2 The package can be installed on all major platforms (e.g., BSD, GNU/Linux, OS X, Windows) from, amongst other sources, the Python Package Index (PyPI), 3 Continuum Analytics Anaconda, 4 Enthought Canopy, 5 Python(x,y), 6 NeuroDebian ( Halchenko & Hanke, 2012) and GNU/Linux distributions such as Ubuntu. The scikit-image project started in August of 2009 and has received contributions from more than 100 individuals. Scikit-image thus makes it possible to perform sophisticated image processing tasks with only a few function calls. # Label image regions.Īx5.set_title('Labeled items', fontsize=24) Finally, physical information such as the position, area, eccentricity, perimeter, and moments can be extracted using measure.regionprops. Then, we attribute to each coin a label ( morphology.label) that can be used to extract a sub-picture. Next, a Canny filter ( filter.canny) ( Canny, 1986) detects the edge of each coin. # Find maxima.įrom skimage.feature import peak_local_maxĬoordinates = peak_local_max(image, min_distance=20)Īx3.set_title('Peak local maxima', fontsize=24) The function feature.peak_local_max can be used to return the coordinates of local maxima in an image.
We can easily detect interesting features, such as local maxima and edges. # Apply threshold.įrom skimage.filter import threshold_adaptiveīw = threshold_adaptive(image, 95, offset=-15)Īx2.set_title('Adaptive threshold', fontsize=24) Here, we employ filter.threshold_adaptive where the threshold value is the weighted mean for the local neighborhood of a pixel. Several threshold algorithms are available. To divide the foreground and background, we threshold the image to produce a binary image. Since the image is represented by a NumPy array, we can easily perform operations such as building a histogram of the intensity values. import numpy as npįig, axes = plt.subplots(ncols=2, nrows=3, The use of NumPy arrays as our data container also enables the use of NumPy’s built-in histogram function. Illustration of several functions available in scikit-image: adaptive threshold, local maxima, edge detection and labels. The rising popularity of Python as a scientific programming language, together with the increasing availability of a large eco-system of complementary tools, makes it an ideal environment in which to produce an image processing toolkit. This paper describes scikit-image, a collection of image processing algorithms implemented in the Python programming language by an active community of volunteers and available under the liberal BSD Open Source license.
#Scipy 2014 3d earthquake activity software#
Exploring these rich data sources requires sophisticated software tools that should be easy to use, free of charge and restrictions, and able to address all the challenges posed by such a diverse field of analysis.
Examples include DNA microarrays, microscopy slides, astronomical observations, satellite maps, robotic vision capture, synthetic aperture radar images, and higher-dimensional images such as 3-D magnetic resonance or computed tomography imaging. In our data-rich world, images represent a significant subset of all measurements made.