OpenCv-004-图像操作（反差图像）

一，读取图像

Mat src, gray_src;
  src = imread("F:/opencv/InputImg/h4.jpg");
  if (src.empty()) cout << "图像打开失败" << endl;
  namedWindow("input", CV_WINDOW_AUTOSIZE);
  imshow("input img", src);

二，反差图像

是图像为字节类型上午，大小是0-255，所以255减去所有的像素值,就实现了图像的反差
一，单通道反差
这个适用于只有一个通道的图片的反差，首先将读入的图片转为单通道
​​​cvtColor(src, gray_src, CV_BGR2GRAY);​​​ //改变为单通道
​​​gray_src.at<uchar>(row, col)​​ 读取每个像素值

cvtColor(src, gray_src, CV_BGR2GRAY);  //改变为单通道
  int width = gray_src.cols;
  int height = gray_src.rows;
  for (int row = 0; row < height; row++) {
    for (int col = 0; col < width; col++) {
      int gray=gray_src.at<uchar>(row, col); //读取像素值
      gray_src.at<uchar>(row, col) = 255 - gray;//反差图像
    }
  }
  namedWindow("output", CV_WINDOW_AUTOSIZE);
  imshow("反差图像", gray_src);  //单通道反差

2，多通道反差
通过 ​​​int r = src.at<Vec3b>(row, col)[0];​​读取每个通道的byte值，然后分别对每个通道进行反差

Mat dst;
  dst.create(src.size(), src.type());
  height = src.rows;
  width = src.cols;
  int channels = src.channels();
  for (int row = 0; row < height; row++) {
    for (int col = 0; col < width; col++) {
      if (channels == 1) {  //单通道
      int gray = gray_src.at<uchar>(row, col); //读取像素值
          gray_src.at<uchar>(row, col) = 255 - gray;//反差图像
      }
      else if (channels == 3) {
        int r = src.at<Vec3b>(row, col)[0];   //vec3b 读取成3个 byte图像
        int g= src.at<Vec3b>(row, col)[1];
        int b = src.at<Vec3b>(row, col)[2];
        dst.at<Vec3b>(row, col)[0] = 255 - r;
        dst.at<Vec3b>(row, col)[1] = 255 - g;
        dst.at<Vec3b>(row, col)[2] = 255 - b;
      }
    }
  }
  namedWindow("output", CV_WINDOW_AUTOSIZE);
  imshow("多通道反差图像", dst);  //多通道反差

三，调用系统api进行反差 bitwise_not

图像的反差同时也能够调用系统的api

bitwise_not(src, dst);

五，案例演示

#include <opencv2\opencv.hpp>
#include <iostream>
using namespace std;
using namespace cv;
int main() {
  Mat src, gray_src;
  src = imread("F:/opencv/InputImg/h4.jpg");
  if (src.empty()) cout << "图像打开失败" << endl;
  namedWindow("input", CV_WINDOW_AUTOSIZE);
  imshow("input img", src);
  cvtColor(src, gray_src, CV_BGR2GRAY);  //改变为单通道
  int width = gray_src.cols;
  int height = gray_src.rows;
  for (int row = 0; row < height; row++) {
    for (int col = 0; col < width; col++) {
      int gray=gray_src.at<uchar>(row, col); //读取像素值
      gray_src.at<uchar>(row, col) = 255 - gray;//反差图像
    }
  }
  namedWindow("output", CV_WINDOW_AUTOSIZE);
  imshow("反差图像", gray_src);  //单通道反差
  //多通道反差
  Mat dst;
  dst.create(src.size(), src.type());
  height = src.rows;
  width = src.cols;
  int channels = src.channels();
  for (int row = 0; row < height; row++) {
    for (int col = 0; col < width; col++) {
      if (channels == 1) {  //单通道
      int gray = gray_src.at<uchar>(row, col); //读取像素值
          gray_src.at<uchar>(row, col) = 255 - gray;//反差图像
      }
      else if (channels == 3) {
        int r = src.at<Vec3b>(row, col)[0];   //vec3b 读取成3个 byte图像
        int g= src.at<Vec3b>(row, col)[1];
        int b = src.at<Vec3b>(row, col)[2];
        dst.at<Vec3b>(row, col)[0] = 255 - r;
        dst.at<Vec3b>(row, col)[1] = 255 - g;
        dst.at<Vec3b>(row, col)[2] = 255 - b;
      }
    }
  }
  namedWindow("output", CV_WINDOW_AUTOSIZE);
  imshow("多通道反差图像", dst);  //多通道反差
  //调用系统api 进行反差
  bitwise_not(src, dst);
  namedWindow("output", CV_WINDOW_AUTOSIZE);
  imshow("多通道反差图像", dst);  //多通道反差
  waitKey(0);
}