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：