def vectorization(N_theta=N_theta, N_azimuth=N_azimuth, N_eccentricity=N_eccentricity, N_phase=N_phase, N_X=N_X, N_Y=N_Y, rho=rho, ecc_max=.8, B_sf=.4, B_theta=np.pi / N_theta / 2, figure_type='', save=False): retina = np.zeros((N_theta, N_azimuth, N_eccentricity, N_phase, N_X * N_Y)) parameterfile = 'https://raw.githubusercontent.com/bicv/LogGabor/master/default_param.py' lg = LogGabor(parameterfile) lg.set_size((N_X, N_Y)) # params = {'sf_0': .1, 'B_sf': lg.pe.B_sf, # 'theta': np.pi * 5 / 7., 'B_theta': lg.pe.B_theta} # phase = np.pi/4 # edge = lg.normalize(lg.invert(lg.loggabor( # N_X/3, 3*N_Y/4, **params)*np.exp(-1j*phase))) for i_theta in range(N_theta): for i_azimuth in range(N_azimuth): for i_eccentricity in range(N_eccentricity): ecc = ecc_max * (1 / rho)**(N_eccentricity - i_eccentricity) r = np.sqrt(N_X**2 + N_Y**2) / 2 * ecc # radius sf_0 = 0.5 * 0.03 / ecc x = N_X/2 + r * \ np.cos((i_azimuth+(i_eccentricity % 2)*.5)*np.pi*2 / N_azimuth) y = N_Y/2 + r * \ np.sin((i_azimuth+(i_eccentricity % 2)*.5)*np.pi*2 / N_azimuth) for i_phase in range(N_phase): params = { 'sf_0': sf_0, 'B_sf': B_sf, 'theta': i_theta * np.pi / N_theta, 'B_theta': B_theta } phase = i_phase * np.pi / 2 # print(r, x, y, phase, params) retina[i_theta, i_azimuth, i_eccentricity, i_phase, :] = lg.normalize( lg.invert( lg.loggabor(x, y, **params) * np.exp(-1j * phase))).ravel() if figure_type == 'retina': FIG_WIDTH = 10 fig, ax = plt.subplots(figsize=(FIG_WIDTH, FIG_WIDTH)) for i_theta in range(N_theta): for i_azimuth in range(N_azimuth): for i_eccentricity in range(N_eccentricity): env = np.sqrt(retina[i_theta, i_azimuth, i_eccentricity, 0, :]**2 + retina[i_theta, i_azimuth, i_eccentricity, 1, :]**2).reshape((N_X, N_Y)) ax.contourf(env, levels=[env.max() / 1.2, env.max() / 1.00001], lw=1, colors=[plt.cm.viridis(i_theta / (N_theta))], alpha=.1) fig.suptitle('Tiling of visual space using the retinal filters') ax.set_xlabel(r'$Y$') ax.set_ylabel(r'$X$') ax.axis('equal') if save: plt.savefig('retina_filter.pdf') plt.tight_layout() return fig, ax elif figure_type == 'colliculus': FIG_WIDTH = 10 fig, ax = plt.subplots(figsize=(FIG_WIDTH, FIG_WIDTH)) for i_azimuth in range(N_azimuth): for i_eccentricity in range(N_eccentricity): env = np.sqrt(colliculus[i_azimuth, i_eccentricity, :]**2.5).reshape( (N_X, N_Y)) #ax.contour(colliculus[i_azimuth, i_eccentricity, :].reshape((N_X, N_Y)), levels=[env.max()/2], lw=1, colors=[plt.cm.viridis(i_theta/(N_theta))]) ax.contourf( env, levels=[env.max() / 1.2, env.max() / 1.00001], lw=1, colors=[plt.cm.viridis(i_eccentricity / (N_eccentricity))], alpha=.1) fig.suptitle('Tiling of visual space using energy') ax.set_xlabel(r'$Y$') ax.set_ylabel(r'$X$') ax.axis('equal') plt.tight_layout() if save: plt.savefig('colliculus_filter.pdf') return fig, ax else: return retina
def generate_gabors_coordinates(theta, params, N_X, N_Y, centers_coordinates, B_theta=15, sf_0=.05, B_sf=.5, distrib_size=8, grid_res=3, on_thresh=.1, off_thresh=-.1, verbose=True): ''' Given some gabor parameters, a set of coordinates for centering gabors, returns a set of coordinates for filters belonging into the gabors Params : theta : gabor theta angle params : the default parameters dictionnary for the gabor generation N_X, N_Y : Gabor size, usually the same as the video centers_coordinates : a 2D array giving the centers of each gabor B_theta, sf_0, B_sf : Parameters for the LogGabor shape B_theta is the opening of the gabor, sf_0 is the spatial frequency b_sf is the bandwidth frequency distrib_size : the size of each group of filters, in image coordinates grid_res : resolution of the group of filters, passed in a np.mgrid on_thresh, off_thresh : threshold at which a filter is selected to be on/off, by scanning the Gabor phi-space verbose : display the filter size as a sanity check ''' xs = centers_coordinates[0] ys = centers_coordinates[1] nbr_gabors = len(xs) N_X = int(N_X) N_Y = int(N_Y) N_phase = 2 lg = LogGabor(params) lg.set_size((N_X, N_Y)) B_theta = B_theta / 180 * np.pi params = {'sf_0': sf_0, 'B_sf': B_sf, 'B_theta': B_theta} params.update(theta=theta) phi = np.zeros((1, N_phase, N_X, N_Y)) filters_per_gab = [] for gab in range(nbr_gabors): x = xs[gab] y = ys[gab] for i_phase in range(N_phase): phase = i_phase * np.pi/2 kernel = lg.invert(lg.loggabor( x, y, **params)*np.exp(-1j*phase)) phi[0, i_phase, :] = lg.normalize(kernel) fx_min = x - distrib_size fx_max = x + distrib_size fy_min = y - distrib_size fy_max = y + distrib_size filters_coordinates = np.mgrid[fx_min:fx_max:grid_res, fy_min:fy_max:grid_res].reshape(2, -1).T if verbose and gab == 0: print('Thread started !\nFilter grid shape', filters_coordinates.shape, '\n') filters_in_gabor = gabor_connectivity(filters=filters_coordinates, phi=phi, theta=0, threshold=on_thresh) off_filters_in_gabor = gabor_connectivity(filters=filters_coordinates, phi=phi, theta=0, threshold=off_thresh, on=False) filters_per_gab.append((filters_in_gabor, off_filters_in_gabor)) return filters_per_gab