def render_image(config):
config = config['image'].copy()
generator = config.pop('generator')
if generator == 'renderman':
return renderman_render(config)
elif generator == 'cairo':
return cairo_render(config)
else:
raise ValueError, 'image generator not recognized'
def center_surround_orth(model_config):
conv_mode = model_config['conv_mode']
L = np.empty((2*len( model_config['filter']['base_images'] ),) + tuple(model_config['filter']['kshape']) )
for (ind,image_config) in enumerate(model_config['filter']['base_images']):
image_fh = rendering.cairo_render(image_config,returnfh=True)
#preprocessing
array = processing.image2array(model_config ,image_fh)
preprocessed,orig_imga = processing.preprocess(array,model_config )
norm_in = norm(preprocessed,conv_mode,model_config.get('normin'))
array = make_array(norm_in)
array = array[:,:,:2].max(2).astype(np.float)
arr_box = cut_bounding_box(array)
s = model_config['filter']['kshape']
X = np.zeros(s)
(hx,wx) = X.shape
(ha,wa) = arr_box.shape
hx0 = max((hx - ha) / 2, 0)
hx1 = min(ha + hx0,hx)
ha0 = max((ha - hx) / 2, 0)
ha1 = min(hx + ha0, ha)
wx0 = max((wx - wa) / 2, 0)
wx1 = min(wa + wx0,wx)
wa0 = max((wa - wx) / 2, 0)
wa1 = min(wx + wa0, wa)
X[hx0:hx1, wx0:wx1] = arr_box[ha0:ha1, wa0:wa1]
X = normalize(X)
L[2*ind,:,:] = X
L[2*ind + 1,:,:] = X.T
return 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
