imops 0.8.7

Efficient parallelizable algorithms for multidimensional arrays to speed up your data pipelines

Imops

Efficient parallelizable algorithms for multidimensional arrays to speed up your data pipelines.

• Documentation
• Benchmarks

Install

pip install imops  # default install with Cython backend
pip install imops[numba]  # additionally install Numba backend

How fast is it?

Time comparisons (ms) for Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz using 8 threads. All inputs are C-contiguous NumPy arrays. For morphology functions bool dtype is used and float64 for all others.

function / backend	Scipy()	Cython(fast=False)	Cython(fast=True)	Numba()
zoom(..., order=0)	2072	1114	867	3590
zoom(..., order=1)	6527	596	575	3757
interp1d	780	149	146	420
radon	59711	5982	4837	-
inverse_radon	52928	8254	6535	-
binary_dilation	2207	310	298	-
binary_erosion	2296	326	304	-
binary_closing	4158	544	469	-
binary_opening	4410	567	522	-
center_of_mass	2237	64	64	-

We use airspeed velocity to benchmark our code. For detailed results visit benchmark page.

Features

Fast Radon transform

from imops import radon, inverse_radon

Fast 0/1-order zoom

from imops import zoom, zoom_to_shape

# fast zoom with optional fallback to scipy's implementation
y = zoom(x, 2, axis=[0, 1])
# a handy function to zoom the array to a given shape 
# without the need to compute the scale factor
z = zoom_to_shape(x, (4, 120, 67))

Works faster only for ndim<=4, dtype=float32 or float64 (and bool-int16-32-64-uint8-16-32 if order == 0), output=None, order=0 or 1, mode='constant', grid_mode=False

Fast 1d linear interpolation

from imops import interp1d  # same as `scipy.interpolate.interp1d`

Works faster only for ndim<=3, dtype=float32 or float64, order=1

Fast 2d linear interpolation

import numpy as np
from imops.interp2d import Linear2DInterpolator
n, m = 1024, 2
points = np.random.randint(low=0, high=1024, size=(n, m))
points = np.unique(points, axis=0)
x_points = points[: n // 2]
values = np.random.uniform(low=0.0, high=1.0, size=(len(x_points),))
interp_points = points[n // 2:]
num_threads = -1 # will be equal to num of CPU cores
# You can optionally pass your own triangulation as an np.array of shape [num_triangles, 3], element at (i, j) position is an index of a point from x_points
interpolator = Linear2DInterpolator(x_points, values, num_threads=num_threads, triangles=None)
# Also you can pass values to __call__ and rewrite the ones that were passed to __init__
interp_values = interpolator(interp_points, values + 1.0, fill_value=0.0)

Fast binary morphology

from imops import binary_dilation, binary_erosion, binary_opening, binary_closing

These functions mimic scikit-image counterparts

Padding

from imops import pad, pad_to_shape

y = pad(x, 10, axis=[0, 1])
# `ratio` controls how much padding is applied to left side:
# 0 - pad from right
# 1 - pad from left
# 0.5 - distribute the padding equally
z = pad_to_shape(x, (4, 120, 67), ratio=0.25)

Cropping

from imops import crop_to_shape

# `ratio` controls the position of the crop
# 0 - crop from right
# 1 - crop from left
# 0.5 - crop from the middle
z = crop_to_shape(x, (4, 120, 67), ratio=0.25)

Labeling

from imops import label

# same as `skimage.measure.label`
labeled, num_components = label(x, background=1, return_num=True)

Backends

For all heavy image routines except label you can specify which backend to use. Backend can be specified by a string or by an instance of Backend class. The latter allows you to customize some backend options:

from imops import Cython, Numba, Scipy, zoom

y = zoom(x, 2, backend='Cython')
y = zoom(x, 2, backend=Cython(fast=False))  # same as previous
y = zoom(x, 2, backend=Cython(fast=True))  # -ffast-math compiled cython backend
y = zoom(x, 2, backend=Scipy())  # use scipy original implementation
y = zoom(x, 2, backend='Numba')
y = zoom(x, 2, backend=Numba(parallel=True, nogil=True, cache=True))  # same as previous

Also backend can be specified globally or locally:

from imops import imops_backend, set_backend, zoom

set_backend('Numba')  # sets Numba as default backend
with imops_backend('Cython'):  # sets Cython backend via context manager
    zoom(x, 2)

Note that for Numba backend setting num_threads argument has no effect for now and you should use NUMBA_NUM_THREADS environment variable. Available backends:

function / backend	Scipy	Cython	Numba
zoom	✓	✓	✓
interp1d	✓	✓	✓
radon	✗	✓	✗
inverse_radon	✗	✓	✗
binary_dilation	✓	✓	✗
binary_erosion	✓	✓	✗
binary_closing	✓	✓	✗
binary_opening	✓	✓	✗
center_of_mass	✓	✓	✗

Acknowledgements

Some parts of our code for radon/inverse radon transform as well as the code for linear interpolation are inspired by the implementations from scikit-image and scipy. Also we used fastremap and cc3d out of the box.