#! /usr/bin/env python
# -*- coding: utf-8 -*-
#
#  Copyright (C) Mar 2011 Angelos Tzotsos <tzotsos@gmail.com>		  

#  This program is free software: you can redistribute it and/or modify
#  it under the terms of the GNU General Public License as published by
#  the Free Software Foundation, either version 3 of the License, or
#  (at your option) any later version.

#  This program is distributed in the hope that it will be useful,
#  but WITHOUT ANY WARRANTY; without even the implied warranty of
#  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
#  GNU General Public License for more details.

#  You should have received a copy of the GNU General Public License
#  along with this program.  If not, see <http://www.gnu.org/licenses/>

import numpy
import scipy
import pylab
import matplotlib
from scipy import ndimage
from scipy import signal

###open image
img = scipy.misc.imread('lena_std_gray.tif')

###show histogram from matplotlib
hist, bins, patches = matplotlib.pyplot.hist(img.flatten(), 256)
print hist
matplotlib.pyplot.show()

###gaussian filter
img_g = ndimage.gaussian_filter(img, 10)

###rank filter
img_rnk = ndimage.filters.rank_filter(img,rank=3, size=5)

###median filter
img_med = ndimage.filters.median_filter(img,size=3)

###maximum filter
img_max = ndimage.filters.maximum_filter(img, size=3)
maxima = img_max - img

###prewitt filter
img_pwt = numpy.zeros(img.shape, dtype=float)
ndimage.filters.prewitt(img, output=img_pwt)

###sobel filter
img_sobel = numpy.zeros(img.shape, dtype=float)
ndimage.filters.sobel(img, axis=-1, output=img_sobel)

###laplacian filter
img_gen_laplace = numpy.zeros(img.shape, dtype=float)
ndimage.filters.laplace(img, output=img_gen_laplace)

###log
img_log = numpy.zeros(img.shape, dtype=float)
ndimage.filters.gaussian_laplace(img, sigma=1, output=img_log)

###gaussian gradient
img_ggrand = numpy.zeros(img.shape, dtype=float)
ndimage.filters.gaussian_gradient_magnitude(img, sigma=3, output=img_ggrand)

###sobel gradient
img_sgrad = numpy.zeros(img.shape, dtype=float)
ndimage.filters.generic_gradient_magnitude(img, ndimage.filters.sobel, output=img_sgrad)

###generic filter
fltr = numpy.array([[1,2,3],[3,2,1],[2,2,2]],float)
deriv = signal.convolve2d(img, fltr, mode='same',boundary='symm')

###show result
matplotlib.pyplot.imshow(img)
matplotlib.pyplot.gray()
matplotlib.pyplot.show()

matplotlib.pyplot.imshow(deriv)
matplotlib.pyplot.gray()
matplotlib.pyplot.show()