ClearVolume

Description

ClearVolume is a real-time live 3D visualization library designed for high-end volumetric microscopes such as SPIM and DLSM microscopes. With ClearVolume you can see live on your screen the stacks acquired by your microscope instead of waiting for offline post-processing to give you an intuitive and comprehensive view on your data. The biologists can immediately decide whether a sample is worth imaging. ClearVolume can easily be integrated into existing Java, C/C++, Python, or LabVIEW based microscope software. It has a dedicated interface to MicroManager/OpenSpim/OpenSpin control software. ClearVolume supports multi-channels, live 3D data streaming from remote microscopes, and uses a multi-pass Fibonacci rendering algorithm that can handle large volumes. Moreover, ClearVolume is integrated into the Fiji/ImageJ2/KNIME ecosystem. You can now open your stacks with ClearVolume from within these popular frameworks for offline viewing.

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ClearCL

Description

ClearCL is a Multi-backend Java Object Oriented Facade API for OpenCL.

OpenCL libraries come and go in Java, some are great but then one day the lead developper goes on to greener pastures and you are left with code that needs to be rewritten to take advantage of a new up-to-date library with better support. Maybe a particular library has a bug or does not support the function you need? or it does not give you access to the underlying native pointers, making difficult to process large buffers/images or interoperate with hardware? or maybe it just does not support your exotic OS of choice. To protect your code from complete rewrites ClearCL offers a very clean and complete API to write your code against. Changing backend requires just changing one line of code.

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OpenCV / CUDA

Description

The OpenCV CUDA module is a set of classes and functions to utilize CUDA computational capabilities. It is implemented using NVIDIA* CUDA* Runtime API and supports only NVIDIA GPUs. The OpenCV CUDA module includes utility functions, low-level vision primitives, and high-level algorithms. The utility functions and low-level primitives provide a powerful infrastructure for developing fast vision algorithms taking advantage of CUDA whereas the high-level functionality includes some state-of-the-art algorithms (such as stereo correspondence, face and people detectors, and others) ready to be used by the application developers.

The CUDA module is designed as a host-level API. This means that if you have pre-compiled OpenCV CUDA binaries, you are not required to have the CUDA Toolkit installed or write any extra code to make use of the CUDA.

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