本博客主要解释opencv中分水岭算法源码中的难点：1）将点push到队列中；2）将点pop出队列。有关分水岭算法原理网上有很多解释，请自行查阅，这里不解释。
下面三张图就是push和pop过程演示（不知道为何图片插进来就自动旋转了，尴尬
，如果人懂，望一定告知解决方法，谢谢）
下面是opencv中分水岭算法源码：
void cv::watershed( InputArray _src, InputOutputArray _markers )
{
CV_INSTRUMENT_REGION()
// 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;
}
}
}