三 (3.1 core 模块) 矩阵掩膜与图像线性操作

一、掩膜操作

1.1掩膜操作函数：

1.1.1filter2D 函数的定义如下：

void filter2D( InputArray src, OutputArray dst, int ddepth,
                            InputArray kernel, Point anchor=Point(-1,-1),
                            double delta=0, int borderType=BORDER_DEFAULT );

1.1.2像素范围处理saturate_cast<typename _Tp>()

• saturate_cast<uchar>(-100)，返回0
• saturate_cast<uchar>(288)，返回255
• saturate_cast<uchar>(100)，返回100
  这个函数的功能是确保RGB值范围在0~255之间。

1.2 实例：理解filter2D函数的作用

#include <iostream>
#include <opencv2/opencv.hpp>
#include <math.h>

using namespace cv;

int main(int argc, const char * argv[]) {
    Mat src, dst;
    //加载图像
    src = imread("/Users/Longxia/Downloads/552566-XXL.jpg");
    
    if (!src.data) {
        printf("could not load image\n");
        return -1;
    }
    //显示
    namedWindow("input Image", CV_WINDOW_AUTOSIZE);
    imshow("input Image", src);
    
    /*
     //掩膜操作
     int cols = (src.cols-1) * src.channels();
     int offsetx = src.channels();
     int rows = src.rows;
     dst = Mat::zeros(src.size(), src.type());

     for (int row = 1; row < rows-1; row++) {
       const uchar *previous = src.ptr<uchar>(row-1);
       const uchar *current = src.ptr<uchar>(row);
       const uchar *next = src.ptr<uchar>(row+1);
       uchar *output = dst.ptr<uchar>(row);
       for (int col = offsetx; col < cols; col++) {
         output[col] = saturate_cast<uchar>(5*current[col] - (current[col-offsetx] + current[col+offsetx] + previous[col] + next[col]));
       }
     }
     */

    // openCV API 掩膜操作
    //定义一个掩膜
    double t = getTickCount();  //获得当前时间
    Mat kernel = (Mat_<char>(3, 3) << 0, -1, 0, -1, 5, -1, 0, -1 ,0);
    //src.depth() 表示与原图深度一样，-1也表示一样
    filter2D(src, dst, src.depth(), kernel);
    double time = (getTickCount() - t) / getTickFrequency();
    printf("time consume %.5f", time);
    //显示
    namedWindow("contrast Image", CV_WINDOW_AUTOSIZE);
    imshow("contrast Image", dst);
    
    waitKey(0);
    return 0;
}

二、图像线性操作

实现自己的线性滤波器 — OpenCV 2.3.2 documentation http://www.opencv.org.cn/opencvdoc/2.3.2/html/doc/tutorials/imgproc/imgtrans/filter_2d/filter_2d.html#filter-2d

实例

#include <opencv2/opencv.hpp>
#include <iostream>
#include <math.h>

using namespace cv;
int main(int argc, char** argv) {
    Mat src, dst;
    int ksize = 0;

    src = imread("D:/vcprojects/images/test1.png");
    if (!src.data) {
        printf("could not load image...\n");
        return -1;
    }

    char INPUT_WIN[] = "input image";
    char OUTPUT_WIN[] = "Custom Blur Filter Result";
    namedWindow(INPUT_WIN, CV_WINDOW_AUTOSIZE);
    namedWindow(OUTPUT_WIN, CV_WINDOW_AUTOSIZE);

    imshow(INPUT_WIN, src);
    
    // Sobel X 方向
    // Mat kernel_x = (Mat_<int>(3, 3) << -1, 0, 1, -2,0,2,-1,0,1);
    // filter2D(src, dst, -1, kernel_x, Point(-1, -1), 0.0);

    // Sobel Y 方向
    // Mat yimg;
    // Mat kernel_y = (Mat_<int>(3, 3) << -1, -2, -1, 0,0,0, 1,2,1);
    // filter2D(src, yimg, -1, kernel_y, Point(-1, -1), 0.0);

    // 拉普拉斯算子
    //Mat kernel_y = (Mat_<int>(3, 3) << 0, -1, 0, -1, 4, -1, 0, -1, 0);
    //filter2D(src, dst, -1, kernel_y, Point(-1, -1), 0.0);
    int c = 0;
    int index = 0;
    while (true) {
        c = waitKey(500);
        if ((char)c == 27) {// ESC 
            break;
        }
        ksize = 5 + (index % 8) * 2;
        Mat kernel = Mat::ones(Size(ksize, ksize), CV_32F) / (float)(ksize * ksize);
        filter2D(src, dst, -1, kernel, Point(-1, -1));
        index++;
        imshow(OUTPUT_WIN, dst);    
    }

    // imshow("Sobel Y", yimg);
    return 0;
}

OpenCV学习之路（四）——矩阵的掩膜操作 - 简书 https://www.jianshu.com/p/c6d1c01c900b

图像卷积与滤波 - 简书 https://www.jianshu.com/p/cbd1a1f86d1b?utm_campaign=maleskine&utm_content=note&utm_medium=seo_notes&utm_source=recommendation