rois in labview ....... an interesting feature to study of the software

85

5

Image Processing

Once you have acquired images, they are often not in an appropriate format for you

to analyze and must be processed Þrst. The Vision Toolkit contains many tools to

help you clean up and alter images, from deÞning a region of interest to Þltering

and spatial manipulation.

5.1 THE ROI (REGION OF INTEREST)

A ROI is a speciÞcation structure that allows for the deÞnition of arbitrarily shaped

regions within a given image, often called subimages (although a ROI can encompass

the entire image if so deÞned). A ROI contains no image data � it is not an image

itself, but a placeholder that remembers a deÞned location within an image.

Why would you need a ROI within an image? Why not just acquire the region

of interest as the complete image? There are many answers to this question: Þrst,

ROIs can be irregular shapes � not necessarily the rectangle or square that your

camera supplies. ROIs can be rectangles or squares (a square is often called an

area

of interest

, or AOI), circles, annuluses, polygons and freehand shapes. Secondly, you

may want to perform different image processing routines on different areas of an

image � there is no sense in wasting valuable time on processing the whole raw

image, when you are only interested in a few small parts of it. Another reason for

ROIs is that you can dynamically deÞne and change them � users can draw a ROI

over a portion of an image at run time, selecting their particular region of interest.

The license plate in Figure 5.1 has a deÞned ROI around the alphanumeric

characters, to make it much simpler and faster for an optical character recognition

(OCR) routine to decode. Also, the routine would try to recognize characters where

none are present, perhaps returning erroneous results.

FIGURE 5.1

Car license plate with a deÞned ROI

!

1480_book.fm!!Page!85!!Friday,!April!25,!2003!!3:25!PM

86

Image Acquisition Processing with LabVIEW

By traditional deÞnition, every point of an image is either inside or outside of

a given ROI. The Vision Toolkit takes this deÞnition one step further by adding two

new ROI concepts: the line and the point. Strictly speaking, these ROI types are not

actually lines and points, but a single pixel width rectangle and square respectively.

Points are useful to return single value data about a region of your image (point

intensity, color data, etc) and the line can return one-dimensional information.

5.1.1 S

IMPLE

ROI U

SE

5.1.1.1 Line

A simple ROI example is that of an intensity contour measurement, as shown in

Figure 5.2 and Figure 5.3. The

IMAQ

WindLastEvent

VI is used to detect the event

that last occurred in the nominated IMAQ window and looks for a draw event

(suggesting that the user has drawn a ROI of some type).

IMAQ WindGetROI

then

returns the deÞnition of the created ROI and

IMAQ ROI Profile

returns the intensity

contours along the length of the ROI (if a two-dimensional ROI is deÞned, the

intensity contours follow the outer shape of the ROI, starting at the Þrst deÞned

point).

!

!!

As you can see, the initial ROI point is that on the far left and as the line is

followed, the intensity levels are returned (suggesting that the background intensity

of the license plate is approximately 240 units, whereas the alphanumeric characters

are approximately 50 units).

5.1.1.2 Square and Rectangle

A square ROI is the simplest two-dimensional ROI � it allows the user to select a

region with four equal sides, although the rectangle ROI is probably the most popular,

allowing the user more ßexibility in deÞning the required region.

FIGURE 5.2

Simple ROI intensity contour � wiring diagram.

!


Image Processing

87

Figure 5.4 shows intensity contour information for a rectangular ROI � the

contours are plotted from the Þrst deÞned corner of the ROI (the top left corner in

this case), along the ROI in a clockwise direction. As they are two-dimensional

ROIs, they have several other applications, including image portion selection (See

Figure 5.5).

FIGURE 5.3

A line ROI, with its calculated intensity contour.

FIGURE 5.4

Rectangle ROI contour example

!


88


Image portion selection is achieved by Þrst converting a detected ROI to a

rectangle and then extracting the portion of the source image that is bound by that

rectangle using

IMAQ Extract

, as shown in Figure 5.6.

5.1.1.3 Oval

An oval ROI is deÞned by Þrst holding down the left mouse button to deÞne the

center and then dragging to deÞne the radii of the ROI:

As you can see from Figure 5.7, the position of the mouse cursor during the

drag operation may not be particularly intuitive, but it is a corner of the oval�s

bounding rectangle.

5.1.2 C

OMPLEX

ROI

S

Although simple ROIs can be very useful in particular situations, several other types

are available, including the rotated rectangle and annulus arc.

FIGURE 5.5

Two-dimensional ROI image portion selection.

FIGURE 5.6

Two-Dimensional ROI image portion selection � wiring diagram.

!


Image Processing

89

5.1.2.1 Rotated Rectangle

As its name would suggest, rotated rectangles are similar to their simple counterparts,

but with the added functionality of allowing the user to rotate them. When a rotated

square or rectangle is deÞned, several extra construction lines are evident (Figure 5.8).

The masked rotated rectangle ROI in Figure 5.8 was achieved with the code

shown in Figure 5.9.

5.1.2.2 Annulus Arc

An annulus is deÞned as the Þgure bounded by and containing the area between

two concentric circles and an annulus arc is, as you might expect, an arc component

of that annulus (Figure 5.10).

An annulus arc is very useful when unwrapping text, or any otherwise wrapped

image data, for analysis. DeÞning an annulus arc takes place in four steps, as shown

in Figure 5.11(a) deÞning the outer radius, Figure 5.11(b) the starting edge, Figure

5.11(c) the ending edge and Figure 5.11(d) the inner radius.

Using the Annulus Arc selection tool alone may seem of little or no use, if it is

combined with an unwrapping routine (

IMAQ Unwrap,

for example) you can unwrap

a semicircular portion of an image, distorting it to a rectangular shape, as shown in

Figure 5.12. This example waits for a Draw Event on either of the IMAQ windows

and once one is detected, that window�s ROI is returned, converted from a raw ROI

to an annulus arc and then

IMAQ Unwrap

is called to unwrap the annulus arc into a

rectangle.

The unwrapped image (the ßoating window containing the text in Figure 5.13)

can then be fed through optical character recognition (OCR) software, including the

FIGURE 5.7

Oval ROI deÞnition.

!


90


National Instruments OCR Toolkit add-on for the Vision Toolkit (see Chapter 8 for

more information about OCR).

FIGURE 5.8

(a) Drag out a standard rectangular ROI. (b) Rotate the ROI with the handles.

(c) Rotated rectangular ROI.

FIGURE 5.9

Rotated rectangle ROI image portion selection � wiring diagram.

!


Image Processing

91

FIGURE 5.10

Annulus arc components.

FIGURE 5.11

(a) DeÞning the outer radius. (b) DeÞning the starting edge. (c) DeÞning the

ending edge. (d) DeÞning the inner radius.

Drag Point

Outer Radius

Inner

Radius

Start Edge

End Edge

!


92


5.1.2.3 ROI Tracing Example

A user recently posted a question on the National Instruments Developer�s Zone

discussion forum:

I am currently working on a project using LabVIEW 6.0 and IMAQ Vision [and] I

need to get the � coordinates of the cursor in an IMAQ window� .

The project called for the user to drag the mouse pointer over an image and then

track a laser beam along the same coordinates. Although previous examples in this

book have used

IMAQ

WindLastEvent

to determine only the type of event that has

occurred, it can also provide us with some limited information regarding the event�s

FIGURE 5.12

Unwrap annulus arc ROI � wiring diagram.

FIGURE 5.13

Unwrap annulus arc ROI.

!


Image Processing

93

attributes. The VI has three outputs of interest:

event, coordinates

and

other param-

eters,

which are populated as shown in Table 5.1.

In this instance, the user was able to use the

draw vertices

data returned in the

coordinates

array, which was subsequently built into an array of clusters (each

containing the

x,y

pair of the pixels that were traced over). This allowed the user to

trace the path of the mouse (Figure 5.15).

As you can see in Figure 5.14, the

coordinates

array returns the start and end

point of the freehand draw operation, as well as the vertices of each point along the

way:

5.1.3 M

ANUALLY

B

UILDING

A

ROI: T

HE

“ROI D

ESCRIPTOR

”

It is often beneÞcial to programmatically deÞne ROIs, especially if there are several

objects of interest that are always in the same place within a series of images (e.g.,

TABLE 5.1

Event ROI Tool Coordinates [Array] Other Parameters [Array]

None Any NULL NULL

Click Cursor [0,1] xy position of the click [0,1,2] selected pixel intensity

Double click Any [0,1] xy position of the click [0,1,2] selected pixel intensity

Zoom [0,1] xy position of the click

[2,3] image xy center position

[0] new zoom factor

Draw Line [0,1] xy start point

[2,3] xy end point

[0] bounding rectangle width

[1] bounding rectangle height

[2] vertical segment angle

Rectangle [0,1] xy start point

[2,3] xy end point

[0] width

[1] height

Oval [0,1] bounding rectangle start

point

[2,3] bounding rectangle end

point



Polygon [0,1] bounding rectangle start

point


point

[4,5], [6,7], [8,9] � vertices



Freehand [0,1] bounding rectangle start

point


point

[4,5],[6,7],[8,9]� vertices



Move Any [0,1] new position of the window NULL

Size Any [0] new width of the window

[1] new height of the window

NULL

Scroll Any [0,1] new center position of image NULL

!


94


parts on a production line). Setting the upper left and lower right corners is not

enough to fully deÞne a ROI, as it contains several other attributes. The ROI itself

is carried around LabVIEW diagrams as a special cluster called the

ROI Descriptor

(Figure 5.16; Table 5.2).

Figure 5.17 is an example of a dynamically deÞned rectangular ROI. As the user

changes the

Manual Rect ROI

values, a new ROI Descriptor is calculated with an

internal ID. The image data within the ROI is then extracted and displayed as a new

FIGURE 5.14

Trace mouse cursor example.

FIGURE 5.15

Trace mouse cursor example � wiring diagram.

!


Image Processing

95

image (the example in Figure 5.18 uses the global rectangle as the coordinates, as

the type is a rectangle).

One often-useful application of dynamic ROI deÞnition is an

inspection routine

,

which allows the user to pan and zoom a source image to locate features (Figure

5.19).

Although the wiring diagram in Figure 5.20 may look complicated, it is just an

extension of previous examples. An event handler waits on the user pressing one of

the inspection navigation buttons on the front panel and adjusts either the window

zoom or ROI descriptor to suit. The new values are then fed into

IMAQ WindZoom

and

IMAQ WindSetROI

and the inspection window is redrawn to reßect the changes.

FIGURE 5.16

The ROI descriptor.

TABLE 5.2

Item Description

Global rectangle The bounding box of the ROI � a rectangle whose bottom side is parallel

to the bottom of the source image and encloses the complete ROI,

irrespective of the ROI�s shape.

Contours Accurately deÞne the ROI with the following components:

ID:

SpeciÞes whether the ROI is considered to be within or without the

deÞned shape. If the ID is external, the ROI is the complete image

outside of the ROI coordinates and is not bound by the global

rectangle.

Type:

SpeciÞes the contour shape (Point, Line, Rectangle, Oval,

Polygon, Free, Broken Line, or Free Hand Line).

Coordinates:

SpeciÞes the list of contour points (e.g., the top left and

bottom right points for a rectangle)

ROI

Global Rectangle

Source Image

Coordinates

Internal ID

!


96


FIGURE 5.17

Manually building a rectangular ROI.

FIGURE 5.18

Manually building a rectangular ROI � wiring diagram.

FIGURE 5.19

Manual rectangular ROI application.

!


Image Processing

97

FIGURE 5.20

Manual rectangular ROI application � wiring diagram.

!


rois in labview ....... an interesting feature to study of the software

Documents

dened roi

roi region of interesta

created roi

twodimensional roi

roi types

image data

imaq roi profile

initial roi point