我正在寻找一个 OpenCV 函数,它可以找到连接的组件并对其执行一些任务(例如获取像素数、轮廓、对象中的像素列表等。)
是否有类似于 MatLab 的 regionprops 的 OpenCV (C++) 功能?
我正在寻找一个 OpenCV 函数,它可以找到连接的组件并对其执行一些任务(例如获取像素数、轮廓、对象中的像素列表等。)
是否有类似于 MatLab 的 regionprops 的 OpenCV (C++) 功能?
从 3.0 版本开始,OpenCV 具有connectedComponents
功能。
看看cvFindContours函数。它非常通用——它可以找到内部和外部轮廓,并以多种格式返回结果(例如平面列表与树结构)。一旦你有了轮廓,像cvContourArea这样的函数允许你确定与特定轮廓相对应的连接组件的基本属性。
如果您更喜欢使用较新的 C++ 接口(与我上面描述的较旧的 C 样式接口相反),那么函数名称是相似的。
编译时设置 -std=c++0x 选项
.h 文件
//connected_components.h
#ifndef CONNECTED_COMPONENTS_H_
#define CONNECTED_COMPONENTS_H_
#include <opencv2/core/core.hpp>
#include <memory>
class DisjointSet {
private:
std::vector<int> m_disjoint_array;
int m_subset_num;
public:
DisjointSet();
DisjointSet(int size);
~DisjointSet();
int add(); //add a new element, which is a subset by itself;
int find(int x); //return the root of x
void unite(int x, int y);
int getSubsetNum(void);
};
class ConnectedComponent {
private:
cv::Rect m_bb;
int m_pixel_count;
std::shared_ptr< std::vector<cv::Point2i> > m_pixels;
public:
ConnectedComponent();
ConnectedComponent(int x, int y);
~ConnectedComponent();
void addPixel(int x, int y);
int getBoundingBoxArea(void) const;
cv::Rect getBoundingBox(void) const;
int getPixelCount(void) const;
std::shared_ptr< const std::vector<cv::Point2i> > getPixels(void) const;
};
void findCC(const cv::Mat& src, std::vector<ConnectedComponent>& cc);
#endif //CONNECTED_COMPONENTS_H_
.cc 文件
//connected_components.cpp
#include "connected_components.h"
using namespace std;
/** DisjointSet **/
DisjointSet::DisjointSet() :
m_disjoint_array(),
m_subset_num(0)
{ }
DisjointSet::DisjointSet(int size) :
m_disjoint_array(),
m_subset_num(0)
{
m_disjoint_array.reserve(size);
}
DisjointSet::~DisjointSet()
{ }
//add a new element, which is a subset by itself;
int DisjointSet::add()
{
int cur_size = m_disjoint_array.size();
m_disjoint_array.push_back(cur_size);
m_subset_num ++;
return cur_size;
}
//return the root of x
int DisjointSet::find(int x)
{
if (m_disjoint_array[x] < 0 || m_disjoint_array[x] == x)
return x;
else {
m_disjoint_array[x] = this->find(m_disjoint_array[x]);
return m_disjoint_array[x];
}
}
// point the x and y to smaller root of the two
void DisjointSet::unite(int x, int y)
{
if (x==y) {
return;
}
int xRoot = find(x);
int yRoot = find(y);
if (xRoot == yRoot)
return;
else if (xRoot < yRoot) {
m_disjoint_array[yRoot] = xRoot;
}
else {
m_disjoint_array[xRoot] = yRoot;
}
m_subset_num--;
}
int DisjointSet::getSubsetNum()
{
return m_subset_num;
}
/** ConnectedComponent **/
ConnectedComponent::ConnectedComponent() :
m_bb(0,0,0,0),
m_pixel_count(0),
m_pixels()
{
m_pixels = std::make_shared< std::vector<cv::Point2i> > ();
}
ConnectedComponent::ConnectedComponent(int x, int y) :
m_bb(x,y,1,1),
m_pixel_count(1),
m_pixels()
{
m_pixels = std::make_shared< std::vector<cv::Point2i> > ();
}
ConnectedComponent::~ConnectedComponent(void)
{ }
void ConnectedComponent::addPixel(int x, int y) {
m_pixel_count++;
// new bounding box;
if (m_pixel_count == 0) {
m_bb = cv::Rect(x,y,1,1);
}
// extend bounding box if necessary
else {
if (x < m_bb.x ) {
m_bb.width+=(m_bb.x-x);
m_bb.x = x;
}
else if ( x > (m_bb.x+m_bb.width) ) {
m_bb.width=(x-m_bb.x);
}
if (y < m_bb.y ) {
m_bb.height+=(m_bb.y-y);
m_bb.y = y;
}
else if ( y > (m_bb.y+m_bb.height) ) {
m_bb.height=(y-m_bb.y);
}
}
m_pixels->push_back(cv::Point(x,y));
}
int ConnectedComponent::getBoundingBoxArea(void) const {
return (m_bb.width*m_bb.height);
}
cv::Rect ConnectedComponent::getBoundingBox(void) const {
return m_bb;
}
std::shared_ptr< const std::vector<cv::Point2i> > ConnectedComponent::getPixels(void) const {
return m_pixels;
}
int ConnectedComponent::getPixelCount(void) const {
return m_pixel_count;
}
/** find connected components **/
void findCC(const cv::Mat& src, std::vector<ConnectedComponent>& cc) {
if (src.empty()) return;
CV_Assert(src.type() == CV_8U);
cc.clear();
int total_pix = src.total();
int frame_label[total_pix];
DisjointSet labels(total_pix);
int root_map[total_pix];
int x, y;
const uchar* cur_p;
const uchar* prev_p = src.ptr<uchar>(0);
int left_val, up_val;
int cur_idx, left_idx, up_idx;
cur_idx = 0;
//first logic loop
for (y = 0; y < src.rows; y++ ) {
cur_p = src.ptr<uchar>(y);
for (x = 0; x < src.cols; x++, cur_idx++) {
left_idx = cur_idx - 1;
up_idx = cur_idx - src.size().width;
if ( x == 0)
left_val = 0;
else
left_val = cur_p[x-1];
if (y == 0)
up_val = 0;
else
up_val = prev_p[x];
if (cur_p[x] > 0) {
//current pixel is foreground and has no connected neighbors
if (left_val == 0 && up_val == 0) {
frame_label[cur_idx] = (int)labels.add();
root_map[frame_label[cur_idx]] = -1;
}
//current pixel is foreground and has left neighbor connected
else if (left_val != 0 && up_val == 0) {
frame_label[cur_idx] = frame_label[left_idx];
}
//current pixel is foreground and has up neighbor connect
else if (up_val != 0 && left_val == 0) {
frame_label[cur_idx] = frame_label[up_idx];
}
//current pixel is foreground and is connected to left and up neighbors
else {
frame_label[cur_idx] = (frame_label[left_idx] > frame_label[up_idx]) ? frame_label[up_idx] : frame_label[left_idx];
labels.unite(frame_label[left_idx], frame_label[up_idx]);
}
}//endif
else {
frame_label[cur_idx] = -1;
}
} //end for x
prev_p = cur_p;
}//end for y
//second loop logic
cur_idx = 0;
int curLabel;
int connCompIdx = 0;
for (y = 0; y < src.size().height; y++ ) {
for (x = 0; x < src.size().width; x++, cur_idx++) {
curLabel = frame_label[cur_idx];
if (curLabel != -1) {
curLabel = labels.find(curLabel);
if( root_map[curLabel] != -1 ) {
cc[root_map[curLabel]].addPixel(x, y);
}
else {
cc.push_back(ConnectedComponent(x,y));
root_map[curLabel] = connCompIdx;
connCompIdx++;
}
}
}//end for x
}//end for y
}
如果您不介意使用使用 OpenCV 的外部库,则可以使用cvBlobsLib来实现。
一个执行二值图像连接组件标记的库(类似于 regionprops Matlab 函数)。它还提供了从提取的 blob 中操作、过滤和提取结果的功能,有关更多信息,请参阅功能部分。
您可以使用cv::connectedComponentsWithStats()函数。
这是一个例子。
// ...
cv::Mat labels, stats, centroids;
int connectivity = 8; // or 4
int label_count = cv::connectedComponentsWithStats(src, labels, stats, centroids, connectivity);
for (int i = 0; i < label_count; i++)
{
int x = stats.at<int>(i, cv::CC_STAT_LEFT);
int y = stats.at<int>(i, cv::CC_STAT_TOP);
int w = stats.at<int>(i, cv::CC_STAT_WIDTH);
int h = stats.at<int>(i, cv::CC_STAT_HEIGHT);
int area = stats.at<int>(i, cv::CC_STAT_AREA);
double cx = centroids.at<double>(i, 0);
double cy = centroids.at<double>(i, 1);
// ...
}
Following DXM's code above which assumes 4-connected components, here is a version for 'findCC' that detects 8-connected components.
void findCC(const cv::Mat& src, std::vector<ConnectedComponent>& cc) {
if (src.empty()) return;
CV_Assert(src.type() == CV_8U);
cc.clear();
int total_pix = int(src.total());
int *frame_label = new int[total_pix];
DisjointSet labels(total_pix);
int *root_map = new int[total_pix];
int x, y;
const uchar* cur_p;
const uchar* prev_p = src.ptr<uchar>(0);
int left_val, up_val, up_left_val, up_right_val;
int cur_idx, left_idx, up_idx, up_left_idx, up_right_idx;
cur_idx = 0;
//first logic loop
for (y = 0; y < src.rows; y++) {
cur_p = src.ptr<uchar>(y);
for (x = 0; x < src.cols; x++, cur_idx++) {
left_idx = cur_idx - 1;
up_idx = cur_idx - src.size().width;
up_left_idx = up_idx - 1;
up_right_idx = up_idx + 1;
if (x == 0)
{
left_val = 0;
}
else
{
left_val = cur_p[x - 1];
}
if (y == 0)
{
up_val = 0;
}
else
{
up_val = prev_p[x];
}
if (x == 0 || y == 0)
{
up_left_val = 0;
}
else
{
up_left_val = prev_p[x-1];
}
if (x == src.cols - 1 || y == 0)
{
up_right_val = 0;
}
else
{
up_right_val = prev_p[x+1];
}
if (cur_p[x] > 0) {
//current pixel is foreground and has no connected neighbors
if (left_val == 0 && up_val == 0 && up_left_val == 0 && up_right_val == 0) {
frame_label[cur_idx] = (int)labels.add();
root_map[frame_label[cur_idx]] = -1;
}
//Current pixel is foreground and has at least one neighbor
else
{
vector<int> frame_lbl;
frame_lbl.reserve(4);
//Find minimal label
int min_frame_lbl = INT_MAX;
int valid_entries_num = 0;
if (left_val != 0)
{
frame_lbl.push_back(frame_label[left_idx]);
min_frame_lbl = min(min_frame_lbl, frame_label[left_idx]);
valid_entries_num++;
}
if (up_val != 0)
{
frame_lbl.push_back(frame_label[up_idx]);
min_frame_lbl = min(min_frame_lbl, frame_label[up_idx]);
valid_entries_num++;
}
if (up_left_val != 0)
{
frame_lbl.push_back(frame_label[up_left_idx]);
min_frame_lbl = min(min_frame_lbl, frame_label[up_left_idx]);
valid_entries_num++;
}
if (up_right_val != 0)
{
frame_lbl.push_back(frame_label[up_right_idx]);
min_frame_lbl = min(min_frame_lbl, frame_label[up_right_idx]);
valid_entries_num++;
}
CV_Assert(valid_entries_num > 0);
frame_label[cur_idx] = min_frame_lbl;
//Unite if necessary
if (valid_entries_num > 1)
{
for (size_t i = 0; i < frame_lbl.size(); i++)
{
labels.unite(frame_lbl[i], min_frame_lbl);
}
}
}
}//endif
else {
frame_label[cur_idx] = -1;
}
} //end for x
prev_p = cur_p;
}//end for y
//second loop logic
cur_idx = 0;
int curLabel;
int connCompIdx = 0;
for (y = 0; y < src.size().height; y++) {
for (x = 0; x < src.size().width; x++, cur_idx++) {
curLabel = frame_label[cur_idx];
if (curLabel != -1) {
curLabel = labels.find(curLabel);
if (root_map[curLabel] != -1) {
cc[root_map[curLabel]].addPixel(x, y);
}
else {
cc.push_back(ConnectedComponent(x, y));
root_map[curLabel] = connCompIdx;
connCompIdx++;
}
}
}//end for x
}//end for y
//Free up allocated memory
delete[] frame_label;
delete[] root_map;
}