Samuel Hao's questions

我正在使用 dm-script 来处理原始轮廓。我首先计算其一阶导数来检测边缘，然后让用户在导数图中选择这些边缘之间的区域。但是，我希望相同的选择框也同时出现在原始轮廓图中，以直观地指导用户。

目前，我的实现是使用复合函数来垂直堆叠两个图（原始剖面和导数剖面）。虽然可以工作，但是交互区域之间的同步感觉很麻烦，而且不太优雅。

在 DM 脚本中，是否有更好或更有效的方法来链接原始配置文件和其衍生图之间的交互式选择区域？

任何建议或代码示例都值得赞赏！

Class CMyHandleLegend
{
    TagGroup CreateLegendTags(object self, CompoundDocument doc, number selectedCMDid)
    {
        TagGroup Legend = NewTagList();
        
        for(number i = 0; i < doc.CompoundDocumentCountImageDisplays() - 1; i++)
        {
            ImageDisplay ithDisp = doc.CompoundDocumentGetImageDisplay(i);
            string buttonName = ithDisp.ImageDisplayGetImage().ImageGetName();
            number buttonCMDid = 10000 + ithDisp.ComponentGetCompoundID();
            
            TagGroup button = NewTagGroup();
            button.TagGroupSetTagAsUInt32("ID", buttonCMDid);
            button.TagGroupSetTagAsString("Name", buttonName);
            button.TagGroupSetTagAsBoolean("Selected", buttonCMDid == selectedCMDid);
            Legend.TagGroupAddTagGroupAtEnd(button);
        }
        // Legend.TagGroupOpenBrowserWindow(0)
        return Legend;
    }
    
    void GetIthDisplayInfo(object self, CompoundDocument doc, number i, number &compID, number &AR, string &name)
    {
        ImageDisplay ithDisp = doc.CompoundDocumentGetImageDisplay(i);
        Image ithImg = ithDisp.ImageDisplayGetImage();
        
        compID = ithDisp.ComponentGetCompoundID();
        Result("\n" + compID)
        number sx = ithImg.ImageGetDimensionSize(0);
        number sy = ithImg.ImageGetDimensionSize(1);
        AR = (0 < sy) ? sx / sy : 1;
        AR = clip(AR, 0.5, 2);
        name = ithImg.ImageGetName();
    }
    
    TagGroup CreateRowLayout(object self, CompoundDocument doc, number cmdID)
    {
        number rowH = 300;
        
        TagGroup Layout = CreateCompoundDocumentLayoutTagGroup("CompoundDocument Example 2");
        TagGroup row = Layout.CompoundDocumentLayoutAddRow(rowH);
        TagGroup row2 = Layout.CompoundDocumentLayoutAddRow(rowH);
        
        number iCompID, iAR;
        string iName;
        number nImgD = doc.CompoundDocumentCountImageDisplays();
        
        for(number i = 0; i < nImgD; i++)
        {
            self.GetIthDisplayInfo(doc, i, iCompID, iAR, iName);
            
            if ((iCompID + 10000) == cmdID)
                row.CompoundDocumentLayoutAddColumnAsImage(rowH * iAR, iName, iCompID);
            if ((iCompID + 10000) == (cmdID + 1))
                row2.CompoundDocumentLayoutAddColumnAsImage(rowH * iAR, iName, iCompID);
        }
        
        Layout.TagGroupSetTagAsBoolean("Legend", 1);
        Layout.TagGroupSetTagAsTagGroup("Buttons", self.CreateLegendTags(doc, cmdID));
        
        return Layout;
    }
    
    void ChooseLayout(object self, CompoundDocument doc, number cmdID)
    {
        TagGroup Layout = self.CreateRowLayout(doc, cmdID);
        //Layout.TagGroupOpenBrowserWindow(0);
        doc.CompoundDocumentShow(Layout);
    }
    
    void HandleLegendButtonPressed(object self, number flags, CompoundDocument doc, number cmdID)
    {
        Result("\n Received button message from: " + doc.ImageDocumentGetName());
        Result("\n Button ID: " + cmdID);
        self.ChooseLayout(doc, cmdID);
    }
}

Image absolute_first_derivative(Image in)  
{  
    // Edge detected at the peak (maximum absolute value) of the first derivative curve  
  
    Number size_x, size_y, origin, scale  
    String units  
    Image out  
  
    in.ImageGetDimensionCalibration(0, origin, scale, units, 0)  
    in.GetSize(size_x, size_y)  
    if (size_y != 1) Result("Only compatible with 1D image")  
  
    out := RealImage("first_derivative", 4, size_x, size_y)  
  
    out = abs(in[icol + 1, irow] - in[icol, irow])  
    //out[0, 0] = 0  
    //out[size_x - 1, 0] = 0  
  
    out.ImageSetDimensionCalibration(0, origin, scale, units, 0)  
    return out  
}

void ShowLegendCompoundDocument()
{
    CompoundDocument doc = NewCompoundDocument("CompoundDoc Example");
    
    image img1 := RealImage("test", 4, 200);
    img1 = 100

    img1[0,0,1,50] = 50
    img1[0,150,1,200] = 50
    Image img2 := absolute_first_derivative(img1)
    Image img3 = img1
    Image img4 := absolute_first_derivative(img3)

    img1.SetName("test 1")
    img2.SetName("test 2")
    img3.SetName("test 3")
    img4.SetName("test 4")
    
    doc.ImageDocumentAddImageDisplay(img1.ImageCreateImageDisplay(-2));
    doc.ImageDocumentAddImageDisplay(img2.ImageCreateImageDisplay(-2));
    doc.ImageDocumentAddImageDisplay(img3.ImageCreateImageDisplay(-2));
    doc.ImageDocumentAddImageDisplay(img4.ImageCreateImageDisplay(-2));
    
    object obj = Alloc(CMyHandleLegend);
    number listenID = doc.CompoundDocumentAddListener(obj, "compounddocument_buttonpressed:HandleLegendButtonPressed");
    
    obj.ChooseLayout(doc, 10008);
}

ShowLegendCompoundDocument();

我目前正在开发 DigitalMicrograph (DM) 脚本中的边缘检测功能。我已经有两个简单的实现来使用内在变量计算一阶和二阶导数，如下所示：

一阶导数：

Image absolute_first_derivative(Image in)
{
    Number size_x, size_y, origin, scale;
    String units;
    Image out;

    in.ImageGetDimensionCalibration(0, origin, scale, units, 0);
    in.GetSize(size_x, size_y);
    if (size_y != 1) Result("Only compatible with 1D image");

    out := RealImage("first_derivative", 4, size_x, size_y);

    out = in[icol + 1, irow] - in[icol, irow];
    out[0, 0] = 0;
    out[size_x - 1, 0] = 0;

    out.ImageSetDimensionCalibration(0, origin, scale, units, 0);
    return out;
}

二阶导数：

Image second_derivative(Image in)
{
    Number size_x, size_y, origin, scale;
    String units;
    Image out;

    in.ImageGetDimensionCalibration(0, origin, scale, units, 0);
    in.GetSize(size_x, size_y);
    if (size_y != 1) Result("Only compatible with 1D image");

    out := RealImage("second_derivative", 4, size_x, size_y);

    out = in[icol + 1, irow] - 2 * in[icol, irow] + in[icol - 1, irow];
    out[0, 0] = 0;
    out[size_x - 1, 0] = 0;

    out.ImageSetDimensionCalibration(0, origin, scale, units, 0);
    return out;
}

我想使用Warp()DM 提供的函数实现更灵活的基于内核的方法（例如 Sobel、LoG 或类似内核）。但是，它似乎Warp()是专门为 2D 图像转换而设计的。

我的问题是：

1. Warp()DigitalMicrograph 脚本是否可以改编或用于一维图像处理？
2. 如果Warp()不合适，是否有其他内置 DM 函数或推荐的方法来执行基于核的卷积或专门对一维图像进行的变换？

任何建议或例子都将不胜感激！