opencv 图像阈值 二值化处理

图像阈值

设定一个阈值，将图片数据大于这个阈值和小于这个阈值的部分区分开来，常用的二值化处理就是将大于阈值的设置为255，小于阈值的设置为0。二值化处理目前只支持8位单通道图像。
也可以用于消除噪声（即滤除过小或过大的像素）。

threshold 函数

CV_EXPORTS_W double threshold( InputArray src, OutputArray dst,
                               double thresh, double maxval, int type );

@param src			//输入数组（多通道，8位或32位浮点数）。
@param dst 			//具有与src相同大小和类型以及相同通道数的输出数组。
@param thresh 		//阈值。
@param maxval 		//与#THRESH_BINARY和#THRESH_BINARY_INV阈值类型一起使用的最大值。
@param type			//阈值类型（请参阅#ThresholdTypes）。
@return				//如果使用了Otsu或Triangle方法，则计算出的阈值。

阈值类型

enum ThresholdTypes {
    THRESH_BINARY     = 0, //!< \f[\texttt{dst} (x,y) =  \fork{\texttt{maxval}}{if \(\texttt{src}(x,y) > \texttt{thresh}\)}{0}{otherwise}\f]
    THRESH_BINARY_INV = 1, //!< \f[\texttt{dst} (x,y) =  \fork{0}{if \(\texttt{src}(x,y) > \texttt{thresh}\)}{\texttt{maxval}}{otherwise}\f]
    THRESH_TRUNC      = 2, //!< \f[\texttt{dst} (x,y) =  \fork{\texttt{threshold}}{if \(\texttt{src}(x,y) > \texttt{thresh}\)}{\texttt{src}(x,y)}{otherwise}\f]
    THRESH_TOZERO     = 3, //!< \f[\texttt{dst} (x,y) =  \fork{\texttt{src}(x,y)}{if \(\texttt{src}(x,y) > \texttt{thresh}\)}{0}{otherwise}\f]
    THRESH_TOZERO_INV = 4, //!< \f[\texttt{dst} (x,y) =  \fork{0}{if \(\texttt{src}(x,y) > \texttt{thresh}\)}{\texttt{src}(x,y)}{otherwise}\f]
    THRESH_MASK       = 7,
    THRESH_OTSU       = 8, //!< flag, use Otsu algorithm to choose the optimal threshold value
    THRESH_TRIANGLE   = 16 //!< flag, use Triangle algorithm to choose the optimal threshold value
};

图解

蓝色为原始数据值，红色为阈值

1. THRESH_BINARY 大于阈值的部分被置为255，小于部分被置为0
   
2. THRESH_BINARY_INV 大于阈值部分被置为0，小于部分被置为255
   
3. THRESH_TRUNC 大于阈值部分被置为threshold，小于部分保持原样
   
4. THRESH_TOZERO 小于阈值部分被置为0，大于部分保持不变
   
5. THRESH_TOZERO_INV 大于阈值部分被置为0，小于部分保持不变
   

DEMO

int main() {
	cv::namedWindow("src",0);
	cv::namedWindow("dst", 0);
	cv::Mat src(255, 500, CV_8UC1, cv::Scalar(0));
	for (int i = 0; i < src.rows; ++i) {
		for (int j = 0; j < src.cols; ++j)
		{
			src.at(i, j) = i;
		}
	}
	cv::imshow("src", src);
	cv::imwrite("./src.jpg", src);
	cv::Mat dst;
	double r = cv::threshold(src, dst, 128, 255, cv::THRESH_BINARY);
	cv::imshow("dst", dst);
	cv::imwrite("./dst_THRESH_BINARY.jpg", dst);
	cv::waitKey(0);
	r = cv::threshold(src, dst, 128, 255, cv::THRESH_BINARY_INV);
	cv::imshow("dst", dst);
	cv::imwrite("./dst_THRESH_BINARY_INV.jpg", dst);
	cv::waitKey(0);
	r = cv::threshold(src, dst, 128, 255, cv::THRESH_TRUNC);
	cv::imshow("dst", dst);
	cv::imwrite("./dst_THRESH_TRUNC.jpg", dst);
	cv::waitKey(0);
	r = cv::threshold(src, dst, 128, 255, cv::THRESH_TOZERO);
	cv::imshow("dst", dst);
	cv::imwrite("./dst_THRESH_TOZERO.jpg", dst);
	cv::waitKey(0);
	r = cv::threshold(src, dst, 128, 255, cv::THRESH_TOZERO_INV);
	cv::imshow("dst", dst);
	cv::imwrite("./dst_THRESH_TOZERO_INV.jpg", dst);
	cv::waitKey(0);
	r = cv::threshold(src, dst, 128, 255, cv::THRESH_OTSU);
	cv::imshow("dst", dst);
	cv::imwrite("./dst_THRESH_OTSU.jpg", dst);
	cv::waitKey(0);
	r = cv::threshold(src, dst, 128, 255, cv::THRESH_TRIANGLE);
	cv::imshow("dst", dst);
	cv::imwrite("./dst_THRESH_TRIANGLE.jpg", dst);
	cv::waitKey(0);

}

效果

0. src原图
   

1. THRESH_BINARY 大于阈值的部分被置为255，小于部分被置为0
   

2. THRESH_BINARY_INV 大于阈值部分被置为0，小于部分被置为255
   

3. THRESH_TRUNC 大于阈值部分被置为threshold，小于部分保持原样
   

4. THRESH_TOZERO 小于阈值部分被置为0，大于部分保持不变
   

5. THRESH_TOZERO_INV 大于阈值部分被置为0，小于部分保持不变
   

6. THRESH_OTSU 二值化的一个很优的算法Otsu大津算法
   

7. THRESH_TRIANGLE