# ==================================================================================================#
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 )
# ==================================================================================================#
mps, experiments = [], []
v_B_theta = np.logspace(-.5, .5, 5, base=10, endpoint=True)*mp.pe.B_theta
for B_theta in v_B_theta:
mp = init_mp()
mp.pe.B_theta = B_theta
exp = 'efficiency_B_theta_' + str(B_theta).replace('.', '_')
mp.process(exp)
experiments.append(exp)
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)
experiments.append(exp)
mps.append(mp)
import matplotlib.pyplot as plt
fig_width_pt = 900 #318.670*.61 # 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
threshold = None
threshold = .25
databases = ['serre07_distractors'] * len(experiments)
labels = ['%0.2f' % MP_alpha for MP_alpha in v_alpha]
fig = plt.figure(figsize=(fig_width, fig_width/1.618))
fig, a, ax = mp.plot(mps=mps,
experiments=experiments, databases=databases, labels=labels,
fig=fig, color=[0., 1., 0.], threshold=threshold, scale=True)
a.set_xlabel(r' $\alpha$')
import os
for ext in FORMATS: fig.savefig(os.path.join(mp.pe.figpath, 'testing_alpha.' + ext))
## TODO: would be interesting to see how that changes with number of image patches used, i.e. whether it settles down to that particular pattern or just jumps around.
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)
FORMATS = ['pdf', 'eps']
for ext in FORMATS: fig.savefig(mps[0].pe.figpath + 'SparseLets_B.' + ext)
