opencv3/C++基于颜色的目标跟踪

inRange函数

void inRange(InputArray src, InputArray lowerb, InputArray upperb, OutputArray dst);

src:输入图像;
lowerb:下边界数组,阈值下限;
upperb:上边界数组,阈值上限;
dst:输出图像;

颜色范围如图:
opencv3/C++基于颜色的目标跟踪_第1张图片

示例:

捕获摄像头中的黄色方块

#include
using namespace cv;

int main()
{
	VideoCapture capture;
	capture.open(0);
	if(!capture.isOpened())
	{
		printf("can not open video file   \n");
		return -1;
	}
	Mat frame, dst;
	Mat kernel;
	//开操作处理
	kernel = getStructuringElement(MORPH_RECT, Size(5, 5));

	namedWindow("input", CV_WINDOW_AUTOSIZE);
	namedWindow("output", CV_WINDOW_AUTOSIZE);
	std::vector> contours;
	std::vector hireachy;
	Rect rect;
	Point2f center;
	float radius=20;

	while (capture.read(frame))
	{	
		//blur(frame, dst, Size(5,5));
		inRange(frame, Scalar(0,80,80), Scalar(50,255,255), dst);
		//开操作
		morphologyEx(dst,dst,MORPH_OPEN,kernel);
		//获取边界
		findContours(dst, contours, hireachy, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE, Point(0,0));
		//框选面积最大的边界
		if (contours.size() > 0)
		{
			double maxArea=0;
			for (int i = 0; i < contours.size(); i++)
			{
				double area = contourArea(contours[static_cast(i)]);
				if (area > maxArea)
				{
					maxArea = area;
					rect = boundingRect(contours[static_cast(i)]);
					minEnclosingCircle(contours[static_cast(i)], center, radius);
				}
			}
		}
		//矩形框
		//rectangle(frame,rect, Scalar(0,255,0),2);
		//圆形框
		circle(frame, Point(center.x,center.y), (int)radius, Scalar(0,255,0), 2);
		imshow("input", frame);
		imshow("output", dst);

		waitKey(100);
	}

	capture.release();
	return 0;
}

opencv3/C++基于颜色的目标跟踪_第2张图片
opencv3/C++基于颜色的目标跟踪_第3张图片

关于颜色范围的选取:

有朋友问颜色范围的事,比如我们选择某个偏红色的范围,如色环图中这个区间即BGR(0,128,255)到BGR(255,0,213);则B、G、R这三个通道的范围分别为0-255,0-128,213-255。因此阈值下限lowerb=Scalar(0,0,213),阈值上限upperb=Scalar(255,128,255)。
opencv3/C++基于颜色的目标跟踪_第4张图片

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