mirror of https://github.com/opencv/opencv.git
Open Source Computer Vision Library
https://opencv.org/
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
411 lines
16 KiB
411 lines
16 KiB
/*M/////////////////////////////////////////////////////////////////////////////////////// |
|
// |
|
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. |
|
// |
|
// By downloading, copying, installing or using the software you agree to this license. |
|
// If you do not agree to this license, do not download, install, |
|
// copy or use the software. |
|
// |
|
// |
|
// Intel License Agreement |
|
// For Open Source Computer Vision Library |
|
// |
|
// Copyright (C) 2000, Intel Corporation, all rights reserved. |
|
// Third party copyrights are property of their respective owners. |
|
// |
|
// Redistribution and use in source and binary forms, with or without modification, |
|
// are permitted provided that the following conditions are met: |
|
// |
|
// * Redistribution's of source code must retain the above copyright notice, |
|
// this list of conditions and the following disclaimer. |
|
// |
|
// * Redistribution's 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 Intel Corporation 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 Intel Corporation or 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. |
|
// |
|
// 2011 Jason Newton <nevion@gmail.com> |
|
//M*/ |
|
// |
|
#include "precomp.hpp" |
|
#include <vector> |
|
|
|
namespace cv{ |
|
namespace connectedcomponents{ |
|
|
|
struct NoOp{ |
|
NoOp(){ |
|
} |
|
void init(int /*labels*/){ |
|
} |
|
inline |
|
void operator()(int r, int c, int l){ |
|
(void) r; |
|
(void) c; |
|
(void) l; |
|
} |
|
void finish(){} |
|
}; |
|
struct Point2ui64{ |
|
uint64 x, y; |
|
Point2ui64(uint64 _x, uint64 _y):x(_x), y(_y){} |
|
}; |
|
|
|
struct CCStatsOp{ |
|
const _OutputArray* _mstatsv; |
|
cv::Mat statsv; |
|
const _OutputArray* _mcentroidsv; |
|
cv::Mat centroidsv; |
|
std::vector<Point2ui64> integrals; |
|
|
|
CCStatsOp(OutputArray _statsv, OutputArray _centroidsv): _mstatsv(&_statsv), _mcentroidsv(&_centroidsv){ |
|
} |
|
inline |
|
void init(int nlabels){ |
|
_mstatsv->create(cv::Size(CC_STAT_MAX, nlabels), cv::DataType<int>::type); |
|
statsv = _mstatsv->getMat(); |
|
_mcentroidsv->create(cv::Size(2, nlabels), cv::DataType<double>::type); |
|
centroidsv = _mcentroidsv->getMat(); |
|
|
|
for(int l = 0; l < (int) nlabels; ++l){ |
|
int *row = (int *) &statsv.at<int>(l, 0); |
|
row[CC_STAT_LEFT] = INT_MAX; |
|
row[CC_STAT_TOP] = INT_MAX; |
|
row[CC_STAT_WIDTH] = INT_MIN; |
|
row[CC_STAT_HEIGHT] = INT_MIN; |
|
row[CC_STAT_AREA] = 0; |
|
} |
|
integrals.resize(nlabels, Point2ui64(0, 0)); |
|
} |
|
void operator()(int r, int c, int l){ |
|
int *row = &statsv.at<int>(l, 0); |
|
if(c > row[CC_STAT_WIDTH]){ |
|
row[CC_STAT_WIDTH] = c; |
|
}else{ |
|
if(c < row[CC_STAT_LEFT]){ |
|
row[CC_STAT_LEFT] = c; |
|
} |
|
} |
|
if(r > row[CC_STAT_HEIGHT]){ |
|
row[CC_STAT_HEIGHT] = r; |
|
}else{ |
|
if(r < row[CC_STAT_TOP]){ |
|
row[CC_STAT_TOP] = r; |
|
} |
|
} |
|
row[CC_STAT_AREA]++; |
|
Point2ui64 &integral = integrals[l]; |
|
integral.x += c; |
|
integral.y += r; |
|
} |
|
void finish(){ |
|
for(int l = 0; l < statsv.rows; ++l){ |
|
int *row = &statsv.at<int>(l, 0); |
|
row[CC_STAT_LEFT] = std::min(row[CC_STAT_LEFT], row[CC_STAT_WIDTH]); |
|
row[CC_STAT_WIDTH] = row[CC_STAT_WIDTH] - row[CC_STAT_LEFT] + 1; |
|
row[CC_STAT_TOP] = std::min(row[CC_STAT_TOP], row[CC_STAT_HEIGHT]); |
|
row[CC_STAT_HEIGHT] = row[CC_STAT_HEIGHT] - row[CC_STAT_TOP] + 1; |
|
|
|
Point2ui64 &integral = integrals[l]; |
|
double *centroid = ¢roidsv.at<double>(l, 0); |
|
double area = ((unsigned*)row)[CC_STAT_AREA]; |
|
centroid[0] = double(integral.x) / area; |
|
centroid[1] = double(integral.y) / area; |
|
} |
|
} |
|
}; |
|
|
|
//Find the root of the tree of node i |
|
template<typename LabelT> |
|
inline static |
|
LabelT findRoot(const LabelT *P, LabelT i){ |
|
LabelT root = i; |
|
while(P[root] < root){ |
|
root = P[root]; |
|
} |
|
return root; |
|
} |
|
|
|
//Make all nodes in the path of node i point to root |
|
template<typename LabelT> |
|
inline static |
|
void setRoot(LabelT *P, LabelT i, LabelT root){ |
|
while(P[i] < i){ |
|
LabelT j = P[i]; |
|
P[i] = root; |
|
i = j; |
|
} |
|
P[i] = root; |
|
} |
|
|
|
//Find the root of the tree of the node i and compress the path in the process |
|
template<typename LabelT> |
|
inline static |
|
LabelT find(LabelT *P, LabelT i){ |
|
LabelT root = findRoot(P, i); |
|
setRoot(P, i, root); |
|
return root; |
|
} |
|
|
|
//unite the two trees containing nodes i and j and return the new root |
|
template<typename LabelT> |
|
inline static |
|
LabelT set_union(LabelT *P, LabelT i, LabelT j){ |
|
LabelT root = findRoot(P, i); |
|
if(i != j){ |
|
LabelT rootj = findRoot(P, j); |
|
if(root > rootj){ |
|
root = rootj; |
|
} |
|
setRoot(P, j, root); |
|
} |
|
setRoot(P, i, root); |
|
return root; |
|
} |
|
|
|
//Flatten the Union Find tree and relabel the components |
|
template<typename LabelT> |
|
inline static |
|
LabelT flattenL(LabelT *P, LabelT length){ |
|
LabelT k = 1; |
|
for(LabelT i = 1; i < length; ++i){ |
|
if(P[i] < i){ |
|
P[i] = P[P[i]]; |
|
}else{ |
|
P[i] = k; k = k + 1; |
|
} |
|
} |
|
return k; |
|
} |
|
|
|
//Based on "Two Strategies to Speed up Connected Components Algorithms", the SAUF (Scan array union find) variant |
|
//using decision trees |
|
//Kesheng Wu, et al |
|
//Note: rows are encoded as position in the "rows" array to save lookup times |
|
//reference for 4-way: {{-1, 0}, {0, -1}};//b, d neighborhoods |
|
const int G4[2][2] = {{1, 0}, {0, -1}};//b, d neighborhoods |
|
//reference for 8-way: {{-1, -1}, {-1, 0}, {-1, 1}, {0, -1}};//a, b, c, d neighborhoods |
|
const int G8[4][2] = {{1, -1}, {1, 0}, {1, 1}, {0, -1}};//a, b, c, d neighborhoods |
|
template<typename LabelT, typename PixelT, typename StatsOp = NoOp > |
|
struct LabelingImpl{ |
|
LabelT operator()(const cv::Mat &I, cv::Mat &L, int connectivity, StatsOp &sop){ |
|
CV_Assert(L.rows == I.rows); |
|
CV_Assert(L.cols == I.cols); |
|
CV_Assert(connectivity == 8 || connectivity == 4); |
|
const int rows = L.rows; |
|
const int cols = L.cols; |
|
size_t Plength = (size_t(rows + 3 - 1)/3) * (size_t(cols + 3 - 1)/3); |
|
if(connectivity == 4){ |
|
Plength = 4 * Plength;//a quick and dirty upper bound, an exact answer exists if you want to find it |
|
//the 4 comes from the fact that a 3x3 block can never have more than 4 unique labels |
|
} |
|
LabelT *P = (LabelT *) fastMalloc(sizeof(LabelT) * Plength); |
|
P[0] = 0; |
|
LabelT lunique = 1; |
|
//scanning phase |
|
for(int r_i = 0; r_i < rows; ++r_i){ |
|
LabelT *Lrow = (LabelT *)(L.data + L.step.p[0] * r_i); |
|
LabelT *Lrow_prev = (LabelT *)(((char *)Lrow) - L.step.p[0]); |
|
const PixelT *Irow = (PixelT *)(I.data + I.step.p[0] * r_i); |
|
const PixelT *Irow_prev = (const PixelT *)(((char *)Irow) - I.step.p[0]); |
|
LabelT *Lrows[2] = { |
|
Lrow, |
|
Lrow_prev |
|
}; |
|
const PixelT *Irows[2] = { |
|
Irow, |
|
Irow_prev |
|
}; |
|
if(connectivity == 8){ |
|
const int a = 0; |
|
const int b = 1; |
|
const int c = 2; |
|
const int d = 3; |
|
const bool T_a_r = (r_i - G8[a][0]) >= 0; |
|
const bool T_b_r = (r_i - G8[b][0]) >= 0; |
|
const bool T_c_r = (r_i - G8[c][0]) >= 0; |
|
for(int c_i = 0; Irows[0] != Irow + cols; ++Irows[0], c_i++){ |
|
if(!*Irows[0]){ |
|
Lrow[c_i] = 0; |
|
continue; |
|
} |
|
Irows[1] = Irow_prev + c_i; |
|
Lrows[0] = Lrow + c_i; |
|
Lrows[1] = Lrow_prev + c_i; |
|
const bool T_a = T_a_r && (c_i + G8[a][1]) >= 0 && *(Irows[G8[a][0]] + G8[a][1]); |
|
const bool T_b = T_b_r && *(Irows[G8[b][0]] + G8[b][1]); |
|
const bool T_c = T_c_r && (c_i + G8[c][1]) < cols && *(Irows[G8[c][0]] + G8[c][1]); |
|
const bool T_d = (c_i + G8[d][1]) >= 0 && *(Irows[G8[d][0]] + G8[d][1]); |
|
|
|
//decision tree |
|
if(T_b){ |
|
//copy(b) |
|
*Lrows[0] = *(Lrows[G8[b][0]] + G8[b][1]); |
|
}else{//not b |
|
if(T_c){ |
|
if(T_a){ |
|
//copy(c, a) |
|
*Lrows[0] = set_union(P, *(Lrows[G8[c][0]] + G8[c][1]), *(Lrows[G8[a][0]] + G8[a][1])); |
|
}else{ |
|
if(T_d){ |
|
//copy(c, d) |
|
*Lrows[0] = set_union(P, *(Lrows[G8[c][0]] + G8[c][1]), *(Lrows[G8[d][0]] + G8[d][1])); |
|
}else{ |
|
//copy(c) |
|
*Lrows[0] = *(Lrows[G8[c][0]] + G8[c][1]); |
|
} |
|
} |
|
}else{//not c |
|
if(T_a){ |
|
//copy(a) |
|
*Lrows[0] = *(Lrows[G8[a][0]] + G8[a][1]); |
|
}else{ |
|
if(T_d){ |
|
//copy(d) |
|
*Lrows[0] = *(Lrows[G8[d][0]] + G8[d][1]); |
|
}else{ |
|
//new label |
|
*Lrows[0] = lunique; |
|
P[lunique] = lunique; |
|
lunique = lunique + 1; |
|
} |
|
} |
|
} |
|
} |
|
} |
|
}else{ |
|
//B & D only |
|
const int b = 0; |
|
const int d = 1; |
|
const bool T_b_r = (r_i - G4[b][0]) >= 0; |
|
for(int c_i = 0; Irows[0] != Irow + cols; ++Irows[0], c_i++){ |
|
if(!*Irows[0]){ |
|
Lrow[c_i] = 0; |
|
continue; |
|
} |
|
Irows[1] = Irow_prev + c_i; |
|
Lrows[0] = Lrow + c_i; |
|
Lrows[1] = Lrow_prev + c_i; |
|
const bool T_b = T_b_r && *(Irows[G4[b][0]] + G4[b][1]); |
|
const bool T_d = (c_i + G4[d][1]) >= 0 && *(Irows[G4[d][0]] + G4[d][1]); |
|
if(T_b){ |
|
if(T_d){ |
|
//copy(d, b) |
|
*Lrows[0] = set_union(P, *(Lrows[G4[d][0]] + G4[d][1]), *(Lrows[G4[b][0]] + G4[b][1])); |
|
}else{ |
|
//copy(b) |
|
*Lrows[0] = *(Lrows[G4[b][0]] + G4[b][1]); |
|
} |
|
}else{ |
|
if(T_d){ |
|
//copy(d) |
|
*Lrows[0] = *(Lrows[G4[d][0]] + G4[d][1]); |
|
}else{ |
|
//new label |
|
*Lrows[0] = lunique; |
|
P[lunique] = lunique; |
|
lunique = lunique + 1; |
|
} |
|
} |
|
} |
|
} |
|
} |
|
|
|
//analysis |
|
LabelT nLabels = flattenL(P, lunique); |
|
sop.init(nLabels); |
|
|
|
for(int r_i = 0; r_i < rows; ++r_i){ |
|
LabelT *Lrow_start = (LabelT *)(L.data + L.step.p[0] * r_i); |
|
LabelT *Lrow_end = Lrow_start + cols; |
|
LabelT *Lrow = Lrow_start; |
|
for(int c_i = 0; Lrow != Lrow_end; ++Lrow, ++c_i){ |
|
const LabelT l = P[*Lrow]; |
|
*Lrow = l; |
|
sop(r_i, c_i, l); |
|
} |
|
} |
|
|
|
sop.finish(); |
|
fastFree(P); |
|
|
|
return nLabels; |
|
}//End function LabelingImpl operator() |
|
|
|
};//End struct LabelingImpl |
|
}//end namespace connectedcomponents |
|
|
|
//L's type must have an appropriate depth for the number of pixels in I |
|
template<typename StatsOp> |
|
static |
|
int connectedComponents_sub1(const cv::Mat &I, cv::Mat &L, int connectivity, StatsOp &sop){ |
|
CV_Assert(L.channels() == 1 && I.channels() == 1); |
|
CV_Assert(connectivity == 8 || connectivity == 4); |
|
|
|
int lDepth = L.depth(); |
|
int iDepth = I.depth(); |
|
using connectedcomponents::LabelingImpl; |
|
//warn if L's depth is not sufficient? |
|
|
|
CV_Assert(iDepth == CV_8U || iDepth == CV_8S); |
|
|
|
if(lDepth == CV_8U){ |
|
return (int) LabelingImpl<uchar, uchar, StatsOp>()(I, L, connectivity, sop); |
|
}else if(lDepth == CV_16U){ |
|
return (int) LabelingImpl<ushort, uchar, StatsOp>()(I, L, connectivity, sop); |
|
}else if(lDepth == CV_32S){ |
|
//note that signed types don't really make sense here and not being able to use unsigned matters for scientific projects |
|
//OpenCV: how should we proceed? .at<T> typechecks in debug mode |
|
return (int) LabelingImpl<int, uchar, StatsOp>()(I, L, connectivity, sop); |
|
} |
|
|
|
CV_Error(CV_StsUnsupportedFormat, "unsupported label/image type"); |
|
return -1; |
|
} |
|
|
|
} |
|
|
|
int cv::connectedComponents(InputArray _img, OutputArray _labels, int connectivity, int ltype){ |
|
const cv::Mat img = _img.getMat(); |
|
_labels.create(img.size(), CV_MAT_DEPTH(ltype)); |
|
cv::Mat labels = _labels.getMat(); |
|
connectedcomponents::NoOp sop; |
|
if(ltype == CV_16U){ |
|
return connectedComponents_sub1(img, labels, connectivity, sop); |
|
}else if(ltype == CV_32S){ |
|
return connectedComponents_sub1(img, labels, connectivity, sop); |
|
}else{ |
|
CV_Error(CV_StsUnsupportedFormat, "the type of labels must be 16u or 32s"); |
|
return 0; |
|
} |
|
} |
|
|
|
int cv::connectedComponentsWithStats(InputArray _img, OutputArray _labels, OutputArray statsv, |
|
OutputArray centroids, int connectivity, int ltype) |
|
{ |
|
const cv::Mat img = _img.getMat(); |
|
_labels.create(img.size(), CV_MAT_DEPTH(ltype)); |
|
cv::Mat labels = _labels.getMat(); |
|
connectedcomponents::CCStatsOp sop(statsv, centroids); |
|
if(ltype == CV_16U){ |
|
return connectedComponents_sub1(img, labels, connectivity, sop); |
|
}else if(ltype == CV_32S){ |
|
return connectedComponents_sub1(img, labels, connectivity, sop); |
|
}else{ |
|
CV_Error(CV_StsUnsupportedFormat, "the type of labels must be 16u or 32s"); |
|
return 0; |
|
} |
|
}
|
|
|