def test_bow(size=0):
kmeans = cluster()
train_data = fvloader.load_train_data(size=size, balance=False)
for item in train_data:
gene, nimg, gene_label, timestep = item
fv = bow(nimg, kmeans)
print("gene", gene, "count", timestep, "fv", fv.shape)
def test_fisher(size=0):
weights, means, covs = load_gmm()
print("weights", weights.shape)
print("means", means.shape)
print("covriances", covs.shape)
train_data = fvloader.load_train_data(size=size, balance=False)
for item in train_data:
gene, nimg, gene_label, timestep = item
fv = fisher_vector(nimg, weights, means, covs)
print("gene", gene, "count", timestep, "fv", fv.shape)
def load_fv(fv='fv0'):
if fv == 'fv0':
gmm_data = fvloader.load_train_data(size=0, balance=False)
weights, means, covs = load_gmm(gmm_data, gmmdir='gmm/fv0')
train_data = fvloader.load_train_data(size=0, balance=True)
val_data = fvloader.load_val_data(size=0)
test_data = fvloader.load_test_data(size=0)
elif fv == 'matlab':
gmm_data = matloader.load_train_data(size=0, balance=False)
weights, means, covs = load_gmm(gmm_data, gmmdir='gmm/matlab')
train_data = matloader.load_train_data(size=0, balance=True)
val_data = matloader.load_val_data(size=0)
test_data = matloader.load_test_data(size=0)
train_items = data2fisher(train_data, weights, means, covs)
val_items = data2fisher(val_data, weights, means, covs)
test_items = data2fisher(test_data, weights, means, covs)
return train_items, val_items, test_items
def load_fv(fv='fv0'):
if fv == 'fv0':
kmean_data = fvloader.load_train_data(size=0, balance=False)
kmeans = cluster(kmean_data)
train_data = fvloader.load_train_data(size=0, balance=True)
val_data = fvloader.load_val_data(size=0)
test_data = fvloader.load_test_data(size=0)
bovdir = 'bov/fv0'
elif fv == 'matlab':
kmean_data = matloader.load_train_data(size=0, balance=False)
kmeans = cluster(kmean_data)
train_data = matloader.load_train_data(size=0, balance=True)
val_data = matloader.load_val_data(size=0)
test_data = matloader.load_test_data(size=0)
bovdir = 'bov/matlab'
train_items = data2bov(train_data, kmeans, bovdir=bovdir)
val_items = data2bov(val_data, kmeans, bovdir=bovdir)
test_items = data2bov(test_data, kmeans, bovdir=bovdir)
return train_items, val_items, test_items
def load_fv(fv='fv0'):
if fv == 'fv0':
vlad_data = fvloader.load_train_data(size=0, balance=False)
kmeans = cluster(vlad_data)
train_data = fvloader.load_train_data(size=0, balance=True)
val_data = fvloader.load_val_data(size=0)
test_data = fvloader.load_test_data(size=0)
vladdir = 'vlad/fv0'
elif fv == 'matlab':
vlad_data = matloader.load_train_data(size=0, balance=False)
kmeans = cluster(vlad_data)
train_data = matloader.load_train_data(size=0, balance=True)
val_data = matloader.load_val_data(size=0)
test_data = matloader.load_test_data(size=0)
vladdir = 'vlad/matlab'
train_items = data2vlad(train_data, kmeans, vladdir=vladdir)
val_items = data2vlad(val_data, kmeans, vladdir=vladdir)
test_items = data2vlad(test_data, kmeans, vladdir=vladdir)
return train_items, val_items, test_items