import matplotlib
matplotlib.use("Agg") # agg-backend, so we can create figures without x-server (no PDF, just PNG etc.)
from SparseEdges import SparseEdges
FORMATS = ['pdf', 'eps']
# TODO: here, we are more interested in the processing of the database, not the comparison - use the correct function
# TODO : annotate the efficiency of different LogGabor bases (RMSE?)
# TODO: make a circular mask to avoid border effects coming with whitening...
#! comparing databases
#!--------------------
mp = SparseEdges('https://raw.githubusercontent.com/bicv/SparseEdges/master/default_param.py')
mp.N = 128
mp.pe.datapath = '../../SLIP/database/'
mp.process('testing_vanilla')
# TODO: CRF
mp.process('testing_noise', noise=mp.pe.noise)
mp.process('testing_vanilla', name_database='serre07_targets')
# TODO : make an experiment showing that using scale does not bring much
mps, experiments = [], []
v_alpha = np.linspace(0.3, 1., 9)
for MP_alpha in v_alpha:
mp = SparseEdges('https://raw.githubusercontent.com/bicv/SparseEdges/master/default_param.py')
mp.N = 128
mp.pe.datapath = '../../SLIP/database/'
mp.pe.MP_alpha = MP_alpha
mp.init()
exp = 'testing_MP_alpha_' + str(MP_alpha).replace('.', '_')
mp.process(exp)
import matplotlib.pyplot as plt
fig_width_pt = 800 #318.67085 # Get this from LaTeX using \showthe\columnwidth
inches_per_pt = 1.0/72.27 # Convert pt to inches
fig_width = fig_width_pt*inches_per_pt # width in inches
# ==================================================================================================#
fig, [[A, B], [C, D]] = plt.subplots(2, 2, figsize=(fig_width, fig_width), subplot_kw={'axisbg':'w'})
# ==================================================================================================#
mps, experiments = [], []
v_B_sf = np.logspace(-.2, .2, 5, base=10, endpoint=True)*mp.pe.B_sf
for B_sf in v_B_sf:
mp = init_mp()
mp.pe.B_sf = B_sf
exp = 'efficiency_B_sf_' + str(B_sf).replace('.', '_')
mp.process(exp)
experiments.append(exp)
mps.append(mp)
databases = ['serre07_distractors'] * len(experiments)
labels = ['%0.2f' % B_sf for B_sf in v_B_sf]
try:
fig, A, inset = mp.plot(mps=mps,
experiments=experiments, databases=databases, labels=labels, ref=2,
fig=fig, ax=A, color=[0., 1., 0.], threshold=threshold, scale=False)
A.set_xlabel(r'frequency bandwith $B_{sf}$')
#A.set_yticks([0., 0.02, 0.04, 0.06])
except Exception as e:
print('Failed to plot with error : %s ' % e )
# ==================================================================================================#
rm -fr **/prior_firstorder* **/**/prior_firstorder*
"""
import numpy as np
from SparseEdges import SparseEdges
for name_database in ['serre07_distractors']:#, 'serre07_distractors_urban', 'laboratory']:
mp = SparseEdges('https://raw.githubusercontent.com/bicv/SparseEdges/master/default_param.py')
mp.pe.datapath = '../../SLIP/database/'
mp.pe.seed = 21341353 # this ensures that all image lists are the same for the different experiments
mp.pe.N_image = 20
mp.pe.N = 1024
# control experiment
#mp.theta = np.linspace(-np.pi/2, np.pi/2, mp.n_theta+1)[1:]
imageslist, edgeslist, RMSE = mp.process(exp='prior_vanilla', name_database=name_database)
imageslist_noise, edgeslist_noise, RMSE_noise = mp.process(exp='prior_vanilla_noise_' + str(mp.pe.noise).replace('.', '_'), name_database=name_database, noise=mp.pe.noise)
if True:#try:
six, N, N_image = edgeslist.shape
# first-order prior
v_hist, v_theta_edges = mp.histedges_theta(edgeslist, display=False)
v_theta_middles, v_theta_bin = (v_theta_edges[1:]+v_theta_edges[:-1])/2, v_theta_edges[1]-v_theta_edges[0]
v_hist, v_theta_edges = mp.histedges_theta(edgeslist, display=False)
v_theta_middles, v_theta_bin = (v_theta_edges[1:]+v_theta_edges[:-1])/2, v_theta_edges[1]-v_theta_edges[0]
z = np.linspace(0, 1., mp.pe.n_theta+1)
P = np.cumsum(np.hstack((0, v_hist[-1]/2, v_hist[:-1], v_hist[-1]/2)))
theta_prior = np.interp(z, P, np.hstack((v_theta_edges[0]-v_theta_bin/2, v_theta_edges[:-1], v_theta_edges[-1]-v_theta_bin/2)))
FORMATS = ['pdf', 'eps']
mps = []
sizes = [16, 32, 64, 128, 256]
N_image = 32
N = 1024
for size, size_str in zip(sizes, ['_016', '_032', '_064', '_128', '']):
mp = SparseEdges('https://raw.githubusercontent.com/bicv/SparseEdges/master/default_param.py')
mp.pe.seed = 42
mp.pe.datapath = '../../SLIP/database/'
mp.set_size((size, size))
downscale_factor = sizes[-1]/size # > 1
mp.pe.N_image = int(N_image*downscale_factor)
mp.pe.N = int(N/downscale_factor**2)
mp.init()
mp.process('SparseLets' + size_str)
mps.append(mp)
import matplotlib.pyplot as plt
fig_width_pt = 600 # Get this from LaTeX using \showthe\columnwidth
inches_per_pt = 1.0/72.27 # Convert pt to inches
fig_width = fig_width_pt*inches_per_pt # width in inches
fig = plt.figure(figsize=(fig_width, fig_width/1.618))
sizes = [16, 32, 64, 128, 256]
experiments = ['SparseLets_' + '%0.3d' % size for size in sizes]
experiments[-1] = 'SparseLets'
databases = ['serre07_distractors'] * len(experiments)
labels = [str(size) for size in sizes]
fig, ax, inset = mp.plot(fig=fig, mps=mps, experiments=experiments, databases=databases,
labels=labels, scale=True)
"""
$ python experiment_retina_sparseness.py
rm -fr **/retina_sparseness* **/**/retina_sparseness*
"""
import numpy as np
from SparseEdges import SparseEdges
mps = []
for name_database in ["serre07_distractors"]: # , 'serre07_distractors_urban', 'laboratory']:
mp = SparseEdges("https://raw.githubusercontent.com/bicv/SparseEdges/master/default_param.py")
mp.pe.datapath = "../../SLIP/database/"
mp.pe.N_image = 100
mp.pe.do_mask = True
mp.pe.N = 2 ** 12
mp.pe.n_theta = 1
mp.pe.B_theta = np.inf
mp.pe.line_width = 0
mp.init()
# normal experiment
imageslist, edgeslist, RMSE = mp.process(exp="retina_sparseness", name_database=name_database)
mps.append(mp)
# control experiment
if True:
mp.pe.MP_alpha = np.inf
mp.init()
imageslist, edgeslist, RMSE = mp.process(exp="retina_sparseness_linear", name_database=name_database)
mps.append(mp)
