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#include "precomp.hpp"
/****************************************************************************************\
* Watershed *
\****************************************************************************************/
namespace cv
{
// A node represents a pixel to label
struct WSNode
{
int next;
int mask_ofs;
int img_ofs;
};
// Queue for WSNodes
struct WSQueue
{
WSQueue() { first = last = 0; }
int first, last;
};
static int
allocWSNodes( std::vector<WSNode>& storage )
{
int sz = (int)storage.size();
int newsz = MAX(128, sz*3/2);
storage.resize(newsz);
if( sz == 0 )
{
storage[0].next = 0;
sz = 1;
}
for( int i = sz; i < newsz-1; i++ )
storage[i].next = i+1;
storage[newsz-1].next = 0;
return sz;
}
}
void cv::watershed( InputArray _src, InputOutputArray _markers )
{
// Labels for pixels
const int IN_QUEUE = -2; // Pixel visited
const int WSHED = -1; // Pixel belongs to watershed
// possible bit values = 2^8
const int NQ = 256;
Mat src = _src.getMat(), dst = _markers.getMat();
Size size = src.size();
// Vector of every created node
std::vector<WSNode> storage;
int free_node = 0, node;
// Priority queue of queues of nodes
// from high priority (0) to low priority (255)
WSQueue q[NQ];
// Non-empty queue with highest priority
int active_queue;
int i, j;
// Color differences
int db, dg, dr;
int subs_tab[513];
// MAX(a,b) = b + MAX(a-b,0)
#define ws_max(a,b) ((b) + subs_tab[(a)-(b)+NQ])
// MIN(a,b) = a - MAX(a-b,0)
#define ws_min(a,b) ((a) - subs_tab[(a)-(b)+NQ])
// Create a new node with offsets mofs and iofs in queue idx
#define ws_push(idx,mofs,iofs) \
{ \
if( !free_node ) \
free_node = allocWSNodes( storage );\
node = free_node; \
free_node = storage[free_node].next;\
storage[node].next = 0; \
storage[node].mask_ofs = mofs; \
storage[node].img_ofs = iofs; \
if( q[idx].last ) \
storage[q[idx].last].next=node; \
else \
q[idx].first = node; \
q[idx].last = node; \
}
// Get next node from queue idx
#define ws_pop(idx,mofs,iofs) \
{ \
node = q[idx].first; \
q[idx].first = storage[node].next; \
if( !storage[node].next ) \
q[idx].last = 0; \
storage[node].next = free_node; \
free_node = node; \
mofs = storage[node].mask_ofs; \
iofs = storage[node].img_ofs; \
}
// Get highest absolute channel difference in diff
#define c_diff(ptr1,ptr2,diff) \
{ \
db = std::abs((ptr1)[0] - (ptr2)[0]);\
dg = std::abs((ptr1)[1] - (ptr2)[1]);\
dr = std::abs((ptr1)[2] - (ptr2)[2]);\
diff = ws_max(db,dg); \
diff = ws_max(diff,dr); \
assert( 0 <= diff && diff <= 255 ); \
}
CV_Assert( src.type() == CV_8UC3 && dst.type() == CV_32SC1 );
CV_Assert( src.size() == dst.size() );
// Current pixel in input image
const uchar* img = src.ptr();
// Step size to next row in input image
int istep = int(src.step/sizeof(img[0]));
// Current pixel in mask image
int* mask = dst.ptr<int>();
// Step size to next row in mask image
int mstep = int(dst.step / sizeof(mask[0]));
for( i = 0; i < 256; i++ )
subs_tab[i] = 0;
for( i = 256; i <= 512; i++ )
subs_tab[i] = i - 256;
// draw a pixel-wide border of dummy "watershed" (i.e. boundary) pixels
for( j = 0; j < size.width; j++ )
mask[j] = mask[j + mstep*(size.height-1)] = WSHED;
// initial phase: put all the neighbor pixels of each marker to the ordered queue -
// determine the initial boundaries of the basins
for( i = 1; i < size.height-1; i++ )
{
img += istep; mask += mstep;
mask[0] = mask[size.width-1] = WSHED; // boundary pixels
for( j = 1; j < size.width-1; j++ )
{
int* m = mask + j;
if( m[0] < 0 ) m[0] = 0;
if( m[0] == 0 && (m[-1] > 0 || m[1] > 0 || m[-mstep] > 0 || m[mstep] > 0) )
{
// Find smallest difference to adjacent markers
const uchar* ptr = img + j*3;
int idx = 256, t;
if( m[-1] > 0 )
c_diff( ptr, ptr - 3, idx );
if( m[1] > 0 )
{
c_diff( ptr, ptr + 3, t );
idx = ws_min( idx, t );
}
if( m[-mstep] > 0 )
{
c_diff( ptr, ptr - istep, t );
idx = ws_min( idx, t );
}
if( m[mstep] > 0 )
{
c_diff( ptr, ptr + istep, t );
idx = ws_min( idx, t );
}
// Add to according queue
assert( 0 <= idx && idx <= 255 );
ws_push( idx, i*mstep + j, i*istep + j*3 );
m[0] = IN_QUEUE;
}
}
}
// find the first non-empty queue
for( i = 0; i < NQ; i++ )
if( q[i].first )
break;
// if there is no markers, exit immediately
if( i == NQ )
return;
active_queue = i;
img = src.ptr();
mask = dst.ptr<int>();
// recursively fill the basins
for(;;)
{
int mofs, iofs;
int lab = 0, t;
int* m;
const uchar* ptr;
// Get non-empty queue with highest priority
// Exit condition: empty priority queue
if( q[active_queue].first == 0 )
{
for( i = active_queue+1; i < NQ; i++ )
if( q[i].first )
break;
if( i == NQ )
break;
active_queue = i;
}
// Get next node
ws_pop( active_queue, mofs, iofs );
// Calculate pointer to current pixel in input and marker image
m = mask + mofs;
ptr = img + iofs;
// Check surrounding pixels for labels
// to determine label for current pixel
t = m[-1]; // Left
if( t > 0 ) lab = t;
t = m[1]; // Right
if( t > 0 )
{
if( lab == 0 ) lab = t;
else if( t != lab ) lab = WSHED;
}
t = m[-mstep]; // Top
if( t > 0 )
{
if( lab == 0 ) lab = t;
else if( t != lab ) lab = WSHED;
}
t = m[mstep]; // Bottom
if( t > 0 )
{
if( lab == 0 ) lab = t;
else if( t != lab ) lab = WSHED;
}
// Set label to current pixel in marker image
assert( lab != 0 );
m[0] = lab;
if( lab == WSHED )
continue;
// Add adjacent, unlabeled pixels to corresponding queue
if( m[-1] == 0 )
{
c_diff( ptr, ptr - 3, t );
ws_push( t, mofs - 1, iofs - 3 );
active_queue = ws_min( active_queue, t );
m[-1] = IN_QUEUE;
}
if( m[1] == 0 )
{
c_diff( ptr, ptr + 3, t );
ws_push( t, mofs + 1, iofs + 3 );
active_queue = ws_min( active_queue, t );
m[1] = IN_QUEUE;
}
if( m[-mstep] == 0 )
{
c_diff( ptr, ptr - istep, t );
ws_push( t, mofs - mstep, iofs - istep );
active_queue = ws_min( active_queue, t );
m[-mstep] = IN_QUEUE;
}
if( m[mstep] == 0 )
{
c_diff( ptr, ptr + istep, t );
ws_push( t, mofs + mstep, iofs + istep );
active_queue = ws_min( active_queue, t );
m[mstep] = IN_QUEUE;
}
}
}
/****************************************************************************************\
* Meanshift *
\****************************************************************************************/
void cv::pyrMeanShiftFiltering( InputArray _src, OutputArray _dst,
double sp0, double sr, int max_level,
TermCriteria termcrit )
{
Mat src0 = _src.getMat();
if( src0.empty() )
return;
_dst.create( src0.size(), src0.type() );
Mat dst0 = _dst.getMat();
const int cn = 3;
const int MAX_LEVELS = 8;
if( (unsigned)max_level > (unsigned)MAX_LEVELS )
CV_Error( CV_StsOutOfRange, "The number of pyramid levels is too large or negative" );
std::vector<cv::Mat> src_pyramid(max_level+1);
std::vector<cv::Mat> dst_pyramid(max_level+1);
cv::Mat mask0;
int i, j, level;
//uchar* submask = 0;
#define cdiff(ofs0) (tab[c0-dptr[ofs0]+255] + \
tab[c1-dptr[(ofs0)+1]+255] + tab[c2-dptr[(ofs0)+2]+255] >= isr22)
double sr2 = sr * sr;
int isr2 = cvRound(sr2), isr22 = MAX(isr2,16);
int tab[768];
if( src0.type() != CV_8UC3 )
CV_Error( CV_StsUnsupportedFormat, "Only 8-bit, 3-channel images are supported" );
if( src0.type() != dst0.type() )
CV_Error( CV_StsUnmatchedFormats, "The input and output images must have the same type" );
if( src0.size() != dst0.size() )
CV_Error( CV_StsUnmatchedSizes, "The input and output images must have the same size" );
if( !(termcrit.type & CV_TERMCRIT_ITER) )
termcrit.maxCount = 5;
termcrit.maxCount = MAX(termcrit.maxCount,1);
termcrit.maxCount = MIN(termcrit.maxCount,100);
if( !(termcrit.type & CV_TERMCRIT_EPS) )
termcrit.epsilon = 1.f;
termcrit.epsilon = MAX(termcrit.epsilon, 0.f);
for( i = 0; i < 768; i++ )
tab[i] = (i - 255)*(i - 255);
// 1. construct pyramid
src_pyramid[0] = src0;
dst_pyramid[0] = dst0;
for( level = 1; level <= max_level; level++ )
{
src_pyramid[level].create( (src_pyramid[level-1].rows+1)/2,
(src_pyramid[level-1].cols+1)/2, src_pyramid[level-1].type() );
dst_pyramid[level].create( src_pyramid[level].rows,
src_pyramid[level].cols, src_pyramid[level].type() );
cv::pyrDown( src_pyramid[level-1], src_pyramid[level], src_pyramid[level].size() );
//CV_CALL( cvResize( src_pyramid[level-1], src_pyramid[level], CV_INTER_AREA ));
}
mask0.create(src0.rows, src0.cols, CV_8UC1);
//CV_CALL( submask = (uchar*)cvAlloc( (sp+2)*(sp+2) ));
// 2. apply meanshift, starting from the pyramid top (i.e. the smallest layer)
for( level = max_level; level >= 0; level-- )
{
cv::Mat src = src_pyramid[level];
cv::Size size = src.size();
const uchar* sptr = src.ptr();
int sstep = (int)src.step;
uchar* mask = 0;
int mstep = 0;
uchar* dptr;
int dstep;
float sp = (float)(sp0 / (1 << level));
sp = MAX( sp, 1 );
if( level < max_level )
{
cv::Size size1 = dst_pyramid[level+1].size();
cv::Mat m( size.height, size.width, CV_8UC1, mask0.ptr() );
dstep = (int)dst_pyramid[level+1].step;
dptr = dst_pyramid[level+1].ptr() + dstep + cn;
mstep = (int)m.step;
mask = m.ptr() + mstep;
//cvResize( dst_pyramid[level+1], dst_pyramid[level], CV_INTER_CUBIC );
cv::pyrUp( dst_pyramid[level+1], dst_pyramid[level], dst_pyramid[level].size() );
m.setTo(cv::Scalar::all(0));
for( i = 1; i < size1.height-1; i++, dptr += dstep - (size1.width-2)*3, mask += mstep*2 )
{
for( j = 1; j < size1.width-1; j++, dptr += cn )
{
int c0 = dptr[0], c1 = dptr[1], c2 = dptr[2];
mask[j*2 - 1] = cdiff(-3) || cdiff(3) || cdiff(-dstep-3) || cdiff(-dstep) ||
cdiff(-dstep+3) || cdiff(dstep-3) || cdiff(dstep) || cdiff(dstep+3);
}
}
cv::dilate( m, m, cv::Mat() );
mask = m.ptr();
}
dptr = dst_pyramid[level].ptr();
dstep = (int)dst_pyramid[level].step;
for( i = 0; i < size.height; i++, sptr += sstep - size.width*3,
dptr += dstep - size.width*3,
mask += mstep )
{
for( j = 0; j < size.width; j++, sptr += 3, dptr += 3 )
{
int x0 = j, y0 = i, x1, y1, iter;
int c0, c1, c2;
if( mask && !mask[j] )
continue;
c0 = sptr[0], c1 = sptr[1], c2 = sptr[2];
// iterate meanshift procedure
for( iter = 0; iter < termcrit.maxCount; iter++ )
{
const uchar* ptr;
int x, y, count = 0;
int minx, miny, maxx, maxy;
int s0 = 0, s1 = 0, s2 = 0, sx = 0, sy = 0;
double icount;
int stop_flag;
//mean shift: process pixels in window (p-sigmaSp)x(p+sigmaSp)
minx = cvRound(x0 - sp); minx = MAX(minx, 0);
miny = cvRound(y0 - sp); miny = MAX(miny, 0);
maxx = cvRound(x0 + sp); maxx = MIN(maxx, size.width-1);
maxy = cvRound(y0 + sp); maxy = MIN(maxy, size.height-1);
ptr = sptr + (miny - i)*sstep + (minx - j)*3;
for( y = miny; y <= maxy; y++, ptr += sstep - (maxx-minx+1)*3 )
{
int row_count = 0;
x = minx;
#if CV_ENABLE_UNROLLED
for( ; x + 3 <= maxx; x += 4, ptr += 12 )
{
int t0 = ptr[0], t1 = ptr[1], t2 = ptr[2];
if( tab[t0-c0+255] + tab[t1-c1+255] + tab[t2-c2+255] <= isr2 )
{
s0 += t0; s1 += t1; s2 += t2;
sx += x; row_count++;
}
t0 = ptr[3], t1 = ptr[4], t2 = ptr[5];
if( tab[t0-c0+255] + tab[t1-c1+255] + tab[t2-c2+255] <= isr2 )
{
s0 += t0; s1 += t1; s2 += t2;
sx += x+1; row_count++;
}
t0 = ptr[6], t1 = ptr[7], t2 = ptr[8];
if( tab[t0-c0+255] + tab[t1-c1+255] + tab[t2-c2+255] <= isr2 )
{
s0 += t0; s1 += t1; s2 += t2;
sx += x+2; row_count++;
}
t0 = ptr[9], t1 = ptr[10], t2 = ptr[11];
if( tab[t0-c0+255] + tab[t1-c1+255] + tab[t2-c2+255] <= isr2 )
{
s0 += t0; s1 += t1; s2 += t2;
sx += x+3; row_count++;
}
}
#endif
for( ; x <= maxx; x++, ptr += 3 )
{
int t0 = ptr[0], t1 = ptr[1], t2 = ptr[2];
if( tab[t0-c0+255] + tab[t1-c1+255] + tab[t2-c2+255] <= isr2 )
{
s0 += t0; s1 += t1; s2 += t2;
sx += x; row_count++;
}
}
count += row_count;
sy += y*row_count;
}
if( count == 0 )
break;
icount = 1./count;
x1 = cvRound(sx*icount);
y1 = cvRound(sy*icount);
s0 = cvRound(s0*icount);
s1 = cvRound(s1*icount);
s2 = cvRound(s2*icount);
stop_flag = (x0 == x1 && y0 == y1) || std::abs(x1-x0) + std::abs(y1-y0) +
tab[s0 - c0 + 255] + tab[s1 - c1 + 255] +
tab[s2 - c2 + 255] <= termcrit.epsilon;
x0 = x1; y0 = y1;
c0 = s0; c1 = s1; c2 = s2;
if( stop_flag )
break;
}
dptr[0] = (uchar)c0;
dptr[1] = (uchar)c1;
dptr[2] = (uchar)c2;
}
}
}
}
///////////////////////////////////////////////////////////////////////////////////////////////
CV_IMPL void cvWatershed( const CvArr* _src, CvArr* _markers )
{
cv::Mat src = cv::cvarrToMat(_src), markers = cv::cvarrToMat(_markers);
cv::watershed(src, markers);
}
CV_IMPL void
cvPyrMeanShiftFiltering( const CvArr* srcarr, CvArr* dstarr,
double sp0, double sr, int max_level,
CvTermCriteria termcrit )
{
cv::Mat src = cv::cvarrToMat(srcarr);
const cv::Mat dst = cv::cvarrToMat(dstarr);
cv::pyrMeanShiftFiltering(src, dst, sp0, sr, max_level, termcrit);
}