More Morphology Transformations

Goal

In this tutorial you will learn how to:

• Use the OpenCV function morphologyEx to apply Morphological Transformation such as:
  • Opening
  • Closing
  • Morphological Gradient
  • Top Hat
  • Black Hat

Theory

Note

The explanation below belongs to the book Learning OpenCV by Bradski and Kaehler.

In the previous tutorial we covered two basic Morphology operations:

• Erosion
• Dilation.

Based on these two we can effectuate more sophisticated transformations to our images. Here we discuss briefly 05 operations offered by OpenCV:

Opening

• It is obtained by the erosion of an image followed by a dilation.

• Useful for removing small objects (it is assumed that the objects are bright on a dark foreground)

• For instance, check out the example below. The image at the left is the original and the image at the right is the result after applying the opening transformation. We can observe that the small spaces in the corners of the letter tend to dissapear.

Closing

• It is obtained by the dilation of an image followed by an erosion.

• Useful to remove small holes (dark regions).

Morphological Gradient

• It is the difference between the dilation and the erosion of an image.

• It is useful for finding the outline of an object as can be seen below:

Top Hat

• It is the difference between an input image and its opening.

Black Hat

• It is the difference between the closing and its input image

Code

This tutorial code’s is shown lines below. You can also download it from here

#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/highgui/highgui.hpp"
#include <stdlib.h>
#include <stdio.h>

using namespace cv;

/// Global variables
Mat src, dst;

int morph_elem = 0;
int morph_size = 0;
int morph_operator = 0;
int const max_operator = 4;
int const max_elem = 2;
int const max_kernel_size = 21;

char* window_name = "Morphology Transformations Demo";

/** Function Headers */
void Morphology_Operations( int, void* );

/** @function main */
int main( int argc, char** argv )
{
  /// Load an image
  src = imread( argv[1] );

  if( !src.data )
  { return -1; }

 /// Create window
 namedWindow( window_name, CV_WINDOW_AUTOSIZE );

 /// Create Trackbar to select Morphology operation
 createTrackbar("Operator:\n 0: Opening - 1: Closing \n 2: Gradient - 3: Top Hat \n 4: Black Hat", window_name, &morph_operator, max_operator, Morphology_Operations );

 /// Create Trackbar to select kernel type
 createTrackbar( "Element:\n 0: Rect - 1: Cross - 2: Ellipse", window_name,
                 &morph_elem, max_elem,
                 Morphology_Operations );

 /// Create Trackbar to choose kernel size
 createTrackbar( "Kernel size:\n 2n +1", window_name,
                 &morph_size, max_kernel_size,
                 Morphology_Operations );

 /// Default start
 Morphology_Operations( 0, 0 );

 waitKey(0);
 return 0;
 }

 /**
  * @function Morphology_Operations
  */
void Morphology_Operations( int, void* )
{
  // Since MORPH_X : 2,3,4,5 and 6
  int operation = morph_operator + 2;

  Mat element = getStructuringElement( morph_elem, Size( 2*morph_size + 1, 2*morph_size+1 ), Point( morph_size, morph_size ) );

  /// Apply the specified morphology operation
  morphologyEx( src, dst, operation, element );
  imshow( window_name, dst );
  }

Explanation

1. Let’s check the general structure of the program:

   • Load an image

   • Create a window to display results of the Morphological operations

   • Create 03 Trackbars for the user to enter parameters:

     • The first trackbar “Operator” returns the kind of morphology operation to use (morph_operator).

       createTrackbar("Operator:\n 0: Opening - 1: Closing \n 2: Gradient - 3: Top Hat \n 4: Black Hat",
                      window_name, &morph_operator, max_operator,
                      Morphology_Operations );
       

     • The second trackbar “Element” returns morph_elem, which indicates what kind of structure our kernel is:

       createTrackbar( "Element:\n 0: Rect - 1: Cross - 2: Ellipse", window_name,
                       &morph_elem, max_elem,
                       Morphology_Operations );
       

     • The final trackbar “Kernel Size” returns the size of the kernel to be used (morph_size)

       createTrackbar( "Kernel size:\n 2n +1", window_name,
                       &morph_size, max_kernel_size,
                       Morphology_Operations );
       

   • Every time we move any slider, the user’s function Morphology_Operations will be called to effectuate a new morphology operation and it will update the output image based on the current trackbar values.

      /**
       * @function Morphology_Operations
       */
     void Morphology_Operations( int, void* )
     {
       // Since MORPH_X : 2,3,4,5 and 6
       int operation = morph_operator + 2;
     
       Mat element = getStructuringElement( morph_elem, Size( 2*morph_size + 1, 2*morph_size+1 ), Point( morph_size, morph_size ) );
     
       /// Apply the specified morphology operation
       morphologyEx( src, dst, operation, element );
       imshow( window_name, dst );
      }
     

     We can observe that the key function to perform the morphology transformations is morphologyEx. In this example we use four arguments (leaving the rest as defaults):

     • src : Source (input) image

     • dst: Output image

     • operation: The kind of morphology transformation to be performed. Note that we have 5 alternatives:

       • Opening: MORPH_OPEN : 2
       • Closing: MORPH_CLOSE: 3
       • Gradient: MORPH_GRADIENT: 4
       • Top Hat: MORPH_TOPHAT: 5
       • Black Hat: MORPH_BLACKHAT: 6

       As you can see the values range from <2-6>, that is why we add (+2) to the values entered by the Trackbar:

       int operation = morph_operator + 2;
       

     • element: The kernel to be used. We use the function getStructuringElement to define our own structure.

Results

• After compiling the code above we can execute it giving an image path as an argument. For this tutorial we use as input the image: baboon.png:

• And here are two snapshots of the display window. The first picture shows the output after using the operator Opening with a cross kernel. The second picture (right side, shows the result of using a Blackhat operator with an ellipse kernel.

====================

Smoothing with MORPH_OPEN

The MORPH_OPEN function applies the opening operation, which is erosion followed by dilation, to a binary or grayscale image. The opening operation removes noise from an image while maintaining the overall sizes of objects in the foreground. Opening is a useful process for smoothing contours, removing pixel noise, eliminating narrow extensions, and breaking thin links between features. After using an opening operation to darken small objects and remove noise, thresholding or other morphological processes can be applied to the image to further refine the display of the primary shapes within the image.

The following example applies the opening operation to an image of microscopic spherical organisms, Rhinosporidium seeberi protozoans. After applying the opening operation and thresholding the image, only the largest elements of the image are retained, the mature R.seeberi organisms. Complete the following steps for a detailed description of the process.

Example Code See morphopenexample.pro in the examples/doc/image subdirectory of the IDL installation directory for code that duplicates this example.

Prepare the display device and load grayscale color table:

DEVICE, DECOMPOSED = 0, RETAIN = 2  
LOADCT, 0  

Select and open the image file:

file = FILEPATH('r_seeberi.jpg', $  
    SUBDIRECTORY = ['examples', 'data'])  
READ_JPEG, file, image, /GRAYSCALE  

Get the image dimensions, prepare a window and display the image:

dims = SIZE(image, /DIMENSIONS)  
WINDOW, 0, XSIZE = 2*dims[0], YSIZE = 2*dims[1], $  
    TITLE = 'Defining Shapes with Opening Operation'  
TVSCL, image, 0  

Define the radius of the structuring element and create a disk-shaped element to extract circular features:

radius = 7  
strucElem = SHIFT(DIST(2*radius+1), radius, radius) LE radius  

Compared to the previous example, a larger element is used in order to retain only the larger image elements, discarding all of the smaller background features. Further increases in the size of the structuring element would extract even larger image features.




Tip Enter PRINT, strucElem to view the structure created by the previous statement.



Apply the MORPH_OPEN function to the image, specifying the GRAY keyword for the grayscale image:

morphImg = MORPH_OPEN(image, strucElem, /GRAY)  

Display the image:

TVSCL, morphImg, 1  

The following figure shows the original image (left) and the application of the opening operation to the original image (right). The opening operation has enhanced and maintained the sizes of the large bright objects within the image while blending the smaller background features.

Figure 9-6: Application of the Opening Operation to a Grayscale Image

Figure 9-6: Application of the Opening Operation to a Grayscale Image

The following steps apply the opening operator to a binary image.

Create a window and use HISTOGRAM in conjunction with PLOT, displaying an intensity histogram to help determine the threshold intensity value:

WINDOW, 1, XSIZE = 400, YSIZE = 300  
PLOT, HISTOGRAM(img)  




Note Using an intensity histogram as a guide for determining threshold values is described in the section, Determining Intensity Values for Threshold and Stretch.



Using the histogram as a guide, create a binary image. To prepare to remove background noise, retain only areas of the image where pixel values are equal to or greater than 160:

threshImg = image GE 160  
WSET, 0  
TVSCL, threshImg, 2  

Apply the opening operation to the binary image to remove noise and smooth contours, and then display the image:

morphThresh = MORPH_OPEN(threshImg, strucElem)  
TVSCL, morphThresh, 3  

The combination of thresholding and applying the opening operation has successfully extracted the primary foreground features as shown in the following figure.

Figure 9-7: Binary Image (left) and Application of the Opening Operator to the Binary Image (right)

Figure 9-7: Binary Image (left) and Application of the Opening Operator to the Binary Image (right)