regionprops与findContours
有没有办法获得相同的结果 cDist = regionprops（bwImg，'Area'）; 和openCV的findContours？
输入图片：
Bw输入图片：
这是我到目前为止所尝试的内容：
dst.convertTo(dst,CV_8U);
cv::vector<cv::vector<cv::Point> > contours_1;
cv::vector<cv::Vec4i> hierarchy_1;
cv::findContours(dst,contours_1,hierarchy_1,CV_RETR_CCOMP, CV_CHAIN_APPROX_SIMPLE);
double maxLabelSize = (dst.rows/4.0) * (dst.cols/6.0);
double minLabelSize = ((dst.rows/40.0) * (dst.cols/60.0));
cv::vector<cv::vector<cv::Point> > goodContours;
for (int i = 0; i < contours_1.size(); i++)
{
double size = cv::contourArea(contours_1[i]);
if (size < maxLabelSize && size > minLabelSize)
{
goodContours.push_back(contours_1[i]);
}
}
cv::Mat filterContours = cv::Mat::zeros(dst.size(),CV_8UC3);
for (int i = 0; i < goodContours.size(); i++)
{
cv::RNG rng(12345);
cv::Scalar color = cv::Scalar( rng.uniform(0, 255), rng.uniform(0,255), rng.uniform(0,255) );
drawContours( filterContours, goodContours, i, color, 2, 8, hierarchy_1, 0, cv::Point() );
}
cv::imshow( "Contours", filterContours );
cv::waitKey(0);
OpenCV结果：
Matlab的版本：
% Calculate each separated object area
cDist=regionprops(bwImg, 'Area');
cDist=[cDist.Area];
% Label each object
[bwImgLabeled, ~]=bwlabel(bwImg);
% Calculate min and max object size based on assumptions
maxLabelSize = prod(size(imageData)./[4 6]);
minLabelSize = prod(size(imageData)./[4 6]./10);
% Find label indices for objects that are too large or too small
remInd = find(cDist > maxLabelSize);
remInd = [remInd find(cDist < minLabelSize)];
% Remove over/undersized objects
for n=1:length(remInd)
ri = bwImgLabeled == remInd(n);
bwImgLabeled(ri) = 0;
end
Matlab的结果：
请注意openCV图像中缺少左下方。
2 个答案:
答案 0 :(得分：2)
OpenCV 3.0版测试版有＆＃34; connectedComponents＆＃34;功能。此函数创建标签图像并计算一些区域属性：区域，边界框和质心。
对于OpenCV 2.4，您可以包含connectedcomponents.cpp 从current OpenCV source code到您的项目并使用＆＃34; connectedComponentsWithStats＆＃34;功能：
nLabels = connectedComponentsWithStats(mask, labelImage, stats, centroids, connectivity, CV_32S);
变量：
-
nLabels - 区域数量;
-
mask - 输入二进制图像;
-
labelImage - 带有标记区域的输出图像;
-
统计 - 区域属性（边界框，区域）;
-
质心;
-
连接 - 区域连接（4或8）。
connectedcomponents.cpp：
#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);
row[CC_STAT_LEFT] = MIN(row[CC_STAT_LEFT], c);
row[CC_STAT_WIDTH] = MAX(row[CC_STAT_WIDTH], c);
row[CC_STAT_TOP] = MIN(row[CC_STAT_TOP], r);
row[CC_STAT_HEIGHT] = MAX(row[CC_STAT_HEIGHT], 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_WIDTH] = row[CC_STAT_WIDTH] - row[CC_STAT_LEFT] + 1;
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;
//A quick and dirty upper bound for the maximimum number of labels. The 4 comes from
//the fact that a 3x3 block can never have more than 4 unique labels for both 4 & 8-way
const size_t Plength = 4 * (size_t(rows + 3 - 1)/3) * (size_t(cols + 3 - 1)/3);
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 * const Lrow = L.ptr<LabelT>(r_i);
LabelT * const Lrow_prev = (LabelT *)(((char *)Lrow) - L.step.p[0]);
const PixelT * const Irow = I.ptr<PixelT>(r_i);
const PixelT * const 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 = L.ptr<LabelT>(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;
}
}
答案 1 :(得分：1)
使用以下代码获取连接的组件标签（对于matlab的bwlabel，类似）。 Opencv findContours和matlab的bwlabel是不同的。花一些时间来研究它。同时，以下代码将暂时解决您的问题。 （要熟悉opencv findcontours - 尝试使用Contour检索模式和Contour逼近方法 - Ref）。
void bwlabelMat(Mat &binary, vector<vector <Point>> &lablidx, int &labels)
{
if (binary.type() != CV_32F)
{
cout << "convert the input image to CV_32FC1 with 0 & 1 as pixel elements" << endl;
exit(EXIT_FAILURE);
}
// starts at 2 because 0,1 are used already
int labelCount = 2;
for (int y = 0; y < binary.rows; y++)
{
for (int x = 0; x < binary.cols; x++)
{
if (1 == (int)binary.at<float>(y, x))
{
Rect rect;
floodFill(binary, Point(x, y), Scalar(labelCount), &rect, Scalar(0), Scalar(0), 4);
vector <Point> blob;
for (int i = rect.y; i < (rect.y + rect.height); i++)
{
for (int j = rect.x; j < (rect.x + rect.width); j++)
{
if (labelCount == (int)binary.at<float>(i, j))
{
blob.push_back(Point(j, i));
}
}
}
lablidx.push_back(blob);
labelCount++;
}
}
}
for (int y = 0; y < binary.rows; y++)
{
for (int x = 0; x < binary.cols; x++)
{
if ((0 != (int)binary.at<float>(y, x)) && (1 != (int)binary.at<float>(y, x)))
binary.at<float>(y, x) = binary.at<float>(y, x) - 1.0;
}
}
labelCount = labelCount - 2;
labels = labelCount;
}
关于：matlab的regionprops：直接向前的Opencv没有相应的regionprops，但仍有可能重新产生精确的结果，因为它只涉及数学背后。我正在分享您的链接，因为我无法在此处完整发布代码。我已经引用了这个python实现并在c ++中重现。它工作正常。前进。
希望它有所帮助。
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