"OpenCV Development Notes (XXIII): basis of morphological filtering algorithm - open operation (after the first expansion of corrosion)": To be published

"OpenCV Development Notes (24): the basis of morphological filtering algorithm - closing operation (after the first expansion of corrosion)": To be published

"OpenCV Development Notes (XXV): basis of morphological filtering algorithm - white hat": to be released

"OpenCV Development Notes (26): the basis of morphological filtering algorithm - Black Hat": to be released

Continued to supplement ...

OpenCV Development Notes (xv): Foundations of linear filtering algorithm - mean filter

Foreword

This chapter learning mean linear filtering with filtering.

Demo

Mean Filter

Outline

Mean filter to cover only a weighted average of all pixel values within the template accounting sub-region, with which the average gray value of a pixel point field instead of the return point. As is common 3x3 sub-accounting, the elements within the template region 9, mean filtering means to the current center pixel of FIG replaced by the following equation.

Is well understood from the equation: accounting for the 3x3 sub-matrix, the size of the intermediate point is replaced with the average of 9 points.

Mean filter algorithm faster rate.

Mean Filter Algorithm presence fixed defect that it can not well protect the image detail, and then the image denoising also undermined the details of the image, whereby an image is blurred, can not properly remove the noise points.

Function prototype

Mean filter function prototype is as follows:

void blur( InputArray src, 
           OutputArray dst,
           Size ksize, 
           Point anchor = Point(-1,-1),
           int borderType = BORDER_DEFAULT );

A parameter: InputArray type generally cv :: Mat, and can handle any number of image channels. Note, however, the depth of the picture should be treated as CV_8U, CV_16U, CV_16S, CV_32F, CV_64F one.
Parameter Two; OutputArray type, output target image, you need the original picture and have the same size and type.
Three parameters: Size type ksize, the size of the sub-accounts. General use Size (w, h) to indicate the size of the sub-accounting, Size (3,3) on a 3x3 size represented by the sub-accounts.
Four parameters: Point type, showing the anchor (the point value of the smoothed). Note: The default value Point (-1, -1). If the point is negative, it means taking the center core of the anchor.
Five parameters: int type borderType, inferring mode for a certain image of the external boundary pixels, the default values BORDER_DEFULAT, generally without use.

Source Demo

void OpenCVManager::testBlurFilter()
{
    QString fileName1 = "E:/qtProject/openCVDemo/openCVDemo/modules/openCVManager/images/1.jpg";
    cv::Mat matSrc = cv::imread(fileName1.toStdString());

    cv::String windowName = _windowTitle.toStdString();
    cvui::init(windowName);

    if(!matSrc.data)
    {
        qDebug() << __FILE__ << __LINE__
                 << "Failed to load image:" << fileName1;
        return;
    }

    cv::Mat dstMat;
    dstMat = cv::Mat::zeros(matSrc.size(), matSrc.type());
    cv::Mat windowMat = cv::Mat(cv::Size(dstMat.cols * 3, dstMat.rows),
                                matSrc.type());
    int ksize = 3;      // 核心大小
    int anchor = -1;    // 锚点, 正数的时候必须小于核心大小，即：-1 <= anchor < ksize
    cvui::window(windowMat, dstMat.cols, 0, dstMat.cols, dstMat.rows, "settings");
    while(true)
    {
        windowMat = cv::Scalar(0, 0, 0);
        // 原图先copy到左边
        cv::Mat leftMat = windowMat(cv::Range(0, matSrc.rows),
                                    cv::Range(0, matSrc.cols));
        cv::addWeighted(leftMat, 1.0f, matSrc, 1.0f, 0.0f, leftMat);
        // 中间为调整滤波参数的相关设置
        cvui::printf(windowMat, 375, 40, "ksize");
        cvui::trackbar(windowMat, 375, 50, 165, &ksize, 1, 10);
        if(anchor >= ksize)
        {
            anchor = ksize - 1;
        }
        cvui::printf(windowMat, 375, 100, "anchor");
        cvui::trackbar(windowMat, 375, 110, 165, &anchor, -1, ksize-1);

        // 均值滤波：方框滤波比均值滤波多了颜色深度的参数
        cv::blur(matSrc,
                 dstMat,
                 cv::Size(ksize, ksize),
                 cv::Point(anchor, anchor));

        // 效果图copy到右边
        // 注意：rang从位置1到位置2，不是位置1+宽度
        cv::Mat rightMat = windowMat(cv::Range(0, matSrc.rows),
                                     cv::Range(matSrc.cols * 2, matSrc.cols * 3));
        cv::addWeighted(rightMat, 0.0f, dstMat, 1.0f, 0.0f, rightMat);
        // 更新
        cvui::update();
        // 显示
        cv::imshow(windowName, windowMat);
        // esc键退出
        if(cv::waitKey(25) == 27)
        {
            break;
        }
    }
}