def __init__(self,pc_file_u,pc_file_v,
covfile,
covfile_sublayer=None,
pc_size=-1,
params={},
preset=None):
"""
Initialize PCAFlow object.
Parameters
----------
pc_file_u, pc_file_v : string
Files containing the principal components in horizontal and
vertical direction, respectively.
These files should be .npy files, in which each row is a flattened
principal component (i.e., the total size of these principal
component matrices is NUM_PC x (WIDTH*HEIGHT).
cov_file : string
File containing the covariance matrix of size NUM_PC x NUM_PC for
PCA-Flow.
covfile_sublayer : string, optional
File containing the covariance matrix for the layers (usually
biased towards the first PCs).
If PCA-Layers is used and this file is not given, use cov_file.
pc_size : tuple, optional
Size of principal components. Only required if PCs are not of size
512x256 or 1024x436.
params : dict, optional
Parameters. See parameters.py for documentation of parameters.
preset : string
Preset with useful parameter values for different datasets.
Can be one of
'pcaflow_sintel'
'pcalayers_sintel'
'pcaflow_kitti'
'pcalayers_kitti'
"""
np.random.seed(1)
self.params = defaults.get_parameters(params,preset)
cprint('[PCAFlow] Initializing.', self.params)
NC = int(self.params['NC'])
self.NC = NC
pc_u = np.load(pc_file_u)
pc_v = np.load(pc_file_v)
cov_matrix = np.load(covfile).astype('float32')
if covfile_sublayer is not None:
cov_matrix_sublayer = np.load(covfile_sublayer).astype('float32')
else:
cov_matrix_sublayer = None
pc_w = 0
pc_h = 0
if pc_size==-1:
# Try to guess principal component dimensions
if pc_u.shape[1] == 1024*436:
cprint('[PCAFLOW] Using PC dimensionality 1024 x 436', self.params)
pc_w = 1024
pc_h = 436
elif pc_v.shape[1] == 512*256:
cprint('[PCAFLOW] Using PC dimensionality 512 x 256', self.params)
pc_w = 512
pc_h = 256
else:
print('[PCAFLOW] *** ERROR *** ')
print('[PCAFLOW] Could not guess dimensionality of principal components.')
print('[PCAFLOW] Please provide as parameter.')
sys.exit(1)
self.PC = []
# Smooth principal components.
self.pc_u = self.filter_pcs(pc_u,(pc_w,pc_h)).astype('float32')
self.pc_v = self.filter_pcs(pc_v,(pc_w,pc_h)).astype('float32')
self.cov_matrix = cov_matrix
self.pc_w = pc_w
self.pc_h = pc_h
self.reshape_features=True
###############################
# Feature matcher
###############################
if self.params['features'].lower() == 'libviso' and libviso_available:
self.feature_matcher = FeatureMatcherLibviso(self.params)
elif self.params['features'].lower() == 'orb':
self.feature_matcher = FeatureMatcherORB(self.params)
elif self.params['features'].lower() == 'fast':
self.feature_matcher = FeatureMatcherFast(self.params)
elif self.params['features'].lower() == 'akaze' or not libviso_available:
self.feature_matcher = FeatureMatcherAKAZE(self.params)
else:
print('[PCAFLOW] *** ERROR ***')
print('[PCAFLOW] Unknown feature type {}. Please use "libviso" or "fast".'.format(self.params['features']))
sys.exit(1)
if self.params['n_models'] <= 1:
##############################
# Solver for PCA-Flow
##############################
self.solver = RobustQuadraticSolver(self.pc_u,
self.pc_v,
self.cov_matrix,
pc_size=(pc_w,pc_h),
params=self.params)
else:
##############################
# Solver for PCA-Layers
##############################
self.solver = EMSolver(self.pc_u, self.pc_v,
self.cov_matrix,
pc_size = (pc_w,pc_h),
params=self.params,
cov_matrix_sublayer=cov_matrix_sublayer)
self.images = deque(maxlen=2)
cprint('[PCAFLOW] Finished initializing.',self.params)
