OpenCV基本数据类型实战

    • 〇、实战内容
    • 1 OpenCV helloworld
        • 1.1 文件结构类型
        • 1.2 CMakeList.txt
        • 1.3 Helloworld
    • 2. Image的基本操作
    • 3. OpenCV 基本数据类型
    • 4. 读取图片的像素 & 遍历图片
        • 4.1 获取制定像素
        • 4.2 遍历图片
    • 5. 图片反色
        • 5.1 方法1 ：遍历
        • 5.2 方法2 ：矩阵减法
    • 6. 矩阵基本运算

〇、实战内容

  1. OpenCV helloworld
  2. Image的基本操作
  3. OpenCV 基本数据类型
  4. 遍历图片，读取图片的像素
  5. 图片反色
  6. 矩阵基本操作

1 OpenCV helloworld

1.1 文件结构类型

assign_1build [cmake build所用]assign_1.cppCMakeLists.txtimg.webp

图片地址

1.2 CMakeList.txt

cmake_minimum_required(VERSION 3.10)set(CMAKE_CXX_STANDARD 11)set(CMAKE_CXX_STANDARD_REQUIRED True)project(assign1)find_package(OpenCV 3 REQUIRED HINTS /usr/local/opt/opencv@3) add_executable(assign1 assign_1.cpp)target_link_libraries(assign1 ${OpenCV_LIBS})
  1. cmake 3.10版本
  2. 使用C++ 11
  3. project 名字为assign1
  4. find_package寻找opencv@3库

1.3 Helloworld

assign_1.cpp

#include <opencv2/opencv.hpp>#include <iostream>#include <vector>#include <string>using namespace cv;using namespace std;int main(int argc, char *argv[]){Mat image = imread("/Users/..../computerphotography/course_zhengjiangdaxue/opencv-logo.png"); // 载入名为 "opencv-logo.png" 的图片namedWindow("hello");// 创建一个标题为 "hello" 的窗口imshow("hello", result); // 在窗口 "hello" 中显示图片waitKey(0);// 等待用户按下键盘destroyWindow("hello");// 销毁窗口 "hello"return 0;}

2. Image的基本操作

#include <opencv2/opencv.hpp>#include <iostream>#include <vector>#include <string>using namespace cv;using namespace std;int main(int argc, char *argv[]){Mat image = imread("/Users/..../computerphotography/course_zhengjiangdaxue/opencv-logo.png"); // 载入名为 "opencv-logo.png" 的图片cout << "image size 1: " << image.size() << endl;cout << "image 行数: " << image.rows << endl;cout << "image 列数: " << image.cols << endl;cout << "image 通道数: " << image.channels() << endl;cout << "image type: " << image.type() << endl;return 0;}

输出结果

image size 1: [200 x 200]image 行数: 200image 列数: 200image 通道数: 3image type: 16

3. OpenCV 基本数据类型

int main(int argc, char *argv[]){cout << "CV_8UC1:" << CV_8UC1 << endl;cout << "CV_8UC2:" << CV_8UC2 << endl;cout << "CV_8UC3:" << CV_8UC3 << endl;cout << "CV_8UC4:" << CV_8UC4 << endl;cout << "CV_8UC5:" << CV_8UC(5) << endl;cout << "CV_8SC1:" << CV_8SC1 << endl;cout << "CV_8SC2:" << CV_8SC2 << endl;cout << "CV_8SC3:" << CV_8SC3 << endl;cout << "CV_8SC4:" << CV_8SC4 << endl;cout << "CV_8SC5:" << CV_8SC(5) << endl;cout << "CV_16UC1:" << CV_16UC1 << endl;cout << "CV_16UC2:" << CV_16UC2 << endl;cout << "CV_16UC3:" << CV_16UC3 << endl;cout << "CV_16UC4:" << CV_16UC4 << endl;cout << "CV_16UC5:" << CV_16UC(5) << endl;cout << "CV_16SC1:" << CV_16SC1 << endl;cout << "CV_32SC1:" << CV_32SC1 << endl;cout << "CV_32FC1:" << CV_32FC1 << endl;cout << "CV_64FC1:" << CV_64FC1 << endl;}

输出结果

CV_8UC1:0CV_8UC2:8CV_8UC3:16CV_8UC4:24CV_8UC5:32CV_8SC1:1CV_8SC2:9CV_8SC3:17CV_8SC4:25CV_8SC5:33CV_16UC1:2CV_16UC2:10CV_16UC3:18CV_16UC4:26CV_16UC5:34CV_16SC1:3CV_32SC1:4CV_32FC1:5CV_64FC1:6
  1. CV_8UC1 8字节无符号类型，通道为1
  2. CV_8UC3 8字节无符号类型，通道为3 即一个长度为3的数据例如[255,255,255] 三通道基本代表R, G, B
  3. image.type() == 16 == CV_8UC3 即改图片是3通道
  4. 单通道，增加一通道，值增加8
    CV_8UC1->0 -> uchar
    CV_8SC1->1 -> char
    CV_16UC1->2 -> ushort
    CV_16SC1->3 -> short
    CV_32SC1->4 -> int
    CV_32FC1->5 -> float
    CV_64FC1->6 -> double

4. 读取图片的像素 & 遍历图片

4.1 获取制定像素

int main(int argc, char *argv[]){// 3. 获取某一个像素值cout << "image at 0: " << image.at<Vec3b>(0) << endl;cout << "image at 10000000: " << image.at<Vec3b>(10000000) << endl;cout << "image at 39999: " << image.at<Vec3b>(39999) << endl;cout << "image at 199,199: " << image.at<Vec3b>(199, 199) << endl;}

输出：

image at 0: [255, 255, 255]image at 10000000: [0, 0, 0]image at 39999: [255, 255, 255]image at 199,199: [255, 255, 255]
  1. at方法
    a. 需要制定对应的类型，单通道见Section3 说明；二通道Vec2b Vec2i Vec2f Vec2d
    b. 参数可为1个，200 * 200 即 0<=index <=39999；参数为2个，则对应的行和列
  2. 超出索引也可获取值

4.2 遍历图片

int main(int argc, char *argv[]){// //5. 遍历图片像素，方法1，便利，判断是白色，赋值为黑色for(int i = 0;i<image.rows;i++){for(int j=0;j<image.cols;j++){if(image.at<Vec3b>(i,j) == white){image.at<Vec3b>(i,j) = black;}}}}

5. 图片反色

5.1 方法1 ：遍历

int main(int argc, char *argv[]){Vec3b white(255, 255, 255); for(int i = 0;i<image.rows;i++){ for(int j=0;j<image.cols;j++){ image.at<Vec3b>(i,j) = white - image.at<Vec3b>(i,j); } }}
  1. 定义白色Vec3b white(255, 255, 255);
  2. 遍历图片用white减去每个像素颜色

5.2 方法2 ：矩阵减法

Mat m(image.rows,image.cols,CV_8UC3,Scalar(255,255,255));image = m-image;
  1. Mat 代表opencv里的矩阵
  2. 初始化的时候传入行数，列数，每个像素的数据格式，以及初始值
    a. 如果CV_8UC1 就是Scalar(255)
    b. 如果CV_8UC2 就是Scalar(255, 255)
  3. 初始化了一个CV_8UC3， 和原始图片一样大的矩阵，然后做减法

6. 矩阵基本运算

int main(){Mat origin(10, 10, CV_32FC1, Scalar(0));for (int i = 0; i < 10; i++){for (int j = 0; j < 10; j++){if (i == j){cout << "i=" << i << "j=" << j << endl;origin.at<float>(i, j) = 2.0;}else if ((i == j - 1) || (i == j + 1)){origin.at<float>(i, j) = -1.0;}}}// 矩阵 的逆Mat invert = origin.inv();cout << "origin mat:"<<endl;print(origin);cout << endl<<"invert mat:"<<endl;print(invert);//矩阵加法cout << endl<< "add mat:"<<endl;origin = origin+invert;print(origin);//矩阵乘法cout << endl<< "multiply mat:"<<endl;origin = origin*invert;print(origin);//初始化对角线cout << endl<< "eye mat:"<<endl;Mat eye = Mat::eye(10,10,CV_32FC1);print(eye);cout << endl<< "normalize mat:"<<endl;Mat result;//归一化，最大的位白色，最小的为黑色normalize(invert, result, 1.0, 0.0, CV_MINMAX);// 现实窗口逻辑print(result);cout << endl;return 0;}

输出结果：

origin mat:[2, -1, 0, 0, 0, 0, 0, 0, 0, 0; -1, 2, -1, 0, 0, 0, 0, 0, 0, 0; 0, -1, 2, -1, 0, 0, 0, 0, 0, 0; 0, 0, -1, 2, -1, 0, 0, 0, 0, 0; 0, 0, 0, -1, 2, -1, 0, 0, 0, 0; 0, 0, 0, 0, -1, 2, -1, 0, 0, 0; 0, 0, 0, 0, 0, -1, 2, -1, 0, 0; 0, 0, 0, 0, 0, 0, -1, 2, -1, 0; 0, 0, 0, 0, 0, 0, 0, -1, 2, -1; 0, 0, 0, 0, 0, 0, 0, 0, -1, 2]invert mat:[0.90909088, 0.81818181, 0.72727281, 0.63636357, 0.54545444, 0.45454538, 0.36363626, 0.27272728, 0.18181814, 0.090909071; 0.81818181, 1.6363636, 1.4545456, 1.2727271, 1.0909089, 0.90909076, 0.72727251, 0.54545456, 0.36363629, 0.18181814; 0.72727281, 1.4545456, 2.1818185, 1.9090908, 1.6363634, 1.3636361, 1.0909088, 0.81818181, 0.54545444, 0.27272722; 0.63636369, 1.2727274, 1.909091, 2.5454543, 2.1818178, 1.8181814, 1.4545449, 1.090909, 0.72727257, 0.36363629; 0.54545456, 1.0909091, 1.6363636, 2.1818178, 2.7272723, 2.2727268, 1.8181812, 1.3636363, 0.9090907, 0.45454535; 0.45454544, 0.90909088, 1.3636363, 1.8181814, 2.2727268, 2.7272723, 2.1818175, 1.6363635, 1.0909089, 0.54545444; 0.36363637, 0.72727275, 1.090909, 1.4545451, 1.8181815, 2.1818178, 2.545454, 1.9090909, 1.2727271, 0.63636357; 0.27272728, 0.54545456, 0.81818181, 1.0909089, 1.3636363, 1.6363634, 1.9090906, 2.1818182, 1.4545454, 0.72727269; 0.18181817, 0.36363634, 0.54545456, 0.72727257, 0.90909082, 1.0909089, 1.2727271, 1.4545454, 1.6363635, 0.81818175; 0.090909094, 0.18181819, 0.27272728, 0.36363631, 0.45454541, 0.54545444, 0.63636357, 0.72727275, 0.81818175, 0.90909088]add mat:[2.909091, -0.18181819, 0.72727281, 0.63636357, 0.54545444, 0.45454538, 0.36363626, 0.27272728, 0.18181814, 0.090909071; -0.18181819, 3.6363635, 0.45454562, 1.2727271, 1.0909089, 0.90909076, 0.72727251, 0.54545456, 0.36363629, 0.18181814; 0.72727281, 0.45454562, 4.1818185, 0.90909076, 1.6363634, 1.3636361, 1.0909088, 0.81818181, 0.54545444, 0.27272722; 0.63636369, 1.2727274, 0.909091, 4.545454, 1.1818178, 1.8181814, 1.4545449, 1.090909, 0.72727257, 0.36363629; 0.54545456, 1.0909091, 1.6363636, 1.1818178, 4.727272, 1.2727268, 1.8181812, 1.3636363, 0.9090907, 0.45454535; 0.45454544, 0.90909088, 1.3636363, 1.8181814, 1.2727268, 4.727272, 1.1818175, 1.6363635, 1.0909089, 0.54545444; 0.36363637, 0.72727275, 1.090909, 1.4545451, 1.8181815, 1.1818178, 4.545454, 0.90909088, 1.2727271, 0.63636357; 0.27272728, 0.54545456, 0.81818181, 1.0909089, 1.3636363, 1.6363634, 0.90909064, 4.181818, 0.45454538, 0.72727269; 0.18181817, 0.36363634, 0.54545456, 0.72727257, 0.90909082, 1.0909089, 1.2727271, 0.45454538, 3.6363635, -0.18181825; 0.090909094, 0.18181819, 0.27272728, 0.36363631, 0.45454541, 0.54545444, 0.63636357, 0.72727275, -0.18181825, 2.909091]multiply mat:[4.181818, 5.4545455, 6.909091, 7.6363621, 7.7272706, 7.2727251, 6.3636341, 5.0909085, 3.5454535, 1.8181813; 5.454545, 11.090909, 13.090909, 14.63636, 14.909087, 14.090905, 12.363631, 9.9090891, 6.9090891, 3.5454535; 6.9090915, 13.09091, 18.818182, 20.363632, 20.999994, 19.999994, 17.636356, 14.181816, 9.9090881, 5.0909076; 7.636363, 14.636362, 20.363634, 25.18181, 25.454536, 24.545444, 21.818171, 17.63636, 12.363632, 6.3636341; 7.727272, 14.909089, 20.999996, 25.454536, 28.727262, 27.272717, 24.545443, 19.999994, 14.090904, 7.2727246; 7.2727261, 14.090907, 19.999996, 24.545444, 27.272717, 28.727262, 25.454533, 20.999994, 14.909085, 7.7272701; 6.3636355, 12.363635, 17.636362, 21.818174, 24.545446, 25.454536, 25.181808, 20.363632, 14.63636, 7.6363616; 5.090909, 9.90909, 14.181817, 17.636358, 19.999994, 20.999994, 20.36363, 18.81818, 13.090906, 6.9090896; 3.5454543, 6.9090905, 9.90909, 12.363633, 14.090905, 14.909086, 14.636359, 13.090907, 11.090907, 5.4545441; 1.8181818, 3.5454545, 5.090909, 6.3636351, 7.2727256, 7.7272706, 7.6363616, 6.9090905, 5.4545445, 4.181818]eye mat:[1, 0, 0, 0, 0, 0, 0, 0, 0, 0; 0, 1, 0, 0, 0, 0, 0, 0, 0, 0; 0, 0, 1, 0, 0, 0, 0, 0, 0, 0; 0, 0, 0, 1, 0, 0, 0, 0, 0, 0; 0, 0, 0, 0, 1, 0, 0, 0, 0, 0; 0, 0, 0, 0, 0, 1, 0, 0, 0, 0; 0, 0, 0, 0, 0, 0, 1, 0, 0, 0; 0, 0, 0, 0, 0, 0, 0, 1, 0, 0; 0, 0, 0, 0, 0, 0, 0, 0, 1, 0; 0, 0, 0, 0, 0, 0, 0, 0, 0, 1]normalize mat:[0.31034487, 0.27586213, 0.24137938, 0.20689656, 0.17241378, 0.13793103, 0.10344826, 0.068965539, 0.034482758, 1.3546499e-09; 0.27586213, 0.58620697, 0.51724154, 0.44827589, 0.37931034, 0.31034482, 0.24137928, 0.17241383, 0.10344827, 0.034482758; 0.24137938, 0.51724154, 0.79310369, 0.68965524, 0.58620691, 0.48275861, 0.37931028, 0.27586213, 0.17241378, 0.068965517; 0.2068966, 0.44827598, 0.6896553, 0.93103451, 0.7931034, 0.65517235, 0.51724124, 0.37931037, 0.24137929, 0.10344827; 0.17241383, 0.37931043, 0.58620697, 0.7931034, 1, 0.82758617, 0.65517229, 0.48275867, 0.31034482, 0.13793102; 0.13793106, 0.31034487, 0.48275867, 0.65517235, 0.82758617, 1, 0.79310334, 0.58620691, 0.37931034, 0.17241378; 0.1034483, 0.24137937, 0.37931037, 0.51724136, 0.65517241, 0.7931034, 0.93103445, 0.68965524, 0.44827589, 0.20689656; 0.068965539, 0.17241383, 0.27586213, 0.37931034, 0.48275867, 0.58620691, 0.68965518, 0.79310358, 0.51724142, 0.24137934; 0.03448277, 0.10344829, 0.17241383, 0.24137929, 0.31034485, 0.37931034, 0.44827589, 0.51724142, 0.58620691, 0.2758621; 9.8328981e-09, 0.034482773, 0.068965539, 0.10344828, 0.13793105, 0.17241378, 0.20689656, 0.24137937, 0.2758621, 0.31034487]a123456@lucky build %