def get_shape_of_filtered_image(image_file_path):
image = imread(image_file_path)
lg = LogGabor('default_param.py')
lg.set_size(image)
i_level = 0
theta = 0
params = {
'sf_0': 1. / (2**i_level),
'B_sf': lg.pe.B_sf,
'theta': theta,
'B_theta': lg.pe.B_theta
}
FT_lg = lg.loggabor(0, 0, **params)
filtered_img_mat = lg.FTfilter(image, FT_lg, full=True)
return filtered_img_mat.shape
def get_gabor_features(image_file_path, num_levels, num_orientations):
image = imread(image_file_path)
opts = {
'vmin': 0.,
'vmax': 1.,
'interpolation': 'nearest',
'origin': 'upper'
}
# image = skimage.io.imread
lg = LogGabor('default_param.py')
lg.set_size(image)
# num_features = num_levels*num_orientations*2
# feature_vec = np.zeros((num_features,1))
# phi = (np.sqrt(5) +1.)/2. # golden number
# fig = plt.figure(figsize=(fig_width, fig_width/phi))
# xmin, ymin, size = 0, 0, 1.
i = 0
for i_level in range(num_levels):
# for theta in np.linspace(0, np.pi, num_orientations, endpoint=False):
for theta in np.linspace(0, np.pi, num_orientations, endpoint=False):
params = {
'sf_0': 1. / (2**i_level),
'B_sf': lg.pe.B_sf,
'theta': theta,
'B_theta': lg.pe.B_theta
}
# loggabor takes as args: u, v, sf_0, B_sf, theta, B_theta)
FT_lg = lg.loggabor(0, 0, **params)
filtered_img_mat = lg.FTfilter(image, FT_lg, full=True)
# print "SHAPE OF FILTERED IMAGE IS (%s, %s)" % filtered_img_mat.shape
im_abs_feature = np.absolute(filtered_img_mat).flatten()
# im_abs_feature = np.sum(np.absolute(filtered_img_mat))
# im_sqr_feature = np.sum(np.square(np.real(filtered_img_mat)))
# im_sqr_feature = np.sum(np.square(filtered_img_mat))
if i == 0:
feature_vec = im_abs_feature
else:
feature_vec = np.hstack((feature_vec, im_abs_feature))
i += 1
print
return feature_vec
if whoswho:
return dictx_fit, dictx_fit_param, idx_unfitted, names
else:
return dictx_fit, dictx_fit_param, idx_unfitted
else:
if whoswho:
return dictx_fit, dictx_fit_param, names
else:
return dictx_fit, dictx_fit_param
def _test():
import doctest
doctest.testmod()
#####################################
#
if __name__ == '__main__':
_test()
#### Main
"""
Some examples of use for the class
"""
lg = LogGabor('./lg_para.py')
from SLIP import imread
image = imread('database/lena512.png')[:, :, 0]
lg.set_size(image)
self.trans(u*1., v*1.)
if preprocess : env *= self.f_mask
# normalizing energy:
env /= np.sqrt((np.abs(env)**2).mean())
# in the case a a single bump (see ``orientation``), we should compensate the fact that the distribution gets complex:
env *= np.sqrt(2.)
return env
def show_loggabor(self, u, v, sf_0, B_sf, theta, B_theta, title='', phase=0.):
FT_lg = self.loggabor(u, v, sf_0, B_sf, theta, B_theta)
fig, a1, a2 = self.show_FT(FT_lg * np.exp(-1j*phase))
return fig, a1, a2
def _test():
import doctest
doctest.testmod()
#####################################
#
if __name__ == '__main__':
_test()
#### Main
"""
Some examples of use for the class
"""
lg = LogGabor('default_param.py')
image = imread('database/lena512.png')[:,:,0]
lg.set_size(image)