用户手册 — National Instruments IMAQ Vision for LabWindows TM /CVI

目录

• IMAQ Vision for LabWindows/CVI User Manual

  1
• Support

  2
  • Worldwide Technical Support and Product Information

    2
  • National Instruments Corporate Headquarters

    2
  • Worldwide Offices

    2
• Important Information

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  • Warranty

    3
  • Copyright

    3
  • Trademarks

    3
  • Patents

    3
  • WARNING REGARDING USE OF NATIONAL INSTRUMENTS PRODUCTS

    3
• Contents

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• About This Manual

  8
  • Conventions

    8
  • Related Documentation

    9
    • IMAQ Vision

      9
    • NI Vision Assistant

      9
    • NI Vision Builder for Automated Inspection

      9
    • Other Documentation

      10
• Chapter 1 Introduction to IMAQ Vision

  11
  • About IMAQ Vision

    11
  • Application Development Environments

    11
  • IMAQ Vision Function Tree

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    • Table 1-1. IMAQ Vision Function Types

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  • IMAQ Machine Vision Function Tree

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    • Table 1-2. IMAQ Machine Vision Function Types

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  • Creating IMAQ Vision Applications

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

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

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• Chapter 2 Getting Measurement-Ready Images

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  • Set Up Your Imaging System

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  • Calibrate Your Imaging System

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  • Create an Image

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

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    • Source and Destination Images

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  • Acquire or Read an Image

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    • Acquiring an Image

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    • Reading a File

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    • Converting an Array to an Image

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  • Display an Image

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  • Attach Calibration Information

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  • Analyze an Image

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  • Improve an Image

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    • Lookup Tables

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    • Filters

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      • Convolution Filter

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      • Nth Order Filter

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    • Grayscale Morphology

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    • FFT

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      • Complex Image Operations

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• Chapter 3 Making Grayscale and Color Measurements

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

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  • Define Regions of Interest

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    • Defining Regions Interactively

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      • Table 3-1. Tools Palette Functions

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      • Figure 3-2. ROI Constructor

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      • Tools Palette Transformation

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      • Figure 3-3. Tools Palette Tools and Information

        34
    • Defining Regions Programmatically

      35
    • Defining Regions with Masks

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  • Measure Grayscale Statistics

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  • Measure Color Statistics

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    • Figure 3-4. Primary Components of a 32-Bit Color Image

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    • Figure 3-5. Primary Components of a 64-Bit Color Image

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    • Comparing Colors

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    • Learning Color Information

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      • Specifying the Color Information to Learn

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      • Figure 3-6. Template Color Information

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      • Figure 3-7. Using the Entire Image to Learn Color Distribution

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      • Figure 3-8. Using a Single Region to Learn Color Distribution

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      • Figure 3-9. Using Multiple Regions to Learn Color Distribution

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      • Choosing a Color Representation Sensitivity

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      • Ignoring Learned Colors

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• Chapter 4 Performing Particle Analysis

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  • Figure 4-1. Steps to Performing Particle Analysis

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  • Create a Binary Image

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  • Improve the Binary Image

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    • Removing Unwanted Particles

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    • Separating Touching Particles

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    • Improving Particle Shapes

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  • Make Particle Measurements

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    • Table 4-1. Particle Measurements

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• Chapter 5 Performing Machine Vision Tasks

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  • Figure 5-1. Steps to Performing Machine Vision

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  • Locate Objects to Inspect

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    • Using Edge Detection to Build a Coordinate Transform

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      • Figure 5-2. Coordinate Systems of a Reference Image and Inspection Image

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      • Figure 5-3. Locating Coordinate System Axes with Two Search Areas

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    • Using Pattern Matching to Build a Coordinate Transform

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    • Choosing a Method to Build the Coordinate Transform

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      • Figure 5-4. Building a Coordinate Transform

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  • Set Search Areas

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    • Defining Regions Interactively

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    • Defining Regions Programmatically

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  • Find Measurement Points

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    • Finding Features Using Edge Detection

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      • Finding Lines or Circles

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      • Figure 5-5. Finding a Straight Feature

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      • Figure 5-6. Finding a Circular Feature

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      • Finding Edge Points Along One Search Contour

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      • Finding Edge Points Along Multiple Search Contours

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    • Finding Points Using Pattern Matching

      65
      • Defining and Creating Good Template Images

        65
      • Figure 5-7. Symmetry

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      • Figure 5-8. Feature Detail

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      • Figure 5-9. Positional Information

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      • Figure 5-10. Background Information

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      • Training the Pattern Matching Algorithm

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      • Defining a Search Area

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      • Figure 5-11. Selecting a Search Area for Grayscale Pattern Matching

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      • Setting Matching Parameters and Tolerances

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      • Testing the Search Algorithm on Test Images

        70
      • Using a Ranking Method to Verify Results

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    • Finding Points Using Color Pattern Matching

      71
      • Defining and Creating Good Color Template Images

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      • Training the Color Pattern Matching Algorithm

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      • Defining a Search Area

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      • Figure 5-12. Selecting a Search Area for Color Pattern Matching

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      • Setting Matching Parameters and Tolerances

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      • Testing the Search Algorithm on Test Images

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    • Finding Points Using Color Location

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  • Convert Pixel Coordinates to Real-World Coordinates

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  • Make Measurements

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    • Distance Measurements

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    • Analytic Geometry Measurements

      79
    • Instrument Reader Measurements

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  • Identify Parts Under Inspection

    80
    • Classifying Samples

      80
    • Reading Characters

      81
    • Reading Barcodes

      82
      • Reading 1D Barcodes

        82
      • Reading Data Matrix Barcodes

        82
      • Reading PDF417 Barcodes

        83
  • Display Results

    83
• Chapter 6 Calibrating Images

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  • Perspective and Nonlinear Distortion Calibration

    86
    • Defining a Calibration Template

      87
      • Figure 6-1. Defining a Calibration Grid

        87
    • Defining a Reference Coordinate System

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      • Figure 6-2. Axis Direction in the Image Plane

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      • Figure 6-3. A Calibration Grid and an Image of the Grid

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      • Figure 6-4. Defining a Coordinate System

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    • Learning Calibration Information

      90
      • Specifying Scaling Factors

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      • Choosing a Region of Interest

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      • Choosing a Learning Algorithm

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      • Figure 6-5. Types of Image Distortion

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      • Figure 6-6. Calibration ROIs

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      • Using the Learning Score

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      • Learning the Error Map

        93
      • Learning the Correction Table

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      • Setting the Scaling Method

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      • Calibration Invalidation

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  • Simple Calibration

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    • Figure 6-7. Defining a Simple Calibration

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  • Save Calibration Information

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  • Attach Calibration Information

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• Appendix A Technical Support and Professional Services

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• Glossary

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  • Numbers

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  • A-B

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  • C

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  • D-E

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  • F-G

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  • H

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  • I

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  • J-L

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  • M

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  • N-O

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  • P

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  • Q-R

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  • S

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  • T

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  • V

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• Index

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  • Numerics

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  • A-C

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  • D-E

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  • F-I

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  • K-M

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  • N-S

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  • T-W

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