Added otsu thresholding

otsu.py

@@ -0,0 +1,127 @@
+#!/usr/local/Cellar/python/2.7.6/bin/python
+# -*- coding: utf-8 -*-
+
+import sys
+from numpy import *
+
+import scipy.misc
+from matplotlib import pyplot
+
+def otsu2( hist, total ):
+	"""
+	This is the more common (optimized) implementation of otsu algorithm, the one you see on Wikipedia pages
+	"""
+	no_of_bins = len( hist ) # should be 256
+
+	sum_total = 0
+	for x in range( 0, no_of_bins ):
+		sum_total += x * hist[x]
+
+	weight_background 	  = 0.0
+	sum_background 		  = 0.0
+	inter_class_variances = []
+
+	for threshold in range( 0, no_of_bins ):
+		# background weight will be incremented, while foreground's will be reduced
+		weight_background += hist[threshold]
+		if weight_background == 0 :
+			continue
+
+		weight_foreground = total - weight_background
+		if weight_foreground == 0 :
+			break
+
+		sum_background += threshold * hist[threshold]
+		mean_background = sum_background / weight_background
+		mean_foreground = (sum_total - sum_background) / weight_foreground
+
+		inter_class_variances.append( weight_background * weight_foreground * (mean_background - mean_foreground)**2 )
+
+	# find the threshold with maximum variances between classes
+	return argmax(inter_class_variances)
+
+def otsu1( hist, total ):
+	"""
+	This is the original otsu thresholding algorithm. No optimization is applied, in order 
+	to illustrate what the algorithm is trying to do
+	'hist' is the histogram of the image or data
+	'total' is the total number of pixels of the image, or the size of data
+	"""
+	no_of_bins = len( hist ) # should be 256
+
+	intra_class_variances = []
+
+	for threshold in range( 0, no_of_bins ):
+		# first we try to find the weight and variance on the background
+		sum_background		= float(sum( hist[0:threshold] ))
+		weight_background 	= sum_background / total
+		mean_background 	= 0.0
+		variance_background = 0.0
+
+		if sum_background > 0.0: # avoid division by zero
+			for x in range( 0, threshold ):
+				mean_background += x * hist[x]
+			mean_background /= sum_background
+
+			for x in range( 0, threshold ):
+				variance_background += (x - mean_background) ** 2 * hist[x]
+			variance_background /= sum_background	
+
+
+		# then we do it for the foreground
+		sum_foreground		= float(sum( hist[threshold:no_of_bins] ))
+		weight_foreground 	= sum_foreground / total
+		mean_foreground 	= 0.0
+		variance_foreground = 0.0
+
+		if sum_foreground > 0.0:
+			for x in range( threshold, no_of_bins ):
+				mean_foreground += x * hist[x]
+			mean_foreground /= sum_foreground
+
+			for x in range( threshold, no_of_bins ):
+				variance_foreground += (x - mean_foreground) ** 2 * hist[x]
+			variance_foreground /= sum_foreground	
+
+		# find the variances within these two classes
+		intra_class_variances.append( weight_background * variance_background + weight_foreground * variance_foreground )
+
+	# use the threshold that has the minimum intra class variance
+	return argmin( intra_class_variances ) - 1
+
+
+def main():
+	img = scipy.misc.imread( '/Users/saburookita/Desktop/Bright_green_tree_-_Waikato.jpg' )
+
+	# resize to a more managable size
+	img = scipy.misc.imresize( img, (1944 / 4, 2592 / 4) ) 
+
+	# convert to grayscale
+	grayscale = img.dot( [0.299, 0.587, 0.144] ) 
+	rows, cols = shape( grayscale )
+
+	# create 256 bins histogram
+	hist = histogram( grayscale, 256 )[0]
+
+	# apply the otsu thresholding
+	thresh = otsu2( hist, rows * cols )
+
+	figure  = pyplot.figure( figsize=(14, 6) )
+	figure.canvas.set_window_title( 'Otsu thresholding' )
+
+	axes    = figure.add_subplot(121)
+	axes.set_title('Original')
+	axes.get_xaxis().set_visible( False )
+	axes.get_yaxis().set_visible( False )
+	axes.imshow( img, cmap='Greys_r' )
+
+	axes    = figure.add_subplot(122)
+	axes.set_title('Otsu thresholding')
+	axes.get_xaxis().set_visible( False )
+	axes.get_yaxis().set_visible( False )
+	axes.imshow( grayscale >= thresh, cmap='Greys_r' )
+
+	pyplot.show()
+
+if __name__ == '__main__':
+	main()