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
def get_gabor_features_texture_classification(image_file_path, num_levels,
num_orientations):
# image = imread(image_file_path)
# image = skimage.io.imread(image_file_path)
print 'length of image'
print len(image_file_path.shape)
if (len(image_file_path.shape) == 3):
image = image_file_path[:, :, 0]
else:
image = image_file_path[:, :]
opts = {
'vmin': 0.,
'vmax': 1.,
'interpolation': 'nearest',
'origin': 'upper'
}
print image.shape
# image = image_file_path
# image = image[30:70,30:70]
sum_sqr_num_feat = num_levels * num_orientations
sum_sqr_feat_vec = np.zeros((1, sum_sqr_num_feat))
print 'shape of sum sqr vec is:'
print sum_sqr_feat_vec.shape
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
first_response = None
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:
first_response = im_abs_feature.reshape(
1, im_abs_feature.shape[0])
# print type(im_abs_feature)
# print im_abs_feature.shape
# print im_abs_feature
# print LA.norm(im_abs_feature.reshape(1, im_abs_feature.shape[0]))
sum_sqr_feat_vec[0, i] = LA.norm(
im_abs_feature.reshape(1, im_abs_feature.shape[0]))
# print 'L2 norm squared is:'
# print sum_sqr_feat_vec[0,i]
# if i == 0:
# feature_vec = im_abs_feature
# else:
# feature_vec = np.hstack((feature_vec, im_abs_feature))
i += 1
print
return sum_sqr_feat_vec
