阈值分割之自适应阈值原理及adaptiveThreshold参数意义

opencv自适应阈值原理

1、先将原图像进行平滑(均值平滑，中值平滑，高斯平滑…),得到一个平滑后的图像记为smoothImg;
2、得到自适应矩阵 Thresh = (1-ratio)*smoothImg ; ratio一般为0.15
3、利用局部阈值进行分割 , 即利用Thresh和原图进行比较分割
优点是对于光照不均匀的图像进行二值化处理，相对会比全局性的二值化(OTSU算法和最大熵算法、全局Thresh)效果会好。

adaptiveThreshold参数意义

@param src Source 8-bit single-channel image.
@param dst Destination image of the same size and the same type as src.
@param maxValue Non-zero value assigned to the pixels for which the condition is satisfied
@param adaptiveMethod Adaptive thresholding algorithm to use, see #AdaptiveThresholdTypes.
The #BORDER_REPLICATE | #BORDER_ISOLATED is used to process boundaries.
@param thresholdType Thresholding type that must be either #THRESH_BINARY or #THRESH_BINARY_INV,
see #ThresholdTypes.
@param blockSize Size of a pixel neighborhood that is used to calculate a threshold value for the
pixel: 3, 5, 7, and so on.
@param C Constant subtracted from the mean or weighted mean (see the details below). Normally, it
is positive but may be zero or negative as well.

@sa  threshold, blur, GaussianBlur
 */
CV_EXPORTS_W void adaptiveThreshold( InputArray src, OutputArray dst,
                                     double maxValue, int adaptiveMethod,
                                     int thresholdType, int blockSize, double C );

参数	意义
src	源图像
dst	输出图像
maxValue	超过阈值的部分值设置为 maxvalue
adaptiveMethod	在一个邻域内计算阈值所采用的算法， 有两个取值，分别为 ADAPTIVE_THRESH_MEAN_C 和 ADAPTIVE_THRESH_GAUSSIAN_C (1)、ADAPTIVE_THRESH_MEAN_C的计算方法是计算出领域的平均值再减去第七个参数2的值。 (2)、ADAPTIVE_THRESH_GAUSSIAN_C的计算方法是计算出领域的高斯均值再减去第七个参数2的值
thresholdType	1、THRESH_BINARY 原图灰度值大于矩阵取最大值 2、THRESH_BINARY_INV，与 1 相反
blockSize	矩阵大小
c	确定完阈值之后的一个微调，在原有的基础上加上 C

void MainWindow::on_pushButton_clicked()
{
    
    
    Mat srcImg = imread("D:\\5.jpg",0);
    if(srcImg.empty())
    {
    
    
        QMessageBox::information(this,"警告","图片读取失败，请检查图片路径！");
        return;
    }

    QImage qImg = QImage((unsigned char*)(srcImg.data), srcImg.cols,
                         srcImg.rows, srcImg.cols*srcImg.channels(), QImage::Format_Grayscale8);
    ui->label->resize(640,480);
    ui->label->setPixmap(QPixmap::fromImage(qImg.scaled(ui->label->size(), Qt::KeepAspectRatio)));
}

//自适应阈值
int MainWindow::myjAutoThresh(Mat& p_srcImg , Mat &p_dstImg)
{
    
    
    adaptiveThreshold(p_srcImg , p_dstImg , 255 , 1 , 1 , 15 , 0);
    return 0;
}