Halcon初探（C++版）之Edges上
1、一个简单的加载并显示图像的Halcon(C++)程序
结果
C++代码
#include <iostream>
#include<HalconCpp.h>
using namespace std;
using namespace HalconCpp;
static void test() {
// Local iconic variables
HObject ho_Image;
// Local control variables
HTuple hv_Width, hv_Height, hv_WindowHandle;
HTuple path = "E:/picture/renwu1.jpg";
ReadImage(&ho_Image, path);
GetImageSize(ho_Image, &hv_Width, &hv_Height);
OpenWindow(0, 0, hv_Width, hv_Height, 0, "", "", &hv_WindowHandle);
HDevWindowStack::Push(hv_WindowHandle);
if (HDevWindowStack::IsOpen())
DispObj(ho_Image, HDevWindowStack::GetActive());
}
int main()
{
test();
system("pause");
return 0;
}
总结
- 1、读取图像：ReadImage
- 2、Open a graphics window：OpenWindow
- 3、显示图像：DispObj
2、边缘检测器
1、EdgesImage
Lanser结果
canny结果
C++代码
#include<HalconCpp.h>
using namespace std;
using namespace HalconCpp;
static void test() {
// Local iconic variables
HObject ho_Image, ho_ImaAmp, ho_ImaDir, ho_Edges;
HObject ho_Skeleton, ho_Contours;
// Local control variables
HTuple hv_Width, hv_Height, hv_WindowHandle, hv_WindowHandle1, hv_WindowHandle2;
HTuple path = "E:/picture/renwu1.jpg";
ReadImage(&ho_Image, path);
GetImageSize(ho_Image, &hv_Width, &hv_Height);
SetWindowAttr("background_color", "black");
EdgesImage(ho_Image, &ho_ImaAmp, &ho_ImaDir, "lanser2", 0.5, "nms", 12, 22);
Threshold(ho_ImaAmp, &ho_Edges, 1, 255);
Skeleton(ho_Edges, &ho_Skeleton);
GenContoursSkeletonXld(ho_Skeleton, &ho_Contours, 1, "filter");
OpenWindow(100, 100, hv_Width / 2, hv_Height / 2, "root", "visible", "", &hv_WindowHandle);
HDevWindowStack::Push(hv_WindowHandle);
if (HDevWindowStack::IsOpen())
DispObj(ho_Image, HDevWindowStack::GetActive());
OpenWindow(100, 100 + hv_Width, hv_Width / 2, hv_Height / 2, 0, "", "", &hv_WindowHandle2);
HDevWindowStack::Push(hv_WindowHandle2);
if (HDevWindowStack::IsOpen())
DispObj(ho_ImaAmp, HDevWindowStack::GetActive());
OpenWindow(100, 100+ hv_Width / 2, hv_Width / 2, hv_Height / 2, 0, "", "", &hv_WindowHandle1);
HDevWindowStack::Push(hv_WindowHandle1);
if (HDevWindowStack::IsOpen())
DispObj(ho_Contours, HDevWindowStack::GetActive());
}
int main()
{
test();
system("pause");
return 0;
}
总结
EdgesImage 使用递归实现的过滤器（根据 Deriche、Lanser 和 Shen）或 Canny 提出的传统实现的“高斯导数”过滤器（使用过滤器掩码）检测阶梯边缘。 此外，可以使用 Sobel 滤波器的一种非常快速的变体。 因此，可以使用以下边缘运算符：
void EdgesImage
(
const HObject& Image, //Input image.
HObject* ImaAmp, //Edge amplitude (gradient magnitude) image.
HObject* ImaDir, //Edge direction image.
const HTuple& Filter, //Edge operator to be applied.
const HTuple& Alpha, //Filter parameter: small values result in strong smoothing, and thus less detail (opposite for 'canny').
const HTuple& NMS, //Non-maximum suppression ('none', if not desired).
const HTuple& Low, //Lower threshold for the hysteresis threshold operation (negative, if no thresholding is desired).
const HTuple& High //Upper threshold for the hysteresis threshold operation (negative, if no thresholding is desired).
)
2、CloseEdges
结果
C++代码
#include<HalconCpp.h>
using namespace std;
using namespace HalconCpp;
static void test()
{
// Local iconic variables
HObject ho_Image, ho_EdgeAmplitude, ho_Edges;
HObject ho_EdgesExtended;
// Local control variables
HTuple hv_Width, hv_Height, hv_WindowHandle, hv_WindowHandle1, hv_WindowHandle2;
// Local control variables
HTuple path = "E:/picture/renwu1.jpg";
ReadImage(&ho_Image, path);
if (HDevWindowStack::IsOpen())
CloseWindow(HDevWindowStack::Pop());
GetImageSize(ho_Image, &hv_Width, &hv_Height);
SetWindowAttr("background_color", "black");
//原始图像
OpenWindow(0, 0, hv_Width, hv_Height, 0, "", "", &hv_WindowHandle);
HDevWindowStack::Push(hv_WindowHandle);
SobelAmp(ho_Image, &ho_EdgeAmplitude, "thin_sum_abs", 3);
if (HDevWindowStack::IsOpen())
DispObj(ho_Image, HDevWindowStack::GetActive());
//......
OpenWindow(0, hv_Width, hv_Width, hv_Height, 0, "", "", &hv_WindowHandle1);
HDevWindowStack::Push(hv_WindowHandle1);
Threshold(ho_EdgeAmplitude, &ho_Edges, 30, 255);
CloseEdges(ho_Edges, ho_EdgeAmplitude, &ho_EdgesExtended, 15);
if (HDevWindowStack::IsOpen())
SetColor(HDevWindowStack::GetActive(), "green");
if (HDevWindowStack::IsOpen())
DispObj(ho_EdgesExtended, HDevWindowStack::GetActive());
OpenWindow(0, hv_Width+hv_Width, hv_Width, hv_Height, 0, "", "", &hv_WindowHandle2);
HDevWindowStack::Push(hv_WindowHandle2);
if (HDevWindowStack::IsOpen())
SetColor(HDevWindowStack::GetActive(), "red");
if (HDevWindowStack::IsOpen())
DispObj(ho_Edges, HDevWindowStack::GetActive());
}
int main()
{
test();
system("pause");
return 0;
}
总结
CloseEdges使用边缘幅度图像关闭边缘间隙。
CloseEdges 关闭边缘检测器输出中的间隙，从而尝试生成完整的对象轮廓。 这是通过检查每个边缘点的邻居来确定具有最大幅度（即最大梯度）的点，如果其幅度大于通过 MinAmplitude 的最小幅度，则将该点添加到边缘。 该算子期望典型边缘算子（例如 EdgesImage 或 SobelAmp）返回的边缘 (Edges) 和幅度图像 (EdgeImage) 作为输入。 CloseEdges 不考虑边缘运算符可能返回的边缘方向。 因此，在梯度几乎恒定的区域，边缘可能会变得相当“摇摆不定”。
void CloseEdges
(
const HObject& Edges, //包含一个像素厚边缘的区域
const HObject& EdgeImage, //边缘幅度（梯度）图像
HObject* RegionResult, //包含闭合边的区域
const HTuple& MinAmplitude //最小边缘幅度
)
3、CloseEdgesLength
结果
C++代码
#include<HalconCpp.h>
using namespace std;
using namespace HalconCpp;
static void test()
{
// Local iconic variables
HObject ho_Image, ho_EdgeAmplitude, ho_Edges;
HObject ho_ClosedEdges;
// Local control variables
HTuple hv_Width, hv_Height, hv_WindowHandle, hv_WindowHandle1, hv_WindowHandle2;
// Local control variables
HTuple path = "E:/picture/renwu1.jpg";
ReadImage(&ho_Image, path);
GetImageSize(ho_Image, &hv_Width, &hv_Height);
if (HDevWindowStack::IsOpen())
CloseWindow(HDevWindowStack::Pop());
SetWindowAttr("background_color", "black");
//原始图像
OpenWindow(0, 0, hv_Width, hv_Height, 0, "", "", &hv_WindowHandle);
HDevWindowStack::Push(hv_WindowHandle);
if (HDevWindowStack::IsOpen())
DispObj(ho_Image, HDevWindowStack::GetActive());
SobelAmp(ho_Image, &ho_EdgeAmplitude, "thin_sum_abs", 3);
Threshold(ho_EdgeAmplitude, &ho_Edges, 30, 255);
CloseEdgesLength(ho_Edges, ho_EdgeAmplitude, &ho_ClosedEdges, 8, 100);
//CloseEdgesLength........
OpenWindow(0, 0+ hv_Width, hv_Width, hv_Height, 0, "", "", &hv_WindowHandle1);
HDevWindowStack::Push(hv_WindowHandle1);
if (HDevWindowStack::IsOpen())
SetColor(HDevWindowStack::GetActive(), "green");
if (HDevWindowStack::IsOpen())
DispObj(ho_ClosedEdges, HDevWindowStack::GetActive());
//Threshold........
OpenWindow(0, hv_Width+ hv_Width, hv_Width, hv_Height, 0, "", "", &hv_WindowHandle2);
HDevWindowStack::Push(hv_WindowHandle2);
if (HDevWindowStack::IsOpen())
SetColor(HDevWindowStack::GetActive(), "red");
if (HDevWindowStack::IsOpen())
DispObj(ho_Edges, HDevWindowStack::GetActive());
}
int main()
{
test();
system("pause");
return 0;
}
总结
CloseEdgesLength - 使用边缘幅度图像关闭边缘间隙
CloseEdgesLength 关闭边缘检测器输出中的间隙，从而尝试生成完整的对象轮廓。该算子期望由典型边缘算子（例如 EdgesImage 或 SobelAmp）返回的边缘 (Edges) 和幅度图像 (??Gradient) 作为输入。
轮廓的闭合分两步：首先，输入轮廓中一个像素宽的间隙被闭合，孤立点被消除。在此之后，通过添加边缘点将开放轮廓最多扩展 MaxGapLength 点，直到轮廓闭合或找不到更多重要的边缘点。如果梯度大于 MinAmplitude，则认为它是显着的。作为可能的新边缘点检查的相邻点是轮廓方向上的点及其在8邻域中的两个相邻点。对于这些点中的每一个，计算其梯度和该点三个可能邻居的最大梯度的总和（向前看长度 1）。然后选择总和最大的点作为新的边缘点。
void CloseEdgesLength
(
const HObject& Edges, //包含一个像素厚边缘的区域。
const HObject& Gradient, //边缘幅度（梯度）图像。
HObject* ClosedEdges, //包含闭合边的区域。
const HTuple& MinAmplitude, //最小边缘幅度
const HTuple& MaxGapLength //边缘延伸的最大点数。
)
4、DerivateGauss
DerivateGauss — 用高斯的导数对图像进行卷积。
DerivateGauss 将图像与高斯的导数进行卷积，并计算从中导出的各种特征。 Sigma 是高斯的参数（即平滑量）。 如果在 Sigma 中传递一个值，则在列和行方向上的平滑量是相同的。 如果在 Sigma 中传递两个值，第一个值指定列方向的平滑量，而第二个值指定行方向的平滑量。 Component的可能值为：
见：Halcon边缘提取之高斯导数卷积图像——derivate_gauss.hdev
5、SobelDir
结果
C++代码
#include<HalconCpp.h>
using namespace std;
using namespace HalconCpp;
static void test()
{
// Local iconic variables
HObject ho_Image, ho_EdgeAmplitude, ho_EdgeDirection;
HObject ho_ImageResult, ho_Region;
// Local control variables
HTuple hv_Width, hv_Height, hv_WindowHandle, hv_WindowHandle1, hv_WindowHandle2;
// Local control variables
HTuple path = "E:/picture/renwu1.jpg";
ReadImage(&ho_Image, path);
GetImageSize(ho_Image, &hv_Width, &hv_Height);
//原始图像
OpenWindow(0, 0, hv_Width, hv_Height, 0, "", "", &hv_WindowHandle);
HDevWindowStack::Push(hv_WindowHandle);
if (HDevWindowStack::IsOpen())
DispObj(ho_Image, HDevWindowStack::GetActive());
//SobelDir
SobelDir(ho_Image, &ho_EdgeAmplitude, &ho_EdgeDirection, "sum_sqrt", 13);
OpenWindow(0, 0 + hv_Width, hv_Width, hv_Height, 0, "", "", &hv_WindowHandle1);
HDevWindowStack::Push(hv_WindowHandle1);
if (HDevWindowStack::IsOpen())
SetColor(HDevWindowStack::GetActive(), "green");
if (HDevWindowStack::IsOpen())
DispObj(ho_EdgeDirection, HDevWindowStack::GetActive());
NonmaxSuppressionDir(ho_EdgeAmplitude, ho_EdgeDirection, &ho_ImageResult, "nms");
//Threshold
Threshold(ho_ImageResult, &ho_Region, 10, 255);
OpenWindow(0, hv_Width + hv_Width, hv_Width, hv_Height, 0, "", "", &hv_WindowHandle2);
HDevWindowStack::Push(hv_WindowHandle2);
if (HDevWindowStack::IsOpen())
SetColor(HDevWindowStack::GetActive(), "red");
if (HDevWindowStack::IsOpen())
DispObj(ho_Region, HDevWindowStack::GetActive());
}
int main()
{
test();
system("pause");
return 0;
}
总结
SobelDir - 使用 Sobel 算子检测边缘（幅度和方向）。
SobelDir 计算图像的一阶导数并用作边缘检测器。 该过滤器基于以下过滤器掩码：
void SobelDir
(
const HObject& Image,
HObject* EdgeAmplitude,
HObject* EdgeDirection,
const HTuple& FilterType,
const HTuple& Size
)
未完待续…
