__author__ = "(c) Laurent Perrinet INT - CNRS"
import numpy as np
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()
To run:
$ python experiment_example.py
To remove cache:
$ rm -fr **/example*
"""
__author__ = "(c) Laurent Perrinet INT - CNRS"
import numpy as np
from SparseEdges import SparseEdges
mp = SparseEdges("https://raw.githubusercontent.com/bicv/SparseEdges/master/default_param.py")
mp.N = 128
image = mp.imread("https://raw.githubusercontent.com/bicv/SparseEdges/master/database/lena256.png")
name = "example"
image = mp.normalize(image, center=True)
# print image.mean(), image.std()
import os
matname = os.path.join(mp.pe.matpath, name + ".npy")
try:
edges = np.load(matname)
except:
edges, C_res = mp.run_mp(image, verbose=True)
np.save(matname, edges)
import numpy as np
from SparseEdges import SparseEdges
mp = SparseEdges('https://raw.githubusercontent.com/bicv/SparseEdges/master/default_param.py')
mp.N = 128 # number of edges
mp.pe.figsize_edges = 9
#! defining a reference test image (see test_Image)
image = np.zeros((mp.pe.N_X, mp.pe.N_Y))
image[mp.pe.N_X/2:mp.pe.N_X/2+mp.pe.N_X/4, mp.pe.N_X/2:mp.pe.N_X/2+mp.pe.N_X/4] = 1
image[mp.pe.N_X/2:mp.pe.N_X/2+mp.pe.N_X/4, mp.pe.N_X/4:mp.pe.N_X/2] = -1
import os
matname = os.path.join(mp.pe.matpath, 'experiment_test_MP.npy')
try:
edges = np.load(matname)
except:
edges, C_res = mp.run_mp(image, verbose=False)
np.save(matname, edges)
fig, a = mp.show_edges(edges, image=mp.whitening(image))
