像二值化阈值,二值化处理英语

opencv 图像阈值 二值化处理

文章目录 opencv 图像阈值 二值化处理图像阈值threshold 函数阈值类型图解DEMO效果

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

THRESH_BINARY 大于阈值的部分被置为255，小于部分被置为0
THRESH_BINARY_INV 大于阈值部分被置为0，小于部分被置为255
THRESH_TRUNC 大于阈值部分被置为threshold，小于部分保持原样
THRESH_TOZERO 小于阈值部分被置为0，大于部分保持不变
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<unsigned char>(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

src原图

THRESH_BINARY 大于阈值的部分被置为255，小于部分被置为0

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

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

THRESH_TOZERO 小于阈值部分被置为0，大于部分保持不变

THRESH_TOZERO_INV 大于阈值部分被置为0，小于部分保持不变

THRESH_OTSU 二值化的一个很优的算法Otsu大津算法

THRESH_TRIANGLE