[−][src]Trait opencv::hub_prelude::CUDA_StereoConstantSpaceBP
Class computing stereo correspondence using the constant space belief propagation algorithm. :
The class implements algorithm described in Yang2010 . StereoConstantSpaceBP supports both local minimum and global minimum data cost initialization algorithms. For more details, see the paper mentioned above. By default, a local algorithm is used. To enable a global algorithm, set use_local_init_data_cost to false .
StereoConstantSpaceBP uses a truncated linear model for the data cost and discontinuity terms:
For more details, see Yang2010 .
By default, StereoConstantSpaceBP uses floating-point arithmetics and the CV_32FC1 type for messages. But it can also use fixed-point arithmetics and the CV_16SC1 message type for better performance. To avoid an overflow in this case, the parameters must satisfy the following requirement:
Required methods
pub fn as_raw_CUDA_StereoConstantSpaceBP(&self) -> *const c_void
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pub fn as_raw_mut_CUDA_StereoConstantSpaceBP(&mut self) -> *mut c_void
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Provided methods
pub fn get_nr_plane(&self) -> Result<i32>
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number of active disparity on the first level
pub fn set_nr_plane(&mut self, nr_plane: i32) -> Result<()>
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pub fn get_use_local_init_data_cost(&self) -> Result<bool>
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pub fn set_use_local_init_data_cost(
&mut self,
use_local_init_data_cost: bool
) -> Result<()>
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&mut self,
use_local_init_data_cost: bool
) -> Result<()>
Implementations
impl<'_> dyn CUDA_StereoConstantSpaceBP + '_
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pub fn estimate_recommended_params(
width: i32,
height: i32,
ndisp: &mut i32,
iters: &mut i32,
levels: &mut i32,
nr_plane: &mut i32
) -> Result<()>
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width: i32,
height: i32,
ndisp: &mut i32,
iters: &mut i32,
levels: &mut i32,
nr_plane: &mut i32
) -> Result<()>
Uses a heuristic method to compute parameters (ndisp, iters, levelsand nrplane) for the specified image size (widthand height).