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42
43 #include "precomp.hpp"
44
45 /****************************************************************************************\
46 * Watershed *
47 \****************************************************************************************/
48
49 namespace cv
50 {
51 // A node represents a pixel to label
52 struct WSNode
53 {
54 int next;
55 int mask_ofs;
56 int img_ofs;
57 };
58
59 // Queue for WSNodes
60 struct WSQueue
61 {
WSQueuecv::WSQueue62 WSQueue() { first = last = 0; }
63 int first, last;
64 };
65
66
67 static int
allocWSNodes(std::vector<WSNode> & storage)68 allocWSNodes( std::vector<WSNode>& storage )
69 {
70 int sz = (int)storage.size();
71 int newsz = MAX(128, sz*3/2);
72
73 storage.resize(newsz);
74 if( sz == 0 )
75 {
76 storage[0].next = 0;
77 sz = 1;
78 }
79 for( int i = sz; i < newsz-1; i++ )
80 storage[i].next = i+1;
81 storage[newsz-1].next = 0;
82 return sz;
83 }
84
85 }
86
87
watershed(InputArray _src,InputOutputArray _markers)88 void cv::watershed( InputArray _src, InputOutputArray _markers )
89 {
90 // Labels for pixels
91 const int IN_QUEUE = -2; // Pixel visited
92 const int WSHED = -1; // Pixel belongs to watershed
93
94 // possible bit values = 2^8
95 const int NQ = 256;
96
97 Mat src = _src.getMat(), dst = _markers.getMat();
98 Size size = src.size();
99
100 // Vector of every created node
101 std::vector<WSNode> storage;
102 int free_node = 0, node;
103 // Priority queue of queues of nodes
104 // from high priority (0) to low priority (255)
105 WSQueue q[NQ];
106 // Non-empty queue with highest priority
107 int active_queue;
108 int i, j;
109 // Color differences
110 int db, dg, dr;
111 int subs_tab[513];
112
113 // MAX(a,b) = b + MAX(a-b,0)
114 #define ws_max(a,b) ((b) + subs_tab[(a)-(b)+NQ])
115 // MIN(a,b) = a - MAX(a-b,0)
116 #define ws_min(a,b) ((a) - subs_tab[(a)-(b)+NQ])
117
118 // Create a new node with offsets mofs and iofs in queue idx
119 #define ws_push(idx,mofs,iofs) \
120 { \
121 if( !free_node ) \
122 free_node = allocWSNodes( storage );\
123 node = free_node; \
124 free_node = storage[free_node].next;\
125 storage[node].next = 0; \
126 storage[node].mask_ofs = mofs; \
127 storage[node].img_ofs = iofs; \
128 if( q[idx].last ) \
129 storage[q[idx].last].next=node; \
130 else \
131 q[idx].first = node; \
132 q[idx].last = node; \
133 }
134
135 // Get next node from queue idx
136 #define ws_pop(idx,mofs,iofs) \
137 { \
138 node = q[idx].first; \
139 q[idx].first = storage[node].next; \
140 if( !storage[node].next ) \
141 q[idx].last = 0; \
142 storage[node].next = free_node; \
143 free_node = node; \
144 mofs = storage[node].mask_ofs; \
145 iofs = storage[node].img_ofs; \
146 }
147
148 // Get highest absolute channel difference in diff
149 #define c_diff(ptr1,ptr2,diff) \
150 { \
151 db = std::abs((ptr1)[0] - (ptr2)[0]);\
152 dg = std::abs((ptr1)[1] - (ptr2)[1]);\
153 dr = std::abs((ptr1)[2] - (ptr2)[2]);\
154 diff = ws_max(db,dg); \
155 diff = ws_max(diff,dr); \
156 assert( 0 <= diff && diff <= 255 ); \
157 }
158
159 CV_Assert( src.type() == CV_8UC3 && dst.type() == CV_32SC1 );
160 CV_Assert( src.size() == dst.size() );
161
162 // Current pixel in input image
163 const uchar* img = src.ptr();
164 // Step size to next row in input image
165 int istep = int(src.step/sizeof(img[0]));
166
167 // Current pixel in mask image
168 int* mask = dst.ptr<int>();
169 // Step size to next row in mask image
170 int mstep = int(dst.step / sizeof(mask[0]));
171
172 for( i = 0; i < 256; i++ )
173 subs_tab[i] = 0;
174 for( i = 256; i <= 512; i++ )
175 subs_tab[i] = i - 256;
176
177 // draw a pixel-wide border of dummy "watershed" (i.e. boundary) pixels
178 for( j = 0; j < size.width; j++ )
179 mask[j] = mask[j + mstep*(size.height-1)] = WSHED;
180
181 // initial phase: put all the neighbor pixels of each marker to the ordered queue -
182 // determine the initial boundaries of the basins
183 for( i = 1; i < size.height-1; i++ )
184 {
185 img += istep; mask += mstep;
186 mask[0] = mask[size.width-1] = WSHED; // boundary pixels
187
188 for( j = 1; j < size.width-1; j++ )
189 {
190 int* m = mask + j;
191 if( m[0] < 0 ) m[0] = 0;
192 if( m[0] == 0 && (m[-1] > 0 || m[1] > 0 || m[-mstep] > 0 || m[mstep] > 0) )
193 {
194 // Find smallest difference to adjacent markers
195 const uchar* ptr = img + j*3;
196 int idx = 256, t;
197 if( m[-1] > 0 )
198 c_diff( ptr, ptr - 3, idx );
199 if( m[1] > 0 )
200 {
201 c_diff( ptr, ptr + 3, t );
202 idx = ws_min( idx, t );
203 }
204 if( m[-mstep] > 0 )
205 {
206 c_diff( ptr, ptr - istep, t );
207 idx = ws_min( idx, t );
208 }
209 if( m[mstep] > 0 )
210 {
211 c_diff( ptr, ptr + istep, t );
212 idx = ws_min( idx, t );
213 }
214
215 // Add to according queue
216 assert( 0 <= idx && idx <= 255 );
217 ws_push( idx, i*mstep + j, i*istep + j*3 );
218 m[0] = IN_QUEUE;
219 }
220 }
221 }
222
223 // find the first non-empty queue
224 for( i = 0; i < NQ; i++ )
225 if( q[i].first )
226 break;
227
228 // if there is no markers, exit immediately
229 if( i == NQ )
230 return;
231
232 active_queue = i;
233 img = src.ptr();
234 mask = dst.ptr<int>();
235
236 // recursively fill the basins
237 for(;;)
238 {
239 int mofs, iofs;
240 int lab = 0, t;
241 int* m;
242 const uchar* ptr;
243
244 // Get non-empty queue with highest priority
245 // Exit condition: empty priority queue
246 if( q[active_queue].first == 0 )
247 {
248 for( i = active_queue+1; i < NQ; i++ )
249 if( q[i].first )
250 break;
251 if( i == NQ )
252 break;
253 active_queue = i;
254 }
255
256 // Get next node
257 ws_pop( active_queue, mofs, iofs );
258
259 // Calculate pointer to current pixel in input and marker image
260 m = mask + mofs;
261 ptr = img + iofs;
262
263 // Check surrounding pixels for labels
264 // to determine label for current pixel
265 t = m[-1]; // Left
266 if( t > 0 ) lab = t;
267 t = m[1]; // Right
268 if( t > 0 )
269 {
270 if( lab == 0 ) lab = t;
271 else if( t != lab ) lab = WSHED;
272 }
273 t = m[-mstep]; // Top
274 if( t > 0 )
275 {
276 if( lab == 0 ) lab = t;
277 else if( t != lab ) lab = WSHED;
278 }
279 t = m[mstep]; // Bottom
280 if( t > 0 )
281 {
282 if( lab == 0 ) lab = t;
283 else if( t != lab ) lab = WSHED;
284 }
285
286 // Set label to current pixel in marker image
287 assert( lab != 0 );
288 m[0] = lab;
289
290 if( lab == WSHED )
291 continue;
292
293 // Add adjacent, unlabeled pixels to corresponding queue
294 if( m[-1] == 0 )
295 {
296 c_diff( ptr, ptr - 3, t );
297 ws_push( t, mofs - 1, iofs - 3 );
298 active_queue = ws_min( active_queue, t );
299 m[-1] = IN_QUEUE;
300 }
301 if( m[1] == 0 )
302 {
303 c_diff( ptr, ptr + 3, t );
304 ws_push( t, mofs + 1, iofs + 3 );
305 active_queue = ws_min( active_queue, t );
306 m[1] = IN_QUEUE;
307 }
308 if( m[-mstep] == 0 )
309 {
310 c_diff( ptr, ptr - istep, t );
311 ws_push( t, mofs - mstep, iofs - istep );
312 active_queue = ws_min( active_queue, t );
313 m[-mstep] = IN_QUEUE;
314 }
315 if( m[mstep] == 0 )
316 {
317 c_diff( ptr, ptr + istep, t );
318 ws_push( t, mofs + mstep, iofs + istep );
319 active_queue = ws_min( active_queue, t );
320 m[mstep] = IN_QUEUE;
321 }
322 }
323 }
324
325
326 /****************************************************************************************\
327 * Meanshift *
328 \****************************************************************************************/
329
330
pyrMeanShiftFiltering(InputArray _src,OutputArray _dst,double sp0,double sr,int max_level,TermCriteria termcrit)331 void cv::pyrMeanShiftFiltering( InputArray _src, OutputArray _dst,
332 double sp0, double sr, int max_level,
333 TermCriteria termcrit )
334 {
335 Mat src0 = _src.getMat();
336
337 if( src0.empty() )
338 return;
339
340 _dst.create( src0.size(), src0.type() );
341 Mat dst0 = _dst.getMat();
342
343 const int cn = 3;
344 const int MAX_LEVELS = 8;
345
346 if( (unsigned)max_level > (unsigned)MAX_LEVELS )
347 CV_Error( CV_StsOutOfRange, "The number of pyramid levels is too large or negative" );
348
349 std::vector<cv::Mat> src_pyramid(max_level+1);
350 std::vector<cv::Mat> dst_pyramid(max_level+1);
351 cv::Mat mask0;
352 int i, j, level;
353 //uchar* submask = 0;
354
355 #define cdiff(ofs0) (tab[c0-dptr[ofs0]+255] + \
356 tab[c1-dptr[(ofs0)+1]+255] + tab[c2-dptr[(ofs0)+2]+255] >= isr22)
357
358 double sr2 = sr * sr;
359 int isr2 = cvRound(sr2), isr22 = MAX(isr2,16);
360 int tab[768];
361
362
363 if( src0.type() != CV_8UC3 )
364 CV_Error( CV_StsUnsupportedFormat, "Only 8-bit, 3-channel images are supported" );
365
366 if( src0.type() != dst0.type() )
367 CV_Error( CV_StsUnmatchedFormats, "The input and output images must have the same type" );
368
369 if( src0.size() != dst0.size() )
370 CV_Error( CV_StsUnmatchedSizes, "The input and output images must have the same size" );
371
372 if( !(termcrit.type & CV_TERMCRIT_ITER) )
373 termcrit.maxCount = 5;
374 termcrit.maxCount = MAX(termcrit.maxCount,1);
375 termcrit.maxCount = MIN(termcrit.maxCount,100);
376 if( !(termcrit.type & CV_TERMCRIT_EPS) )
377 termcrit.epsilon = 1.f;
378 termcrit.epsilon = MAX(termcrit.epsilon, 0.f);
379
380 for( i = 0; i < 768; i++ )
381 tab[i] = (i - 255)*(i - 255);
382
383 // 1. construct pyramid
384 src_pyramid[0] = src0;
385 dst_pyramid[0] = dst0;
386 for( level = 1; level <= max_level; level++ )
387 {
388 src_pyramid[level].create( (src_pyramid[level-1].rows+1)/2,
389 (src_pyramid[level-1].cols+1)/2, src_pyramid[level-1].type() );
390 dst_pyramid[level].create( src_pyramid[level].rows,
391 src_pyramid[level].cols, src_pyramid[level].type() );
392 cv::pyrDown( src_pyramid[level-1], src_pyramid[level], src_pyramid[level].size() );
393 //CV_CALL( cvResize( src_pyramid[level-1], src_pyramid[level], CV_INTER_AREA ));
394 }
395
396 mask0.create(src0.rows, src0.cols, CV_8UC1);
397 //CV_CALL( submask = (uchar*)cvAlloc( (sp+2)*(sp+2) ));
398
399 // 2. apply meanshift, starting from the pyramid top (i.e. the smallest layer)
400 for( level = max_level; level >= 0; level-- )
401 {
402 cv::Mat src = src_pyramid[level];
403 cv::Size size = src.size();
404 const uchar* sptr = src.ptr();
405 int sstep = (int)src.step;
406 uchar* mask = 0;
407 int mstep = 0;
408 uchar* dptr;
409 int dstep;
410 float sp = (float)(sp0 / (1 << level));
411 sp = MAX( sp, 1 );
412
413 if( level < max_level )
414 {
415 cv::Size size1 = dst_pyramid[level+1].size();
416 cv::Mat m( size.height, size.width, CV_8UC1, mask0.ptr() );
417 dstep = (int)dst_pyramid[level+1].step;
418 dptr = dst_pyramid[level+1].ptr() + dstep + cn;
419 mstep = (int)m.step;
420 mask = m.ptr() + mstep;
421 //cvResize( dst_pyramid[level+1], dst_pyramid[level], CV_INTER_CUBIC );
422 cv::pyrUp( dst_pyramid[level+1], dst_pyramid[level], dst_pyramid[level].size() );
423 m.setTo(cv::Scalar::all(0));
424
425 for( i = 1; i < size1.height-1; i++, dptr += dstep - (size1.width-2)*3, mask += mstep*2 )
426 {
427 for( j = 1; j < size1.width-1; j++, dptr += cn )
428 {
429 int c0 = dptr[0], c1 = dptr[1], c2 = dptr[2];
430 mask[j*2 - 1] = cdiff(-3) || cdiff(3) || cdiff(-dstep-3) || cdiff(-dstep) ||
431 cdiff(-dstep+3) || cdiff(dstep-3) || cdiff(dstep) || cdiff(dstep+3);
432 }
433 }
434
435 cv::dilate( m, m, cv::Mat() );
436 mask = m.ptr();
437 }
438
439 dptr = dst_pyramid[level].ptr();
440 dstep = (int)dst_pyramid[level].step;
441
442 for( i = 0; i < size.height; i++, sptr += sstep - size.width*3,
443 dptr += dstep - size.width*3,
444 mask += mstep )
445 {
446 for( j = 0; j < size.width; j++, sptr += 3, dptr += 3 )
447 {
448 int x0 = j, y0 = i, x1, y1, iter;
449 int c0, c1, c2;
450
451 if( mask && !mask[j] )
452 continue;
453
454 c0 = sptr[0], c1 = sptr[1], c2 = sptr[2];
455
456 // iterate meanshift procedure
457 for( iter = 0; iter < termcrit.maxCount; iter++ )
458 {
459 const uchar* ptr;
460 int x, y, count = 0;
461 int minx, miny, maxx, maxy;
462 int s0 = 0, s1 = 0, s2 = 0, sx = 0, sy = 0;
463 double icount;
464 int stop_flag;
465
466 //mean shift: process pixels in window (p-sigmaSp)x(p+sigmaSp)
467 minx = cvRound(x0 - sp); minx = MAX(minx, 0);
468 miny = cvRound(y0 - sp); miny = MAX(miny, 0);
469 maxx = cvRound(x0 + sp); maxx = MIN(maxx, size.width-1);
470 maxy = cvRound(y0 + sp); maxy = MIN(maxy, size.height-1);
471 ptr = sptr + (miny - i)*sstep + (minx - j)*3;
472
473 for( y = miny; y <= maxy; y++, ptr += sstep - (maxx-minx+1)*3 )
474 {
475 int row_count = 0;
476 x = minx;
477 #if CV_ENABLE_UNROLLED
478 for( ; x + 3 <= maxx; x += 4, ptr += 12 )
479 {
480 int t0 = ptr[0], t1 = ptr[1], t2 = ptr[2];
481 if( tab[t0-c0+255] + tab[t1-c1+255] + tab[t2-c2+255] <= isr2 )
482 {
483 s0 += t0; s1 += t1; s2 += t2;
484 sx += x; row_count++;
485 }
486 t0 = ptr[3], t1 = ptr[4], t2 = ptr[5];
487 if( tab[t0-c0+255] + tab[t1-c1+255] + tab[t2-c2+255] <= isr2 )
488 {
489 s0 += t0; s1 += t1; s2 += t2;
490 sx += x+1; row_count++;
491 }
492 t0 = ptr[6], t1 = ptr[7], t2 = ptr[8];
493 if( tab[t0-c0+255] + tab[t1-c1+255] + tab[t2-c2+255] <= isr2 )
494 {
495 s0 += t0; s1 += t1; s2 += t2;
496 sx += x+2; row_count++;
497 }
498 t0 = ptr[9], t1 = ptr[10], t2 = ptr[11];
499 if( tab[t0-c0+255] + tab[t1-c1+255] + tab[t2-c2+255] <= isr2 )
500 {
501 s0 += t0; s1 += t1; s2 += t2;
502 sx += x+3; row_count++;
503 }
504 }
505 #endif
506 for( ; x <= maxx; x++, ptr += 3 )
507 {
508 int t0 = ptr[0], t1 = ptr[1], t2 = ptr[2];
509 if( tab[t0-c0+255] + tab[t1-c1+255] + tab[t2-c2+255] <= isr2 )
510 {
511 s0 += t0; s1 += t1; s2 += t2;
512 sx += x; row_count++;
513 }
514 }
515 count += row_count;
516 sy += y*row_count;
517 }
518
519 if( count == 0 )
520 break;
521
522 icount = 1./count;
523 x1 = cvRound(sx*icount);
524 y1 = cvRound(sy*icount);
525 s0 = cvRound(s0*icount);
526 s1 = cvRound(s1*icount);
527 s2 = cvRound(s2*icount);
528
529 stop_flag = (x0 == x1 && y0 == y1) || std::abs(x1-x0) + std::abs(y1-y0) +
530 tab[s0 - c0 + 255] + tab[s1 - c1 + 255] +
531 tab[s2 - c2 + 255] <= termcrit.epsilon;
532
533 x0 = x1; y0 = y1;
534 c0 = s0; c1 = s1; c2 = s2;
535
536 if( stop_flag )
537 break;
538 }
539
540 dptr[0] = (uchar)c0;
541 dptr[1] = (uchar)c1;
542 dptr[2] = (uchar)c2;
543 }
544 }
545 }
546 }
547
548
549 ///////////////////////////////////////////////////////////////////////////////////////////////
550
cvWatershed(const CvArr * _src,CvArr * _markers)551 CV_IMPL void cvWatershed( const CvArr* _src, CvArr* _markers )
552 {
553 cv::Mat src = cv::cvarrToMat(_src), markers = cv::cvarrToMat(_markers);
554 cv::watershed(src, markers);
555 }
556
557
558 CV_IMPL void
cvPyrMeanShiftFiltering(const CvArr * srcarr,CvArr * dstarr,double sp0,double sr,int max_level,CvTermCriteria termcrit)559 cvPyrMeanShiftFiltering( const CvArr* srcarr, CvArr* dstarr,
560 double sp0, double sr, int max_level,
561 CvTermCriteria termcrit )
562 {
563 cv::Mat src = cv::cvarrToMat(srcarr);
564 const cv::Mat dst = cv::cvarrToMat(dstarr);
565
566 cv::pyrMeanShiftFiltering(src, dst, sp0, sr, max_level, termcrit);
567 }
568