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National Instruments IMAQ Vision for Measurement Studio Programmatically Defining Regions, Defining Regions with Masks

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Chapter3 Grayscaleand Color Measurements

IMAQVision for LabWindows/CVI User Manual 3-6 ni.com

Youcan also use imaqSelectPoint(),imaqSelectLine(),

imaqSelectRect(),andimaqSelectAnnulus() to defineregions of

interest. Followthese steps to use these functions:

1. Call the function to display an image in an ROIConstructor window.

Only the tools specific to that function are availablefor you to use.

2. Drawan ROI on your image. Resize or reposition the ROI until it

coversthe area you want to process.

3. Clickthe OK button to populate a structure represent ing the ROI.You

can uset his structure as an input to a varietyof functions, such as the

followingfunctions that measure grayscale intensity:

•imaqLightMeterPoint()—Usesthe output of

imaqSelectPoint()

•imaqLightMeterLine()—Usesthe output of

imaqSelectLine()

•imaqLightMeterRect()—Usesthe output of

imaqSelectRect()

Programmatically Defining Regions

Whenyou have an aut omated application, you may need to define regions

ofinterest programmatically. To programmatically define an ROI, create

the ROI using imaqCreateROI()and t hen add the individual contours.

A contour is a shape that defines an ROI. You can create contours from

points,lines, rectangles, ovals, polygons, and annuli. For example, t o add a

rectangular contour to an ROI, use imaqAddRectContour().

Specifyregions by providing basic parameters that describe the region you

wantto define. For example, define a point by providing the x-coordinate

andy-coordinate. Define a line by specifying the start and end coordinates.

Definea rectangle by specifying the coordinates of the top, left point; the

widthand height; and the rotation angle (in the case of a rotated rectangle).

Defining Regions with Masks

Youcan define regions to process with image masks. An image mask is

an8-bit image of the same size as or smaller than the image you want to

process. Pixels in the mask image determine whether the corresponding

pixelin the source image needs to be processed. If a pixel in the image

mask has a value different than 0, the corresponding pixel in the source

image is processed. If a pixel in the image mask has a value of 0, the

corresponding pixel in the source image is left unchanged.

Contents

Main IMAQ Page Important Information Warranty Copyright Trademarks WARNINGREGARDING USE OF NATIONAL INSTRUMENTS PRODUCTS Conventions Contents Chapter 1 Introduction to IMAQ Vision Chapter 2 Getting Measurement-Ready Images Chapter 3 Grayscale and Color Measurements Chapter 4 Blob Analysis Chapter 5 Machine Vision Page Introduction to IMAQ Vision About IMAQ Vision Documentation and Examples Application Development Environments IMAQ Vision Function Tree Page IMAQ Machine Vision Function Tree Creating IMAQ Vision Applications Chapter1 Introductionto IMAQ Vision Figure1-2. InspectionSteps for Building a Vision Application IMAQVision for LabWindows/CVI User Manual 1-6 ni.com Getting Measurement-Ready Images Set Up Your Imaging System Calibrate Your Imaging System Create an Image Page Source and Destination Images AcquireorReadanImage 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 FFT Page Complex Image Operations Grayscale and Color Measurements Define Regions of Interest Interactively Defining Regions Page Page Page Page Programmatically Defining Regions Defining Regions with Masks Measure Grayscale Statistics Measure Color Statistics Figure3-4. Primary Components of a Color Image Comparing Colors Learning Color Information Choosing the Right Color Information Specifying the Color Information to Learn Using the Entire Image Using a Region in the Image Using Multiple Regions in the Image Choosing a Color Representation Sensitivity Ignoring Learned Colors Blob Analysis Correct Image Distortion Create a Binary Image Improve the Binary Image Removing Unwanted Blobs Separating TouchingBlobs Improving Blob Shapes Make Particle Measurements Page Page Convert Pixel Coordinates to Real-World Coordinates Machine Vision Locate Objects to Inspect UsingEdgeDetectiontoBuildaCoordinateTransform Page Using Pattern Matching to Build a Coordinate Transform Page Chapter5 MachineVision NationalInstruments Corporation 5-7 IMAQVision for LabWindows/CVI User Manual ChoosingaMethodtoBuildtheCoordinateTransform Figure5-4. Buildinga Coordinate Transform Set Search Areas Interactively Defining Regions Programmatically Defining Regions Find Measurement Points Finding Features Using Edge Detection Finding Lines or Circles Figure5-5. Findinga Straight Feature Finding Edge Points Along One Search Contour Finding Edge Points Along Multiple Search Contours Finding Points Using Pattern Matching Defining and Create Good TemplateImages Symmetry Feature detail Positional information Background information Trainingthe Pattern Matching Algorithm Defining a Search Area Setting Matching Parameters and Tolerances Match Mode Minimum Contrast Rotation Angle Ranges Testingthe Search Algorithm on Test Images Using a Ranking Method to Verify Results Finding Points Using Color Pattern Matching Defining and Creating Good Color TemplateImages Color Information Symmetry Feature Detail Positional Information Trainingthe Color Pattern Matching Algorithm Defining a Search Area Setting Matching Parameters and Tolerances Color Sensitivity Search Strategy Color Score Weight Minimum Contrast Rotation Angle Ranges Testingthe Search Algorithm on Test Images Finding Points Using Color Location Convert Pixel Coordinates to Real-World Coordinates Make Measurements Distance Measurements Analytic Geometry Measurements Instrument Reader Measurements Display Results Page Calibration Perspective and Nonlinear Distortion Calibration Defining a Calibration Template Defining a Reference Coordinate System Page Page Learning Calibration Information Specifying Scaling Factors Choosing a Region of Interest Choosing a Learning Algorithm Using the Learning Score Learning the Error Map Learning the Correction Table Setting the Scaling Method Calibration Invalidation Simple Calibration Save Calibration Information Attach Calibration Information A Technical Support Resources Web Support NI Developer Zone Customer Education System Integration Worldwide Support Glossary Numbers/Symbols A B C Page D E F G H I Page J K L M N O P Page Q R S T Page Index A B C D E F G H I Page Page L M N O P R S T V W Z