OpenCV图像的阈值化

图像阈值化的基本思想是，给定一个数组和一个阈值，然后根据数组中每个元素是低于还是高于阈值而进行一些处理。

cvThreshold()函数如下：

double cvThreshold(CvArr* src, CvArr* dst, double threshold, double max_value, int threshold_type);

cvShold函数只能处理8位或者浮点灰度图像，目标图像必须与源图像一致，或者为8为图像 。

实现阈值化的代码如下：

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

#pragma comment(lib, "ml.lib")
#pragma comment(lib, "cv.lib")
#pragma comment(lib, "cvaux.lib")
#pragma comment(lib, "cvcam.lib")
#pragma comment(lib, "cxcore.lib")
#pragma comment(lib, "cxts.lib")
#pragma comment(lib, "highgui.lib")
#pragma comment(lib, "cvhaartraining.lib")

int main()
{
	IplImage* sourceImage;
	IplImage* dstImage;   
	if(!(sourceImage = cvLoadImage("D:\\Testing_Images\\view.png")))
		return -1;
	dstImage = cvCreateImage(cvGetSize(sourceImage), sourceImage->depth, 1);

	IplImage* r = cvCreateImage(cvGetSize(sourceImage), IPL_DEPTH_8U, 1);
	IplImage* g = cvCreateImage(cvGetSize(sourceImage), IPL_DEPTH_8U, 1);
	IplImage* b = cvCreateImage(cvGetSize(sourceImage), IPL_DEPTH_8U, 1);
	IplImage* tempImage = cvCreateImage(cvGetSize(sourceImage), IPL_DEPTH_8U, 1);
	cvSplit(sourceImage, r, g, b, NULL);

	cvAddWeighted(r, 1./3., g, 1./3., 0.0, tempImage);
	cvAddWeighted(tempImage, 1, b, 1./3., 0.0, tempImage);
	cvThreshold(tempImage, dstImage, 100, 255, CV_THRESH_BINARY);
	// 对于大于100的设为255
	cvNamedWindow("sourceImage");
	cvNamedWindow("dstImage");
	cvShowImage("sourceImage",sourceImage);
	cvShowImage("dstImage",dstImage);

	cvWaitKey(-1);
	cvReleaseImage(&r);
	cvReleaseImage(&g);
	cvReleaseImage(&b);
	cvDestroyWindow("sourceImage");
	cvDestroyWindow("dstImage");

	cvReleaseImage(&sourceImage);
	cvReleaseImage(&dstImage);
	return 0;
}

结果如下：

在自适应阈值中，阈值本身就是一个变量，实现自适应阈值的代码如下：

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

#pragma comment(lib, "ml.lib")
#pragma comment(lib, "cv.lib")
#pragma comment(lib, "cvaux.lib")
#pragma comment(lib, "cvcam.lib")
#pragma comment(lib, "cxcore.lib")
#pragma comment(lib, "cxts.lib")
#pragma comment(lib, "highgui.lib")
#pragma comment(lib, "cvhaartraining.lib")

int main()
{
	IplImage* sourceImage;

	//直接以灰度图像载入
	if(!(sourceImage = cvLoadImage("D:\\Testing_Images\\view.png", CV_LOAD_IMAGE_GRAYSCALE)))
		return -1;
	IplImage* dstImage = cvCreateImage(cvGetSize(sourceImage), IPL_DEPTH_8U, 1);

	//这个函数只能处理单通道图像或者8位图像，并且要求源图像	与目标图像不能为同一个图像
	cvAdaptiveThreshold(
		sourceImage,
		dstImage,
		255,     //max_val
		CV_ADAPTIVE_THRESH_MEAN_C,
		CV_THRESH_BINARY,
		3,      //block_size
		5       //offset
		);
	cvNamedWindow("AdaptiveThreshold", 0);
	cvShowImage("AdaptiveThreshold", dstImage);

	//单一阈值
	IplImage *dstImage2 = cvCreateImage(cvGetSize(sourceImage), IPL_DEPTH_8U, 1);
	cvThreshold(sourceImage, dstImage2, 100, 255, CV_THRESH_BINARY);

	cvNamedWindow("sourceImage", 0);
	cvNamedWindow("Threshold", 0);
	cvShowImage("sourceImage", sourceImage);
	cvShowImage("Threshold", dstImage2);

	cvWaitKey(-1);

	//释放资源
	cvDestroyWindow("sourceImage");
	cvDestroyWindow("Threshold");
	cvDestroyWindow("AdaptiveThreshold");
	cvReleaseImage(&sourceImage);
	cvReleaseImage(&dstImage);
	cvReleaseImage(&dstImage2);
	return 0;
}

结果如下：