def get_gabor_filters(params):
""" Return a Gabor filterbank (generate it if needed)
Inputs:
params -- filters parameters (dict)
Outputs:
filt_l -- filterbank (list)
"""
global filt_l
if filt_l is not None:
return filt_l
# -- get parameters
fh, fw = params['kshape']
orients = params['orients']
freqs = params['freqs']
phases = params['phases']
nf = len(orients) * len(freqs) * len(phases)
fbshape = nf, fh, fw
xc = fw/2
yc = fh/2
filt_l = []
# -- build the filterbank
for freq in freqs:
for orient in orients:
for phase in phases:
# create 2d gabor
filt = gabor2d(xc,yc,xc,yc,
freq,orient,phase,
(fw,fh))
filt_l += [filt]
return filt_l
def get_filterbank(config):
model_config = config
config = config['filter']
model_name = config['model_name']
fh, fw = config.get('kshape',config.get('ker_shape'))
if model_name == 'really_random':
num_filters = config['num_filters']
filterbank = get_random_filterbank((num_filters,fh,fw),normalization=config.get('normalize',True))
elif model_name == 'random_gabor':
num_filters = config['num_filters']
xc = fw/2
yc = fh/2
filterbank = np.empty((num_filters,fh,fw))
orients = []
freqs = []
phases = []
df = config.get('divfreq')
for i in range(num_filters):
orient = config.get('orient',2*np.pi*np.random.random())
orients.append(orient)
if not df:
freq = 1./np.random.randint(config['min_wavelength'],high = config['max_wavelength'])
else:
freq = 1./df
freqs.append(freq)
phase = config.get('phase',2*np.pi*np.random.random())
phases.append(phase)
filterbank[i,:,:] = v1m.gabor2d(xc,yc,xc,yc,
freq,orient,phase,
(fw,fh))
#return SON([('filterbank',filterbank),('orients',orients),('phases',phases),('freqs',freqs)])
return filterbank
elif model_name == 'gridded_gabor':
norients = config['norients']
orients = [ o*np.pi/norients for o in xrange(norients) ]
divfreqs = config['divfreqs']
freqs = [1./d for d in divfreqs]
phases = config['phases']
xc = fw/2
yc = fh/2
values = list(itertools.product(freqs,orients,phases))
num_filters = len(values)
filterbank = np.empty((num_filters,fh,fw))
for (i,(freq,orient,phase)) in enumerate(values):
filterbank[i,:,:] = v1m.gabor2d(xc,yc,xc,yc,
freq,orient,phase,
(fw,fh))
elif model_name == 'pixels':
return np.ones((1,fh,fw))
elif model_name == 'specific_gabor':
orients = config['orients']
divfreqs = config['divfreqs']
phases = config['phases']
xc = fw/2
yc = fh/2
freqs = [1./d for d in divfreqs]
values = zip(freqs,orients,phases)
num_filters = len(values)
filterbank = np.empty((num_filters,fh,fw))
for (i,(freq,orient,phase)) in enumerate(values):
filterbank[i,:,:] = v1m.gabor2d(xc,yc,xc,yc,
freq,orient,phase,
(fw,fh))
elif model_name == 'cairo_generated':
specs = config.get('specs')
if not specs:
specs = [spec['image'] for spec in rendering.cairo_config_gen(config['spec_gen'])]
filterbank = np.empty((len(specs),fh,fw))
for (i,spec) in enumerate(specs):
im_fh = rendering.cairo_render(spec,returnfh=True)
arr = processing.image2array({'color_space':'rgb'},im_fh).astype(np.int32)
arr = arr[:,:,0] - arr[:,:,1]
arrx0 = arr.shape[0]/2
arry0 = arr.shape[1]/2
dh = fh/2; dw = fw/2
filterbank[i,:,:] = normalize(arr[arrx0-dh:arrx0+(fh - dh),arry0-dw:arry0+(fw-dw)])
elif model_name == 'center_surround':
if config.get('orth',True):
return center_surround_orth(model_config)
else:
return center_surround(model_config)
filterbank = np.cast[np.float32](filterbank)
return filterbank