Python image processing libraries performance: OpenCV vs Scipy vs Scikit-Image

image-processing python numpy scipy opencv scikit-image

We are going to compare the performance of different methods of image processing using three Python libraries (scipy, opencv and scikit-image). All the tests will be done using timeit. Also, in the case of OpenCV the tests will be done with the optimization enabled (by default if supported by CPU) and disabled:

>>> cv2.useOptimized()
True
>>> cv2.setUseOptimized(False)
>>> cv2.useOptimized()
False

Requirements

% sudo apt-get install -y build-essential libblas-dev liblapack-dev gfortran cmake git \
  libgtk2.0-dev pkg-config libavcodec-dev libavformat-dev libswscale-dev python-dev \
  python-numpy libtbb2 libtbb-dev libjpeg-dev libpng-dev libtiff-dev libjasper-dev libdc1394-22-dev
% sudo pip install scipy scikit-image
% mkdir tmp && cd tmp
% git clone https://github.com/Itseez/opencv.git
% git clone https://github.com/Itseez/opencv_contrib.git
% cd opencv
% mkdir build && cd build
% cmake -D CMAKE_BUILD_TYPE=Release -D CMAKE_INSTALL_PREFIX=/usr/local ..
% make -j7
% sudo make install
% bin/opencv_test_core

Linear filter: gaussian (3-channel)

import numpy as np
import scipy.ndimage as nd
from skimage import data, morphology, filter as imfilter
import cv2
img = data.lena()

# scipy
dst = np.zeros(img.shape, img.dtype)
for i in xrange(img.shape[2]):
    dst[:, :, i] = nd.gaussian_filter(img[:, :, i], 5)

# opencv
dst = cv2.GaussianBlur(img, (0, 0), 5)

# scikit-image
dst = imfilter.gaussian_filter(img, 5, multichannel=True)
image filter: gaussian filter

Linear filter: sobel (1-channel)

img = data.lena()[:, :, 1]

# scipy
dst = nd.sobel(img, 1)

# opencv
dst = cv2.Sobel(img, cv2.CV_8U, 1, 0)

# scikit-image
dst = imfilter.hsobel(img)
image filter: sobel filter

Non-linear filter: median (3-channel)

img = data.lena()

# scipy
dst = np.zeros(img.shape, img.dtype)
for i in xrange(img.shape[2]):
    dst[:, :, i] = nd.median_filter(img[:, :, i], 5)

# opencv
dst = cv2.medianBlur(img, 5)
image filter: median filter

Morphological operation: dilation (1-channel)

img = np.where(data.coins() > 128, 1, 0).astype(np.uint8)
kernel = np.ones((5,5), np.uint8)

# opencv
dst = cv2.dilate(img, kernel, iterations=1)

# scipy
dst = nd.binary_dilation(img, kernel, iterations=1)

# scikit-image
dst = morphology.binary_dilation(img, kernel)
image morphology: dilation

Results

We can see a general better performance in OpenCV (with optimization enabled), specially in a non-linear operation (median filter); a light difference in an easy computable filter as the sobel; and the scikit-image in the last position of performance.

gaussian filter (rgb) sobel filter (gray) median filter (rgb) dilation (binary)
scipy 0.06260.004190.5680.00452
opencv* 0.05880.005920.1730.00118
opencv 0.01390.006840.00520.000104
scikit-image 0.07290.007460.0154
Time (in seconds) used per process for each of the four libraries (the less the better)
* OpenCV with optimization disabled
import pandas as pd
import matplotlib.pyplot as plt


data = pd.DataFrame(
    [[0.0626, 0.00419, 0.568, 0.00452],
     [0.0588, 0.00592, 0.173, 0.00118],
     [0.0139, 0.00684, 0.005, 0.000104],
     [0.0729, 0.00746, np.nan, 0.0154]],
    index=['scipy', 'opencv*', 'opencv', 'scikit-image'],
    columns=['gaussian_filter', 'sober_filter', 'median_filter', 'dilation'])

data.T.plot(kind='barh', grid=False)
plt.xlabel('seconds')
plt.axes().xaxis.grid(True)
plt.show()
python image processing performance plot