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Machine vision uses sensors (cameras), processing hardware and software algorithms to automate complex or mundane visual inspection tasks and precisely guide handling equipment during product assembly. Applications include Positioning, Identification, Verification, Measurement, and Flaw Detection.

You will want to select lighting that 'amplifies' the elements of the part that you want to inspect and 'attenuates' elements that you don't want to inspect. In the left picture, poor lighting makes it difficult to read the letters on this part. In the right picture, the lighting has been selected to clearly show the lettering.

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The lens gathers the light reflected (or transmitted) from the part being inspected, and forms an image in the camera sensor. The proper lens allows you to see the field-of-view you want and to place the camera at a convenient working distance from the part.

Given all of these issues, we recommend that you work closely with your DALSA IPD distributor to choose the appropriate lens for your application.

Staging usually is mechanical. It also usually includes a Part-in-Place sensor that tells the machine vision system when a part is in front of the camera. This sensor is usually a simple light source and photoelectric detector, for example.

A machine vision system consists of several critical components, from the sensor (camera) that captures a picture for inspection, to the processing engine itself (vision appliance) that renders and communicates the result. For any machine vision system to work reliably and generate repeatable results, it is important to understand how these crticial components interact.

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Teledyne DALSA offers a full range of Area Scan (2D sensors) and Line Scan (1D sensors) cameras that interface with our Vision Appliance controllers.

A machine vision system will work tirelessly performing 100% online inspection, resulting in improved product quality, higher yields and lower production costs. Consistent product appearance and quality drives customer satisfaction and ultimately market share.

In addition to the standard library, there is an active collection of hundreds of thousands of components (from individual programs and modules to packages and entire application development frameworks), available from the Python Package Index.

Here is a typical example: If the part to be inspected is 4" wide and 2" high, you would need a FOV that is slightly larger than 4", assuming your staging can position the part within this FOV. In specifying the FOV you have to also consider the camera's "aspect ratio" - the ratio of the width to height view. The cameras used with Vision Appliances™ have a 4:3 aspect ratio. In the previous example, the 4" x 2" part size would fit in a 4:3 aspect ratio, but a 4" x 3.5" part would require a larger FOV to be entirely seen.

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From the FOV and working distance and the camera specifications, the focal length of the lens can be estimated. The focal length is a common way to specify lenses and is, in theory, the distance behind the lens where light rays 'from infinity' (parallel light rays) are brought to a focus. Common focal lengths for lenses in machine vision are 12 mm, 16 mm, 25 mm, 35 mm and 55 mm. When the calculations are done, the estimated focal length will probably not exactly match any of these common values. We typically pick a focal length that is close and then adjust the working distance to get the desired FOV.

The following sections will provide you with an introduction to lighting, staging, optics and cameras, all critical components of a successful machine vision solution. Additional help on these topics is available from your distributor or integrator, from IPD, and from vendors of lighting and lenses

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The camera contains a sensor that converts light from the lens into electrical signals. These signals are digitized into an array of values called pixels and processed by a Vision Appliance™ to perform the inspection.

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To pick the proper lens you will first need to know the field-of-view (FOV) and the working distance. The FOV is the size of the area you want to capture.

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The sensors used by machine vision cameras are highly specialized, and hence more expensive than say, a web cam. First, it is desirable to have square physical pixels. This makes measurement calculations easier and more precise. Second, the cameras can be triggered by the machine vision system to take a picture based on the Part-in-Place signal. Third, the cameras have sophisticated exposure and fast electronic shutters that can 'freeze' the motion of most parts.

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The working distance is approximately the distance from the front of the camera to the part being inspected. A more exact definition takes into account the structure of the lens.

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The human eye can see well over a wide range of lighting conditions, but a machine vision system is not as capable. You must therefore carefully light the part being inspected so that the machine vision system can clearly 'see' them.

Proper lighting makes inspection faster and more accurate. Poor lighting is a major cause of failure in machine vision inspection systems.

In general, the available or ambient light is poor lighting and will not work. For example, the overhead lights in a factory can burn out, dim or be blocked, and these changes might be interpreted as part failures by the machine vision system.

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Industrial cameras are great for applications that don't require extreme sensitivity and extremely low noise, like machine vision.

The resolution (precision) of the inspection depends upon the working distance, the field-of-view (FOV), and the number of physical pixels in the camera's sensor. A standard VGA camera has 640 x 480 physical pixels (width x height), and each physical pixel is about 7.4 microns square. From these numbers, resolution can be estimated for your "real world" units. We usually specify resolution as a fraction of a physical pixel, as this is independent of your particular imaging set-up.

The Python installers for the Windows platform usually include the entire standard library and often also include many additional components. For Unix-like operating systems Python is normally provided as a collection of packages, so it may be necessary to use the packaging tools provided with the operating system to obtain some or all of the optional components.

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The light must be regulated and constant so that the light changes seen by the machine vision system are due to changes in the parts being inspected and not changes in the light source.

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While The Python Language Reference describes the exact syntax and semantics of the Python language, this library reference manual describes the standard library that is distributed with Python. It also describes some of the optional components that are commonly included in Python distributions.

Staging, sometimes called fixturing, holds the part to be inspected at a precise location in front of the camera for a Vision Appliance™ to 'see'. Staging is required for three reasons:

There are other important specifications for lenses, such as resolution (image detail - depends on the camera and the lens), the amount and type of optical distortion the lens introduces and how closely the lens can focus.

Selecting the proper lighting requires some knowledge and experience. Our distributors and lighting vendors will be able to do an analysis of the parts you want to inspect and recommend proper lighting.

Python’s standard library is very extensive, offering a wide range of facilities as indicated by the long table of contents listed below. The library contains built-in modules (written in C) that provide access to system functionality such as file I/O that would otherwise be inaccessible to Python programmers, as well as modules written in Python that provide standardized solutions for many problems that occur in everyday programming. Some of these modules are explicitly designed to encourage and enhance the portability of Python programs by abstracting away platform-specifics into platform-neutral APIs.