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# -*- coding: utf-8 -*-

"""

Created on Sun Jun 22 00:22:31 2014

@author: Edward

"""

# Segmenting the picture in regions

import numpy as np

import pylab as pl

from scipy.ndimage.interpolation import zoom

from sklearn.feature_extraction import image

from sklearn.cluster import spectral_clustering

def SpectralClusterImage(input_image, beta=5, eps=1e-6, n_regions=11, assign_labels='discretize',downsample_factor=np.NaN, order=3):

""" Spectral Cluster an image

Inputs:

input_image: ndarray of image

beta: Take a decreasing function of the gradient: an exponential

The smaller beta is, the more independent the segmentation is of

the acutal image. For beta=1, the segmentation is close to a

voronoi. Default is 5.

eps: error term. Default is 1E-6

n_regions: number of regions to decompose into. Default is 11.

assign_labels: ways of decomposition. Selecting from 'discretize' and

'kmeans'. Default is 'discretize'.

downsample_factor: downsampling before spectral decomposition. Default

is to keep the original sampling. Enter a single number to apply

the kernel for both dimensions of the image, or enter as a sequence

to apply different kernel for each dimension

order: downsampling method, order of B-spline interpolation

"""

# Downsample the image

if not np.isnan(downsample_factor):

zoom(input_image, zoom=downsample_factor, order=order)

# Convert the image into a graph with the value of the gradient on the edges

graph = image.img_to_graph(input_image)

# Take a decreasing function of the gradient: an exponential

# The smaller beta is, the more independent the segmentation is of the

# acutal image. For beta=1, the segmentation is close to a voronoi

graph.data = np.exp(-beta * graph.data / input_image.std()) + eps

# Apply spectral clustering (this step goes much faster if yuo have pyamg

# installed)

labels = spectral_clustering(graph, n_clusters=n_regions,

assign_labels='discretize')

labels = labels.reshape(input_image.shape)

# Visualizing the resulting regions

pl.figure(figsize=(5,5))

pl.imshow(input_image, cmap=pl.cm.gray)

for lb in range(n_regions):

pl.contour(labels == lb, contour=1,

color=[pl.cm.spectral(lb / float(n_regions)), ])

# Get rid of x, y tick marks

pl.xticks(())

pl.yticks(())