Detect Quality of Focus on a Camera

[Wes_P]

Overview

This example demonstrates how to calculate the relative edge sharpness of an image to determine the best camera focus. 

Description

It may be necessary in some applications to programmatically determine the quality of an image. "Quality" is a subjective term, but it is almost universally accepted that sharp focus is one characteristic of a high quality image. We can measure the sharpness of an image, at least relatively, by taking advantave of filters and convolution in NI Vision.

Basically, we are taking an image in, and then applying various convolution and filtering algorithms. These algorithms effectively differentiate the image, and highlight features in the image where there is a high change in pixel value in a small change of space. In other words, the derivative of a function which represents pixel values with respect to space will be the highest on sharp color (or intensity changes).

In our resulting image, the edges will show up as white, and everything else as black. We then take the mean value of the resulting image. If we pan through the various lens settings while on a constant field of view, we can surmise that the highest mean value of the set corresponds to the lens setting that provides the sharpest focus. It is important to remember that this is relative, but it can be helpful for many applications.

Requirements

 Software

• LabVIEW Base Development System 2012 (or compatible)
• NI Vision Development Module 2012 (or compatible)
• NI IMAQdx 4.2 driver (or compatible, part of the NI Vision Acquisition Software)
• If running in a Real-Time context, you will need the LabVIEW Real-Time Module 2012 (or compatible)

 Hardware

• IMAQdx 4.2 compatible camera. IMAQdx supports this camera standards:
  
  • USB 3 Vision
  • GigE Vision
  • IEEE-1394 (FireWire)
  • USB 2.0 (DirectShow)

Steps to Implement or Execute Code

1. Download and open the attached VI
2. Run the program

Additional Information or References

This example requires IMAQdx. If you do not have a license for IMAQdx, you can still download this example and replace the IMAQdx VIs with IMAQ VIs.

**The code for this example has been edited to meet the new Community Example Style Guidelines. The edited copy is marked with the text ‘NIVerified’. Read here for more information about the new Example Guidelines and Community Platform.**

[Wes_P]

Overview

This example demonstrates how to calculate the relative edge sharpness of an image to determine the best camera focus. 

Description

It may be necessary in some applications to programmatically determine the quality of an image. "Quality" is a subjective term, but it is almost universally accepted that sharp focus is one characteristic of a high quality image. We can measure the sharpness of an image, at least relatively, by taking advantave of filters and convolution in NI Vision.

Basically, we are taking an image in, and then applying various convolution and filtering algorithms. These algorithms effectively differentiate the image, and highlight features in the image where there is a high change in pixel value in a small change of space. In other words, the derivative of a function which represents pixel values with respect to space will be the highest on sharp color (or intensity changes).

In our resulting image, the edges will show up as white, and everything else as black. We then take the mean value of the resulting image. If we pan through the various lens settings while on a constant field of view, we can surmise that the highest mean value of the set corresponds to the lens setting that provides the sharpest focus. It is important to remember that this is relative, but it can be helpful for many applications.

Requirements

 Software

• LabVIEW Base Development System 2012 (or compatible)
• NI Vision Development Module 2012 (or compatible)
• NI IMAQdx 4.2 driver (or compatible, part of the NI Vision Acquisition Software)
• If running in a Real-Time context, you will need the LabVIEW Real-Time Module 2012 (or compatible)

 Hardware

• IMAQdx 4.2 compatible camera. IMAQdx supports this camera standards:
  
  • USB 3 Vision
  • GigE Vision
  • IEEE-1394 (FireWire)
  • USB 2.0 (DirectShow)

Steps to Implement or Execute Code

1. Download and open the attached VI
2. Run the program

Additional Information or References

This example requires IMAQdx. If you do not have a license for IMAQdx, you can still download this example and replace the IMAQdx VIs with IMAQ VIs.

**The code for this example has been edited to meet the new Community Example Style Guidelines. The edited copy is marked with the text ‘NIVerified’. Read here for more information about the new Example Guidelines and Community Platform.**

[Wes_P]

Overview

This example demonstrates how to calculate the relative edge sharpness of an image to determine the best camera focus. 

Description

It may be necessary in some applications to programmatically determine the quality of an image. "Quality" is a subjective term, but it is almost universally accepted that sharp focus is one characteristic of a high quality image. We can measure the sharpness of an image, at least relatively, by taking advantave of filters and convolution in NI Vision.

Basically, we are taking an image in, and then applying various convolution and filtering algorithms. These algorithms effectively differentiate the image, and highlight features in the image where there is a high change in pixel value in a small change of space. In other words, the derivative of a function which represents pixel values with respect to space will be the highest on sharp color (or intensity changes).

In our resulting image, the edges will show up as white, and everything else as black. We then take the mean value of the resulting image. If we pan through the various lens settings while on a constant field of view, we can surmise that the highest mean value of the set corresponds to the lens setting that provides the sharpest focus. It is important to remember that this is relative, but it can be helpful for many applications.

Requirements

 Software

• LabVIEW Base Development System 2012 (or compatible)
• NI Vision Development Module 2012 (or compatible)
• NI IMAQdx 4.2 driver (or compatible, part of the NI Vision Acquisition Software)
• If running in a Real-Time context, you will need the LabVIEW Real-Time Module 2012 (or compatible)

 Hardware

• IMAQdx 4.2 compatible camera. IMAQdx supports this camera standards:
  
  • USB 3 Vision
  • GigE Vision
  • IEEE-1394 (FireWire)
  • USB 2.0 (DirectShow)

Steps to Implement or Execute Code

1. Download and open the attached VI
2. Run the program

Additional Information or References

This example requires IMAQdx. If you do not have a license for IMAQdx, you can still download this example and replace the IMAQdx VIs with IMAQ VIs.

**The code for this example has been edited to meet the new Community Example Style Guidelines. The edited copy is marked with the text ‘NIVerified’. Read here for more information about the new Example Guidelines and Community Platform.**

[Wes_P]

Overview

This example demonstrates how to calculate the relative edge sharpness of an image to determine the best camera focus. 

Description

It may be necessary in some applications to programmatically determine the quality of an image. "Quality" is a subjective term, but it is almost universally accepted that sharp focus is one characteristic of a high quality image. We can measure the sharpness of an image, at least relatively, by taking advantave of filters and convolution in NI Vision.

Basically, we are taking an image in, and then applying various convolution and filtering algorithms. These algorithms effectively differentiate the image, and highlight features in the image where there is a high change in pixel value in a small change of space. In other words, the derivative of a function which represents pixel values with respect to space will be the highest on sharp color (or intensity changes).

In our resulting image, the edges will show up as white, and everything else as black. We then take the mean value of the resulting image. If we pan through the various lens settings while on a constant field of view, we can surmise that the highest mean value of the set corresponds to the lens setting that provides the sharpest focus. It is important to remember that this is relative, but it can be helpful for many applications.

Requirements

 Software

• LabVIEW Base Development System 2012 (or compatible)
• NI Vision Development Module 2012 (or compatible)
• NI IMAQdx 4.2 driver (or compatible, part of the NI Vision Acquisition Software)
• If running in a Real-Time context, you will need the LabVIEW Real-Time Module 2012 (or compatible)

 Hardware

• IMAQdx 4.2 compatible camera. IMAQdx supports this camera standards:
  
  • USB 3 Vision
  • GigE Vision
  • IEEE-1394 (FireWire)
  • USB 2.0 (DirectShow)

Steps to Implement or Execute Code

1. Download and open the attached VI
2. Run the program

Additional Information or References

This example requires IMAQdx. If you do not have a license for IMAQdx, you can still download this example and replace the IMAQdx VIs with IMAQ VIs.

**The code for this example has been edited to meet the new Community Example Style Guidelines. The edited copy is marked with the text ‘NIVerified’. Read here for more information about the new Example Guidelines and Community Platform.**

[Wes_P]

Overview

This example demonstrates how to calculate the relative edge sharpness of an image to determine the best camera focus. 

Description

It may be necessary in some applications to programmatically determine the quality of an image. "Quality" is a subjective term, but it is almost universally accepted that sharp focus is one characteristic of a high quality image. We can measure the sharpness of an image, at least relatively, by taking advantave of filters and convolution in NI Vision.

Basically, we are taking an image in, and then applying various convolution and filtering algorithms. These algorithms effectively differentiate the image, and highlight features in the image where there is a high change in pixel value in a small change of space. In other words, the derivative of a function which represents pixel values with respect to space will be the highest on sharp color (or intensity changes).

In our resulting image, the edges will show up as white, and everything else as black. We then take the mean value of the resulting image. If we pan through the various lens settings while on a constant field of view, we can surmise that the highest mean value of the set corresponds to the lens setting that provides the sharpest focus. It is important to remember that this is relative, but it can be helpful for many applications.

Requirements

 Software

• LabVIEW Base Development System 2012 (or compatible)
• NI Vision Development Module 2012 (or compatible)
• NI IMAQdx 4.2 driver (or compatible, part of the NI Vision Acquisition Software)
• If running in a Real-Time context, you will need the LabVIEW Real-Time Module 2012 (or compatible)

 Hardware

• IMAQdx 4.2 compatible camera. IMAQdx supports this camera standards:
  
  • USB 3 Vision
  • GigE Vision
  • IEEE-1394 (FireWire)
  • USB 2.0 (DirectShow)

Steps to Implement or Execute Code

1. Download and open the attached VI
2. Run the program

Additional Information or References

This example requires IMAQdx. If you do not have a license for IMAQdx, you can still download this example and replace the IMAQdx VIs with IMAQ VIs.

**The code for this example has been edited to meet the new Community Example Style Guidelines. The edited copy is marked with the text ‘NIVerified’. Read here for more information about the new Example Guidelines and Community Platform.**

[surfnska]

can you convert this to LV 8.6?

[Wes_P]

I added a copy that is saved in LV 8.0.  This means it should be able to be opened in versions 8.0 and higher.

[TusDavid]

the filter vi's are just not working for me. gettin error -1074396080 all the time

[grobas]

I get the same error than TusDavid, did anyone solve it?

[Ajay_kumar_A_S]

Which filter type gave you the best results?