C++ OpenCV實現物體尺寸測量示例詳解

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前言

本文將使用OpenCV C++ 進行物體尺寸測量。具體來說就是先定位到待測物體的位置，然後測量物體的寬高。

一、圖像透視矯正

原圖如圖所示。本案例的需求是測量圖片中兩張卡片的尺寸。首先，我們得定位到兩張卡片的位置。第一步，我們首先得將白色A4紙切割出來，這樣方便定位到兩張卡片所在位置。這裡用到的算法是圖像透視矯正，具體可以參考OpenCV C++案例實戰四《圖像透視矯正》

//圖像矯正
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;

	//找到最大輪廓
	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;
		}
	}

	//獲取矩形四個角點
	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);

	//透視變換進行圖像矯正
	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));

}

效果如圖所示。接下來，我們需要定位兩張卡片所在位置，尋找特征。

二、物體定位

//獲取物體坐標
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());
	//定位卡片四個角點
	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);
		}
	}
}

如圖所示。通過上面代碼段，我們已經定位出卡片的四個角點。接下來，隻需根據角點位置就可以計算卡片的寬高瞭。

三、尺寸測量

//計算距離
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++)
		{
			//尺寸測量
			Point PtA = Point(TargetPts[i][j]);
			Point PtB = Point(TargetPts[i][(j + 1) % TargetPts[i].size()]);
			double dis = round(EuDis(PtA, PtB) * 100) / 100;

			//效果顯示
			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、效果顯示

五、源碼

#include<iostream>
#include<opencv2/opencv.hpp>
using namespace std;
using namespace cv;

//歐式距離
double EuDis(Point pt1, Point pt2)
{
	return sqrt((pt2.x - pt1.x)*(pt2.x - pt1.x) + (pt2.y - pt1.y)*(pt2.y - pt1.y));
}

//圖像矯正
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;

	//找到最大輪廓
	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;
		}
	}

	//獲取矩形四個角點
	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);

	//透視變換進行圖像矯正
	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));

}

//獲取物體坐標
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());
	//定位卡片四個角點
	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);
		}
	}
}

//計算距離
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++)
		{
			//尺寸測量
			Point PtA = Point(TargetPts[i][j]);
			Point PtB = Point(TargetPts[i][(j + 1) % TargetPts[i].size()]);
			double dis = round(EuDis(PtA, PtB) * 100) / 100;

			//效果顯示
			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;
}

總結

本文使用OpenCV C++ 進行物體尺寸測量，關鍵步驟有以下幾點。

1、圖像透視矯正。方便定位物體所在位置。

2、物體定位。定位所需物體位置，獲取特征。

3、根據已知特征進行計算。

以上就是C++ OpenCV實現物體尺寸測量示例詳解的詳細內容，更多關於C++ OpenCV物體尺寸測量的資料請關註WalkonNet其它相關文章！

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