EasySpotDetector

Supported operating systems:

Microsoft Windows 11, 10 for x86-64 (64-bit) processor architecture

Microsoft Windows 11, 10 IoT Enterprise for x86-64 systems

Linux for x86-64 (64-bit) and ARMv8-A (64-bit) processor architectures with a glibc version greater or equal to 2.18

Minimum requirements:

8 GB RAM

Optional NVidia GPU

Supported programming languages :

The Open eVision libraries and tools support C++, Python and the programming languages compatible with the .NET (C#, VB.NET)

C++ requirements: A compiler compatible with the C++ 11 standard is required to use Open eVision

Python requirements: Python 3.11 or later is required to use the Python bindings for Open eVision

.NET requirements: .NET framework 4.8 (or later) or the .NET platform 6.0 (or later) are supported

Supported Integrated Development Environments:

Microsoft Visual Studio 2017 (C++, C#, VB .NET, C++/CLI)

Microsoft Visual Studio 2019 (C++, C#, VB .NET, C++/CLI)

Microsoft Visual Studio 2022 (C++, C#, VB .NET, C++/CLI)

QtCreator 4.15 with Qt 5.12

Image source:

Any 8-bit grey scale image, no size limit

Region of interest:

Explicit or automatic selection of the region of interest (an oriented rectangle is fitted to the part’s edges)

A list of detected spots with their type (particle or scratch), position and size, strength, and pixel level segmentation map

Optionally, if a deep learning classifier is loaded, a class and a probability is set for each spot. The deep learning classifier is a trained EasyClassify tool.

Display functions are provided to draw the spot bounding boxes and segmented pixel

Processing speed on single core Intel i7-10850H:

Particle detection only: 200 MPixels/s

Particle and scratch: 60 MPixels/s

Minimum defect size:

2x2 pixels

No maximum defect size

Released

PC4190 Open EasySpotDetector for USB dongle

PC4340 Open eVision EasySpotDetector

PC6512 eVision/Open eVision USB Dongle (empty)

PC6514 Neo USB Dongle (empty)

PC6516 Neo USB Header (empty)