int y, int h, int bpc)
589 {
590 int i;
591
592 for (i = 0; i < 49; i++) {
593 int xoff = FFABS(x + ((i % 7) - 3));
594 int yoff = FFABS(y + (i / 7) - 3);
595
596 xoff = xoff >= w ? 2 * w - 1 - xoff : xoff;
597 yoff = yoff >= h ? 2 * h - 1 - yoff : yoff;
598
599 c[i] = src + xoff * bpc + yoff * stride;
600 }
601 }
602
setup_row(int radius,const uint8_t * c[],const uint8_t * src,int stride,int x,int w,int y,int h,int bpc)603 static void setup_row(int radius, const uint8_t *c[], const uint8_t *src, int stride,
604 int x, int w, int y, int h, int bpc)
605 {
606 int i;
607
608 for (i = 0; i < radius * 2 + 1; i++) {
609 int xoff = FFABS(x + i - radius);
610
611 xoff = xoff >= w ? 2 * w - 1 - xoff : xoff;
612
613 c[i] = src + xoff * bpc + y * stride;
614 }
615 }
616
setup_column(int radius,const uint8_t * c[],const uint8_t * src,int stride,int x,int w,int y,int h,int bpc)617 static void setup_column(int radius, const uint8_t *c[], const uint8_t *src, int stride,
618 int x, int w, int y, int h, int bpc)
619 {
620 int i;
621
622 for (i = 0; i < radius * 2 + 1; i++) {
623 int xoff = FFABS(x + i - radius);
624
625 xoff = xoff >= h ? 2 * h - 1 - xoff : xoff;
626
627 c[i] = src + y * bpc + xoff * stride;
628 }
629 }
630
filter_slice(AVFilterContext * ctx,void * arg,int jobnr,int nb_jobs)631 static int filter_slice(AVFilterContext *ctx, void *arg, int jobnr, int nb_jobs)
632 {
633 ConvolutionContext *s = ctx->priv;
634 ThreadData *td = arg;
635 AVFrame *in = td->in;
636 AVFrame *out = td->out;
637 int plane;
638
639 for (plane = 0; plane < s->nb_planes; plane++) {
640 const int mode = s->mode[plane];
641 const int bpc = s->bpc;
642 const int radius = s->size[plane] / 2;
643 const int height = s->planeheight[plane];
644 const int width = s->planewidth[plane];
645 const int stride = in->linesize[plane];
646 const int dstride = out->linesize[plane];
647 const int sizeh = mode == MATRIX_COLUMN ? width : height;
648 const int sizew = mode == MATRIX_COLUMN ? height : width;
649 const int slice_start = (sizeh * jobnr) / nb_jobs;
650 const int slice_end = (sizeh * (jobnr+1)) / nb_jobs;
651 const float rdiv = s->rdiv[plane];
652 const float bias = s->bias[plane];
653 const uint8_t *src = in->data[plane];
654 const int dst_pos = slice_start * (mode == MATRIX_COLUMN ? bpc : dstride);
655 uint8_t *dst = out->data[plane] + dst_pos;
656 const int *matrix = s->matrix[plane];
657 const int step = mode == MATRIX_COLUMN ? 16 : 1;
658 const uint8_t *c[49];
659 int y, x;
660
661 if (s->copy[plane]) {
662 if (mode == MATRIX_COLUMN)
663 av_image_copy_plane(dst, dstride, src + slice_start * bpc, stride,
664 (slice_end - slice_start) * bpc, height);
665 else
666 av_image_copy_plane(dst, dstride, src + slice_start * stride, stride,
667 width * bpc, slice_end - slice_start);
668 continue;
669 }
670 for (y = slice_start; y < slice_end; y += step) {
671 const int xoff = mode == MATRIX_COLUMN ? (y - slice_start) * bpc : radius * bpc;
672 const int yoff = mode == MATRIX_COLUMN ? radius * dstride : 0;
673
674 for (x = 0; x < radius; x++) {
675 const int xoff = mode == MATRIX_COLUMN ? (y - slice_start) * bpc : x * bpc;
676 const int yoff = mode == MATRIX_COLUMN ? x * dstride : 0;
677
678 s->setup[plane](radius, c, src, stride, x, width, y, height, bpc);
679 s->filter[plane](dst + yoff + xoff, 1, rdiv,
680 bias, matrix, c, s->max, radius,
681 dstride, stride, slice_end - step);
682 }
683 s->setup[plane](radius, c, src, stride, radius, width, y, height, bpc);
684 s->filter[plane](dst + yoff + xoff, sizew - 2 * radius,
685 rdiv, bias, matrix, c, s->max, radius,
686 dstride, stride, slice_end - step);
687 for (x = sizew - radius; x < sizew; x++) {
688 const int xoff = mode == MATRIX_COLUMN ? (y - slice_start) * bpc : x * bpc;
689 const int yoff = mode == MATRIX_COLUMN ? x * dstride : 0;
690
691 s->setup[plane](radius, c, src, stride, x, width, y, height, bpc);
692 s->filter[plane](dst + yoff + xoff, 1, rdiv,
693 bias, matrix, c, s->max, radius,
694 dstride, stride, slice_end - step);
695 }
696 if (mode != MATRIX_COLUMN)
697 dst += dstride;
698 }
699 }
700
701 return 0;
702 }
703
param_init(AVFilterContext * ctx)704 static int param_init(AVFilterContext *ctx)
705 {
706 ConvolutionContext *s = ctx->priv;
707 AVFilterLink *inlink = ctx->inputs[0];
708 const AVPixFmtDescriptor *desc = av_pix_fmt_desc_get(inlink->format);
709 int p, i;
710
711 if (!strcmp(ctx->filter->name, "convolution")) {
712 for (i = 0; i < 4; i++) {
713 int *matrix = (int *)s->matrix[i];
714 char *orig, *p, *arg, *saveptr = NULL;
715 float sum = 1.f;
716
717 p = orig = av_strdup(s->matrix_str[i]);
718 if (p) {
719 s->matrix_length[i] = 0;
720 s->rdiv[i] = 0.f;
721 sum = 0.f;
722
723 while (s->matrix_length[i] < 49) {
724 if (!(arg = av_strtok(p, " |", &saveptr)))
725 break;
726
727 p = NULL;
728 sscanf(arg, "%d", &matrix[s->matrix_length[i]]);
729 sum += matrix[s->matrix_length[i]];
730 s->matrix_length[i]++;
731 }
732
733 av_freep(&orig);
734 if (!(s->matrix_length[i] & 1)) {
735 av_log(ctx, AV_LOG_ERROR, "number of matrix elements must be odd\n");
736 return AVERROR(EINVAL);
737 }
738 }
739
740 if (s->mode[i] == MATRIX_ROW) {
741 s->filter[i] = filter_row;
742 s->setup[i] = setup_row;
743 s->size[i] = s->matrix_length[i];
744 } else if (s->mode[i] == MATRIX_COLUMN) {
745 s->filter[i] = filter_column;
746 s->setup[i] = setup_column;
747 s->size[i] = s->matrix_length[i];
748 } else if (s->matrix_length[i] == 9) {
749 s->size[i] = 3;
750
751 if (!memcmp(matrix, same3x3, sizeof(same3x3))) {
752 s->copy[i] = 1;
753 } else {
754 s->filter[i] = filter_3x3;
755 s->copy[i] = 0;
756 }
757 s->setup[i] = setup_3x3;
758 } else if (s->matrix_length[i] == 25) {
759 s->size[i] = 5;
760 if (!memcmp(matrix, same5x5, sizeof(same5x5))) {
761 s->copy[i] = 1;
762 } else {
763 s->filter[i] = filter_5x5;
764 s->copy[i] = 0;
765 }
766 s->setup[i] = setup_5x5;
767 } else if (s->matrix_length[i] == 49) {
768 s->size[i] = 7;
769 if (!memcmp(matrix, same7x7, sizeof(same7x7))) {
770 s->copy[i] = 1;
771 } else {
772 s->filter[i] = filter_7x7;
773 s->copy[i] = 0;
774 }
775 s->setup[i] = setup_7x7;
776 } else {
777 return AVERROR(EINVAL);
778 }
779
780 if (sum == 0)
781 sum = 1;
782 if (s->rdiv[i] == 0)
783 s->rdiv[i] = 1. / sum;
784
785 if (s->copy[i] && (s->rdiv[i] != 1. || s->bias[i] != 0.))
786 s->copy[i] = 0;
787 }
788 } else if (!strcmp(ctx->filter->name, "prewitt")) {
789 for (i = 0; i < 4; i++) {
790 s->filter[i] = filter_prewitt;
791 s->copy[i] = !((1 << i) & s->planes);
792 s->size[i] = 3;
793 s->setup[i] = setup_3x3;
794 s->rdiv[i] = s->scale;
795 s->bias[i] = s->delta;
796 }
797 } else if (!strcmp(ctx->filter->name, "roberts")) {
798 for (i = 0; i < 4; i++) {
799 s->filter[i] = filter_roberts;
800 s->copy[i] = !((1 << i) & s->planes);
801 s->size[i] = 3;
802 s->setup[i] = setup_3x3;
803 s->rdiv[i] = s->scale;
804 s->bias[i] = s->delta;
805 }
806 } else if (!strcmp(ctx->filter->name, "sobel")) {
807 for (i = 0; i < 4; i++) {
808 s->filter[i] = filter_sobel;
809 s->copy[i] = !((1 << i) & s->planes);
810 s->size[i] = 3;
811 s->setup[i] = setup_3x3;
812 s->rdiv[i] = s->scale;
813 s->bias[i] = s->delta;
814 }
815 } else if (!strcmp(ctx->filter->name, "kirsch")) {
816 for (i = 0; i < 4; i++) {
817 s->filter[i] = filter_kirsch;
818 s->copy[i] = !((1 << i) & s->planes);
819 s->size[i] = 3;
820 s->setup[i] = setup_3x3;
821 s->rdiv[i] = s->scale;
822 s->bias[i] = s->delta;
823 }
824 } else if (!strcmp(ctx->filter->name, "scharr")) {
825 for (i = 0; i < 4; i++) {
826 s->filter[i] = filter_scharr;
827 s->copy[i] = !((1 << i) & s->planes);
828 s->size[i] = 3;
829 s->setup[i] = setup_3x3;
830 s->rdiv[i] = s->scale;
831 s->bias[i] = s->delta;
832 }
833 }
834
835 s->depth = desc->comp[0].depth;
836 s->max = (1 << s->depth) - 1;
837
838 s->planewidth[1] = s->planewidth[2] = AV_CEIL_RSHIFT(inlink->w, desc->log2_chroma_w);
839 s->planewidth[0] = s->planewidth[3] = inlink->w;
840 s->planeheight[1] = s->planeheight[2] = AV_CEIL_RSHIFT(inlink->h, desc->log2_chroma_h);
841 s->planeheight[0] = s->planeheight[3] = inlink->h;
842
843 s->nb_planes = av_pix_fmt_count_planes(inlink->format);
844 s->nb_threads = ff_filter_get_nb_threads(ctx);
845 s->bpc = (s->depth + 7) / 8;
846
847 if (!strcmp(ctx->filter->name, "convolution")) {
848 if (s->depth > 8) {
849 for (p = 0; p < s->nb_planes; p++) {
850 if (s->mode[p] == MATRIX_ROW)
851 s->filter[p] = filter16_row;
852 else if (s->mode[p] == MATRIX_COLUMN)
853 s->filter[p] = filter16_column;
854 else if (s->size[p] == 3)
855 s->filter[p] = filter16_3x3;
856 else if (s->size[p] == 5)
857 s->filter[p] = filter16_5x5;
858 else if (s->size[p] == 7)
859 s->filter[p] = filter16_7x7;
860 }
861 }
862 #if CONFIG_CONVOLUTION_FILTER && ARCH_X86_64
863 ff_convolution_init_x86(s);
864 #endif
865 } else if (!strcmp(ctx->filter->name, "prewitt")) {
866 if (s->depth > 8)
867 for (p = 0; p < s->nb_planes; p++)
868 s->filter[p] = filter16_prewitt;
869 } else if (!strcmp(ctx->filter->name, "roberts")) {
870 if (s->depth > 8)
871 for (p = 0; p < s->nb_planes; p++)
872 s->filter[p] = filter16_roberts;
873 } else if (!strcmp(ctx->filter->name, "sobel")) {
874 if (s->depth > 8)
875 for (p = 0; p < s->nb_planes; p++)
876 s->filter[p] = filter16_sobel;
877 } else if (!strcmp(ctx->filter->name, "kirsch")) {
878 if (s->depth > 8)
879 for (p = 0; p < s->nb_planes; p++)
880 s->filter[p] = filter16_kirsch;
881 } else if (!strcmp(ctx->filter->name, "scharr")) {
882 if (s->depth > 8)
883 for (p = 0; p < s->nb_planes; p++)
884 s->filter[p] = filter16_scharr;
885 }
886
887 return 0;
888 }
889
config_input(AVFilterLink * inlink)890 static int config_input(AVFilterLink *inlink)
891 {
892 AVFilterContext *ctx = inlink->dst;
893 return param_init(ctx);
894 }
895
filter_frame(AVFilterLink * inlink,AVFrame * in)896 static int filter_frame(AVFilterLink *inlink, AVFrame *in)
897 {
898 AVFilterContext *ctx = inlink->dst;
899 ConvolutionContext *s = ctx->priv;
900 AVFilterLink *outlink = ctx->outputs[0];
901 AVFrame *out;
902 ThreadData td;
903
904 out = ff_get_video_buffer(outlink, outlink->w, outlink->h);
905 if (!out) {
906 av_frame_free(&in);
907 return AVERROR(ENOMEM);
908 }
909 av_frame_copy_props(out, in);
910
911 td.in = in;
912 td.out = out;
913 ff_filter_execute(ctx, filter_slice, &td, NULL,
914 FFMIN3(s->planeheight[1], s->planewidth[1], s->nb_threads));
915
916 av_frame_free(&in);
917 return ff_filter_frame(outlink, out);
918 }
919
process_command(AVFilterContext * ctx,const char * cmd,const char * args,char * res,int res_len,int flags)920 static int process_command(AVFilterContext *ctx, const char *cmd, const char *args,
921 char *res, int res_len, int flags)
922 {
923 int ret;
924
925 ret = ff_filter_process_command(ctx, cmd, args, res, res_len, flags);
926 if (ret < 0)
927 return ret;
928
929 return param_init(ctx);
930 }
931
932 static const AVFilterPad convolution_inputs[] = {
933 {
934 .name = "default",
935 .type = AVMEDIA_TYPE_VIDEO,
936 .config_props = config_input,
937 .filter_frame = filter_frame,
938 },
939 };
940
941 static const AVFilterPad convolution_outputs[] = {
942 {
943 .name = "default",
944 .type = AVMEDIA_TYPE_VIDEO,
945 },
946 };
947
948 #if CONFIG_CONVOLUTION_FILTER
949
950 const AVFilter ff_vf_convolution = {
951 .name = "convolution",
952 .description = NULL_IF_CONFIG_SMALL("Apply convolution filter."),
953 .priv_size = sizeof(ConvolutionContext),
954 .priv_class = &convolution_class,
955 FILTER_INPUTS(convolution_inputs),
956 FILTER_OUTPUTS(convolution_outputs),
957 FILTER_PIXFMTS_ARRAY(pix_fmts),
958 .flags = AVFILTER_FLAG_SUPPORT_TIMELINE_GENERIC | AVFILTER_FLAG_SLICE_THREADS,
959 .process_command = process_command,
960 };
961
962 #endif /* CONFIG_CONVOLUTION_FILTER */
963
964 static const AVOption common_options[] = {
965 { "planes", "set planes to filter", OFFSET(planes), AV_OPT_TYPE_INT, {.i64=15}, 0, 15, FLAGS},
966 { "scale", "set scale", OFFSET(scale), AV_OPT_TYPE_FLOAT, {.dbl=1.0}, 0.0, 65535, FLAGS},
967 { "delta", "set delta", OFFSET(delta), AV_OPT_TYPE_FLOAT, {.dbl=0}, -65535, 65535, FLAGS},
968 { NULL }
969 };
970
971 AVFILTER_DEFINE_CLASS_EXT(common, "kirsch/prewitt/roberts/scharr/sobel",
972 common_options);
973
974 #if CONFIG_PREWITT_FILTER
975
976 const AVFilter ff_vf_prewitt = {
977 .name = "prewitt",
978 .description = NULL_IF_CONFIG_SMALL("Apply prewitt operator."),
979 .priv_size = sizeof(ConvolutionContext),
980 .priv_class = &common_class,
981 FILTER_INPUTS(convolution_inputs),
982 FILTER_OUTPUTS(convolution_outputs),
983 FILTER_PIXFMTS_ARRAY(pix_fmts),
984 .flags = AVFILTER_FLAG_SUPPORT_TIMELINE_GENERIC | AVFILTER_FLAG_SLICE_THREADS,
985 .process_command = process_command,
986 };
987
988 #endif /* CONFIG_PREWITT_FILTER */
989
990 #if CONFIG_SOBEL_FILTER
991
992 const AVFilter ff_vf_sobel = {
993 .name = "sobel",
994 .description = NULL_IF_CONFIG_SMALL("Apply sobel operator."),
995 .priv_size = sizeof(ConvolutionContext),
996 .priv_class = &common_class,
997 FILTER_INPUTS(convolution_inputs),
998 FILTER_OUTPUTS(convolution_outputs),
999 FILTER_PIXFMTS_ARRAY(pix_fmts),
1000 .flags = AVFILTER_FLAG_SUPPORT_TIMELINE_GENERIC | AVFILTER_FLAG_SLICE_THREADS,
1001 .process_command = process_command,
1002 };
1003
1004 #endif /* CONFIG_SOBEL_FILTER */
1005
1006 #if CONFIG_ROBERTS_FILTER
1007
1008 const AVFilter ff_vf_roberts = {
1009 .name = "roberts",
1010 .description = NULL_IF_CONFIG_SMALL("Apply roberts cross operator."),
1011 .priv_size = sizeof(ConvolutionContext),
1012 .priv_class = &common_class,
1013 FILTER_INPUTS(convolution_inputs),
1014 FILTER_OUTPUTS(convolution_outputs),
1015 FILTER_PIXFMTS_ARRAY(pix_fmts),
1016 .flags = AVFILTER_FLAG_SUPPORT_TIMELINE_GENERIC | AVFILTER_FLAG_SLICE_THREADS,
1017 .process_command = process_command,
1018 };
1019
1020 #endif /* CONFIG_ROBERTS_FILTER */
1021
1022 #if CONFIG_KIRSCH_FILTER
1023
1024 const AVFilter ff_vf_kirsch = {
1025 .name = "kirsch",
1026 .description = NULL_IF_CONFIG_SMALL("Apply kirsch operator."),
1027 .priv_size = sizeof(ConvolutionContext),
1028 .priv_class = &common_class,
1029 FILTER_INPUTS(convolution_inputs),
1030 FILTER_OUTPUTS(convolution_outputs),
1031 FILTER_PIXFMTS_ARRAY(pix_fmts),
1032 .flags = AVFILTER_FLAG_SUPPORT_TIMELINE_GENERIC | AVFILTER_FLAG_SLICE_THREADS,
1033 .process_command = process_command,
1034 };
1035
1036 #endif /* CONFIG_KIRSCH_FILTER */
1037
1038 #if CONFIG_SCHARR_FILTER
1039
1040 const AVFilter ff_vf_scharr = {
1041 .name = "scharr",
1042 .description = NULL_IF_CONFIG_SMALL("Apply scharr operator."),
1043 .priv_size = sizeof(ConvolutionContext),
1044 .priv_class = &common_class,
1045 FILTER_INPUTS(convolution_inputs),
1046 FILTER_OUTPUTS(convolution_outputs),
1047 FILTER_PIXFMTS_ARRAY(pix_fmts),
1048 .flags = AVFILTER_FLAG_SUPPORT_TIMELINE_GENERIC | AVFILTER_FLAG_SLICE_THREADS,
1049 .process_command = process_command,
1050 };
1051
1052 #endif /* CONFIG_SCHARR_FILTER */
1053