- made some of dependencies explicit
- removed dependencies to highgui and some other modules where possible
- modified some samples to build without modules
This patch adds ocl kernels to accelerate Dense Inverse Search
based optical flow algorithm, it acclerates 3 parts in the algorithm,
including 1) Structure tensor elements compute, 2) Patch inverse search,
3) Densification compute.
Perf and accuracy test are also added. The perf test shows it is 30%
faster than the current implementation.
Signed-off-by: Li Peng <peng.li@intel.com>
* Minor fixes
* Start adding correspondence finding
* Added finding of correspondences using GPC
* New evaluation tool for GPC
* Changed default parameters
* Display ground truth in the evaluation tool
* Added training tool for MPI Sintel dataset
* Added the training tool for Middlebury dataset
* Added some OpenCL optimization
* Added explanatory notes
* Minor improvements: time measurements + little ocl optimization
* Added demos
* Fixed warnings
* Make parameter struct assignable
* Fix warning
* Proper command line argument usage
* Prettified training tool, added parameters
* Fixed VS warning
* Fixed VS warning
* Using of compressed forest.yml.gz files by default to save space
* Added OpenCL flag to the evaluation tool
* Updated documentation
* Major speed and memory improvements:
1) Added new (optional) type of patch descriptors which are much faster. Retraining with option --descriptor-type=1 is required.
2) Got rid of hash table for descriptors, less memory usage.
* Fixed various floating point errors related to precision.
SIMD for dot product, forest traversing is a little bit faster now.
* Tolerant floating point comparison
* Triplets
* Added comment
* Choosing negative sample among nearest neighbors
* Fix warning
* Usage of parallel_for_() in critical places. Performance improvments.
* Simulated annealing heuristic
* Moved OpenCL kernel to separate file
* Moved implementation to source file
* Added basic accuracy tests for GPC and PCAFlow
* Fixing warnings
* Test accuracy constraints were too strict
* Test accuracy constraints were too strict
* Make tests more lightweight
Added an option to pass an initial approximation of optical flow in
DISOpticalFlow. Added a python sample that demonstrates the use of this
feature for temporal propagation of flow vectors.
Replaced internal variational refinement implementation inside DeepFlow
with a new optimized one, which provides about the same quality while
being 4-6 times faster.
Added variational refinement as a separate class (based on implementation
inside DeepFlow, but significantly accelerated, about 4-6 times faster),
accelerated the main dense inverse search algorithm. Added several new
features including patch mean normalization for increased robustness to
illumination changes and spatial propagation, which often helps to recover
from errors introduced by the coarse-to-fine scheme. Expanded the
documentation, added new accuracy and perf tests. Refactored some of
the already existing optical flow accuracy tests.
Basic interfaces and a partial implementation of the Dense Inverse
Search (DIS) optical flow algorithm without variational refinement. Also
added a python benchmarking script that can evaluate different optical
flow algorithms on the MPI Sintel and Middlebury datasets and build
overall comparative charts.
* Sparse match interpolator interface and EdgeAwareInterpolator were
added to the ximgproc module
* New optical flow algorithm, based on PyrLK sparse OF and sparse match
interpolation, is added to the optflow module
LaurentBerger
Li Peng
Maksim Shabunin
berak
Alexander Alekhin
Alexander Alekhin
Vladislav Samsonov
Bleach
sbokov
vbystricky
Gary Bradski
Maksim Shabunin
Francisco Facioni