def build_codebook_kmeans_online(bag,
codebook_size,
desc_names,
standardize=False):
desc = [np.array(bag[dn_])
for dn_ in desc_names] # ensures a strict ordering
X = np.hstack(desc) # put all feature vectors in an array
if standardize:
# make sure each variable (column) is mean-centered and has unit standard deviation
Xm = np.mean(X, axis=0)
Xs = np.std(X, axis=0)
Xs[np.isclose(Xs, 1e-16)] = 1.0
X = (X - Xm) / Xs
if codebook_size is None:
# try to estimate a suitable codebook size based on gap statistic:
codebook_size, _ = gap(X,
Ks=np.linspace(start=10,
stop=100,
num=10,
dtype=np.int32),
Wstar=None,
B=20)
print("Best codebook size:", codebook_size)
rng = np.random.RandomState(0)
vq = MiniBatchKMeans(n_clusters=codebook_size,
random_state=rng,
batch_size=500,
compute_labels=True,
verbose=True) # vector quantizer
vq.fit(X)
return vq
def build_codebook_kmeans_online(bag, codebook_size, desc_names, standardize=False):
desc = [np.array(bag[dn_]) for dn_ in desc_names] # ensures a strict ordering
X = np.hstack(desc) # put all feature vectors in an array
if standardize:
# make sure each variable (column) is mean-centered and has unit standard deviation
Xm = np.mean(X, axis=0)
Xs = np.std(X, axis=0)
Xs[np.isclose(Xs, 1e-16)] = 1.0
X = (X - Xm) / Xs
if codebook_size is None:
# try to estimate a suitable codebook size based on gap statistic:
codebook_size,_ = gap(X, Ks=np.linspace(start=10, stop=100, num=10, dtype=np.int32),
Wstar=None, B=20)
print("Best codebook size:", codebook_size)
rng = np.random.RandomState(0)
vq = MiniBatchKMeans(n_clusters=codebook_size, random_state=rng,
batch_size=500, compute_labels=True, verbose=True) # vector quantizer
vq.fit(X)
return vq
def main():
p = opt.ArgumentParser(description="""
Builds a codebook based on a set of local descriptors, previously
computed.
""")
p.add_argument('config', action='store', help='a configuration file')
args = p.parse_args()
cfg_file = args.config
parser = SafeConfigParser()
parser.read(cfg_file)
if not parser.has_section('codebook'):
raise ValueError('"codebook" section is mandatory')
codebook_size = ast.literal_eval(parser.get('codebook', 'size'))
if isinstance(codebook_size, list):
# expect 3 values:
if len(codebook_size) != 3:
raise ValueError('Wrong codebook size specification')
codebook_size = np.linspace(*codebook_size, dtype=np.int32)
elif isinstance(codebook_size, int):
if codebook_size <= 0:
raise ValueError('Wrong codebook size specification')
else:
raise ValueError('Wrong codebook size specification')
verbose = False
if parser.has_option('codebook', 'verbose'):
verbose = parser.getboolean('codebook', 'verbose')
standardize = True
if parser.has_option('codebook', 'standardize_features'):
standardize = parser.getboolean('codebook', 'standardize_features')
result_file = 'output.dat'
if parser.has_option('codebook', 'result'):
result_file = parser.get('codebook', 'result')
# Read the various features:
big_bag = {}
img_names = [] # for each region, the image it belongs to
all_regs = [] # all regions
descriptors = []
for desc_name in [
'gabor', 'haar', 'identity', 'stats', 'hist', 'hog', 'lbp'
]:
if not parser.has_option('codebook', desc_name):
continue
feat_files = []
with open(parser.get('codebook', desc_name), 'r') as f:
feat_files = f.readlines()
if len(feat_files) == 0:
raise UserWarning('No files specified for ' + desc_name +
' feature.')
if verbose:
print('Reading', desc_name)
descriptors.append(desc_name)
desc_values = [
] # all values for this descriptor will be concatenated in a single list
for f in feat_files:
f = f.strip()
if len(f) == 0:
continue
if verbose:
print('\t', f)
bag = read_bag(f, desc_name)
desc_values.extend(bag[desc_name])
if len(big_bag) == 0:
# since the image names and regions are the same (in the same order too) for all
# feature types (gabor, haar,...) it makes sense to add them only once, when the "big_bag"
# is still empty (for the 1st feature type read)
img_names.extend([f] * len(
bag[desc_name])) # for each feature, add the image name
all_regs.extend(bag['regs'])
if len(big_bag) == 0:
# 1st time store some values:
big_bag['regs'] = all_regs
big_bag['fname'] = img_names
big_bag[desc_name] = desc_values
if verbose:
print("Read", len(descriptors), "feature type(s) with a total of",
len(big_bag['regs']), "regions.")
print('\nBuilding codebook:')
desc = [np.array(bag[dn_])
for dn_ in descriptors] # ensures a strict ordering
X = np.hstack(desc) # put all feature vectors in an array
Xm = np.zeros((X.shape[1], ), dtype=np.float32)
Xs = np.ones((X.shape[1], ), dtype=np.float32)
if standardize:
# make sure each variable (column) is mean-centered and has unit standard deviation
Xm = np.mean(X, axis=0)
Xs = np.std(X, axis=0)
Xs[np.isclose(Xs, 1e-16)] = 1.0
X = (X - Xm) / Xs
if not isinstance(codebook_size, int):
# try to estimate a suitable codebook size based on gap statistic:
codebook_size, _ = gap(X, Ks=codebook_size, Wstar=None, B=20)
if verbose:
print("\tBest codebook size:", codebook_size)
rng = np.random.RandomState(0)
vq = MiniBatchKMeans(n_clusters=codebook_size,
random_state=rng,
batch_size=500,
compute_labels=False,
verbose=True) # vector quantizer
vq.fit(X)
with ModelPersistence(result_file, 'c', format='pickle') as d:
d['codebook'] = vq
d['shift'] = Xm
d['scale'] = Xs
d['standardize'] = standardize
return True
def main():
p = opt.ArgumentParser(description="""
Builds a codebook based on a set of local descriptors, previously
computed.
""")
p.add_argument('config', action='store', help='a configuration file')
args = p.parse_args()
cfg_file = args.config
parser = SafeConfigParser()
parser.read(cfg_file)
if not parser.has_section('codebook'):
raise ValueError('"codebook" section is mandatory')
codebook_size = ast.literal_eval(parser.get('codebook', 'size'))
if isinstance(codebook_size, list):
# expect 3 values:
if len(codebook_size) != 3:
raise ValueError('Wrong codebook size specification')
codebook_size = np.linspace(*codebook_size, dtype=np.int32)
elif isinstance(codebook_size, int):
if codebook_size <= 0:
raise ValueError('Wrong codebook size specification')
else:
raise ValueError('Wrong codebook size specification')
verbose = False
if parser.has_option('codebook', 'verbose'):
verbose = parser.getboolean('codebook', 'verbose')
standardize = True
if parser.has_option('codebook', 'standardize_features'):
standardize = parser.getboolean('codebook', 'standardize_features')
result_file = 'output.dat'
if parser.has_option('codebook', 'result'):
result_file = parser.get('codebook', 'result')
# Read the various features:
big_bag = {}
img_names = [] # for each region, the image it belongs to
all_regs = [] # all regions
descriptors = []
for desc_name in ['gabor', 'haar', 'identity', 'stats', 'hist', 'hog', 'lbp']:
if not parser.has_option('codebook', desc_name):
continue
feat_files = []
with open(parser.get('codebook', desc_name), 'r') as f:
feat_files = f.readlines()
if len(feat_files) == 0:
raise UserWarning('No files specified for ' + desc_name + ' feature.')
if verbose:
print('Reading', desc_name)
descriptors.append(desc_name)
desc_values = [] # all values for this descriptor will be concatenated in a single list
for f in feat_files:
f = f.strip()
if len(f) == 0:
continue
if verbose:
print('\t', f)
bag = read_bag(f, desc_name)
desc_values.extend(bag[desc_name])
if len(big_bag) == 0:
# since the image names and regions are the same (in the same order too) for all
# feature types (gabor, haar,...) it makes sense to add them only once, when the "big_bag"
# is still empty (for the 1st feature type read)
img_names.extend([f] * len(bag[desc_name])) # for each feature, add the image name
all_regs.extend(bag['regs'])
if len(big_bag) == 0:
# 1st time store some values:
big_bag['regs'] = all_regs
big_bag['fname'] = img_names
big_bag[desc_name] = desc_values
if verbose:
print("Read", len(descriptors), "feature type(s) with a total of", len(big_bag['regs']), "regions.")
print('\nBuilding codebook:')
desc = [np.array(bag[dn_]) for dn_ in descriptors] # ensures a strict ordering
X = np.hstack(desc) # put all feature vectors in an array
Xm = np.zeros((X.shape[1],), dtype=np.float32)
Xs = np.ones((X.shape[1],), dtype=np.float32)
if standardize:
# make sure each variable (column) is mean-centered and has unit standard deviation
Xm = np.mean(X, axis=0)
Xs = np.std(X, axis=0)
Xs[np.isclose(Xs, 1e-16)] = 1.0
X = (X - Xm) / Xs
if not isinstance(codebook_size, int):
# try to estimate a suitable codebook size based on gap statistic:
codebook_size,_ = gap(X, Ks=codebook_size, Wstar=None, B=20)
if verbose:
print("\tBest codebook size:", codebook_size)
rng = np.random.RandomState(0)
vq = MiniBatchKMeans(n_clusters=codebook_size, random_state=rng,
batch_size=500, compute_labels=False, verbose=True) # vector quantizer
vq.fit(X)
with ModelPersistence(result_file, 'c', format='pickle') as d:
d['codebook'] = vq
d['shift'] = Xm
d['scale'] = Xs
d['standardize'] = standardize
return True
