Opencv step by step - 阈值化

Opencv里面的阈值化做起来比较简单，只需要一个函数即可：

/* Applies fixed-level threshold to grayscale image.
   This is a basic operation applied before retrieving contours */
CVAPI(double)  cvThreshold( const CvArr*  src, CvArr*  dst,
                            double  threshold, double  max_value,
                            int threshold_type );

这里是根据threadshould来决定处理源图像的阈值，使用threshold_type 来决定如何处理。

这里有5种选择，详见：

http://www.opencv.org.cn/opencvdoc/2.3.2/html/doc/tutorials/imgproc/threshold/threshold.html

下面来实践一下：

#include <cv.h>
#include <highgui.h>
#include <stdio.h>

/*

CV_IMPL void
cvAddWeighted( const CvArr* srcarr1, double alpha,
               const CvArr* srcarr2, double beta,
               double gamma, CvArr* dstarr )
{
    cv::Mat src1 = cv::cvarrToMat(srcarr1), src2 = cv::cvarrToMat(srcarr2),
        dst = cv::cvarrToMat(dstarr);
    CV_Assert( src1.size == dst.size && src1.channels() == dst.channels() );
    cv::addWeighted( src1, alpha, src2, beta, gamma, dst, dst.type() );
}

void cv::addWeighted( InputArray src1, double alpha, InputArray src2,
                      double beta, double gamma, OutputArray dst, int dtype )
{
    double scalars[] = {alpha, beta, gamma};
    arithm_op(src1, src2, dst, noArray(), dtype, getAddWeightedTab(), true, scalars);
}

*/

void sum_rgb(IplImage* src, IplImage *dst, int type)
{
	IplImage *r = cvCreateImage(cvGetSize(src), IPL_DEPTH_8U, 1);
	IplImage *g = cvCreateImage(cvGetSize(src), IPL_DEPTH_8U, 1);
	IplImage *b = cvCreateImage(cvGetSize(src), IPL_DEPTH_8U, 1);

	//split the image to three color planes
	cvSplit(src, r, g, b, NULL);

	IplImage *s = cvCreateImage(cvGetSize(src), IPL_DEPTH_8U, 1);

	/*
		void cvAddWeighted(const CvArr* src1, double alpha,
			const CvArr* src2, double beta, double gamma, CvArr* dst)
		 dst = src1 * alpha + src2 * beta + gamma
	*/
	cvAddWeighted(r, 1.0/3.0, g, 1.0/3.0, 0.0, s);
	cvAddWeighted(s, 1.0/1.0, b, 1.0/3.0, 0.0, s);

	cvThreshold(s, dst, 100, 255, type);
	cvReleaseImage(&r);
	cvReleaseImage(&g);
	cvReleaseImage(&b);
	cvReleaseImage(&s);

}

int main(int argc, char **argv)
{
	cvNamedWindow("HI", 1);
	IplImage *src = cvLoadImage(argv[1]);
	IplImage *dst = cvCreateImage(cvGetSize(src), src->depth, 1);

	const int methods[5] = {CV_THRESH_BINARY, CV_THRESH_BINARY_INV,
				CV_THRESH_TRUNC, CV_THRESH_TOZERO_INV,
				CV_THRESH_TOZERO};
	const char* methods_str[5] = {"CV_THRESH_BINARY", "CV_THRESH_BINARY_INV",
				"CV_THRESH_TRUNC", "CV_THRESH_TOZERO_INV",
				"CV_THRESH_TOZERO"};

	for(int i = 0; i < 5; i++) {
		sum_rgb(src, dst, methods[i]);
		cvShowImage(methods_str[i], dst);
	}

	while(1) {

		if(cvWaitKey(10) & 0x7f == 27)
			break;

	}

	cvDestroyWindow("HI");
	cvReleaseImage(&src);
	cvReleaseImage(&dst);

}

这里的关键函数是：

	cvThreshold(s, dst, 100, 255, type);

效果如下：