While this documentation aims to go beyond a simple listing of parameters and instead attempts to explain some of the principles behind the functions, please see the section "Usage" for more details and usage examples including code and flow field visualisations.
This section documents the custom flow class and all its class methods. It is the recommended way of using oflibpytorch and makes the full range of functionality available to the user.
oflibpytorch.Flow
__init__
oflibpytorch
Flow.resize
Flow.pad
Flow.invert
Flow.switch_ref
Flow.combine_with
oflibpytorch
Flow.apply
Flow.track
oflibpytorch
Flow.is_zero
Flow.matrix
Flow.valid_target
Flow.valid_source
Flow.get_padding
oflibpytorch
Flow.visualise
Flow.visualise_arrows
Flow.show
Flow.show_arrows
oflibpytorch.visualise_definition
This section contains functions that take Torch tensors as well as NumPy arrays as inputs, instead of making use of the custom flow class. On the one hand, this avoids having to define flow objects. On the other hand, it requires keeping track of flow attributes manually, and it does not avail itself of the full scope of functionality oflibpytorch has to offer: most importantly, flow masks are not considered or tracked.
oflibpytorch.from_matrix
oflibpytorch.from_transforms
oflibpytorch.load_kitti
oflibpytorch.load_sintel
oflibpytorch.load_sintel_mask
oflibpytorch.resize_flow
oflibpytorch.invert_flow
oflibpytorch.switch_flow_ref
oflibpytorch.combine_flows
oflibpytorch.apply_flow
oflibpytorch.track_pts
oflibpytorch.is_zero_flow
oflibpytorch.get_flow_matrix
oflibpytorch.valid_target
oflibpytorch.valid_source
oflibpytorch.get_flow_padding
oflibpytorch.visualise_flow
oflibpytorch.visualise_flow_arrows
oflibpytorch.show_flow
oflibpytorch.show_flow_arrows