Updated README with more details on the opencv optional dependency
@@ -194,7 +194,8 @@ Optional dependencies
The following dependencies are not automatically installed upon installation of ``VIP`` but may significantly improve your experience:
- ``VIP`` contains a class ``vip_hci.vip_ds9`` that enables, through ``pyds9``, the interaction with a DS9 window (displaying numpy arrays, controlling the display options, etc). To enable this feature, ``pyds9`` must be installed from the latest development version: ``pip install git+git://github.com/ericmandel/pyds9.git#egg=pyds9``
-- Also, you can install the Intel Math Kernel Library (``mkl``) optimizations (provided that you have a recent version of ``conda``) or ``openblas`` libraries. Either of them can be installed with ``conda install``. This is recommended along with ``OpenCV`` for maximum speed on ``VIP`` computations.
+- Also, you can install the Intel Math Kernel Library (``mkl``) optimizations (provided that you have a recent version of ``conda``) or ``openblas`` libraries. Either of them can be installed with ``conda install``.
+- For maximum speed on image operations, such as scaling, shifts or rotations (the latter being a typical bottleneck in most post-processing routines), one can install ``OpenCV`` (`pip install opencv-python`). To use it, just set `imlib='opencv'` (combined e.g. with `interpolation='lanczos4'`) instead of the default `imlib='vip-fft' when using any routine involving image operations. While it can yield up to 3 orders of magnitude speed improvement, note that flux conservation will be worse than using FFT-based methods.
- ``VIP`` offers the possibility of computing SVDs on GPU by using ``CuPy`` (starting from version 0.8.0) or ``PyTorch`` (from version 0.9.2). These remain as optional requirements, to be installed by the user, as well as a proper CUDA environment (and a decent GPU card).
- Finally, bad pixel correction routines can be optimised with ``Numba``, which converts some Python code, particularly ``NumPy``, into fast machine code. A factor up to ~50x times speed improvement can be obtained on large images compared to NumPy. Numba can be installed with ``conda install numba``.
