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Python Tools for Visual Studio (PTVS) is a free and open-source plug-in for versions of Visual Studio up to VS 2015 providing support for programming in Python. Since VS 2017, it is integrated in VS and called Python Support in Visual Studio. It supports IntelliSense, debugging, profiling, MPI cluster debugging, mixed C++/Python debugging, and ...
Aspect-orientation is not limited to programming since it is useful to identify, analyse, trace and modularise concerns through requirements elicitation, specification, and design. Aspects can be multi-dimensional by allowing both functional and non-functional behaviour to crosscut any other concerns, instead of just mapping non-functional ...
In computing, aspect-oriented programming (AOP) is a programming paradigm that aims to increase modularity by allowing the separation of cross-cutting concerns.It does so by adding behavior to existing code (an advice) without modifying the code, instead separately specifying which code is modified via a "pointcut" specification, such as "log all function calls when the function's name begins ...
VPython is Open Source, and a part of the Python Library, combining the Python programming language with a 3D graphics module called Visual. This library application allows users to create 3D objects, such as spheres and cones, and then display these objects in an app window. This aids creation of simple visualizations, allowing programmers to ...
A view frustum The appearance of an object in a pyramid of vision When creating a parallel projection, the viewing frustum is shaped like a box as opposed to a pyramid.. In 3D computer graphics, a viewing frustum [1] or view frustum [2] is the region of space in the modeled world that may appear on the screen; it is the field of view of a perspective virtual camera system.
In image processing, computer vision and related fields, an image moment is a certain particular weighted average of the image pixels' intensities, or a function of such moments, usually chosen to have some attractive property or interpretation. Image moments are useful to describe objects after segmentation.
The technique counts occurrences of gradient orientation in localized portions of an image. This method is similar to that of edge orientation histograms , scale-invariant feature transform descriptors, and shape contexts , but differs in that it is computed on a dense grid of uniformly spaced cells and uses overlapping local contrast ...
The image gradient magnitudes and orientations are sampled around the keypoint location, using the scale of the keypoint to select the level of Gaussian blur for the image. In order to achieve orientation invariance, the coordinates of the descriptor and the gradient orientations are rotated relative to the keypoint orientation.