边缘检测（OpenCV）
作者：互联网
梯度算子
不变矩
#include <opencv2/highgui.hpp>
#include <opencv2/imgproc.hpp>
#include <iostream>
#include <cmath>
using namespace std;
using namespace cv;
//原图，原图的灰度版，目标图
Mat g_srcImage, g_srcGrayImage, g_dstImage;
//Canny边缘检测相关变量
Mat g_cannyDetectedEdges;
int g_cannyLowThreshold = 1;//TrackBar位置参数
//Sobel边缘检测相关变量
Mat g_sobelGradient_X, g_sobelGradient_Y;
Mat g_sobelAbsGradient_X, g_sobelAbsGradient_Y;
int g_sobelKernelSize = 1;//TrackBar位置参数
//Scharr滤波器相关变量
Mat g_scharrGradient_X, g_scharrGradient_Y;
Mat g_scharrAbsGradient_X, g_scharrAbsGradient_Y;
static void ShowHelpText();
static void on_Canny(int, void*);//Canny边缘检测窗口滚动条的回调函数
static void on_Sobel(int, void*);//Sobel边缘检测窗口滚动条的回调函数
void Scharr();// 封装了Scharr边缘检测相关代码的函数
void laplacian(); // 封装了laplacian边缘检测相关代码的函数
/* 不变矩 */
void hu(Mat image);
/* 轮廓 */
Mat src, dst, drawImage;
const char* result = "moments_demo";
int threshold_value = 120;
int threshold_max = 255;
RNG rng(12345);
void Moments_demo(int, void*);
void moments_demo_start();
int main(int argc, char** argv)
{
ShowHelpText();
g_srcImage = imread("1.png");
if (!g_srcImage.data) { printf("Oh，no，读取srcImage错误~！ \n"); return false; }
namedWindow("【原始图】");
imshow("【原始图】", g_srcImage);
g_dstImage.create(g_srcImage.size(), g_srcImage.type());
cvtColor(g_srcImage, g_srcGrayImage, COLOR_BGR2GRAY);
namedWindow("【效果图】Canny边缘检测", WINDOW_AUTOSIZE);
namedWindow("【效果图】Sobel边缘检测", WINDOW_AUTOSIZE);
createTrackbar("参数值：", "【效果图】Canny边缘检测", &g_cannyLowThreshold, 120, on_Canny);
createTrackbar("参数值：", "【效果图】Sobel边缘检测", &g_sobelKernelSize, 3, on_Sobel);
on_Canny(0, 0);
on_Sobel(0, 0);
Scharr();
laplacian();
// moments_demo_start();
waitKey();
return 0;
}
static void ShowHelpText()
{
printf("当前使用的OpenCV版本为：" CV_VERSION);
printf("\n运行成功，请调整滚动条观察图像效果\n");
}
void on_Canny(int, void*)
{
// 先使用 3x3内核来降噪
blur(g_srcGrayImage, g_cannyDetectedEdges, Size(3, 3));
// 运行我们的Canny算子
Canny(g_cannyDetectedEdges, g_cannyDetectedEdges, g_cannyLowThreshold, g_cannyLowThreshold * 3, 3);
//先将g_dstImage内的所有元素设置为0
g_dstImage = Scalar::all(0);
//使用Canny算子输出的边缘图g_cannyDetectedEdges作为掩码，来将原图g_srcImage拷到目标图g_dstImage中
g_srcImage.copyTo(g_dstImage, g_cannyDetectedEdges);
//显示效果图
imshow("【效果图】Canny边缘检测", g_dstImage);
//计算边缘图像不变矩
hu(g_dstImage);
}
void on_Sobel(int, void*)
{
Sobel(g_srcImage, g_sobelGradient_X, CV_16S, 1, 0, (2 * g_sobelKernelSize + 1), 1, 1, BORDER_DEFAULT);
convertScaleAbs(g_sobelGradient_X, g_sobelAbsGradient_X);//计算绝对值，并将结果转换成8位
Sobel(g_srcImage, g_sobelGradient_Y, CV_16S, 0, 1, (2 * g_sobelKernelSize + 1), 1, 1, BORDER_DEFAULT);
convertScaleAbs(g_sobelGradient_Y, g_sobelAbsGradient_Y);//计算绝对值，并将结果转换成8位
addWeighted(g_sobelAbsGradient_X, 0.5, g_sobelAbsGradient_Y, 0.5, 0, g_dstImage);
imshow("【效果图】Sobel边缘检测", g_dstImage);
}
void Scharr() //
{
Scharr(g_srcImage, g_scharrGradient_X, CV_16S, 1, 0, 1, 0, BORDER_DEFAULT);
convertScaleAbs(g_scharrGradient_X, g_scharrAbsGradient_X);//计算绝对值，并将结果转换成8位
Scharr(g_srcImage, g_scharrGradient_Y, CV_16S, 0, 1, 1, 0, BORDER_DEFAULT);
convertScaleAbs(g_scharrGradient_Y, g_scharrAbsGradient_Y);//计算绝对值，并将结果转换成8位
addWeighted(g_scharrAbsGradient_X, 0.5, g_scharrAbsGradient_Y, 0.5, 0, g_dstImage);
imshow("【效果图】Scharr滤波器", g_dstImage);
}
void laplacian()
{
Mat result;
int ksize = 5; // 当ksize==1时，3*3的拉普拉斯算子
int depth = CV_16S; // 目标图像的深度，当depth==-1时，为原图图像深度
Laplacian(g_srcImage, g_dstImage, depth, ksize);
// imshow("【效果图】laplacian滤波器", g_dstImage);
convertScaleAbs(g_dstImage, result); // (3 + 1) * 0.25
imshow("Laplacian", result);
}
void Moments_demo(int, void*)
{
//提取图像边缘
Mat canny_out;
Canny(dst, canny_out, threshold_value, threshold_value * 2, 3, false);
//imshow("canny image", canny_out);
//发现轮廓，找到图像轮廓
vector<vector<Point>> contours;
vector<Vec4i> hierachy;
findContours(canny_out, contours, hierachy, RETR_TREE, CHAIN_APPROX_SIMPLE, Point(0, 0));
//计算每个轮廓对象的矩
vector< Moments> contours_moments(contours.size());
vector<Point2f> centers(contours.size());
for (size_t i = 0; i < contours.size(); i++)
{
//计算矩
contours_moments[i] = moments(contours[i]);
//moments(InputArray array,//输入数据
//bool binaryImage = false // 是否为二值图像
centers[i] = Point(static_cast<float>(contours_moments[i].m10 / contours_moments[i].m00), static_cast<float>(contours_moments[i].m01 / contours_moments[i].m00));
//图像中心Center(x0, y0)=(m10/m00,m01/m00)
}
src.copyTo(drawImage);
for (size_t i = 0; i < contours.size(); i++)
{
printf("centers point x：%.2f,y：%.2f\n", centers[i].x, centers[i].y);
printf("contours %d Area：%.2f Arc length：%.2f \n", i, contourArea(contours[i]), arcLength(contours[i], true));
Scalar color = Scalar(rng.uniform(0, 255), rng.uniform(0, 255), rng.uniform(0, 255));
drawContours(drawImage, contours, i, color, 2, LINE_AA, hierachy, 0, Point(0, 0));//绘制轮廓
circle(drawImage, centers[i], 2, color, 2, LINE_AA);//绘制图形中心
}
imshow(result, drawImage);
return;
}
void moments_demo_start()
{
src = imread("1.png");
if (!src.data)
{
printf("could not load image...\n");
}
char input[] = "gray image";
namedWindow(input, WINDOW_AUTOSIZE);
namedWindow(result, WINDOW_AUTOSIZE);
//输入图像转为灰度图像
cvtColor(src, dst, COLOR_BGR2GRAY);
GaussianBlur(dst, dst, Size(3, 3), 0, 0);
imshow(input, dst);
const char* thresh = "threshold value";
createTrackbar(thresh, result, &threshold_value, threshold_max, Moments_demo);
Moments_demo(0, 0);
}
void hu(Mat image)
{
cvtColor(image, image, COLOR_BGR2GRAY);
Moments mts = moments(image);
double hu[7];
HuMoments(mts, hu);
cout << endl << "Canny算子处理后的图像的不变矩 ：" << endl;
for (int i = 0; i < 7; i++)
{
cout << "η" << i+1 << '=' << abs(log(abs(hu[i]))) << endl;
}
}
edgedetection
---------------------continue------------------------------------------
标签：srcImage,moments,边缘,dstImage,检测,void,OpenCV,int,contours 来源： https://www.cnblogs.com/jianle23/p/13893848.html