medianfilter: OpenCL median filter¶
A module for performing the 1d, 2d and 3d median filter …
The target is to mimic the signature of scipy.signal.medfilt and scipy.medfilt2
The first implementation targets 2D implementation where this operation is costly (~10s/2kx2k image)
MedianFilter2D(shape, kernel_size=(3, 3), ctx=None, devicetype='all', platformid=None, deviceid=None, block_size=None, profile=False)[source]¶
A class for doing median filtering using OpenCL
Send a numpy array to the device, including the cast on the device if possible
data – numpy array with data
dest – name of the buffer as registered in the class
calculate and return the optimal workgroup size for the first dimension, taking into account the 8-height band
kernel_size – 2-tuple of int, shape of the median window
optimal workgroup size
Actually apply the median filtering on the image
image – numpy array with the image
kernel_size – 2-tuple if
median-filtered 2D image
- Nota: for window size 1x1 -> 7x7 up to 49 / 64 elements in 8 threads, 8elt/th
9x9 -> 15x15 up to 225 / 256 elements in 32 threads, 8elt/th 17x17 -> 21x21 up to 441 / 512 elements in 64 threads, 8elt/th
TODO: change window size on the fly,
Median filter a 2-dimensional array.
Apply a median filter to the input array using a local window-size given by kernel_size (must be odd).
ary – A 2-dimensional input array.
kernel_size – A scalar or a list of length 2, giving the size of the median filter window in each dimension. Elements of kernel_size should be odd. If kernel_size is a scalar, then this scalar is used as the size in each dimension. Default is a kernel of size (3, 3).
An array the same size as input containing the median filtered result. always work on float32 values
About the padding:
The filling mode in scipy.signal.medfilt2d is zero-padding
- This implementation is equivalent to:
scipy.ndimage.median_filter(ary, kernel_size, mode=”nearest”)