opencv中 MSER 源码赏析
会慢慢完善，如有错误请告知，谢谢
这个原理转引自http://www.cnblogs.com/frischzenger/p/3334569.html
方便理解后面看似复杂的程序
Component Tree原理
当前元素和所有周围邻近当前区域的元素并且小于当前元素的区域都属于该区域
从最小值Xi像素开始建立一个region，如果Xi的邻域亮度值等于该像素的亮度值，则将Xi合并进邻域像素所在的region（且父节点是邻域像素父节点），如果Xi邻域的像素值小于Xi的像素值，则将邻域像素归入当前region，并将当前region作为邻域像素region的根节点（且邻域像素父节点指向当前像素的父节点），直到遍历完所有像素为止。
Component Tree 代码解读:
4 4 4 4 4 4 4
4 2 2 4 2 1 4
4 2 2 4 1 2 4
4 3 3 4 3 3 4
4 2 2 4 4 3 4
4 1 1 2 4 3 4
4 4 4 4 4 4 4
4 2 2 4 2 1 4
4 2 2 4 1 2 4
4 3 3 4 3 3 4
4 2 2 4 4 3 4
4 1 1 2 4 3 4
4 4 4 4 4 4 4
Extremal regions: 8
ner: 0. value:1,area: 2, parent: 3
ner: 1. value:1,area: 2, parent: 4
ner: 2. value:2,area: 4, parent: 5
ner: 3. value:2,area: 5, parent: 5
ner: 4. value:2,area: 4, parent: 6
ner: 5. value:3,area: 11, parent: 7
ner: 6. value:3,area: 8, parent: 7
ner: 7. value:4,area: 49, parent: 7
ner: 0. value:1,area: 2, parent: 3
ner: 1. value:1,area: 2, parent: 4
ner: 2. value:2,area: 4, parent: 5
ner: 3. value:2,area: 5, parent: 5
ner: 4. value:2,area: 4, parent: 6
ner: 5. value:3,area: 11, parent: 7
ner: 6. value:3,area: 8, parent: 7
ner: 7. value:4,area: 49, parent: 7
从像素值是1的像素开始:
节点0，1属于节点3
节点2，3属于节点5
节点4属于节点6
节点5，6属于节点7
反之则有：
1 1 1 1 1 1 1
1 3 3 2 3 4 1
1 3 3 2 3 4 1
1 1 1 1 1 3 1
1 3 4 2 1 1 1
1 4 3 2 2 2 1
1 1 1 1 1 1 1
1 3 3 2 3 4 1
1 3 3 2 3 4 1
1 1 1 1 1 3 1
1 3 4 2 1 1 1
1 4 3 2 2 2 1
1 1 1 1 1 1 1
ner: 0. value:1,area: 30, parent: 1
ner: 1. value:2,area: 36, parent: 2
ner: 2. value:3,area: 45, parent: 3
ner: 3. value:4,area: 49, parent: 3
ner: 1. value:2,area: 36, parent: 2
ner: 2. value:3,area: 45, parent: 3
ner: 3. value:4,area: 49, parent: 3
从像素值是1的像素开始，刚开始只有节点0一个区域，将像素2的区域并入0后形成节点1，将像素3并入节点1后形成节点2，最后全部合并形成整个区域作为父节点.
Component Tree的步骤:
1.在计算开始之前首先对所有像素按照亮度大小进行排序。
2.
for (i = 0; i < pixel_numbers; i++)
{
1.弹出当前像素的索引和值
2.将邻结点的索引指向当前索引
3.将当前像素结点插入到树中
4.while(依次处理以该像素为中心的8邻域像素)
{
a.邻域像素在图像边界以内
b.邻域像素不是自身
c.邻域像素已经在树中了，也就是说这个像素已经被处理过了
if(满足a,b,c)
{
1.将当前此节点，以及当前此节点的上一层父节点,..., 直到根节点的所有链路节点的shortcut都置为根节点
2.将此邻域节点，以及此邻域节点的上一层父节点,..., 直到根节点的所有链路节点的shortcut都置为根节点
i. 如果当前节点的根节点等于邻域像素的根节点，这种情况下两个树已经加入了同一个树当中了
ii. 如果当前节点的根节点的亮度等于邻域像素的根节点的亮度，此种情况索引将被扩展到具有相同亮度的一个极值区域，因为邻域像素的根节点不会是整个图像的一个极值区域；当前索引的根节点能够安全的被添加到邻域像素的根节点作为邻域像素的孩子结点。
iii.如果当前节点的根节点的亮度大于邻域像素的根节点的亮度并且当前节点是一个极值区域，这时仅仅增加它的层数。这种情况下邻域像素根节点将会是最终整个图像的一个极值区域并且只有唯一的可能性作为添加到当前像素所属的树中当前像素的孩子
}
}
}
{
1.弹出当前像素的索引和值
2.将邻结点的索引指向当前索引
3.将当前像素结点插入到树中
4.while(依次处理以该像素为中心的8邻域像素)
{
a.邻域像素在图像边界以内
b.邻域像素不是自身
c.邻域像素已经在树中了，也就是说这个像素已经被处理过了
if(满足a,b,c)
{
1.将当前此节点，以及当前此节点的上一层父节点,..., 直到根节点的所有链路节点的shortcut都置为根节点
2.将此邻域节点，以及此邻域节点的上一层父节点,..., 直到根节点的所有链路节点的shortcut都置为根节点
i. 如果当前节点的根节点等于邻域像素的根节点，这种情况下两个树已经加入了同一个树当中了
ii. 如果当前节点的根节点的亮度等于邻域像素的根节点的亮度，此种情况索引将被扩展到具有相同亮度的一个极值区域，因为邻域像素的根节点不会是整个图像的一个极值区域；当前索引的根节点能够安全的被添加到邻域像素的根节点作为邻域像素的孩子结点。
iii.如果当前节点的根节点的亮度大于邻域像素的根节点的亮度并且当前节点是一个极值区域，这时仅仅增加它的层数。这种情况下邻域像素根节点将会是最终整个图像的一个极值区域并且只有唯一的可能性作为添加到当前像素所属的树中当前像素的孩子
}
}
}
/* Redistribution and use in source and binary forms, with or
* without modification, are permitted provided that the following
* conditions are met:
* Redistributions of source code must retain the above
* copyright notice, this list of conditions and the following
* disclaimer.
* Redistributions in binary form must reproduce the above
* copyright notice, this list of conditions and the following
* disclaimer in the documentation and/or other materials
* provided with the distribution.
* The name of Contributor may not be used to endorse or
* promote products derived from this software without
* specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND
* CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES,
* INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
* MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
* DISCLAIMED. IN NO EVENT SHALL THE CONTRIBUTORS BE LIABLE FOR ANY
* DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
* CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
* PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA,
* OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
* THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR
* TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT
* OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY
* OF SUCH DAMAGE.
* Copyright© 2009, Liu Liu All rights reserved.
*
* OpenCV functions for MSER extraction
*
* 1. there are two different implementation of MSER, one for grey image, one for color image
* 2. the grey image algorithm is taken from: Linear Time Maximally Stable Extremal Regions;
* the paper claims to be faster than union-find method;
* it actually get 1.5~2m/s on my centrino L7200 1.2GHz laptop.
* 3. the color image algorithm is taken from: Maximally Stable Colour Regions for Recognition and Match;
* it should be much slower than grey image method ( 3~4 times );
* the chi_table.h file is taken directly from paper's source code which is distributed under GPL.
* 4. though the name is *contours*, the result actually is a list of point set.
*/
#include "precomp.hpp"
namespace cv
{
const int TABLE_SIZE = 400;
static double chitab3[]={0, 0.0150057, 0.0239478, 0.0315227,
0.0383427, 0.0446605, 0.0506115, 0.0562786,
0.0617174, 0.0669672, 0.0720573, 0.0770099,
0.081843, 0.0865705, 0.0912043, 0.0957541,
0.100228, 0.104633, 0.108976, 0.113261,
0.117493, 0.121676, 0.125814, 0.12991,
0.133967, 0.137987, 0.141974, 0.145929,
0.149853, 0.15375, 0.15762, 0.161466,
0.165287, 0.169087, 0.172866, 0.176625,
0.180365, 0.184088, 0.187794, 0.191483,
0.195158, 0.198819, 0.202466, 0.2061,
0.209722, 0.213332, 0.216932, 0.220521,
0.2241, 0.22767, 0.231231, 0.234783,
0.238328, 0.241865, 0.245395, 0.248918,
0.252435, 0.255947, 0.259452, 0.262952,
0.266448, 0.269939, 0.273425, 0.276908,
0.280386, 0.283862, 0.287334, 0.290803,
0.29427, 0.297734, 0.301197, 0.304657,
0.308115, 0.311573, 0.315028, 0.318483,
0.321937, 0.32539, 0.328843, 0.332296,
0.335749, 0.339201, 0.342654, 0.346108,
0.349562, 0.353017, 0.356473, 0.35993,
0.363389, 0.366849, 0.37031, 0.373774,
0.377239, 0.380706, 0.384176, 0.387648,
0.391123, 0.3946, 0.39808, 0.401563,
0.405049, 0.408539, 0.412032, 0.415528,
0.419028, 0.422531, 0.426039, 0.429551,
0.433066, 0.436586, 0.440111, 0.44364,
0.447173, 0.450712, 0.454255, 0.457803,
0.461356, 0.464915, 0.468479, 0.472049,
0.475624, 0.479205, 0.482792, 0.486384,
0.489983, 0.493588, 0.4972, 0.500818,
0.504442, 0.508073, 0.511711, 0.515356,
0.519008, 0.522667, 0.526334, 0.530008,
0.533689, 0.537378, 0.541075, 0.54478,
0.548492, 0.552213, 0.555942, 0.55968,
0.563425, 0.56718, 0.570943, 0.574715,
0.578497, 0.582287, 0.586086, 0.589895,
0.593713, 0.597541, 0.601379, 0.605227,
0.609084, 0.612952, 0.61683, 0.620718,
0.624617, 0.628526, 0.632447, 0.636378,
0.64032, 0.644274, 0.648239, 0.652215,
0.656203, 0.660203, 0.664215, 0.668238,
0.672274, 0.676323, 0.680384, 0.684457,
0.688543, 0.692643, 0.696755, 0.700881,
0.70502, 0.709172, 0.713339, 0.717519,
0.721714, 0.725922, 0.730145, 0.734383,
0.738636, 0.742903, 0.747185, 0.751483,
0.755796, 0.760125, 0.76447, 0.768831,
0.773208, 0.777601, 0.782011, 0.786438,
0.790882, 0.795343, 0.799821, 0.804318,
0.808831, 0.813363, 0.817913, 0.822482,
0.827069, 0.831676, 0.836301, 0.840946,
0.84561, 0.850295, 0.854999, 0.859724,
0.864469, 0.869235, 0.874022, 0.878831,
0.883661, 0.888513, 0.893387, 0.898284,
0.903204, 0.908146, 0.913112, 0.918101,
0.923114, 0.928152, 0.933214, 0.938301,
0.943413, 0.94855, 0.953713, 0.958903,
0.964119, 0.969361, 0.974631, 0.979929,
0.985254, 0.990608, 0.99599, 1.0014,
1.00684, 1.01231, 1.01781, 1.02335,
1.02891, 1.0345, 1.04013, 1.04579,
1.05148, 1.05721, 1.06296, 1.06876,
1.07459, 1.08045, 1.08635, 1.09228,
1.09826, 1.10427, 1.11032, 1.1164,
1.12253, 1.1287, 1.1349, 1.14115,
1.14744, 1.15377, 1.16015, 1.16656,
1.17303, 1.17954, 1.18609, 1.19269,
1.19934, 1.20603, 1.21278, 1.21958,
1.22642, 1.23332, 1.24027, 1.24727,
1.25433, 1.26144, 1.26861, 1.27584,
1.28312, 1.29047, 1.29787, 1.30534,
1.31287, 1.32046, 1.32812, 1.33585,
1.34364, 1.3515, 1.35943, 1.36744,
1.37551, 1.38367, 1.39189, 1.4002,
1.40859, 1.41705, 1.42561, 1.43424,
1.44296, 1.45177, 1.46068, 1.46967,
1.47876, 1.48795, 1.49723, 1.50662,
1.51611, 1.52571, 1.53541, 1.54523,
1.55517, 1.56522, 1.57539, 1.58568,
1.59611, 1.60666, 1.61735, 1.62817,
1.63914, 1.65025, 1.66152, 1.67293,
1.68451, 1.69625, 1.70815, 1.72023,
1.73249, 1.74494, 1.75757, 1.77041,
1.78344, 1.79669, 1.81016, 1.82385,
1.83777, 1.85194, 1.86635, 1.88103,
1.89598, 1.91121, 1.92674, 1.94257,
1.95871, 1.97519, 1.99201, 2.0092,
2.02676, 2.04471, 2.06309, 2.08189,
2.10115, 2.12089, 2.14114, 2.16192,
2.18326, 2.2052, 2.22777, 2.25101,
2.27496, 2.29966, 2.32518, 2.35156,
2.37886, 2.40717, 2.43655, 2.46709,
2.49889, 2.53206, 2.56673, 2.60305,
2.64117, 2.6813, 2.72367, 2.76854,
2.81623, 2.86714, 2.92173, 2.98059,
3.04446, 3.1143, 3.19135, 3.27731,
3.37455, 3.48653, 3.61862, 3.77982,
3.98692, 4.2776, 4.77167, 133.333 };
typedef struct LinkedPoint
{
struct LinkedPoint* prev;
struct LinkedPoint* next;
Point pt;
}
LinkedPoint;
// the history of region grown
typedef struct MSERGrowHistory
{
struct MSERGrowHistory* shortcut;
struct MSERGrowHistory* child;
int stable; // when it ever stabled before, record the size
int val;
int size;
}
MSERGrowHistory;
typedef struct MSERConnectedComp
{
LinkedPoint* head;
LinkedPoint* tail;
MSERGrowHistory* history;
unsigned long grey_level;
int size;
int dvar; // the derivative of last var
float var; // the current variation (most time is the variation of one-step back)
}
MSERConnectedComp;
// Linear Time MSER claims by using bsf can get performance gain, here is the implementation
// however it seems that will not do any good in real world test
inline void _bitset(unsigned long * a, unsigned long b) //位设置
{
*a |= 1<<b;
}
inline void _bitreset(unsigned long * a, unsigned long b) //位复位
{
*a &= ~(1<<b);
}
//int delta; //! delta, in the code, it compares (size_{i}-size_{i-delta})/size_{i-delta}
//
//int maxArea; //! prune the area which bigger than maxArea
//
//int minArea; //! prune the area which smaller than minArea
//
//float maxVariation; //! prune the area have simliar size to its children
//
//float minDiversity; //! trace back to cut off mser with diversity < min_diversity
//
//The next few params for MSER of color image:
//
//int maxEvolution; //! for color image, the evolution steps
//
//double areaThreshold; //! the area threshold to cause re-initialize
//
//double minMargin; //! ignore too small margin
//
//int edgeBlurSize; //! the aperture size for edge blur
struct MSERParams
{
MSERParams( int _delta, int _minArea, int _maxArea, double _maxVariation,
double _minDiversity, int _maxEvolution, double _areaThreshold,
double _minMargin, int _edgeBlurSize )
: delta(_delta), minArea(_minArea), maxArea(_maxArea), maxVariation(_maxVariation),
minDiversity(_minDiversity), maxEvolution(_maxEvolution), areaThreshold(_areaThreshold),
minMargin(_minMargin), edgeBlurSize(_edgeBlurSize)
{}
int delta; //这个和原论文有区别
// delta, in the code, it compares (size_{i}-size_{i-delta})/size_{i-delta}
int minArea;
// prune the area which bigger/smaller than max_area/min_area
int maxArea;
// prune the area have simliar size to its children
double maxVariation;
// trace back to cut off mser with diversity < min_diversity
double minDiversity;
/* the next few params for MSER of color image */
// for color image, the evolution steps
int maxEvolution;
// the area threshold to cause re-initialize
double areaThreshold;
// ignore too small margin
double minMargin;
// the aperture size for edge blur
int edgeBlurSize;
};
// clear the connected component in stack
static void
initMSERComp( MSERConnectedComp* comp )
{
comp->size = 0;
comp->var = 0;
comp->dvar = 1;
comp->history = NULL;
}
// add history of size to a connected component
static void
MSERNewHistory( MSERConnectedComp* comp, MSERGrowHistory* history )
{
history->child = history;
if ( NULL == comp->history )
{
history->shortcut = history;
history->stable = 0;
} else {
comp->history->child = history;
history->shortcut = comp->history->shortcut;
history->stable = comp->history->stable;
}
history->val = comp->grey_level;
history->size = comp->size;
comp->history = history;
}
// merging two connected component
//合并两个连通的元素
static void
MSERMergeComp( MSERConnectedComp* comp1,
MSERConnectedComp* comp2,
MSERConnectedComp* comp,
MSERGrowHistory* history )
{
LinkedPoint* head;
LinkedPoint* tail;
comp->grey_level = comp2->grey_level;
history->child = history;
// select the winner by size
if ( comp1->size >= comp2->size )
{
if ( NULL == comp1->history )
{
history->shortcut = history;
history->stable = 0;
} else {
comp1->history->child = history;
history->shortcut = comp1->history->shortcut;
history->stable = comp1->history->stable;
}
if ( NULL != comp2->history && comp2->history->stable > history->stable )
history->stable = comp2->history->stable;
history->val = comp1->grey_level;
history->size = comp1->size;
// put comp1 to history
comp->var = comp1->var;
comp->dvar = comp1->dvar;
if ( comp1->size > 0 && comp2->size > 0 )
{
comp1->tail->next = comp2->head;
comp2->head->prev = comp1->tail;
}
head = ( comp1->size > 0 ) ? comp1->head : comp2->head;
tail = ( comp2->size > 0 ) ? comp2->tail : comp1->tail;
// always made the newly added in the last of the pixel list (comp1 ... comp2)
} else {
if ( NULL == comp2->history )
{
history->shortcut = history;
history->stable = 0;
} else {
comp2->history->child = history;
history->shortcut = comp2->history->shortcut;
history->stable = comp2->history->stable;
}
if ( NULL != comp1->history && comp1->history->stable > history->stable )
history->stable = comp1->history->stable;
history->val = comp2->grey_level;
history->size = comp2->size;
// put comp2 to history
comp->var = comp2->var;
comp->dvar = comp2->dvar;
if ( comp1->size > 0 && comp2->size > 0 )
{
comp2->tail->next = comp1->head;
comp1->head->prev = comp2->tail;
}
head = ( comp2->size > 0 ) ? comp2->head : comp1->head;
tail = ( comp1->size > 0 ) ? comp1->tail : comp2->tail;
// always made the newly added in the last of the pixel list (comp2 ... comp1)
}
comp->head = head;
comp->tail = tail;
comp->history = history;
comp->size = comp1->size + comp2->size;
}
static float
MSERVariationCalc( MSERConnectedComp* comp, int delta )
{
MSERGrowHistory* history = comp->history;
int val = comp->grey_level;
if ( NULL != history )
{
MSERGrowHistory* shortcut = history->shortcut;
while ( shortcut != shortcut->shortcut && shortcut->val + delta > val )
shortcut = shortcut->shortcut;
MSERGrowHistory* child = shortcut->child;
while ( child != child->child && child->val + delta <= val )
{
shortcut = child;
child = child->child;
}
// get the position of history where the shortcut->val <= delta+val and shortcut->child->val >= delta+val
history->shortcut = shortcut;// delta的使用位置
return (float)(comp->size-shortcut->size)/(float)shortcut->size;
// here is a small modification of MSER where cal ||R_{i}-R_{i-delta}||/||R_{i-delta}||
// in standard MSER, cal ||R_{i+delta}-R_{i-delta}||/||R_{i}||
// my calculation is simpler and much easier to implement
}
return 1.;
}
static bool MSERStableCheck( MSERConnectedComp* comp, MSERParams params )//判断区域的大小
{
// tricky part: it actually check the stablity of one-step back
if ( comp->history == NULL || comp->history->size <= params.minArea || comp->history->size >= params.maxArea )
return 0;
float div = (float)(comp->history->size-comp->history->stable)/(float)comp->history->size;//比率
float var = MSERVariationCalc( comp, params.delta );
int dvar = ( comp->var < var || (unsigned long)(comp->history->val + 1) < comp->grey_level );
int stable = ( dvar && !comp->dvar && comp->var < params.maxVariation && div > params.minDiversity );//判断是否为稳定区域
comp->var = var;
comp->dvar = dvar;
if ( stable )
comp->history->stable = comp->history->size;
return stable != 0;
}
// add a pixel to the pixel list
static void accumulateMSERComp( MSERConnectedComp* comp, LinkedPoint* point )
{
if ( comp->size > 0 )
{
point->prev = comp->tail;
comp->tail->next = point;
point->next = NULL;
} else {
point->prev = NULL;
point->next = NULL;
comp->head = point;
}
comp->tail = point;
comp->size++;
}
// convert the point set to CvSeq
static CvContour* MSERToContour( MSERConnectedComp* comp, CvMemStorage* storage )
{
CvSeq* _contour = cvCreateSeq( CV_SEQ_KIND_GENERIC|CV_32SC2, sizeof(CvContour), sizeof(CvPoint), storage );
CvContour* contour = (CvContour*)_contour;
cvSeqPushMulti( _contour, 0, comp->history->size );
LinkedPoint* lpt = comp->head;
for ( int i = 0; i < comp->history->size; i++ )
{
CvPoint* pt = CV_GET_SEQ_ELEM( CvPoint, _contour, i );
pt->x = lpt->pt.x;
pt->y = lpt->pt.y;
lpt = lpt->next;
}
cvBoundingRect( contour );
return contour;
}
// to preprocess src image to following format
// 32-bit image
// > 0 is available, < 0 is visited
// 17~19 bits is the direction
// 8~11 bits is the bucket it falls to (for BitScanForward)
// 0~8 bits is the color
static int* preprocessMSER_8UC1( CvMat* img,
int*** heap_cur,
CvMat* src,
CvMat* mask )
{
int srccpt = src->step-src->cols; //图像是4位对齐的
int cpt_1 = img->cols-src->cols-1; //2^N图像差值
int* imgptr = img->data.i;
int* startptr;
int level_size[256];
for ( int i = 0; i < 256; i++ )
level_size[i] = 0; //256级，初始化每级为0
for ( int i = 0; i < src->cols+2; i++ )
{
*imgptr = -1;
imgptr++;
}
imgptr += cpt_1-1;
uchar* srcptr = src->data.ptr;
if ( mask )
{
startptr = 0;
uchar* maskptr = mask->data.ptr;
for ( int i = 0; i < src->rows; i++ )
{
*imgptr = -1;
imgptr++;
for ( int j = 0; j < src->cols; j++ )
{
if ( *maskptr ) //如果是MASK区域
{
if ( !startptr )
startptr = imgptr;
*srcptr = 0xff-*srcptr; //255-src的值 取反
level_size[*srcptr]++;
*imgptr = ((*srcptr>>5)<<8)|(*srcptr);
} else {
*imgptr = -1; //如果不是MASK 区域 = -1
}
imgptr++;
srcptr++;
maskptr++;
}
*imgptr = -1;
imgptr += cpt_1;
srcptr += srccpt;
maskptr += srccpt;
}
} else {
startptr = imgptr+img->cols+1;
for ( int i = 0; i < src->rows; i++ )
{
*imgptr = -1;
imgptr++;
for ( int j = 0; j < src->cols; j++ )
{
*srcptr = 0xff-*srcptr;//255 - 8bit的图像像素
level_size[*srcptr]++;//计算反过来的直方
*imgptr = ((*srcptr>>5)<<8)|(*srcptr);//不知道用意，大于32的数变大，小的数不变
imgptr++;
srcptr++;
}
*imgptr = -1;
imgptr += cpt_1;//
srcptr += srccpt;//
}
}
for ( int i = 0; i < src->cols+2; i++ )
{
*imgptr = -1;
imgptr++;
}
heap_cur[0][0] = 0;
for ( int i = 1; i < 256; i++ )
{
heap_cur[i] = heap_cur[i-1]+level_size[i-1]+1;//地址增加（level_size[i-1]+1）*sizeof(heap_cur[i-1])
heap_cur[i][0] = 0; //记录每个灰度级的个数
}
return startptr;//mark的话，选取startptr为0
//不然的话startptr为 imgptr+img->cols+1;
}
static void extractMSER_8UC1_Pass( int* ioptr,
int* imgptr,
int*** heap_cur,
LinkedPoint* ptsptr,
MSERGrowHistory* histptr,
MSERConnectedComp* comptr,
int step,
int stepmask,
int stepgap,
MSERParams params,
int color,
CvSeq* contours,
CvMemStorage* storage )
{
comptr->grey_level = 256;
comptr++;
comptr->grey_level = (*imgptr)&0xff;
initMSERComp( comptr );
*imgptr |= 0x80000000;
heap_cur += (*imgptr)&0xff;//对应灰度级current的地址值
int dir[] = { 1, step, -1, -step };
#ifdef __INTRIN_ENABLED__
unsigned long heapbit[] = { 0, 0, 0, 0, 0, 0, 0, 0 };
unsigned long* bit_cur = heapbit+(((*imgptr)&0x700)>>8); //选取9-11位的值 对应heapbit 里面的值
// to preprocess src image to following format
// 32-bit image
// > 0 is available, < 0 is visited
// 17~19 bits is the direction
// 8~11 bits is the bucket it falls to (for BitScanForward)
// 0~8 bits is the color
#endif
for ( ; ; )
{
// take tour of all the 4 directions
while ( ((*imgptr)&0x70000) < 0x40000 )
{
// get the neighbor
int* imgptr_nbr = imgptr+dir[((*imgptr)&0x70000)>>16]; //得到临近的值
if ( *imgptr_nbr >= 0 ) // if the neighbor is not visited yet
{
*imgptr_nbr |= 0x80000000; // mark it as visited
if ( ((*imgptr_nbr)&0xff) < ((*imgptr)&0xff) )
{
// when the value of neighbor smaller than current
// push current to boundary heap and make the neighbor to be the current one
// create an empty comp
(*heap_cur)++;
**heap_cur = imgptr;
*imgptr += 0x10000;//换到下一个方向dir[]
heap_cur += ((*imgptr_nbr)&0xff)-((*imgptr)&0xff);//差值
#ifdef __INTRIN_ENABLED__
_bitset( bit_cur, (*imgptr)&0x1f );
bit_cur += (((*imgptr_nbr)&0x700)-((*imgptr)&0x700))>>8;
#endif
imgptr = imgptr_nbr;
comptr++;
initMSERComp( comptr );
comptr->grey_level = (*imgptr)&0xff;
continue;
} else {
// otherwise, push the neighbor to boundary heap
heap_cur[((*imgptr_nbr)&0xff)-((*imgptr)&0xff)]++;
*heap_cur[((*imgptr_nbr)&0xff)-((*imgptr)&0xff)] = imgptr_nbr;
#ifdef __INTRIN_ENABLED__
_bitset( bit_cur+((((*imgptr_nbr)&0x700)-((*imgptr)&0x700))>>8), (*imgptr_nbr)&0x1f );
#endif
}
}
*imgptr += 0x10000;//换到下一个方向dir[]
}
int imsk = (int)(imgptr-ioptr);
ptsptr->pt = cvPoint( imsk&stepmask, imsk>>stepgap );
// get the current location
accumulateMSERComp( comptr, ptsptr );
ptsptr++;
// get the next pixel from boundary heap
if ( **heap_cur )
{
imgptr = **heap_cur;
(*heap_cur)--;
#ifdef __INTRIN_ENABLED__
if ( !**heap_cur )
_bitreset( bit_cur, (*imgptr)&0x1f );
#endif
} else {
#ifdef __INTRIN_ENABLED__
bool found_pixel = 0;
unsigned long pixel_val;
for ( int i = ((*imgptr)&0x700)>>8; i < 8; i++ )
{
if ( _BitScanForward( &pixel_val, *bit_cur ) )
{
found_pixel = 1;
pixel_val += i<<5;
heap_cur += pixel_val-((*imgptr)&0xff);
break;
}
bit_cur++;
}
if ( found_pixel )
#else
heap_cur++;
unsigned long pixel_val = 0;
for ( unsigned long i = ((*imgptr)&0xff)+1; i < 256; i++ )
{
if ( **heap_cur )
{
pixel_val = i;
break;
}
heap_cur++;
}
if ( pixel_val )
#endif
{
imgptr = **heap_cur;
(*heap_cur)--;
#ifdef __INTRIN_ENABLED__
if ( !**heap_cur )
_bitreset( bit_cur, pixel_val&0x1f );
#endif
if ( pixel_val < comptr[-1].grey_level )
{
// check the stablity and push a new history, increase the grey level
if ( MSERStableCheck( comptr, params ) )
{
CvContour* contour = MSERToContour( comptr, storage );
contour->color = color;
cvSeqPush( contours, &contour );
}
MSERNewHistory( comptr, histptr );
comptr[0].grey_level = pixel_val;
histptr++;
} else {
// keep merging top two comp in stack until the grey level >= pixel_val
for ( ; ; )
{
comptr--;
MSERMergeComp( comptr+1, comptr, comptr, histptr );
histptr++;
if ( pixel_val <= comptr[0].grey_level )
break;
if ( pixel_val < comptr[-1].grey_level )
{
// check the stablity here otherwise it wouldn't be an ER
if ( MSERStableCheck( comptr, params ) )
{
CvContour* contour = MSERToContour( comptr, storage );
contour->color = color;
cvSeqPush( contours, &contour );
}
MSERNewHistory( comptr, histptr );
comptr[0].grey_level = pixel_val;
histptr++;
break;
}
}
}
} else
break;
}
}
}
static void extractMSER_8UC1( CvMat* src,
CvMat* mask,
CvSeq* contours,
CvMemStorage* storage,
MSERParams params )
{
int step = 8;
int stepgap = 3;
while ( step < src->step+2 )
{
step <<= 1;
stepgap++;
}
int stepmask = step-1;
// to speedup the process, make the width to be 2^N
CvMat* img = cvCreateMat( src->rows+2, step, CV_32SC1 );
int* ioptr = img->data.i+step+1;
int* imgptr;
// pre-allocate boundary heap
int** heap = (int**)cvAlloc( (src->rows*src->cols+256)*sizeof(heap[0]) );
int** heap_start[256];
heap_start[0] = heap;
// pre-allocate linked point and grow history
LinkedPoint* pts = (LinkedPoint*)cvAlloc( src->rows*src->cols*sizeof(pts[0]) );
MSERGrowHistory* history = (MSERGrowHistory*)cvAlloc( src->rows*src->cols*sizeof(history[0]) );
MSERConnectedComp comp[257];
// darker to brighter (MSER-)
imgptr = preprocessMSER_8UC1( img, heap_start, src, mask );
extractMSER_8UC1_Pass( ioptr, imgptr, heap_start, pts, history, comp, step, stepmask, stepgap, params, -1, contours, storage );
// brighter to darker (MSER+)
imgptr = preprocessMSER_8UC1( img, heap_start, src, mask );
extractMSER_8UC1_Pass( ioptr, imgptr, heap_start, pts, history, comp, step, stepmask, stepgap, params, 1, contours, storage );
// clean up
cvFree( &history );
cvFree( &heap );
cvFree( &pts );
cvReleaseMat( &img );
}
struct MSCRNode;
struct TempMSCR
{
MSCRNode* head;
MSCRNode* tail;
double m; // the margin used to prune area later
int size;
};
struct MSCRNode
{
MSCRNode* shortcut;
// to make the finding of root less painful
MSCRNode* prev;
MSCRNode* next;
// a point double-linked list
TempMSCR* tmsr;
// the temporary msr (set to NULL at every re-initialise)
TempMSCR* gmsr;
// the global msr (once set, never to NULL)
int index;
// the index of the node, at this point, it should be x at the first 16-bits, and y at the last 16-bits.
int rank;
int reinit;
int size, sizei;
double dt, di;
double s;
};
struct MSCREdge
{
double chi;
MSCRNode* left;
MSCRNode* right;
};
static double ChiSquaredDistance( uchar* x, uchar* y )
{
return (double)((x[0]-y[0])*(x[0]-y[0]))/(double)(x[0]+y[0]+1e-10)+
(double)((x[1]-y[1])*(x[1]-y[1]))/(double)(x[1]+y[1]+1e-10)+
(double)((x[2]-y[2])*(x[2]-y[2]))/(double)(x[2]+y[2]+1e-10);
}
static void initMSCRNode( MSCRNode* node )
{
node->gmsr = node->tmsr = NULL;
node->reinit = 0xffff;
node->rank = 0;
node->sizei = node->size = 1;
node->prev = node->next = node->shortcut = node;
}
// the preprocess to get the edge list with proper gaussian blur
static int preprocessMSER_8UC3( MSCRNode* node,
MSCREdge* edge,
double* total,
CvMat* src,
CvMat* mask,
CvMat* dx,
CvMat* dy,
int Ne,
int edgeBlurSize )
{
int srccpt = src->step-src->cols*3;
uchar* srcptr = src->data.ptr;
uchar* lastptr = src->data.ptr+3;
double* dxptr = dx->data.db;
for ( int i = 0; i < src->rows; i++ )
{
for ( int j = 0; j < src->cols-1; j++ )
{
*dxptr = ChiSquaredDistance( srcptr, lastptr );//求x方向的距离(3通道)
dxptr++;
srcptr += 3;
lastptr += 3;
}
srcptr += srccpt+3;
lastptr += srccpt+3;
}
srcptr = src->data.ptr;
lastptr = src->data.ptr+src->step;
double* dyptr = dy->data.db;
for ( int i = 0; i < src->rows-1; i++ )
{
for ( int j = 0; j < src->cols; j++ )
{
*dyptr = ChiSquaredDistance( srcptr, lastptr );//求y方向的距离(3通道)
dyptr++;
srcptr += 3;
lastptr += 3;
}
srcptr += srccpt;
lastptr += srccpt;
}
// get dx and dy and blur it
if ( edgeBlurSize >= 1 )
{
cvSmooth( dx, dx, CV_GAUSSIAN, edgeBlurSize, edgeBlurSize );//彩色图像进行高斯模糊
cvSmooth( dy, dy, CV_GAUSSIAN, edgeBlurSize, edgeBlurSize );
}
dxptr = dx->data.db;
dyptr = dy->data.db;
// assian dx, dy to proper edge list and initialize mscr node
// the nasty code here intended to avoid extra loops
if ( mask )
{
Ne = 0;
int maskcpt = mask->step-mask->cols+1;
uchar* maskptr = mask->data.ptr;
MSCRNode* nodeptr = node;
initMSCRNode( nodeptr );
nodeptr->index = 0;
*total += edge->chi = *dxptr;
if ( maskptr[0] && maskptr[1] )
{
edge->left = nodeptr;
edge->right = nodeptr+1;
edge++;
Ne++;
}
dxptr++;
nodeptr++;
maskptr++;
for ( int i = 1; i < src->cols-1; i++ )
{
initMSCRNode( nodeptr );
nodeptr->index = i;
if ( maskptr[0] && maskptr[1] )
{
*total += edge->chi = *dxptr;
edge->left = nodeptr;
edge->right = nodeptr+1;
edge++;
Ne++;
}
dxptr++;
nodeptr++;
maskptr++;
}
initMSCRNode( nodeptr );
nodeptr->index = src->cols-1;
nodeptr++;
maskptr += maskcpt;
for ( int i = 1; i < src->rows-1; i++ )
{
initMSCRNode( nodeptr );
nodeptr->index = i<<16;
if ( maskptr[0] )
{
if ( maskptr[-mask->step] )
{
*total += edge->chi = *dyptr;
edge->left = nodeptr-src->cols;
edge->right = nodeptr;
edge++;
Ne++;
}
if ( maskptr[1] )
{
*total += edge->chi = *dxptr;
edge->left = nodeptr;
edge->right = nodeptr+1;
edge++;
Ne++;
}
}
dyptr++;
dxptr++;
nodeptr++;
maskptr++;
for ( int j = 1; j < src->cols-1; j++ )
{
initMSCRNode( nodeptr );
nodeptr->index = (i<<16)|j;
if ( maskptr[0] )
{
if ( maskptr[-mask->step] )
{
*total += edge->chi = *dyptr;
edge->left = nodeptr-src->cols;
edge->right = nodeptr;
edge++;
Ne++;
}
if ( maskptr[1] )
{
*total += edge->chi = *dxptr;
edge->left = nodeptr;
edge->right = nodeptr+1;
edge++;
Ne++;
}
}
dyptr++;
dxptr++;
nodeptr++;
maskptr++;
}
initMSCRNode( nodeptr );
nodeptr->index = (i<<16)|(src->cols-1);
if ( maskptr[0] && maskptr[-mask->step] )
{
*total += edge->chi = *dyptr;
edge->left = nodeptr-src->cols;
edge->right = nodeptr;
edge++;
Ne++;
}
dyptr++;
nodeptr++;
maskptr += maskcpt;
}
initMSCRNode( nodeptr );
nodeptr->index = (src->rows-1)<<16;
if ( maskptr[0] )
{
if ( maskptr[1] )
{
*total += edge->chi = *dxptr;
edge->left = nodeptr;
edge->right = nodeptr+1;
edge++;
Ne++;
}
if ( maskptr[-mask->step] )
{
*total += edge->chi = *dyptr;
edge->left = nodeptr-src->cols;
edge->right = nodeptr;
edge++;
Ne++;
}
}
dxptr++;
dyptr++;
nodeptr++;
maskptr++;
for ( int i = 1; i < src->cols-1; i++ )
{
initMSCRNode( nodeptr );
nodeptr->index = ((src->rows-1)<<16)|i;
if ( maskptr[0] )
{
if ( maskptr[1] )
{
*total += edge->chi = *dxptr;
edge->left = nodeptr;
edge->right = nodeptr+1;
edge++;
Ne++;
}
if ( maskptr[-mask->step] )
{
*total += edge->chi = *dyptr;
edge->left = nodeptr-src->cols;
edge->right = nodeptr;
edge++;
Ne++;
}
}
dxptr++;
dyptr++;
nodeptr++;
maskptr++;
}
initMSCRNode( nodeptr );
nodeptr->index = ((src->rows-1)<<16)|(src->cols-1);
if ( maskptr[0] && maskptr[-mask->step] )
{
*total += edge->chi = *dyptr;
edge->left = nodeptr-src->cols;
edge->right = nodeptr;
Ne++;
}
} else {
MSCRNode* nodeptr = node;
initMSCRNode( nodeptr );
nodeptr->index = 0;
*total += edge->chi = *dxptr;
dxptr++; //对X
edge->left = nodeptr;
edge->right = nodeptr+1;
edge++;
nodeptr++;
for ( int i = 1; i < src->cols-1; i++ )
{
initMSCRNode( nodeptr );
nodeptr->index = i;
*total += edge->chi = *dxptr;
dxptr++;
edge->left = nodeptr;
edge->right = nodeptr+1;
edge++;
nodeptr++;
}
initMSCRNode( nodeptr );
nodeptr->index = src->cols-1;
nodeptr++;
for ( int i = 1; i < src->rows-1; i++ )
{
initMSCRNode( nodeptr );
nodeptr->index = i<<16;
*total += edge->chi = *dyptr;
dyptr++; //对Y
edge->left = nodeptr-src->cols;
edge->right = nodeptr;
edge++;
*total += edge->chi = *dxptr;
dxptr++;
edge->left = nodeptr;
edge->right = nodeptr+1;
edge++;
nodeptr++;
for ( int j = 1; j < src->cols-1; j++ )
{
initMSCRNode( nodeptr );
nodeptr->index = (i<<16)|j;
*total += edge->chi = *dyptr;
dyptr++;
edge->left = nodeptr-src->cols;
edge->right = nodeptr;
edge++;
*total += edge->chi = *dxptr;
dxptr++;
edge->left = nodeptr;
edge->right = nodeptr+1;
edge++;
nodeptr++;
}
initMSCRNode( nodeptr );
nodeptr->index = (i<<16)|(src->cols-1);
*total += edge->chi = *dyptr;
dyptr++;
edge->left = nodeptr-src->cols;
edge->right = nodeptr;
edge++;
nodeptr++;
}
initMSCRNode( nodeptr );
nodeptr->index = (src->rows-1)<<16;
*total += edge->chi = *dxptr;
dxptr++;
edge->left = nodeptr;
edge->right = nodeptr+1;
edge++;
*total += edge->chi = *dyptr;
dyptr++;
edge->left = nodeptr-src->cols;
edge->right = nodeptr;
edge++;
nodeptr++;
for ( int i = 1; i < src->cols-1; i++ )
{
initMSCRNode( nodeptr );
nodeptr->index = ((src->rows-1)<<16)|i;
*total += edge->chi = *dxptr;
dxptr++;
edge->left = nodeptr;
edge->right = nodeptr+1;
edge++;
*total += edge->chi = *dyptr;
dyptr++;
edge->left = nodeptr-src->cols;
edge->right = nodeptr;
edge++;
nodeptr++;
}
initMSCRNode( nodeptr );
nodeptr->index = ((src->rows-1)<<16)|(src->cols-1);
*total += edge->chi = *dyptr;
edge->left = nodeptr-src->cols;
edge->right = nodeptr;
}
return Ne;
}
#define cmp_mscr_edge(edge1, edge2) \
((edge1).chi < (edge2).chi)
static CV_IMPLEMENT_QSORT( QuickSortMSCREdge, MSCREdge, cmp_mscr_edge )
// to find the root of one region//找到根节点
static MSCRNode* findMSCR( MSCRNode* x )
{
MSCRNode* prev = x;
MSCRNode* next;
for ( ; ; )
{
next = x->shortcut;
x->shortcut = prev;
if ( next == x ) break;
prev= x;
x = next;
}
MSCRNode* root = x;
for ( ; ; )
{
prev = x->shortcut;
x->shortcut = root;
if ( prev == x ) break;
x = prev;
}
return root;
}
// the stable mscr should be:
// bigger than minArea and smaller than maxArea
// differ from its ancestor more than minDiversity
static bool MSCRStableCheck( MSCRNode* x, MSERParams params )//将过大过小的区域去除掉
{
if ( x->size <= params.minArea || x->size >= params.maxArea )
return 0;
if ( x->gmsr == NULL )
return 1;
double div = (double)(x->size-x->gmsr->size)/(double)x->size;//计算div
return div > params.minDiversity;//比例大于 minDiversity 返回BOOL
}
static void
extractMSER_8UC3( CvMat* src,
CvMat* mask,
CvSeq* contours,
CvMemStorage* storage,
MSERParams params )
{
MSCRNode* map = (MSCRNode*)cvAlloc( src->cols*src->rows*sizeof(map[0]) );
int Ne = src->cols*src->rows*2-src->cols-src->rows;
MSCREdge* edge = (MSCREdge*)cvAlloc( Ne*sizeof(edge[0]) );
TempMSCR* mscr = (TempMSCR*)cvAlloc( src->cols*src->rows*sizeof(mscr[0]) );
double emean = 0;
CvMat* dx = cvCreateMat( src->rows, src->cols-1, CV_64FC1 );
CvMat* dy = cvCreateMat( src->rows-1, src->cols, CV_64FC1 );
Ne = preprocessMSER_8UC3( map, edge, &emean, src, mask, dx, dy, Ne, params.edgeBlurSize );
emean = emean / (double)Ne;
QuickSortMSCREdge( edge, Ne, 0 );
MSCREdge* edge_ub = edge+Ne;
MSCREdge* edgeptr = edge;
TempMSCR* mscrptr = mscr;
// the evolution process//卷积过程，只对彩色图像有用
for ( int i = 0; i < params.maxEvolution; i++ )
{
double k = (double)i/(double)params.maxEvolution*(TABLE_SIZE-1);
int ti = cvFloor(k);
double reminder = k-ti;
double thres = emean*(chitab3[ti]*(1-reminder)+chitab3[ti+1]*reminder);
// to process all the edges in the list that chi < thres
while ( edgeptr < edge_ub && edgeptr->chi < thres )
{
MSCRNode* lr = findMSCR( edgeptr->left );
MSCRNode* rr = findMSCR( edgeptr->right );
// get the region root (who is responsible)
if ( lr != rr )
{
// rank idea take from: N-tree Disjoint-Set Forests for Maximally Stable Extremal Regions
if ( rr->rank > lr->rank )
{
MSCRNode* tmp;
CV_SWAP( lr, rr, tmp );
} else if ( lr->rank == rr->rank ) {
// at the same rank, we will compare the size
if ( lr->size > rr->size )
{
MSCRNode* tmp;
CV_SWAP( lr, rr, tmp );
}
lr->rank++;
}
rr->shortcut = lr;
lr->size += rr->size;
// join rr to the end of list lr (lr is a endless double-linked list)
lr->prev->next = rr;
lr->prev = rr->prev;
rr->prev->next = lr;
rr->prev = lr;
// area threshold force to reinitialize
if ( lr->size > (lr->size-rr->size)*params.areaThreshold )
{
lr->sizei = lr->size;
lr->reinit = i;
if ( lr->tmsr != NULL )
{
lr->tmsr->m = lr->dt-lr->di;
lr->tmsr = NULL;
}
lr->di = edgeptr->chi;
lr->s = 1e10;
}
lr->dt = edgeptr->chi;
if ( i > lr->reinit )
{
double s = (double)(lr->size-lr->sizei)/(lr->dt-lr->di);
if ( s < lr->s )
{
// skip the first one and check stablity
if ( i > lr->reinit+1 && MSCRStableCheck( lr, params ) )
{
if ( lr->tmsr == NULL )
{
lr->gmsr = lr->tmsr = mscrptr;
mscrptr++;
}
lr->tmsr->size = lr->size;
lr->tmsr->head = lr;
lr->tmsr->tail = lr->prev;
lr->tmsr->m = 0;
}
lr->s = s;
}
}
}
edgeptr++;
}
if ( edgeptr >= edge_ub )
break;
}
for ( TempMSCR* ptr = mscr; ptr < mscrptr; ptr++ )
// to prune area with margin less than minMargin
if ( ptr->m > params.minMargin )
{
CvSeq* _contour = cvCreateSeq( CV_SEQ_KIND_GENERIC|CV_32SC2, sizeof(CvContour), sizeof(CvPoint), storage );
cvSeqPushMulti( _contour, 0, ptr->size );
MSCRNode* lpt = ptr->head;
for ( int i = 0; i < ptr->size; i++ )
{
CvPoint* pt = CV_GET_SEQ_ELEM( CvPoint, _contour, i );
pt->x = (lpt->index)&0xffff;
pt->y = (lpt->index)>>16;
lpt = lpt->next;
}
CvContour* contour = (CvContour*)_contour;
cvBoundingRect( contour );
contour->color = 0;
cvSeqPush( contours, &contour );
}
cvReleaseMat( &dx );
cvReleaseMat( &dy );
cvFree( &mscr );
cvFree( &edge );
cvFree( &map );
}
static void
extractMSER( CvArr* _img,
CvArr* _mask,
CvSeq** _contours,
CvMemStorage* storage,
MSERParams params )
{
CvMat srchdr, *src = cvGetMat( _img, &srchdr );
CvMat maskhdr, *mask = _mask ? cvGetMat( _mask, &maskhdr ) : 0;
CvSeq* contours = 0;
CV_Assert(src != 0);
CV_Assert(CV_MAT_TYPE(src->type) == CV_8UC1 || CV_MAT_TYPE(src->type) == CV_8UC3);
CV_Assert(mask == 0 || (CV_ARE_SIZES_EQ(src, mask) && CV_MAT_TYPE(mask->type) == CV_8UC1));
CV_Assert(storage != 0);
contours = *_contours = cvCreateSeq( 0, sizeof(CvSeq), sizeof(CvSeq*), storage );
// choose different method for different image type
// for grey image, it is: Linear Time Maximally Stable Extremal Regions
// for color image, it is: Maximally Stable Colour Regions for Recognition and Matching
switch ( CV_MAT_TYPE(src->type) )
{
case CV_8UC1: //灰度图像
extractMSER_8UC1( src, mask, contours, storage, params );
break;
case CV_8UC3: //彩色图像
extractMSER_8UC3( src, mask, contours, storage, params );
break;
}
}
MSER::MSER( int _delta, int _min_area, int _max_area,
double _max_variation, double _min_diversity,
int _max_evolution, double _area_threshold,
double _min_margin, int _edge_blur_size )
: delta(_delta), minArea(_min_area), maxArea(_max_area),
maxVariation(_max_variation), minDiversity(_min_diversity),
maxEvolution(_max_evolution), areaThreshold(_area_threshold),
minMargin(_min_margin), edgeBlurSize(_edge_blur_size)
{
}
void MSER::operator()( const Mat& image, vector<vector<Point> >& dstcontours, const Mat& mask ) const
{
CvMat _image = image, _mask, *pmask = 0;
if( mask.data )
pmask = &(_mask = mask);
MemStorage storage(cvCreateMemStorage(0));
Seq<CvSeq*> contours;
extractMSER( &_image, pmask, &contours.seq, storage,
MSERParams(delta, minArea, maxArea, maxVariation, minDiversity,
maxEvolution, areaThreshold, minMargin, edgeBlurSize));
SeqIterator<CvSeq*> it = contours.begin();
size_t i, ncontours = contours.size();
dstcontours.resize(ncontours);
for( i = 0; i < ncontours; i++, ++it )
Seq<Point>(*it).copyTo(dstcontours[i]);//得到轮廓点
}
void MserFeatureDetector::detectImpl( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask ) const
{
vector<vector<Point> > msers;
(*this)(image, msers, mask);
vector<vector<Point> >::const_iterator contour_it = msers.begin();
Rect r(0, 0, image.cols, image.rows);
for( ; contour_it != msers.end(); ++contour_it )
{
// TODO check transformation from MSER region to KeyPoint
RotatedRect rect = fitEllipse(Mat(*contour_it));
float diam = sqrt(rect.size.height*rect.size.width);
if( diam > std::numeric_limits<float>::epsilon() && r.contains(rect.center) )
keypoints.push_back( KeyPoint(rect.center, diam) ); //存储特征点
}
}
}
MSER检测子的基本原理是：对一幅灰度图像，从0~255分别取阈值，将大于
阈值的点设为1，小于阈值的点设为0，从而得到256幅阈值分割的二值图像，通过前后相邻阈值图像间区域比较，得出区域面积关于阈值变化的关系。最后选取当区域面积的变化相对于阈值的变化小于某个值时所检测出的区域即为最大稳定极值区域(MSER)。由于阈值可以向两个相反的方向变化，所以会产生两种不
同的极值区域，因此，最大稳定极值区域包括最大稳定极大值区域（较亮的极值
区域）与最大稳定极小值区域（较暗的极值区域）两种。
这样获得的区域有如下特性：
(1) 图像亮度的单调变化不会影响最后得到的区域，因为它依赖于像素亮度
的大小顺序，在图像亮度单调变化的时候该顺序不会改变，这使得对于一般的光
照变换，区域是稳定的。
(2) 几何变换不会改变区域的邻域关系，符合条件的区域都能检测到，这是
由于连续的几何变换保留了拓扑性，单连通区域内的像素变换后仍然在单连通区
域内。
(3) 由于是极值区域，所以最终得到的区域集合在一个宽泛的几何、光照变
换下保持不变。
MSER的提取算法包括检测极值区域和确定最大稳定极值区域这两大部分，
具体提取过程包括三个步骤：图像像素的排序、极值区域的检测和最大稳定极值
区域的判定。下面将详细介绍MSER检测子的具体实现过程。
图像像素的排序:
提取最大稳定极值区域时所采用的图像为灰度图像。算法首先将图像的像素
按照灰度值的大小进行排序，具体采用箱排序法(Bin Sort)来实现。假设图像的灰度范围S 较小，如{0, , 255} ，则该步骤算法是线性的，时间复杂度为O ( n)
，其中n 为灰度图像的像素数。排序产生了一个256维的序列，该序列的每个单元存放了具有相同灰度值的像素及其在图像中的坐标。
极值区域的检测:
检测算法按照阈值的灰度级从小到大（或从大到小）的顺序，依次计算每个
阈值图像上的极值区域，这个过程最终生成一个树状的数据结构，称为区域树。
其中，区域树的每一层对应一个阈值图像，且层上的每个节点代表了相应阈值图
像上的一个极值区域。区域树可使用合并搜索(Union-find)算法来建立，该算具
有较高的计算效率，其时间复杂度为 O ( n loglog n )，其中 n为灰度图像的像素数。
具体计算时，区域树的各个节点应满足以下规则
(1) 一个节点代表一个极值区域，且该区域内所有像素的灰度值相同，记为该节点的灰度值。
(2) 若节点m 是节点 n 的父节点，则节点m 的灰度值大于或等于节点 n 的灰度值。
(3) 若节点 m 是节点n 的父节点，且节点n 和节点m 的灰度值相同，则节点m被标记为重命名的父节点。
以上是区域树上节点的基本性质，下面详细说明如何计算每个节点所代表的极值区域。在某一阈值图像上，算法按照以下四种不同的情形来计算图像上的连
通区域，称为四邻域法则:
(1) 对于像素 p ，在它的所有邻接像素中，若没有邻接像素属于某个已知的节点，则建立新的节点，像素 p 属于该新建节点。
(2) 对于像素 p ，若它的所有邻接像素均属于某个最高的父节点n，并且该节点的灰度值等于该像素的灰度值，则像素p 属于已知节点 n 。
(3) 对于像素 p ，若它的所有邻接像素均属于某个最高的父节点n ，并且该节点的灰度值不等于该像素的灰度值，则建立新的节点，并把它标记为已知节点n的父节点，像素p 属于该新建节点。
(4) 对于像素 p ，若它的邻接像素属于几个不同的最高父节点，则建立新的节点，并把它标记为这些不同已知节点的父节点，像素 p 属于该新建节点。这个新建节点的过程也是区域合并的过程。
通过上述步骤，我们成功地建立了一棵区域树，但此时的区域树上存在着许多重复的节点，可以通过对重复节点重新命名的方法对其进行简化，简化的结果使得每个节点的灰度值都小于它的父节点的灰度值。
区域树简化的规则如下
(1) 对于被标记为重命名的节点 n ，找到它的最高重命名节点 m。
(2) 把节点 m 标记为节点 n的所有子节点的父节点。
简化后的区域树具有如下特点
(1) 区域树的最大高度是图像的灰度值范围 S的长度。
(2) 节点以及它的所有子节点所代表的区域的集合，是一个极值区域，该极值区域称作是该节点代表的区域。
(3) 从任一个叶节点出发上溯到它的最高父节点，这个过程是一个极值区域生长的过程。
(4) 所有节点所代表的区域的集合是图像本身。
区域树上的任一节点对应的是某个阈值图像上的极值区域。根据区域树的性
质可知，从叶节点到它的最高父节点之间的路径代表了一个区域增长的过程，并
且从一个叶节点出发，可以有多条不同的路径通向它的最高父节点。我们希望从
中选择一条最优路径w，该最优路径需满足以下条件
(1) 对于节点n 和它的父节点m ，在节点m 的所有子节点中n， 代表的极值区域具有最大的区域面积。
(2) 几个区域合并的过程可以看作是把较小区域的所有像素插入较大区域的过程。
最大稳定极值区域的判定:
根据上面的规则确定了区域增长的最优路径 后，对于该路径上的嵌套区
域，算法根据下面的判定法则计算该路径上的最大稳定极值区域
值得一提的是，提取MSER的算法与分水岭算法的计算过程基本上相同，但这两个算法的侧重点不同。分水岭算法是对图像区域的划分，它侧重于分析不同区域融合时的对应阈值，这类阈值对应的区域往往是发生较大变化的不稳定区域。而MSER提取算法则旨在寻找一类在较大阈值范围内变化较小的稳定区域，对于整幅图像而言，MSER检测子没有一个全局的或者最优的阈值，对于每一个嵌套区域组，均需要依次分析和检测它们各自对应的最优值。