National Instruments IMAQ Vision for LabWindows TM CVI User Manual

IMAQ

IMAQ Vision

for LabWindows User Manual

IMAQ Vision for LabWindows/CVI User Manual

/CVITM

August 2004 Edition

Part Number 371266A-01

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About This Manual

Conventions ...................................................................................................................ix

Related Documentation..................................................................................................x

IMAQ Vision...................................................................................................x

NI Vision Assistant..........................................................................................x

NI Vision Builder for Automated Inspection ..................................................x

Other Documentation ......................................................................................xi

Chapter 1 Introduction to IMAQ Vision

About IMAQ Vision ......................................................................................................1-1

Application Development Environments.......................................................................1-1

IMAQ Vision Function Tree..........................................................................................1-2

IMAQ Machine Vision Function Tree...........................................................................1-3

Creating IMAQ Vision Applications.............................................................................1-4

Chapter 2 Getting Measurement-Ready Images

Set Up Your Imaging System ........................................................................................2-1

Calibrate Your Imaging System ....................................................................................2-2

Create an Image .............................................................................................................2-2

Source and Destination Images .......................................................................2-4

Acquire or Read an Image .............................................................................................2-5

Acquiring an Image .........................................................................................2-6

Reading a File..................................................................................................2-6

Converting an Array to an Image ....................................................................2-7

Display an Image ...........................................................................................................2-7

Attach Calibration Information......................................................................................2-8

Analyze an Image ..........................................................................................................2-8

Improve an Image ..........................................................................................................2-9

Lookup Tables .................................................................................................2-10

Filters...............................................................................................................2-10

Convolution Filter .............................................................................2-11

Nth Order Filter.................................................................................2-11

Grayscale Morphology ....................................................................................2-11

FFT ..................................................................................................................2-12

Complex Image Operations ..............................................................2-13

Contents

Chapter 3 Making Grayscale and Color Measurements

Define Regions of Interest............................................................................................. 3-1

Defining Regions Interactively ....................................................................... 3-1

Tools Palette Transformation ........................................................... 3-5

Defining Regions Programmatically............................................................... 3-6

Defining Regions with Masks......................................................................... 3-6

Measure Grayscale Statistics......................................................................................... 3-7

Measure Color Statistics................................................................................................ 3-7

Comparing Colors ........................................................................................... 3-9

Learning Color Information ............................................................................ 3-9

Specifying the Color Information to Learn ...................................... 3-9

Choosing a Color Representation Sensitivity ................................... 3-12

Ignoring Learned Colors................................................................... 3-13

Chapter 4 Performing Particle Analysis

Create a Binary Image ................................................................................................... 4-1

Improve the Binary Image............................................................................................. 4-2

Removing Unwanted Particles........................................................................ 4-3

Separating Touching Particles ........................................................................ 4-3

Improving Particle Shapes .............................................................................. 4-3

Make Particle Measurements ........................................................................................ 4-4

Chapter 5 Performing Machine Vision Tasks

Locate Objects to Inspect .............................................................................................. 5-2

Using Edge Detection to Build a Coordinate Transform ................................ 5-4

Using Pattern Matching to Build a Coordinate Transform .............................5-6

Choosing a Method to Build the Coordinate Transform................................. 5-7

Set Search Areas............................................................................................................ 5-8

Defining Regions Interactively ....................................................................... 5-8

Defining Regions Programmatically............................................................... 5-9

Find Measurement Points .............................................................................................. 5-9

Finding Features Using Edge Detection ......................................................... 5-9

Finding Lines or Circles ................................................................... 5-10

Finding Edge Points Along One Search Contour ............................. 5-11

Finding Edge Points Along Multiple Search Contours ....................5-12

Finding Points Using Pattern Matching.......................................................... 5-13

Defining and Creating Good Template Images ................................ 5-13

Training the Pattern Matching Algorithm ........................................ 5-15

IMAQ Vision for LabWindows/CVI User Manual vi ni.com

Contents

Defining a Search Area .....................................................................5-16

Setting Matching Parameters and Tolerances...................................5-17

Testing the Search Algorithm on Test Images..................................5-18

Using a Ranking Method to Verify Results ......................................5-19

Finding Points Using Color Pattern Matching ................................................5-19

Defining and Creating Good Color Template Images ......................5-20

Training the Color Pattern Matching Algorithm...............................5-21

Defining a Search Area .....................................................................5-22

Setting Matching Parameters and Tolerances...................................5-23

Testing the Search Algorithm on Test Images..................................5-25

Finding Points Using Color Location..............................................................5-25

Convert Pixel Coordinates to Real-World Coordinates.................................................5-26

Make Measurements ......................................................................................................5-26

Distance Measurements...................................................................................5-26

Analytic Geometry Measurements ..................................................................5-27

Instrument Reader Measurements ...................................................................5-27

Identify Parts Under Inspection .....................................................................................5-28

Classifying Samples ........................................................................................5-28

Reading Characters..........................................................................................5-29

Reading Barcodes............................................................................................5-30

Reading 1D Barcodes........................................................................5-30

Reading Data Matrix Barcodes .........................................................5-30

Reading PDF417 Barcodes ...............................................................5-31

Display Results ..............................................................................................................5-31

Chapter 6 Calibrating Images

Perspective and Nonlinear Distortion Calibration .........................................................6-1

Defining a Calibration Template.....................................................................6-2

Defining a Reference Coordinate System .......................................................6-3

Learning Calibration Information....................................................................6-5

Specifying Scaling Factors................................................................6-6

Choosing a Region of Interest...........................................................6-6

Choosing a Learning Algorithm .......................................................6-6

Using the Learning Score..................................................................6-7

Learning the Error Map.....................................................................6-8

Learning the Correction Table ..........................................................6-8

Setting the Scaling Method ...............................................................6-8

Calibration Invalidation ....................................................................6-8

Simple Calibration .........................................................................................................6-9

Save Calibration Information.........................................................................................6-10

Attach Calibration Information......................................................................................6-10

Contents

Appendix A Technical Support and Professional Services

Glossary

Index

IMAQ Vision for LabWindows/CVI User Manual viii ni.com

About This Manual

The IMAQ Vision for LabWindows/CVI User Manual is intended for

engineers and scientists who have knowledge of the LabWindows

programming environment and need to create machine vision and image

processing applications using C functions. The manual guides you through

tasks beginning with setting up your imaging system to taking

measurements.

™

/CVI™

Conventions

The following conventions are used in this manual:

» The » symbol leads you through nested menu items and dialog box options

to a final action. The sequence File»Page Setup»Options directs you to

pull down the File menu, select the Page Setup item, and select Options

from the last dialog box.

This icon denotes a tip, which alerts you to advisory information.

This icon denotes a note, which alerts you to important information.

bold Bold text denotes items that you must select or click in the software, such

as menu items and dialog box options. Bold text also denotes parameter

names.

italic Italic text denotes variables, emphasis, a cross reference, or an introduction

to a key concept. This font also denotes text that is a placeholder for a word

or value that you must supply.

monospace Text in this font denotes text or characters that you should enter from the

keyboard, sections of code, programming examples, and syntax examples.

This font is also used for the proper names of disk drives, paths, directories,

programs, subprograms, subroutines, device names, functions, operations,

variables, filenames, and extensions.

About This Manual

management.

Functions that create and manipulate regions of interest.

Functions that compute the centroid of an image, profile of a line of pixels, and the mean line profile. This type also includes functions that calculate the pixel distribution and statistical parameters of an image.

Functions for grayscale image processing and analysis.

Binary Processing

Color Processing

Pattern Matching

IMAQ Vision for LabWindows/CVI User Manual 1-2 ni.com

Functions for binary image processing and analysis.

Functions for color image processing and analysis.

Functions that learn patterns and search for patterns in images.

Chapter 1 Introduction to IMAQ Vision

Table 1-1. IMAQ Vision Function Types (Continued)

Function Type Description

Caliper Functions designed for gauging, measurement, and inspection applications.

Operators Functions that perform arithmetic, logic, and comparison operations with

two images or with an image and a constant value.

Analytic Geometry

Frequency Domain Analysis

Barcode I/O Functions that find and read barcodes.

LCD Functions that find and read seven-segment LCD characters.

Meter Functions that return the arc information of a meter and read the meter.

Utilities Functions that return structures, and a function that returns a pointer to

OCR Functions that perform optical character recognition on an image.

Classification Functions that classify an image or feature vector.

Obsolete Functions that are no longer necessary but may exist in older applications.

Functions that perform basic geometric calculations on an image.

Functions for the extraction and manipulation of complex planes. Functions of this type perform Fast Fourier Transform (FFT), inverse FFT, truncation, attenuation, addition, subtraction, multiplication, and division of complex images.

predefined convolution matrices.

IMAQ Machine Vision Function Tree

The IMAQ Machine Vision function tree (NIMachineVision.fp) contains separate classes corresponding to groups or types of functions. Table 1-2 lists the IMAQ Machine Vision function types and gives a description of each type.

Table 1-2. IMAQ Machine Vision Function Types

Function Type Description

Coordinate Transform Functions that find coordinate transforms based on image contents.

Count and Measure Objects Function that counts and measures objects in an image.

Find Patterns Function that finds patterns in an image.

Locate Edges Functions that locate different types of edges in an image.

Chapter 1 Introduction to IMAQ Vision

Table 1-2. IMAQ Machine Vision Function Types (Continued)

Function Type Description

Measure Distances Functions that measure distances between objects in an image.

Measure Intensities Functions that measure light intensities in various shaped regions

within an image.

Select Region of Interest Functions that allow a user to select a specific region of interest in

an image.

Creating IMAQ Vision Applications

Figures 1-1 and 1-2 illustrate the steps for creating an application with IMAQ Vision. Figure 1-1 describes the general steps to designing a Vision application. The last step in Figure 1-1 is expanded upon in Figure 1-2. You can use a combination of the items in the last step to create your IMAQ Vision application. Refer to the corresponding chapter listed to the side of the item for more information about items in either diagram.

IMAQ Vision for LabWindows/CVI User Manual 1-4 ni.com

Chapter 1 Introduction to IMAQ Vision

Set Up Your Imaging System

Chapter 2:

Measurement-Ready

Getting

Images

Calibrate Your Imaging System

Create an Image

Acquire or Read an Image

Display an Image

Attach Calibration Information

Analyze an Image

Improve an Image

Make Measurements or Identify Objects

in an Image Using

Grayscale or Color Measurements, and/or

Particle Analysis, and/or

Machine Vision

Chapter 6: Calibration

Figure 1-1. General Steps for Designing a Vision Application

Note

Diagram items enclosed with dashed lines are optional steps.

Chapter 1 Introduction to IMAQ Vision

Chapter 5:

Particle

Analysis

Chapter 4:

Grayscale and Color

Measurements

Create a Binary Image

Improve a Binary Image

Make Particle Measurements

Grayscale Statistics

Define Regions of Interest

Measure

Find Measurement Points

Convert Pixel Coordinates to

Real-World Coordinates

Make Measurements

Measure

Color Statistics

Locate Objects to Inspect

Set Search Areas

Identify Parts Under Inspection

Classify Objects

Display Results

Read

Characters

Read

Symbologies

Chapter 6: Machine Vision

Figure 1-2. Inspection Steps for Building a Vision Application

Note

Diagram items enclosed with dashed lines are optional steps.

IMAQ Vision for LabWindows/CVI User Manual 1-6 ni.com

Getting Measurement-Ready Images

This chapter describes how to set up your imaging system, acquire and display an image, analyze the image, and prepare the image for additional processing.

Set Up Your Imaging System

Before you acquire, analyze, and process images, you must set up your imaging system. How you set up your system depends on your imaging environment and the type of analysis and processing you need to do. Your imaging system should produce images with high enough quality so that you can extract the information you need from the images.

Complete the following steps to set up your imaging system.

1. Determine the type of equipment you need given your space constraints and the size of the object you need to inspect. Refer to Chapter 3, System Setup and Calibration, of the IMAQ Vision Concepts Manual for more information.

a. Make sure your camera sensor is large enough to satisfy your

minimum resolution requirement.

b. Make sure your lens has a depth of field high enough to keep all

of your objects in focus regardless of their distance from the lens. Also, make sure your lens has a focal length that meets your needs.

c. Make sure your lighting provides enough contrast between the

object under inspection and the background for you to extract the information you need from the image.

2. Position your camera so that it is perpendicular to the object under inspection. If your camera acquires images of the object from an angle, perspective errors occur. Even though you can compensate for these errors with software, NI recommends that you use a perpendicular inspection angle to obtain the most accurate results.

Chapter 2 Getting Measurement-Ready Images

3. Select an IMAQ device that meets your needs. National Instruments offers several IMAQ devices, including analog color and monochrome devices as well as digital devices. Visit information about IMAQ devices.

4. Configure the driver software for your image acquisition device. If you have a National Instruments image acquisition device, configure the NI-IMAQ driver software through MAX. Open MAX by double-clicking the Measurement & Automation Explorer icon on your desktop. Refer to the NI-IMAQ User Manual and the Measurement and Automation Explorer Help for IMAQ for more information.

Calibrate Your Imaging System

After you set up your imaging system, you may want to calibrate your system to assign real-world coordinates to pixel coordinates. This allows you to compensate for perspective and nonlinear errors inherent in your imaging system.

Perspective errors occur when your camera axis is not perpendicular to the object under inspection. Nonlinear distortion may occur from aberrations in the camera lens. Perspective errors and lens aberrations cause images to appear distorted. This distortion misplaces information in an image, but it does not necessarily destroy the information in the image.

ni.com/imaq for more

Use simple calibration if you only want to assign real-world coordinates to pixel coordinates. Use perspective and nonlinear distortion calibration if you need to compensate for perspective errors and nonlinear lens distortion. For detailed information about calibration, refer to Chapter 5, Performing

Machine Vision Tasks.

Create an Image

To create an image in IMAQ Vision for LabWindows/CVI, call

imaqCreateImage(). This function returns an image reference you can

use when calling other IMAQ Vision functions. The only limitation to the size and number of images you can acquire and process is the amount of memory on your computer. When you create an image, specify the type of the image. Table 2-1 lists the valid image types.

IMAQ Vision for LabWindows/CVI User Manual 2-2 ni.com

Chapter 2 Getting Measurement-Ready Images

Table 2-1. IMAQ Vision for LabWindows/CVI Image Types

Val ue Description

IMAQ_IMAGE_U8

IMAQ_IMAGE_I16

IMAQ_IMAGE_SGL

IMAQ_IMAGE_COMPLEX 2 × 32 bits per pixel—floating point, native format after a Fast

8 bits per pixel—unsigned, standard monochrome

16 bits per pixel—signed, monochrome

32 bits per pixel—floating point, monochrome

Fourier Transform (FFT)

IMAQ_IMAGE_RGB

IMAQ_IMAGE_HSL

IMAQ_IMAGE_RGB_U64

32 bits per pixel—standard color

32 bits per pixel—color

64 bits per pixel—standard color

You can create multiple images by executing imaqCreateImage() as many times as you want. Determine the number of required images through an analysis of your intended application. The decision is based on different processing phases and your need to keep the original image after each processing step. The decision to keep an image occurs before each processing step.

When you create an image, IMAQ Vision creates an internal image structure to hold properties of the image, such as its name and border size. However, no memory is allocated to store the image pixels at this time. IMAQ Vision functions automatically allocate the appropriate amount of memory when the image size is modified. For example, functions that acquire or resample an image alter the image size, so they allocate the appropriate memory space for the image pixels. The return value of

imaqCreateImage() is a pointer to the image structure. Supply this

pointer as an input to all subsequent IMAQ Vision functions.

Most functions in the IMAQ Vision library require one or more image pointers. The number of image pointers a function takes depends on the image processing function and the type of image you want to use. Some IMAQ Vision functions act directly on the image and require only one image pointer. Other functions that process the contents of images require pointers to the source image(s) and to a destination image.

At the end of your application, dispose of each image that you created using

imaqDispose().

Chapter 2 Getting Measurement-Ready Images

Source and Destination Images

Some IMAQ Vision functions that modify the contents of an image have source image and destination image input parameters. The source image receives the image to process. The destination image receives the processing results. The destination image can receive either another image or the original, depending on your goals. If you do not want the contents of the original image to change, use separate source and destination images. If you want to replace the original image with the processed image, pass the same image as both the source and destination.

Depending on the function, the image type of the destination image can be the same or different than the image type of the source image. The function descriptions in the IMAQ Vision for LabWindows/CVI Function Reference include the type of images you can use as image inputs and outputs. IMAQ Vision resizes the destination image to hold the result if the destination is not the appropriate size.

The following examples illustrate source and destination images with

imaqTranspose():

•

imaqTranspose(myImage, myImage);

This function creates a transposed image using the same image for the source and destination. The contents of

•

imaqTranspose(myTransposedImage, myImage);

This function creates a transposed image and stores it in a destination different from the source. The and

myTransposedImage contains the result.

myImage change.

myImage image remains unchanged,

Functions that perform arithmetic or logical operations between two images have two source images and a destination image. You can perform an operation between two images and then either store the result in a separate destination image or in one of the two source images. In the latter case, make sure you no longer need the original data in the source image before storing the result over the data.

The following examples show the possible combinations using

imaqAdd():

•

imaqAdd(myResultImage, myImageA, myImageB);

This function adds two source images (myImageA and myImageB) and stores the result in a third image (

myResultImage). Both source

images remain intact after processing.

IMAQ Vision for LabWindows/CVI User Manual 2-4 ni.com

• imaqAdd(myImageA, myImageA, myImageB);

This function adds two source images and stores the result in the first source image.

•

imaqAdd(myImageB, myImageA, myImageB);

This function adds two source images and stores the result in the second source image.

Most operations between two images require that the images have the same type and size. However, some arithmetic operations can work between two different types of images, such as 8-bit and 16-bit images.

Some functions perform operations that populate an image. Examples of this type of operation include reading a file, acquiring an image from an IMAQ device, or transforming a 2D array into an image. This type of function can modify the size of an image.

Some functions take an additional mask parameter. The presence of this parameter indicates that the processing or analysis is dependent on the contents of another image, the image mask.

Note The image mask must be an 8-bit image.

Chapter 2 Getting Measurement-Ready Images

If you want to apply a processing or analysis function to the entire image, pass NULL for the image mask. Passing the same image to both the source image and image mask also gives the same effect as passing NULL for the image mask, except in this case the source image must be an 8-bit image.

Acquire or Read an Image

After you create an image reference, you can acquire an image into your imaging system in three ways. You can acquire an image with a camera through your IMAQ device, load an image from a file stored on your computer, or convert data stored in a 2D array to an image. Functions that acquire images, load images from file, or convert data from a 2D array to an image automatically allocate the memory space required to accommodate the image data.

Chapter 2 Getting Measurement-Ready Images

Acquiring an Image

Use one of the following methods to acquire images with a National Instruments IMAQ device.

• Acquire a single image using this function, it initializes the IMAQ device and acquires the next incoming video frame. Use this function for low-speed single capture applications where ease of programming is essential.

• Acquire a single image using function, it acquires the next incoming video frame on an IMAQ device you have already initialized using i

mgSessionOpen(). Use this function for high-speed single capture

applications.

• Acquire images continually through a grab acquisition. Grab functions perform high-speed acquisitions that loop continually on one buffer. Use

imaqSetupGrab() to start the acquisition. Use imaqGrab()

to return a copy of the current image. Use to stop the acquisition.

• Acquire a fixed number of images using a sequence acquisition. Set up the acquisition using

imaqStartAcquisition() to acquire the number of images you

requested during setup. If you want to acquire only certain images, supply of frames to skip after each acquired frame.

• Acquire images continually through a ringed buffer acquisition. Set up the acquisition using

imaqStartAcquisition() to start acquiring images into the

acquired ring buffer. To get an image from the ring, call

imaqExtractFromRing() or imaqCopyRing(). Use imaqStopAcquisition() to stop the acquisition.

imaqEasyAcquire(). When you call

imaqSnap(). When you call this

imgInterfaceOpen() and

imaqStopAcquisition()

imaqSetupSequence(). Use

imaqSetupSequence() with a table describing the number

imaqSetupRing(). Use

Note You must use imgClose() to release resources associated with the image

acquisition device.

Reading a File

Use imaqReadFile() to open and read data from a file stored on your computer into the image reference. You can read from image files stored in several standard formats: BMP, TIFF, JPEG, PNG, and AIPD. The software automatically converts the pixels it reads into the type of image you pass in.

IMAQ Vision for LabWindows/CVI User Manual 2-6 ni.com

Chapter 2 Getting Measurement-Ready Images

Use imaqReadVisionFile() to open an image file containing additional information, such as calibration information, template information for pattern matching, or overlay information. For more information about pattern matching templates and overlays, refer to Chapter 5, Performing

Machine Vision Tasks.

You can also use properties—such as image size, recommended image type, and calibration units—without actually reading all the image data.

Converting an Array to an Image

Use imaqArrayToImage() to convert a 2D array to an image. You can also use

imaqImageToArray() to convert an image to a 2D array.

Display an Image

Display an image in an external window using imaqDisplayImage(). You can display images in 16 different external windows. Use the other display functions to configure the appearance of each external window. Properties you can set include whether the window has scroll bars, a title bar, and whether it is resizable. You can also use position the external image window at a particular location on you monitor. Refer to the IMAQ Vision for LabWindows/CVI Function Reference for a complete list of Display functions.

Note Image windows are not LabWindows/CVI panels. They are managed directly by

IMAQ Vision .

You can use a color palette to display grayscale images by applying a color palette to the window. Use color palettes. For example, if you need to display a binary image—an image containing particle regions with pixel values of 1 and a background region with pixel values of 0—apply the predefined binary palette. For more information about color palettes, refer to Chapter 2, Display, of the IMAQ Vision Concepts Manual.

imaqGetFileInfo() to retrieve image

imaqMoveWindow() to

imaqSetWindowPalette() to set predefined

Note At the end of your application, close all open external windows using

imaqCloseWindow().

Chapter 2 Getting Measurement-Ready Images

Attach Calibration Information

If you want to attach the calibration information of the current setup to each image you acquire, use function takes in a source image containing the calibration information and a destination image that you want to calibrate. The output image is your inspection image with the calibration information attached to it. For detailed information about calibration, refer to Chapter 6, Calibrating

Images.

Note Because calibration information is part of the image, it is propagated throughout

the processing and analysis of the image. Functions that modify the image size, such as geometrical transforms, void the calibration information. Use to save the image and all of the attached calibration information to a file.

Analyze an Image

After you acquire and display an image, you may want to analyze the contents of the image for the following reasons:

• To determine whether the image quality is high enough for your inspection task.

• To obtain the values of parameters that you want to use in processing functions during the inspection process.

imaqCopyCalibrationInfo(). This

imaqWriteVisionFile()

The histogram and line profile tools can help you analyze the quality of your images.

Use

imaqHistogram() to analyze the overall grayscale distribution in the

image. Use the histogram of the image to analyze two important criteria that define the quality of an image—saturation and contrast. If your image is underexposed, or does not have enough light, the majority of your pixels will have low intensity values, which appear as a concentration of peaks on the left side of your histogram. If your image is overexposed, or has too much light, the majority of your pixels will have high intensity values, which appear as a concentration of peaks on the right side of your histogram. If your image has an appropriate amount of contrast, your histogram will have distinct regions of pixel concentrations. Use the histogram information to decide if the image quality is high enough to separate objects of interest from the background.

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Chapter 2 Getting Measurement-Ready Images

If the image quality meets your needs, use the histogram to determine the range of pixel values that correspond to objects in the image. You can use this range in processing functions, such as determining a threshold range during particle analysis.

If the image quality does not meet your needs, try to improve the imaging conditions to get the necessary image quality. You may need to re-evaluate and modify each component of your imaging setup, including lighting equipment and setup, lens tuning, camera operation mode, and acquisition device parameters. If you reach the best possible conditions with your setup but the image quality still does not meet your needs, try to improve the image quality using the image processing techniques described in the Improve an Image section of this chapter.

Use

imaqLineProfile() to get the pixel distribution along a line in the

image, or use one-dimensional path in the image. By looking at the pixel distribution, you can determine if the image quality is high enough to provide you with sharp edges at object boundaries. Also, you can determine if the image is noisy and identify the characteristics of the noise.

If the image quality meets your needs, use the pixel distribution information to determine some parameters of the inspection functions you want to use. For example, use the information from the line profile to determine the strength of the edge at the boundary of an object. You can input this information into along the line.

imaqROIProfile() to get the pixel distribution along a

imaqEdgeTool2() to find the edges of objects

Improve an Image

Using the information you gathered from analyzing your image, you may want to improve the quality of your image for inspection. You can improve your image with lookup tables, filters, grayscale morphology, and FFTs.

Chapter 2 Getting Measurement-Ready Images

Lookup Tables

Apply lookup table (LUT) transformations to highlight image details in areas containing significant information at the expense of other areas. A LUT transformation converts input grayscale values in the source image into other grayscale values in the transformed image. IMAQ Vision provides four functions that directly or indirectly apply lookup tables to images.

•

imaqMathTransform()—Converts the pixel values of an image

by eplacing them with values from a predefined lookup table. IMAQ Vision has seven predefined lookup tables based on mathematical transformations. For more information about these lookup tables, refer to Chapter 5, Image Processing, of the IMAQ Vision Concepts Manual.

•

imaqLookup()—Converts the pixel values of an image by replacing

them with values from a user-defined lookup table.

•

imaqEqualize()—Distributes the grayscale values evenly within a

given grayscale range. Use in images containing few grayscale values.

•

imaqInverse()—Inverts the pixel intensities of an image to

compute the negative of the image. For example, use before applying an automatic threshold to your image if the background pixels are brighter than the object pixels.

imaqEqualize() to increase the contrast

imaqInverse()

Filters

Filter your image when you need to improve the sharpness of transitions in the image or increase the overall signal-to-noise ratio of the image. You can choose either a lowpass or highpass filter depending on your needs.

Lowpass filters remove insignificant details by smoothing the image,

removing sharp details, and smoothing the edges between the objects and the background. You can use lowpass filter with

imaqConvolve() or imaqNthOrderFilter().

Highpass filters emphasize details, such as edges, object boundaries,

or cracks. These details represent sharp transitions in intensity value. You can define your own highpass filter with

imaqNthOrderFilter(), or you can use a predefined highpass filter

with

imaqEdgeFilter() or imaqCannyEdgeFilter(). The

imaqEdgeFilter() function allows you to find edges in an image using

predefined edge detection kernels, such as the Sobel, Prewitt, and Roberts kernels.

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imaqLowPass() or define your own

imaqConvolve() or

Convolution Filter

The imaqConvolve() function allows you to use a predefined set of lowpass and highpass filters. Each filter is defined by a kernel of coefficients. Use predefined kernels do not meet your needs, define your own custom filter using a 2D array of floating point numbers.

Nth Order Filter

The imaqNthOrderFilter() function allows you to define a lowpass or highpass filter depending on the value of N that you choose. One specific Nth order filter, the median filter, removes speckle noise, which appears as small black and white dots. Use filter. For more information about Nth order filters, refer to Chapter 5, Image Processing, of the IMAQ Vision Concepts Manual.

Grayscale Morphology

Perform grayscale morphology when you want to filter grayscale features of an image. Grayscale morphology helps you remove or enhance isolated features, such as bright pixels on a dark background. Use these transformations on a grayscale image to enhance non-distinct features before thresholding the image in preparation for particle analysis.

Chapter 2 Getting Measurement-Ready Images

imaqGetKernel() to retrieve predefined kernels. If the

imaqMedianFilter() to apply a median

Grayscale morphological transformations compare a pixel to those pixels surrounding it. The transformation keeps the smallest pixel values when performing an erosion or keeps the largest pixel values when performing a dilation.

Refer to Chapter 5, Image Processing, of the IMAQ Vision Concepts Manual for more information about grayscale morphology transformations.

Use

imaqGrayMorphology() to perform one of the following seven

transformations:

• Erosion—Reduces the brightness of pixels that are surrounded by neighbors with a lower intensity.

• Dilation—Increases the brightness of pixels surrounded by neighbors with a higher intensity. A dilation produces the opposite effect of an erosion.

• Opening—Removes bright pixels isolated in dark regions and smooths boundaries.

Chapter 2 Getting Measurement-Ready Images

• Closing—Removes dark pixels isolated in bright regions and smooths boundaries.

• Proper-opening—Removes bright pixels isolated in dark regions and smooths the inner contours of particles.

• Proper-closing—Removes dark pixels isolated in bright regions and smooths the inner contours of particles.

• Auto-median—Generates simpler particles that have fewer details.

FFT

Use the Fast Fourier Transform (FFT) to convert an image into its frequency domain. In an image, details and sharp edges are associated with mid to high spatial frequencies because they introduce significant gray-level variations over short distances. Gradually varying patterns are associated with low spatial frequencies.

An image can have extraneous noise, such as periodic stripes, introduced during the digitization process. In the frequency domain, the periodic pattern is reduced to a limited set of high spatial frequencies. Also, the imaging setup may produce non-uniform lighting of the field of view, which produces an image with a light drift superimposed on the information you want to analyze. In the frequency domain, the light drift appears as a limited set of low frequencies around the average intensity of the image, the DC component.

You can use algorithms working in the frequency domain to isolate and remove these unwanted frequencies from your image. Complete the following steps to obtain an image in which the unwanted pattern has disappeared but the overall features remain.

1. Use

imaqFFT() to convert an image from the spatial domain to the

frequency domain. This function computes the FFT of the image and results in a complex image representing the frequency information of your image.

2. Improve your image in the frequency domain with a lowpass or highpass frequency filter. Specify which type of filter to use with

imaqAttenuate() or imaqTruncate(). Lowpass filters smooth

noise, details, textures, and sharp edges in an image. Highpass filters emphasize details, textures, and sharp edges in images, but they also emphasize noise.

• Lowpass attenuation—The amount of attenuation is directly

proportional to the frequency information. At low frequencies, there is little attenuation. As the frequencies increase, the

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Chapter 2 Getting Measurement-Ready Images

attenuation increases. This operation preserves all of the zero frequency information. Zero frequency information corresponds to the DC component of the image or the average intensity of the image in the spatial domain.

• Highpass attenuation—The amount of attenuation is inversely proportional to the frequency information. At high frequencies, there is little attenuation. As the frequencies decrease, the attenuation increases. The zero frequency component is removed entirely.

• Lowpass truncation—Frequency components above the ideal cutoff frequency are removed, and the frequencies below it remain unaltered.

• Highpass truncation—Frequency components above the ideal cutoff frequency remain unaltered, and the frequencies below it are removed.

3. To transform your image back to the spatial domain, use

imaqInverseFFT().

Complex Image Operations

The imaqExtractComplexPlane() and

imaqReplaceComplexPlane() functions allow you to access, process,

and update independently the real and imaginary planes of a complex image. You can also convert planes of a complex image to an array and back with

imaqArrayToComplexPlane().

imaqComplexPlaneToArray() and

Making Grayscale and Color Measurements

This chapter describes how to take measurements from grayscale and color images. You can make inspection decisions based on image statistics, such as the mean intensity level in a region. Based on the image statistics, you can perform many machine vision inspection tasks on grayscale or color images, such as detecting the presence or absence of components, detecting flaws in parts, and comparing a color component with a reference. Figure 3-1 illustrates the basic steps involved in making grayscale and color measurements.

Define Regions of Interest

Figure 3-1. Steps to Taking Grayscale and Color Measurements

Define Regions of Interest

A region of interest (ROI) is an area of an image in which you want to focus your image analysis. You can define an ROI interactively, programmatically, or with an image mask.

Defining Regions Interactively

You can interactively define an ROI in a window that displays an image. Use the tools from the IMAQ Vision tools palette to interactively define and manipulate an ROI.

Measure

Grayscale Statistics

Measure

Color Statistics

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Contents

About This Manual

Conventions

Related Documentation

IMAQ Vision

NI Vision Assistant

NI Vision Builder for Automated Inspection

Other Documentation

About IMAQ Vision

Application Development Environments

IMAQ Vision Function Tree

Table 1-1. IMAQ Vision Function Types

IMAQ Machine Vision Function Tree

Table 1-2. IMAQ Machine Vision Function Types

Creating IMAQ Vision Applications

Figure 1-1. General Steps for Designing a Vision Application

Figure 1-2. Inspection Steps for Building a Vision Application

Set Up Your Imaging System

Calibrate Your Imaging System

Create an Image

Table 2-1. IMAQ Vision for LabWindows/CVI Image Types

Source and Destination Images

Acquire or Read an Image

Acquiring an Image

Reading a File

Converting an Array to an Image

Display an Image

Attach Calibration Information

Analyze an Image

Improve an Image

Lookup Tables

Filters

Convolution Filter

Nth Order Filter

Grayscale Morphology

Complex Image Operations

Define Regions of Interest

Figure 3-1. Steps to Taking Grayscale and Color Measurements

Defining Regions Interactively