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In one of my projects, there is a need to implement a server-less SQL database.  The result of my exploration led me to SQLITE.  I’m lucky to have found a good wrapper since I’m using C#.  All is well until the need to re-create the database arises. This is required when the application is being readied for deployment.  In my application, one of the requirements was to be able to reset the database  or re create the database inside the application.

To serve this purpose, I developed a small C#console application to create the database for me.  The code is quiet simple , far from optimized and buggy but of course it is up to you to improve it.


public static class Util
<pre> {
public static bool CreateDb(string db)
{
try
{
Console.WriteLine("Please wait creating database " + db);
SQLiteConnection.CreateFile(db);
}
catch (Exception ex)
{
Console.WriteLine(ex.Message);
return false;
}

return true;
}

public static bool ExecuteScript(string script, string connectionString)
{
try
{
if (!File.Exists(script)) throw new FileNotFoundException(script);

DbProviderFactory fact = DbProviderFactories.GetFactory("System.Data.SQLite");
using (DbConnection cnn = fact.CreateConnection())
{
cnn.ConnectionString = connectionString;

using (var cmd = cnn.CreateCommand())
{
var fileInfo = new FileInfo(script);
var sql = fileInfo.OpenText().ReadToEnd();

var tables = System.Text.RegularExpressions.Regex.Split(sql, "\r\n\r\n");

if (tables.Length > 0)
{
cnn.Open();

foreach (var table in tables)
{
Console.WriteLine("Writing TABLE: \n" + table);
cmd.CommandText = table;
cmd.CommandType = CommandType.Text;

cmd.ExecuteNonQuery();
}
}

cnn.Close();
return true;
}
}

}
catch (Exception ex)
{
Console.WriteLine(ex.Message);
return false;
}
}
}</pre>
<pre>

In your app.config, you need the following entry:
</pre>
<pre><?xml version="1.0"?>
<configuration>
  <startup useLegacyV2RuntimeActivationPolicy="true">
    <supportedRuntime version="v4.0" sku=".NETFramework,Version=v4.0"/>
  </startup>

  <system.data>
    <DbProviderFactories>
      <remove invariant="System.Data.SQLite"/>
      <add name="SQLite Data Provider" invariant="System.Data.SQLite"?
      description=".Net Framework Data Provider for SQLite"
      type="System.Data.SQLite.SQLiteFactory, System.Data.SQLite"/></pre>
<pre>    </DbProviderFactories>
  </system.data>
<startup><supportedRuntime version="v4.0" sku=".NETFramework,Version=v4.0"/></startup>
</configuration></pre>
<pre>
Finally, add a reference to the first file, the second file below should be in the same location as the executable:
  1. System.Data.SQLite.dll
  2. System.Data.SQLite.lib

I have been trying to learn OpenCV for a while now and I came across Emgu.CV, a C# wrapper.  Although the Emgu.CV is still under development, it has a many features that will make your work a lot easier if you’re doing a project in computer vision and image processing areas.  Some of its capabilities enable you to develop  web applications that can do processing of video and images via TCP/IP.

My goal in this article is to demonstrate simple background segmentation techniques using Emgu.CV.  Background segmentation is important if you are trying to extract features from an image and you want to remove or filter out unwanted objects.  You may also find this technique important when the recovery of color information from foreground objects is important.

Our objective is to remove the background from the rice image below. We want to get the color information from the rice kernels and be able to extract features for for each kernel, maybe for shape analysis or classification purposes.

 

Original image

I am sure there are other ways to perform background subtraction but this article will cover three methods that .

  1. Color filtering in Cielab color space
  2. Image masking (as i call it)
  3. Cielab + image masking

The three methods will be explained shortly and the link to the source code is available at the visioneer forum.

Color Filtering in Cielab Color Space

In this method, a copy of RGB image in Cielab space is obtained, and then process this image (in Cielab space) pixel by pixel.  Each pixel is checked if its color value is within specified range, otherwise, the pixel in the corresponding RGB image is set to black color.  An implementation is visible below:

 1: // background subtraction in cielab color space
 2: private void CielabColorFilteringBGSubtraction(string filename, bool displayResult)
 3: {
 4:     // create new image from file
 5:     Image<Bgr, Byte> rgbimage = new Image<Bgr, byte>(filename);
 6:
 7:     // make a copy of the image in lab color space
 8:     Image<Lab, Byte> labimage = rgbimage.Convert<Lab, Byte>();
 9:
 10:     // get the width and size
 11:     int width = labimage.Width;
 12:     int height = labimage.Height;
 13:
 14:     // get the filter range for each channel in lab color space
 15:     IntRange l = new IntRange(Byte.Parse(min1TextBox.Text), Byte.Parse(max1TextBox.Text));
 16:     IntRange a = new IntRange(Byte.Parse(min2TextBox.Text), Byte.Parse(max2TextBox.Text));
 17:     IntRange b = new IntRange(Byte.Parse(min3TextBox.Text), Byte.Parse(max3TextBox.Text));
 18:
 19:     // process each row in the image
 20:     for (int i = 0; i < width; i++)
 21:     {
 22:         // process each pixel
 23:         for (int j = 0; j < height; j++)
 24:         {
 25:             if (
 26:                 (labimage[j, i].X >=l.Max ) || (labimage[j, i].X <= l.Min) ||
 27:                  (labimage[j, i].Y >= a.Max) || (labimage[j, i].Y <= a.Min) ||
 28:                  (labimage[j, i].Z >= b.Max) || (labimage[j, i].Z <= b.Min)
 29:                 )
 30:             {
 31:                 // if outside the filter range, set the pixel to black color
 32:                 rgbimage[j, i] = new Bgr(0, 0, 0);
 33:             }
 34:         }
 35:     }
 36:
 37:     // display
 38:     if (displayResult) this.NewImage("Cielab-based background segmented image", rgbimage);
 39: }

The resulting image looks like this.  In my PC, it took about 3.20 sec to perform background segmentation.

output

Image Masking

In this method, what we do is convert an RGB image into binary via binary thresholding.  (The choice of threshold value depends on the image, so you have to experiment on this.)  This binary image will serve as the mask image to copy pixels from the original image to the destination image, if the corresponding pixel in the mask image is nonzero. This is given by the following representation:

DestinatioImage(x,y) = SourceImage(x,y) if MaskImage(x,y) <> 0

where x and y is the pixel coordinates of the image. Source, destination and mask image have the same size.

Code implementation is as follows:

 1: // background subtraction by converting the RGB image to binary to create the mask
 2: // then use this mask to copy foreground objects in the image
 3: private void RGBImageMaskBGSubtraction(string filename, bool displayResult)
 4: {
 5:     // load the threshold value for grayscale image
 6:     double threshold = double.Parse(max2TextBox.Text);
 7:
 8:     // create new image
 9:     Image<Bgr, Byte> img = new Image<Bgr, byte>(filename);
 10:
 11:     //convert to grayscale
 12:     Image<Gray, Byte> gray = img.Convert<Gray, Byte>();
 13:
 14:     //convert to binary image using the threshold
 15:     gray = gray.ThresholdBinary(new Gray(threshold), new Gray(255));
 16:
 17:     // copy pixels from the original image where pixels in 
 18:     // mask image is nonzero
 19:     Image<Bgr, Byte> newimg = img.Copy(gray);
 20:
 21:     // display result
 22:     if (displayResult) this.NewImage("Background segmented", newimg);
 23:
 24: }

One thing we should be aware of is that, all channels in RGB image is used to convert the image in grayscale image, then eventually binary image.  The output image is shown below:

output

Obviously, this is far worst then the first method.  But the advantage of segmentation using this technique is the speed.  The image was segmented for only 0.25 sec with more than 12X improvement, but we have to live with the quality. According to my tests, the performance improvement increases as the image size increased.  If we are very much concerned with the pixels in foreground objects, then the Image Masking method will not satisfy our requirements.

However, there is another method to achieve the segmentation quality of cielab color filtering method, and the speed of the image masking method.

Cielab + Image masking

In this method, we combine the first two techniques to get the cream of both ice creams. ;)   What we do is to perform the processing as in image masking method, but instead of using 3 channels (in RGB format) to convert the image to grayscale, we convert the image into Cielab space, select the a*-channel of the image, and use this channel to derive the mask image.

Code implementation here:

 1: // background subtraction by extracting one channel in cielab image to 
 2:  // create binary mask, and use this mask to copy foreground objects in 
 3:  // the original image
 4:  private void CielabChannelMaskBGSubtraction(string filename, bool displayResult)
 5:  {
 6:      double threshold = double.Parse(max2TextBox.Text);
 7:
 8:      Image<Bgr, byte> rgb = new Image<Bgr, byte>(filename);
 9:      Image<Lab, Byte> img = rgb.Convert<Lab, Byte>();
 10:
 11:      //get the a* channel 
 12:      Image<Gray, Byte> gray = img[channel];
 13:
 14:      //threshold and invert
 15:      gray = gray.ThresholdBinary(new Gray(threshold), new Gray(255)).Not();
 16:
 17:     // display the result
 18:      if (displayResult) this.NewImage("Background segmented", image);
 19:  }

Resulting output image here, note that it has almost the same quality as in the first method but with little performance penalty (processing takes around 0.03 sec longer than the second method).

output

Please note that you need to tweak the filter range(threshold value) for this to work in your problem area. Also,

  1. In Method 1, all three channels are used as filter ranges.
  2. Method 2, uses channel 1(maximum) for grayscale threshold value
  3. In method 3, you can select among the radio buttons (on the right) which channels to choose as the source for the mask image.  Then modify the filter range for that selected channel mask. You can also modify the source code to select one or more channels if it gives better performance.
  4. I have included two images located in the source code folder for testing purposes.

Screenshot is given below:

image

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Writing technical reports and papers for conference or journal publication can be a challenging task specially when it involves a lot of equations. This is also true for those who are doing their thesis and dissertation.  And sometimes centering an equation can be so frustrating. I myself, for one, have gone through this difficulty.

I’m using Microsoft Word 2003 with MathType 6.0.  What we want to achieve is to center the equation within the margin of our document and align the equation numbers[ e.g., (1), (2), and (3)] to the right edge of the margin as shown below:

image

Now, here’s how we do it:

1.  Make sure the STYLES AND FORMATTING is clicked so the pane at the right is visible (see the arrows).

image

2.  Once visible, the STYLES AND FORMATTING can be edited by clicking the MODIFY menu item in the drop-down menu:

image

3. A dialog box appears, click Format -> Tabs, make sure that you are editing the MTDisplayEquation style. See the example below.

image

4. Clicking the TABS menu item pops up another dialog box.  Assuming you are using A4-size paper with a left and right margin of 1.25″, you should edit the TAB STOP POSITION as shown, 0″ (left alignment),  3″ (center alignment) and 6 (right alignment).

image

5. Close the dialog boxes and try to insert an equation.  You should have the result as shown. Tab stops being pointed by arrows are now visible at the ruler area:

image