前言
本文将使用OpenCV C++ 进行物体尺寸测量。具体来说就是先定位到待测物体的位置，然后测量物体的宽高。
一、图像透视矫正
原图如图所示。本案例的需求是测量图片中两张卡片的尺寸。首先，我们得定位到两张卡片的位置。第一步，我们首先得将白色A4纸切割出来，这样方便定位到两张卡片所在位置。这里用到的算法是图像透视矫正，具体可以参考OpenCV C++案例实战四《图像透视矫正》
//图像矫正
void getWarp(Mat src, Mat &Warp)
{
Mat gray;
cvtColor(src, gray, COLOR_BGR2GRAY);
Mat thresh;
threshold(gray, thresh, 0, 255, THRESH_BINARY | THRESH_OTSU);
Mat kernel = getStructuringElement(MORPH_RECT, Size(5, 5));
Mat open;
morphologyEx(thresh, open, MORPH_OPEN, kernel);
vector<vector<Point>>contours;
findContours(open, contours, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE);
vector<vector<Point>>conPoly(contours.size());
vector<Point>srcPts;
//找到最大轮廓
int MaxIndex = 0;
double Area = 0;
for (int i = 0; i < contours.size(); i++)
{
double area = contourArea(contours[i]);
if (area > Area)
{
Area = area;
MaxIndex = i;
}
}
//获取矩形四个角点
double peri = arcLength(contours[MaxIndex], true);
approxPolyDP(contours[MaxIndex], conPoly[MaxIndex], 0.02*peri, true);
srcPts = { conPoly[MaxIndex][0],conPoly[MaxIndex][1],conPoly[MaxIndex][2],conPoly[MaxIndex][3] };
int T_L, B_L, B_R, T_R;
int width = src.cols / 2;
int height = src.rows / 2;
for (int i = 0; i < srcPts.size(); i++)
{
if (srcPts[i].x < width && srcPts[i].y < height)
{
T_L = i;
}
if (srcPts[i].x < width && srcPts[i].y > height)
{
B_L = i;
}
if (srcPts[i].x > width && srcPts[i].y > height)
{
B_R = i;
}
if (srcPts[i].x > width && srcPts[i].y < height)
{
T_R = i;
}
}
double UpWidth = EuDis(srcPts[T_L], srcPts[T_R]);
double DownWidth = EuDis(srcPts[B_L], srcPts[B_R]);
double MaxWidth = max(UpWidth, DownWidth);
double UpHeight = EuDis(srcPts[T_L], srcPts[B_L]);
double DownHeight = EuDis(srcPts[T_R], srcPts[B_R]);
double MaxHeight = max(UpHeight, DownHeight);
//透视变换进行图像矫正
Point2f SrcAffinePts[4] = { Point2f(srcPts[T_L]),Point2f(srcPts[T_R]) ,Point2f(srcPts[B_R]) ,Point2f(srcPts[B_L]) };
Point2f DstAffinePts[4] = { Point2f(0,0),Point2f(MaxWidth,0),Point2f(MaxWidth,MaxHeight),Point2f(0,MaxHeight) };
Mat M = getPerspectiveTransform(SrcAffinePts, DstAffinePts);
warpPerspective(src, Warp, M, Point(MaxWidth, MaxHeight));
}
效果如图所示。接下来，我们需要定位两张卡片所在位置，寻找特征。
二、物体定位
//获取物体坐标
void FindPts(Mat &Warp, vector<vector<Point>>&TargetPts)
{
Mat gray;
cvtColor(Warp, gray, COLOR_BGR2GRAY);
Mat thresh;
threshold(gray, thresh, 0, 255, THRESH_BINARY_INV | THRESH_OTSU);
Mat kernel = getStructuringElement(MORPH_RECT, Size(3, 3));
Mat open;
morphologyEx(thresh, open, MORPH_OPEN, kernel);
vector<vector<Point>>contours;
findContours(open, contours, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE);
vector<vector<Point>>conPoly(contours.size());
//定位卡片四个角点
for (int i = 0; i < contours.size(); i++)
{
double area = contourArea(contours[i]);
if (area > 1000)
{
double peri = arcLength(contours[i], true);
approxPolyDP(contours[i], conPoly[i], 0.02*peri, true);
vector<Point>temp;
temp = { conPoly[i][0],conPoly[i][1], conPoly[i][2], conPoly[i][3] };
TargetPts.push_back(temp);
}
}
}
如图所示。通过上面代码段，我们已经定位出卡片的四个角点。接下来，只需根据角点位置就可以计算卡片的宽高了。
三、尺寸测量
//计算距离
void DrawAndCompute(Mat &Warp, vector<vector<Point>>&TargetPts)
{
for (int i = 0; i < TargetPts.size(); i++)
{
for (int j = 0; j < TargetPts[i].size(); j++)
{
//尺寸测量
Point PtA = Point(TargetPts[i][j]);
Point PtB = Point(TargetPts[i][(j + 1) % TargetPts[i].size()]);
double dis = round(EuDis(PtA, PtB) * 100) / 100;
//效果显示
circle(Warp, TargetPts[i][j], 5, Scalar(0, 255, 0), -1);
line(Warp, PtA, PtB, Scalar(0, 0, 255), 2);
char text[20];
sprintf_s(text, "%.2f", dis);
Point point = Point((PtA.x + PtB.x) / 2, (PtA.y + PtB.y) / 2);
putText(Warp, text, point, FONT_HERSHEY_SIMPLEX, 1, Scalar(255, 0, 255), 2);
}
}
}
四、效果显示
五、源码
#include<iostream>
#include<opencv2/opencv.hpp>
using namespace std;
using namespace cv;
//欧式距离
double EuDis(Point pt1, Point pt2)
{
return sqrt((pt2.x - pt1.x)*(pt2.x - pt1.x) + (pt2.y - pt1.y)*(pt2.y - pt1.y));
}
//图像矫正
void getWarp(Mat src, Mat &Warp)
{
Mat gray;
cvtColor(src, gray, COLOR_BGR2GRAY);
Mat thresh;
threshold(gray, thresh, 0, 255, THRESH_BINARY | THRESH_OTSU);
Mat kernel = getStructuringElement(MORPH_RECT, Size(5, 5));
Mat open;
morphologyEx(thresh, open, MORPH_OPEN, kernel);
vector<vector<Point>>contours;
findContours(open, contours, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE);
vector<vector<Point>>conPoly(contours.size());
vector<Point>srcPts;
//找到最大轮廓
int MaxIndex = 0;
double Area = 0;
for (int i = 0; i < contours.size(); i++)
{
double area = contourArea(contours[i]);
if (area > Area)
{
Area = area;
MaxIndex = i;
}
}
//获取矩形四个角点
double peri = arcLength(contours[MaxIndex], true);
approxPolyDP(contours[MaxIndex], conPoly[MaxIndex], 0.02*peri, true);
srcPts = { conPoly[MaxIndex][0],conPoly[MaxIndex][1],conPoly[MaxIndex][2],conPoly[MaxIndex][3] };
int T_L, B_L, B_R, T_R;
int width = src.cols / 2;
int height = src.rows / 2;
for (int i = 0; i < srcPts.size(); i++)
{
if (srcPts[i].x < width && srcPts[i].y < height)
{
T_L = i;
}
if (srcPts[i].x < width && srcPts[i].y > height)
{
B_L = i;
}
if (srcPts[i].x > width && srcPts[i].y > height)
{
B_R = i;
}
if (srcPts[i].x > width && srcPts[i].y < height)
{
T_R = i;
}
}
double UpWidth = EuDis(srcPts[T_L], srcPts[T_R]);
double DownWidth = EuDis(srcPts[B_L], srcPts[B_R]);
double MaxWidth = max(UpWidth, DownWidth);
double UpHeight = EuDis(srcPts[T_L], srcPts[B_L]);
double DownHeight = EuDis(srcPts[T_R], srcPts[B_R]);
double MaxHeight = max(UpHeight, DownHeight);
//透视变换进行图像矫正
Point2f SrcAffinePts[4] = { Point2f(srcPts[T_L]),Point2f(srcPts[T_R]) ,Point2f(srcPts[B_R]) ,Point2f(srcPts[B_L]) };
Point2f DstAffinePts[4] = { Point2f(0,0),Point2f(MaxWidth,0),Point2f(MaxWidth,MaxHeight),Point2f(0,MaxHeight) };
Mat M = getPerspectiveTransform(SrcAffinePts, DstAffinePts);
warpPerspective(src, Warp, M, Point(MaxWidth, MaxHeight));
}
//获取物体坐标
void FindPts(Mat &Warp, vector<vector<Point>>&TargetPts)
{
Mat gray;
cvtColor(Warp, gray, COLOR_BGR2GRAY);
Mat thresh;
threshold(gray, thresh, 0, 255, THRESH_BINARY_INV | THRESH_OTSU);
Mat kernel = getStructuringElement(MORPH_RECT, Size(3, 3));
Mat open;
morphologyEx(thresh, open, MORPH_OPEN, kernel);
vector<vector<Point>>contours;
findContours(open, contours, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE);
vector<vector<Point>>conPoly(contours.size());
//定位卡片四个角点
for (int i = 0; i < contours.size(); i++)
{
double area = contourArea(contours[i]);
if (area > 1000)
{
double peri = arcLength(contours[i], true);
approxPolyDP(contours[i], conPoly[i], 0.02*peri, true);
vector<Point>temp;
temp = { conPoly[i][0],conPoly[i][1], conPoly[i][2], conPoly[i][3] };
TargetPts.push_back(temp);
}
}
}
//计算距离
void DrawAndCompute(Mat &Warp, vector<vector<Point>>&TargetPts)
{
for (int i = 0; i < TargetPts.size(); i++)
{
for (int j = 0; j < TargetPts[i].size(); j++)
{
//尺寸测量
Point PtA = Point(TargetPts[i][j]);
Point PtB = Point(TargetPts[i][(j + 1) % TargetPts[i].size()]);
double dis = round(EuDis(PtA, PtB) * 100) / 100;
//效果显示
circle(Warp, TargetPts[i][j], 5, Scalar(0, 255, 0), -1);
line(Warp, PtA, PtB, Scalar(0, 0, 255), 2);
char text[20];
sprintf_s(text, "%.2f", dis);
Point point = Point((PtA.x + PtB.x) / 2, (PtA.y + PtB.y) / 2);
putText(Warp, text, point, FONT_HERSHEY_SIMPLEX, 1, Scalar(255, 0, 255), 2);
}
}
}
int main()
{
Mat src = imread("src.jpg");
if (src.empty())
{
cout << "No Image!" << endl;
system("pause");
return -1;
}
Mat Warp;
getWarp(src, Warp);
vector<vector<Point>>TargetPts;
FindPts(Warp, TargetPts);
DrawAndCompute(Warp, TargetPts);
imshow("Warp", Warp);
waitKey(0);
destroyAllWindows();
system("pause");
return 0;
}
总结
本文使用OpenCV C++ 进行物体尺寸测量，关键步骤有以下几点。
1、图像透视矫正。方便定位物体所在位置。
2、物体定位。定位所需物体位置，获取特征。
3、根据已知特征进行计算。
以上就是C++ OpenCV实现物体尺寸测量示例详解的详细内容，更多关于C++ OpenCV物体尺寸测量的资料请关注服务器之家其它相关文章！
原文链接：https://blog.csdn.net/Zero___Chen/article/details/122390167
