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Merge branch 'master' of https://github.com/nevion/opencv into cc

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This commit is contained in:
Vadim Pisarevsky 2012-12-15 21:45:55 +04:00
commit 2a42960ff2
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42
modules/imgproc/doc/structural_analysis_and_shape_descriptors.rst
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@ -118,6 +118,48 @@ These values are proved to be invariants to the image scale, rotation, and refle
.. seealso:: :ocv:func:`matchShapes`
connectedComponents
-----------
computes the connected components labeled image of boolean image I with 4 or 8 way connectivity - returns N, the total
number of labels [0, N-1] where 0 represents the background label. L's value type determines the label type, an important
consideration based on the total number of labels or alternatively the total number of pixels.
.. ocv:function:: uint64 connectedComponents(Mat &L, const Mat &I, int connectivity = 8)
.. ocv:function:: uint64 connectedComponentsWithStats(Mat &L, const Mat &I, std::vector<ConnectedComponentStats> &statsv, int connectivity = 8)
:param L: destitination Labeled image
:param I: the image to be labeled
:param connectivity: 8 or 4 for 8-way or 4-way connectivity respectively
:param statsv: statistics for each label, including the background label
Statistics information such as bounding box, area, and centroid is exported via the ``ConnectComponentStats`` structure defined as: ::
class CV_EXPORTS ConnectedComponentStats
{
public:
//! lower left corner column
int lower_x;
//! lower left corner row
int lower_y;
//! upper right corner column
int upper_x;
//! upper right corner row
int upper_y;
//! centroid column
double centroid_x;
//! centroid row
double centroid_y;
//! sum of all columns where the image was non-zero
uint64 integral_x;
//! sum of all rows where the image was non-zero
uint64 integral_y;
//! count of all non-zero pixels
unsigned int area;
};
findContours
----------------

9
modules/imgproc/include/opencv2/imgproc/imgproc.hpp
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@ -1102,6 +1102,15 @@ enum { TM_SQDIFF=0, TM_SQDIFF_NORMED=1, TM_CCORR=2, TM_CCORR_NORMED=3, TM_CCOEFF
CV_EXPORTS_W void matchTemplate( InputArray image, InputArray templ,
OutputArray result, int method );
enum { CC_STAT_LEFT=0, CC_STAT_TOP=1, CC_STAT_WIDTH=2, CC_STAT_HEIGHT=3, CC_STAT_AREA=4, CC_STAT_MAX = 5};
//! computes the connected components labeled image of boolean image I with 4 or 8 way connectivity - returns N, the total
//number of labels [0, N-1] where 0 represents the background label. L's value type determines the label type, an important
//consideration based on the total number of labels or alternatively the total number of pixels.
CV_EXPORTS_W int connectedComponents(InputArray image, OutputArray labels, int connectivity = 8, int ltype=CV_32S);
CV_EXPORTS_W int connectedComponentsWithStats(InputArray image, OutputArray labels, OutputArray stats, OutputArray centroids, int connectivity = 8, int ltype=CV_32S);
//! mode of the contour retrieval algorithm
enum
{

437
modules/imgproc/src/connectedcomponents.cpp Normal file
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@ -0,0 +1,437 @@
/*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>
#if defined _MSC_VER
#pragma warning(disable: 4127)
#endif
namespace cv{
namespace connectedcomponents{
template<typename LabelT>
struct NoOp{
NoOp(){
}
void init(const LabelT labels){
(void) labels;
}
inline
void operator()(int r, int c, LabelT l){
(void) r;
(void) c;
(void) l;
}
void finish(){}
};
struct Point2ui64{
uint64 x, y;
Point2ui64(uint64 _x, uint64 _y):x(_x), y(_y){}
};
template<typename LabelT>
struct CCStatsOp{
OutputArray _mstatsv;
cv::Mat statsv;
OutputArray _mcentroidsv;
cv::Mat centroidsv;
std::vector<Point2ui64> integrals;
CCStatsOp(OutputArray _statsv, OutputArray _centroidsv): _mstatsv(_statsv), _mcentroidsv(_centroidsv){
}
inline
void init(const LabelT nlabels){
_mstatsv.create(cv::Size(nlabels, CC_STAT_MAX), cv::DataType<int>::type);
statsv = _mstatsv.getMat();
_mcentroidsv.create(cv::Size(nlabels, 2), cv::DataType<double>::type);
centroidsv = _mcentroidsv.getMat();
for(int l = 0; l < (int) nlabels; ++l){
unsigned int *row = (unsigned int *) &statsv.at<int>(l, 0);
row[CC_STAT_LEFT] = std::numeric_limits<LabelT>::max();
row[CC_STAT_TOP] = std::numeric_limits<LabelT>::max();
row[CC_STAT_WIDTH] = std::numeric_limits<LabelT>::min();
row[CC_STAT_HEIGHT] = std::numeric_limits<LabelT>::min();
//row[CC_STAT_CX] = 0;
//row[CC_STAT_CY] = 0;
row[CC_STAT_AREA] = 0;
}
integrals.resize(nlabels, Point2ui64(0, 0));
}
void operator()(int r, int c, LabelT l){
int *row = &statsv.at<int>(l, 0);
unsigned int *urow = (unsigned int *) row;
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;
}
}
urow[CC_STAT_AREA]++;
Point2ui64 &integral = integrals[l];
integral.x += c;
integral.y += r;
}
void finish(){
for(int l = 0; l < statsv.rows; ++l){
unsigned int *row = (unsigned int *) &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 = &centroidsv.at<double>(l, 0);
centroid[0] = double(integral.x) / row[CC_STAT_AREA];
centroid[1] = double(integral.y) / row[CC_STAT_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<LabelT>, int connectivity = 8>
struct LabelingImpl{
LabelT operator()(const cv::Mat &I, cv::Mat &L, StatsOp &sop){
CV_Assert(L.rows == I.rows);
CV_Assert(L.cols == I.cols);
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
assert(connectivity == 4);
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?
if(lDepth == CV_8U){
if(iDepth == CV_8U || iDepth == CV_8S){
if(connectivity == 4){
return (int) LabelingImpl<uchar, uchar, StatsOp, 4>()(I, L, sop);
}else{
return (int) LabelingImpl<uchar, uchar, StatsOp, 8>()(I, L, sop);
}
}else{
CV_Assert(false);
}
}else if(lDepth == CV_16U){
if(iDepth == CV_8U || iDepth == CV_8S){
if(connectivity == 4){
return (int) LabelingImpl<ushort, uchar, StatsOp, 4>()(I, L, sop);
}else{
return (int) LabelingImpl<ushort, uchar, StatsOp, 8>()(I, L, sop);
}
}else{
CV_Assert(false);
}
}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
if(iDepth == CV_8U || iDepth == CV_8S){
if(connectivity == 4){
return (int) LabelingImpl<int, uchar, StatsOp, 4>()(I, L, sop);
}else{
return (int) LabelingImpl<int, uchar, StatsOp, 8>()(I, L, sop);
}
}else{
CV_Assert(false);
}
}
CV_Error(CV_StsUnsupportedFormat, "unsupported label/image type");
return -1;
}
int connectedComponents(InputArray _I, OutputArray _L, int connectivity, int ltype){
const cv::Mat I = _I.getMat();
_L.create(I.size(), CV_MAT_TYPE(ltype));
cv::Mat L = _L.getMat();
if(ltype == CV_16U){
connectedcomponents::NoOp<ushort> sop; return connectedComponents_sub1(I, L, connectivity, sop);
}else if(ltype == CV_32S){
connectedcomponents::NoOp<unsigned> sop; return connectedComponents_sub1(I, L, connectivity, sop);
}else{
CV_Assert(false);
return 0;
}
}
int connectedComponentsWithStats(InputArray _I, OutputArray _L, OutputArray statsv, OutputArray centroids, int connectivity, int ltype){
const cv::Mat I = _I.getMat();
_L.create(I.size(), CV_MAT_TYPE(ltype));
cv::Mat L = _L.getMat();
if(ltype == CV_16U){
connectedcomponents::CCStatsOp<ushort> sop(statsv, centroids); return connectedComponents_sub1(I, L, connectivity, sop);
}else if(ltype == CV_32S){
connectedcomponents::CCStatsOp<unsigned> sop(statsv, centroids); return connectedComponents_sub1(I, L, connectivity, sop);
}else{
CV_Assert(false);
return 0;
}
}
}

108
modules/imgproc/test/test_connectedcomponents.cpp Normal file
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@ -0,0 +1,108 @@
/*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.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., 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 the copyright holders 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.
//
//M*/
#include "test_precomp.hpp"
#include <string>
using namespace cv;
using namespace std;
class CV_ConnectedComponentsTest : public cvtest::BaseTest
{
public:
CV_ConnectedComponentsTest();
~CV_ConnectedComponentsTest();
protected:
void run(int);
};
CV_ConnectedComponentsTest::CV_ConnectedComponentsTest() {}
CV_ConnectedComponentsTest::~CV_ConnectedComponentsTest() {}
void CV_ConnectedComponentsTest::run( int /* start_from */)
{
string exp_path = string(ts->get_data_path()) + "connectedcomponents/ccomp_exp.png";
Mat exp = imread(exp_path, 0);
Mat orig = imread(string(ts->get_data_path()) + "connectedcomponents/concentric_circles.png", 0);
if (orig.empty())
{
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
return;
}
Mat bw = orig > 128;
Mat labelImage;
int nLabels = connectedComponents(bw, labelImage, 8, CV_32S);
for(int r = 0; r < labelImage.rows; ++r){
for(int c = 0; c < labelImage.cols; ++c){
int l = labelImage.at<int>(r, c);
bool pass = l >= 0 && l <= nLabels;
if(!pass){
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
return;
}
}
}
if( exp.empty() || orig.size() != exp.size() )
{
imwrite(exp_path, labelImage);
exp = labelImage;
}
if (0 != norm(labelImage > 0, exp > 0, NORM_INF))
{
ts->set_failed_test_info( cvtest::TS::FAIL_MISMATCH );
return;
}
if (nLabels != norm(labelImage, NORM_INF)+1)
{
ts->set_failed_test_info( cvtest::TS::FAIL_MISMATCH );
return;
}
ts->set_failed_test_info(cvtest::TS::OK);
}
TEST(Imgproc_ConnectedComponents, regression) { CV_ConnectedComponentsTest test; test.safe_run(); }

11
modules/python/src2/cv2.cpp
View File

@ -410,7 +410,7 @@ static bool pyopencv_to(PyObject* obj, bool& value, const char* name = "<unknown
static PyObject* pyopencv_from(size_t value)
{
return PyLong_FromUnsignedLong((unsigned long)value);
return PyLong_FromSize_t(value);
}
static bool pyopencv_to(PyObject* obj, size_t& value, const char* name = "<unknown>")
@ -497,9 +497,16 @@ static bool pyopencv_to(PyObject* obj, float& value, const char* name = "<unknow
static PyObject* pyopencv_from(int64 value)
{
return PyFloat_FromDouble((double)value);
return PyLong_FromLongLong(value);
}
#if !defined(__LP64__)
static PyObject* pyopencv_from(uint64 value)
{
return PyLong_FromUnsignedLongLong(value);
}
#endif
static PyObject* pyopencv_from(const string& value)
{
return PyString_FromString(value.empty() ? "" : value.c_str());

36
samples/cpp/connected_components.cpp
View File

@ -11,25 +11,21 @@ int threshval = 100;
static void on_trackbar(int, void*)
{
Mat bw = threshval < 128 ? (img < threshval) : (img > threshval);
vector<vector<Point> > contours;
vector<Vec4i> hierarchy;
findContours( bw, contours, hierarchy, CV_RETR_CCOMP, CV_CHAIN_APPROX_SIMPLE );
Mat dst = Mat::zeros(img.size(), CV_8UC3);
if( !contours.empty() && !hierarchy.empty() )
{
// iterate through all the top-level contours,
// draw each connected component with its own random color
int idx = 0;
for( ; idx >= 0; idx = hierarchy[idx][0] )
{
Scalar color( (rand()&255), (rand()&255), (rand()&255) );
drawContours( dst, contours, idx, color, CV_FILLED, 8, hierarchy );
}
Mat labelImage(img.size(), CV_32S);
int nLabels = connectedComponents(bw, labelImage, 8);
std::vector<Vec3b> colors(nLabels);
colors[0] = Vec3b(0, 0, 0);//background
for(int label = 1; label < nLabels; ++label){
colors[label] = Vec3b( (rand()&255), (rand()&255), (rand()&255) );
}
Mat dst(img.size(), CV_8UC3);
for(int r = 0; r < dst.rows; ++r){
for(int c = 0; c < dst.cols; ++c){
int label = labelImage.at<int>(r, c);
Vec3b &pixel = dst.at<Vec3b>(r, c);
pixel = colors[label];
}
}
imshow( "Connected Components", dst );
}
@ -45,14 +41,14 @@ static void help()
const char* keys =
{
"{@image |stuff.jpg|image for converting to a grayscale}"
"{@image|stuff.jpg|image for converting to a grayscale}"
};
int main( int argc, const char** argv )
{
help();
CommandLineParser parser(argc, argv, keys);
string inputImage = parser.get<string>(1);
string inputImage = parser.get<string>("@image");
img = imread(inputImage.c_str(), 0);
if(img.empty())