Should resolve compilation error on some platforms when using linux and
compilation error of libmv on FreeBSB.
It was a regression caused by not applied changes on config_linux.h
and some changes made to utilities.cc were also occasionally missed.
This version of libmv includes new gflags and glog libraries which makes
it possible to compile libmv with clang compiler.
Also remove code from CMakeLists which was disabling libmv if using clang.
Tested on linux with gcc-4.6 and clang-3.0, windows cmake+msvc and scons+mingw.
Could be some issues with other platforms/build system which shall be simple to resolve.
Needed to make constants like M_E defined in msvc. Was occasionally
removed on moving main changes in libmv from patch files in blender
repo to won repo (rev44190).
Keir's comment:
Add support for detecting tracking failure in the ESM tracker component of
libmv. Since both KLT and Hybrid rely on ESM underneath, KLT and Hybrid now
have a minimum correlation setting to match. With this fix, track failures
should get detected quicker, with the issue that sometimes the tracker will
give up too easily. That is fixable by reducing the required correlation (in
the track properties).
Command used for merge: svn merge -r 42396:42397 -r 42399:42400 ^/branches/soc-2011-tomato
Comment from Keir's commit:
Add a new hybrid region tracker for motion tracking to libmv, and
add it as an option (under "Hybrid") in the tracking settings. The
region tracker is a combination of brute force tracking for coarse
alignment, then refinement with the ESM/KLT algorithm already in
libmv that gives excellent subpixel precision (typically 1/50'th
of a pixel)
This also adds a new "brute force" region tracker which does a
brute force search through every pixel position in the destination
for the pattern in the first frame. It leverages SSE if available,
similar to the SAD tracker, to do this quickly. Currently it does
some unnecessary conversions to/from floating point that will get
fixed later.
The hybrid tracker glues the two trackers (brute & ESM) together
to get an overall better tracker. The algorithm is simple:
1. Track from frame 1 to frame 2 with the brute force tracker.
This tries every possible pixel position for the pattern from
frame 1 in frame 2. The position with the smallest
sum-of-absolute-differences is chosen. By definition, this
position is only accurate up to 1 pixel or so.
2. Using the result from 1, initialize a track with ESM. This does
a least-squares fit with subpixel precision.
3. If the ESM shift was more than 2 pixels, report failure.
4. If the ESM track shifted less than 2 pixels, then the track is
good and we're done. The rationale here is that if the
refinement stage shifts more than 1 pixel, then the brute force
result likely found some random position that's not a good fit.
svn command used: svn merge -r 42375:42376 -r 42377:42379 ^/branches/soc-2011-tomato
It was error in CMakeLists.txt caused by automatic bundling script which
expanded variables instead of substituting them as-is.
Fixed both of bundling script and CMakeLists.txt
In some cases solving can take a while (especially when refining is used)
and keeping interface locked is a bit annoying. Now camera solver is moved
to job system and interface isn't locking.
Reporting progress isn't really accurate, but trying to make it more linear
can lead to spending more effort on it than having benefit. Also, changing
status in the information line helps to understand that blender isn't hang
up and solving is till working nicely.
Main changes in code:
- libmv_solveReconstruction now accepts additional parameters:
* progress_update_callback - a function which is getting called
from solver algorithm to report progress back to Blender.
* callback_customdata - a user-defined context which is passing
to progress_update_callback so progress can be updated in needed
blender-side data structures.
This parameters are optional.
- Added structure MovieTrackingStats which is placed in MovieTracking
structure. It's supposed to be used for displaying information about
different operations (currently it's only camera solver, but can be
easily used for something else in the future) in clip editor.
This statistics structure is getting allocated for time operator is
working and not saving into .blend file.
- Clip Editor now displays statistics stored in MovieTrackingStats structure
like it's done for rendering.
Reporting progress isn't really accurate, but trying to make it more linear
can lead to spending more effort on it than having benefit. Also, changing
status in the information line helps to understand that blender isn't hang
up and solving is till working nicely.
Main changes in code:
- libmv_solveReconstruction now accepts additional parameters:
* progress_update_callback - a function which is getting called
from solver algorithm to report progress back to Blender.
* callback_customdata - a user-defined context which is passing
to progress_update_callback so progress can be updated in needed
blender-side data structures.
This parameters are optional.
- Added structure MovieTrackingStats which is placed in MovieTracking
structure. It's supposed to be used for displaying information about
different operations (currently it's only camera solver, but can be
easily used for something else in the future) in clip editor.
This statistics structure is getting allocated for time operator is
working and not saving into .blend file.
- Clip Editor now displays statistics stored in MovieTrackingStats structure
like it's done for rendering.
- Fixed incorrect memory access on distoritons more than 128 pixels
- Do not use UNDO operators flags for delete proxy operator (files can't be restored form disk),
and also do not use UNDO for set as background operator (background images are storing in
3d viewport which isn't getting re-loaded on undo which can lead to incorrect users count
of movie clip user).
- Add support for refining the camera's intrinsic parameters
during a solve. Currently, refining supports only the following
combinations of intrinsic parameters:
f
f, cx, cy
f, cx, cy, k1, k2
f, k1
f, k1, k2
This is not the same as autocalibration, since the user must
still make a reasonable initial guess about the focal length and
other parameters, whereas true autocalibration would eliminate
the need for the user specify intrinsic parameters at all.
However, the solver works well with only rough guesses for the
focal length, so perhaps full autocalibation is not that
important.
Adding support for the last two combinations, (f, k1) and (f,
k1, k2) required changes to the library libmv depends on for
bundle adjustment, SSBA. These changes should get ported
upstream not just to libmv but to SSBA as well.
- Improved the region of convergence for bundle adjustment by
increasing the number of Levenberg-Marquardt iterations from 50
to 500. This way, the solver is able to crawl out of the bad
local minima it gets stuck in when changing from, for example,
bundling k1 and k2 to just k1 and resetting k2 to 0.
- Add several new region tracker implementations. A region tracker
is a libmv concept, which refers to tracking a template image
pattern through frames. The impact to end users is that tracking
should "just work better". I am reserving a more detailed
writeup, and maybe a paper, for later.
- Other libmv tweaks, such as detecting that a tracker is headed
outside of the image bounds.
This includes several changes made directly to the libmv extern
code rather expecting to get those changes through normal libmv
channels, because I, the libmv BDFL, decided it was faster to work
on libmv directly in Blender, then later reverse-port the libmv
changes from Blender back into libmv trunk. The interesting part
is that I added a full Levenberg-Marquardt loop to the region
tracking code, which should lead to a more stable solutions. I
also added a hacky implementation of "Efficient Second-Order
Minimization" for tracking, which works nicely. A more detailed
quantitative evaluation will follow.
Original patch by Keir, cleaned a bit by myself.
- Replace set of booleans with menu, so now you'll simply be unable to choose
unsupported refine combination
- Some internal code cleanup and minor refactor
- Add support for refining the camera's intrinsic parameters
during a solve. Currently, refining supports only the following
combinations of intrinsic parameters:
f
f, cx, cy
f, cx, cy, k1, k2
f, k1
f, k1, k2
This is not the same as autocalibration, since the user must
still make a reasonable initial guess about the focal length and
other parameters, whereas true autocalibration would eliminate
the need for the user specify intrinsic parameters at all.
However, the solver works well with only rough guesses for the
focal length, so perhaps full autocalibation is not that
important.
Adding support for the last two combinations, (f, k1) and (f,
k1, k2) required changes to the library libmv depends on for
bundle adjustment, SSBA. These changes should get ported
upstream not just to libmv but to SSBA as well.
- Improved the region of convergence for bundle adjustment by
increasing the number of Levenberg-Marquardt iterations from 50
to 500. This way, the solver is able to crawl out of the bad
local minima it gets stuck in when changing from, for example,
bundling k1 and k2 to just k1 and resetting k2 to 0.
- Add several new region tracker implementations. A region tracker
is a libmv concept, which refers to tracking a template image
pattern through frames. The impact to end users is that tracking
should "just work better". I am reserving a more detailed
writeup, and maybe a paper, for later.
- Other libmv tweaks, such as detecting that a tracker is headed
outside of the image bounds.
This includes several changes made directly to the libmv extern
code rather expecting to get those changes through normal libmv
channels, because I, the libmv BDFL, decided it was faster to work
on libmv directly in Blender, then later reverse-port the libmv
changes from Blender back into libmv trunk. The interesting part
is that I added a full Levenberg-Marquardt loop to the region
tracking code, which should lead to a more stable solutions. I
also added a hacky implementation of "Efficient Second-Order
Minimization" for tracking, which works nicely. A more detailed
quantitative evaluation will follow.
