Changes in 2.4.6.1:
* Hotfix for camera pipeline for Linux (V4L).
Changes in 2.4.6:
* Windows RT: added video file i/o and sample application using camera,
enabled parallelization with TBB or MS Concurrency
* CUDA 5.5: added support for desktop and ARM
* Added Qt 5 support
* Binary compatiblility with both OpenCL 1.1/1.2 platforms. Now the binaries
compiled with any of AMD/Intel/Nvidia's SDK can run on all other platforms.
* New functions ported, CLAHE, GoodFeaturesToTrack, TVL1 optical flow and more
* Performance optimizations, HOG and more.
* More kernel binary cache options though setBinaryDiskCache interface.
* OpenCL binaries are now included into the superpack for Windows (for VS2010
and VS2012 only)
* Switched all the remaining parallel loops from TBB-only
'tbb::parallel_for()' to universal 'cv::parallel_for_()' with many possible
backends (MS Concurrency, Apple's GDC, OpenMP, Intel TBB etc.)
* iOS build scripts (together with Android ones) moved to 'opencv/platforms'
directory
* Fixed bug with incorrect saved video from camera through CvVideoCamera
* Added 'rotateVideo' flag to the CvVideoCamera class to control camera
preview rotation on device rotation
* Added functions to convert between UIImage and cv::Mat (just include
opencv2/highgui/ios.h)
* Numerous bug-fixes across all the library
2.4.5
April, 2013
Experimental WinRT support (build for WindowsRT guide)
the new video super-resolution module has been added that
implements the following papers:
- S. Farsiu, D. Robinson, M. Elad, P. Milanfar. Fast and robust
Super-Resolution. Proc 2003 IEEE Int Conf on Image Process,
pp. 291â294, 2003.
- D. Mitzel, T. Pock, T. Schoenemann, D. Cremers. Video super
resolution using duality based TV-L1 optical flow. DAGM, 2009.
CLAHE (adaptive histogram equalization) algorithm has been
implemented, both CPU and GPU-accelerated versions (in imgproc
and gpu modules, respectively)
there are further improvements and extensions in ocl module:
- 2 stereo correspondence algorithms: stereobm (block matching)
and stereobp (belief propagation) have been added
- many bugs fixed, including some crashes on Intel HD4000
The tutorial on displaying cv::Mat inside Visual Studio 2012
debugger has been contributed by Wolf Kienzle from Microsoft
Research. See
http://opencv.org/image-debugger-plug-in-for-visual-studio.html
78 pull requests have been merged. Big thanks to everybody who
contributed!
At least 25 bugs have been fixed since 2.4.4 (see
http://code.opencv.org/projects/opencv/issues select closed
issues with target version set to "2.4.5").
2.4.4
March, 2013
This is the biggest news in 2.4.4 - we've got full-featured
OpenCV Java bindings on a desktop, not only Android! In fact
you can use any JVM language, including functional Java or
handy Groovy. Big thanks to Eric Christiansen for the contribution!
Check the tutorial for details and code samples.
Android application framework, samples, tutorials, OpenCV
Manager are updated, see Android Release Notes for details.
Numerous improvements in gpu module and the following new
functionality & optimizations:
Optimizations for the NVIDIA Kepler architecture
NVIDIA CARMA platform support
HoughLinesP for line segments detection
Lab/Luv <-> RGB conversions
Let us be more verbose here. The openCL-based hardware acceleration
(ocl) module is now mature, and, with numerous bug fixes, it
is largely bug-free. Correct operation has been verified on
all tested platforms, including discrete GPUs (tested on NVIDIA
and AMD boards), as well as integrated GPUs (AMD APUs as well
as Intel Ivy Bridge iGPUs). On the host side, there has been
exhaustive testing on 32/64 bit, Windows/Linux systems, making
the ocl module a very serious and robust cross-platform GPU
hardware acceleration solution. While we currently do not test
on other devices that implement OpenCL (e.g. FPGA, ARM or other
processors), it is expected that the ocl module will work well
on such devices as well (provided the minimum requirements
explained in the user guide are met).
Here are specific highlights of the 2.4.4 release:
The ocl::Mat can now use âspecialâ memory (e.g. pinned
memory, host-local or device-local).
The ocl module can detect if the underlying hardware supports
âintegrated memory,â and if so use âdevice-localâ memory
by default for all operations.
New arithmetic operations for ocl::Mat, providing significant
ease of use for simple numerical manipulations.
Interop with OpenCL enables very easy integration of OpenCV
in existing OpenCL applications, and vice versa.
New algorithms include Hough circles, more color conversions
(including YUV, YCrCb), and Hu Moments.
Numerous bug fixes, and optimizations, including in:
blendLinear, square samples, erode/dilate, Canny, convolution
fixes with AMD FFT library, mean shift filtering, Stereo
BM.
Platform specific bug fixes: PyrLK, bruteForceMatcher,
faceDetect now works also on Intel Ivy Bridge chips (as
well as on AMD APUs/GPUs and NVIDIA GPUs); erode/dilate
also works on NVIDIA GPUs (as well as AMD APUs/GPUs and
Intel iGPUs).
Many people contributed their code in the form of pull requests.
Here are some of the most interesting contributions, that were
included into 2.4 branch:
>100 reported problems have been resolved since 2.4.3
Oscar Deniz submitted smile detector and sample.
Alexander Smorkalov created a tutorial on cross-compilation
of OpenCV for Linux on ARM platforms.
to address issues with NetBSD-6(and earlier)'s fontconfig not being
new enough for pango.
While doing that, also bump freetype2 dependency to current pkgsrc
version.
Suggested by tron in PR 47882
Changelog:
* Add universal parallell mechianism support
* Add sample codes
* Add some new algorithms
* Many improvements in GPU support
* Many bugfixes
not tested. New ffmpeg support not enabled in package.
2.4.1
June, 2012
The changes since 2.4.0
The GPU module now supports CUDA 4.1 and CUDA 4.2 and can be
compiled with CUDA 5.0 preview.
Added API for storing OpenCV data structures to text string
and reading them back:
cv::calcOpticalFlowPyrLK now supports precomputed pyramids as
input.
Function signatures in documentation are made consistent with
source code.
Restored python wrappers for SURF and MSER.
45 more bugs in our bug tracker have been fixed
2.4.0
May, 2012
The major changes since 2.4 beta
OpenCV now provides pretty complete build information via
(surprise) cv::getBuildInformation().
reading/writing video via ffmpeg finally works and it's now
available on MacOSX too.
note 1: we now demand reasonably fresh versions of ffmpeg/libav
with libswscale included.
note 2: if possible, do not read or write more than 1 video
simultaneously (even within a single thread) with ffmpeg 0.7.x
or earlier versions, since they seem to use some global structures
that are destroyed by simultaneously executed codecs. Either
build and install a newer ffmpeg (0.10.x is recommended), or
serialize your video i/o, or use parallel processes instead of
threads.
MOG2 background subtraction by Zoran Zivkovic was optimized
using TBB.
The reference manual has been updated to match OpenCV 2.4.0
better (though, not perfectly).
>20 more bugs in our bug tracker have been closed
(http://code.opencv.org/projects/opencv/roadmap).
Asus Xtion is now properly supported for HighGUI. For now, you
have to manually specify this device by using
VideoCapture(CV_CAP_OPENNI_ASUS) instead of
VideoCapture(CV_CAP_OPENNI).
2.4 beta
April, 2012
As usual, we created 2.4 branch in our repository
(http://code.opencv.org/svn/opencv/branches/2.4), where we will
further stabilize the code. You can check this branch periodically,
before as well as after 2.4 release.
Common changes
At the age of 12, OpenCV got its own home! http://code.opencv.org
is now the primary site for OpenCV development and http://opencv.org
(to be launched soon) will be the official OpenCV user site.
Some of the old functionality from the modules imgproc, video,
calib3d, features2d, objdetect has been moved to legacy.
CMake scripts have been substantially modified. Now it's very
easy to add new modules - just put the directory with include,
src, doc and test sub-directories to the modules directory,
create a very simple CMakeLists.txt and your module will be
built as a part of OpenCV. Also, it's possible to exclude
certain modules from build (the CMake variables
"BUILD_opencv_<modulename>" control that).
New functionality
The new very base cv::Algorithm class has been introduced. It's
planned to be the base of all the "non-trivial" OpenCV
functionality. All Algorithm-based classes have the following
features:
"virtual constructor", i.e. an algorithm instance can be created by name;
there is a list of available algorithms;
one can retrieve and set algorithm parameters by name;
one can save algorithm parameters to XML/YAML file and then load them.
A new ffmpeg wrapper has been created that features multi-threaded
decoding, more robust video positioning etc. It's used with
ffmpeg starting with 0.7.x versions.
features2d API has been cleaned up. There are no more numerous
classes with duplicated functionality. The base classes
FeatureDetector and DescriptorExtractor are now derivatives of
cv::Algorithm. There is also the base Feature2D, using which
you can detect keypoints and compute the descriptors in a single
call. This is also more efficient.
SIFT and SURF have been moved to a separate module named nonfree
to indicate possible legal issues of using those algorithms in
user applications. Also, SIFT performance has been substantially
improved (by factor of 3-4x).
The current state-of-art textureless detection algorithm,
Line-Mod by S. Hinterstoisser, has been contributed by Patrick
Mihelich. See objdetect/objdetect.hpp, class Detector.
3 face recognition algorithms have been contributed by Philipp
Wagner. Please, check opencv/contrib/contrib.hpp, FaceRecognizer
class, and opencv/samples/cpp/facerec_demo.cpp.
2 algorithms for solving PnP problem have been added. Please,
check flags parameter in solvePnP and solvePnPRansac functions.
Enhanced LogPolar implementation (that uses Blind-Spot model)
has been contributed by Fabio Solari and Manuela Chessa, see
opencv/contrib/contrib.hpp, LogPolar_* classes and
opencv/samples/cpp/logpolar_bsm.cpp sample.
A stub module photo has been created to support a quickly
growing "computational photography" area. Currently, it only
contains inpainting algorithm, moved from imgproc, but it's
planned to add much more functionality.
Another module videostab (beta version) has been added that
solves a specific yet very important task of video stabiliion.
The module is under active development. Please, check
opencv/samples/cpp/videostab.cpp sample.
findContours can now find contours on a 32-bit integer image
of labels (not only on a black-and-white 8-bit image). This is
a step towards more convenhich results in better edge maps
Python bindings can now be used within python threads, so one
can write multi-threaded computer vision applications in Python.
OpenCV on GPU
Different Optical Flow algorithms have been added:
Brox (contrtions;
Improved performance.
pyrUp/pyrDown implementations.
Matrix multiplication on GPU (wrapper for the CUBLAS library).
This is optional, user need to compile OpenCV with CUBLAS
support.
OpenGL back-end has been implemented for highgui module, that
allows to display GpuMat directly without downloading them to
CPU.
Performance
A few OpenCV functions, like color conversion, morphology, data
type conversions, brute-force feature mer have been optimized
using TBB and/or SSE intrinisics.
Along with regression tests, now many OpenCV functions have
got performance tests. Now for most modules one can build
opencv_perf_<modulename> executables that run various functions
from the particular module and produce a XML file. Note that
if you want to run those tests, as well as the normal regression
tests, you will need to get (a rather big)
http://code.opencv.org/svn/opencv/trunk/opencv_extra directory
and set environment variable OPENCV_TEST_DATA_PATH to
"<your_copy_of_opencv_extra>/testdata".
Bug fixes
In this version we fixed literally hundreds of bugs. Please,
check http://code.opencv.org/projects/opencv/versions/1 for a
list of fixed bugs.
Known issues
When OpenCV is built statically, dynamically created classes
(via Algorithm::create) can fail because linker excludes the
"unused" object files. To avoid this problem, create classes
explicitly, e.g
1 Ptr<DescriptorExtractor> d = new BriefDescriptorExtractor;
Add a python option (off by default).
2.3.1
(August, 2011)
New Functionality and Features
* Retina module has been contributed by Alexandre Benoit (in opencv_contrib
module).
* Planar subdivisions construction (Delaunay triangulation and Voronoi
tesselation) have been ported to C++. See the new delaunay2.cpp sample.
* Several new Python samples have been added.
* FLANN in OpenCV has been upgraded to v1.6. Also, added Python bindings
for FLANN.
* We now support the latest FFMPEG (0.8.x) that features multi-threaded
decoding. Reading videos in OpenCV has never been that fast.
* Over 100 issues have been resolved since 2.3 release.
2.3
(July, 2011)
Modifications and Improvements since 2.3rc
* A few more bugs reported in the OpenCV bug tracker have been fixed.
* Documentation has been improved a lot!
2.3rc
(June, 2011)
New Functionality, Features
* Many functions and methods now take InputArray/OutputArray instead of
"cv::Mat" references. It retains compatibility with the existing code and
yet brings more natural support for STL vectors and potentially other
"foreign" data structures to OpenCV.
core:
* LAPACK is not used by OpenCV anymore.
* Arithmetic operations now support mixed-type operands and arbitrary number
of channels.
features2d:
* Completely new patent-free BRIEF and ORB feature descriptors have been
added.
* Very fast LSH matcher for BRIEF and ORB descriptors will be added in 2.3.1.
calib3d:
* calibration.cpp sample. With the new pattern calibration accuracy is
usually much higher.
stitching:
* opencv_stitching is a beta version of new application that makes a panorama
out of a set of photos taken from the same point.
python:
* Now there are 2 extension modules: cv and cv2. cv2 includes wrappers for
OpenCV 2.x functionality. opencv/samples/python2 contain a few samples
demonstrating cv2 in use.
* Over 250 issues have been resolved.
alternative from mk/jpeg.buildlink3.mk
This allows selection of an alternative jpeg library (namely the x86 MMX,
SSE, SSE2 accelerated libjpeg-turbo) via JPEG_DEFAULT=libjpeg-turbo, and
follows the current standard model for alternatives (fam, motif, fuse etc).
The mechanical edits were applied via the following script:
#!/bin/sh
for d in */*; do
[ -d "$d" ] || continue
for i in "$d/"Makefile* "$d/"*.mk; do
case "$i" in *.orig|*"*"*) continue;; esac
out="$d/x"
sed -e 's;graphics/jpeg/buildlink3\.mk;mk/jpeg.buildlink3.mk;g' \
-e 's;BUILDLINK_PREFIX\.jpeg;JPEGBASE;g' \
< "$i" > "$out"
if cmp -s "$i" "$out"; then
rm -f "$out"
else
echo "Edited $i"
mv -f "$i" "$i.orig" && mv "$out" "$i"
fi
done
done
2.2
(December, 2010)
General Modifications and Improvements
* The library has been reorganized. Instead of cxcore, cv, cvaux,
highgui and ml we now have several smaller modules:
* opencv_core - core functionality (basic structures, arithmetics
and linear algebra, dft, XML and YAML I/O ...).
* opencv_imgproc - image processing (filter, GaussianBlur, erode,
dilate, resize, remap, cvtColor, calcHist etc.)
* opencv_highgui - GUI and image & video I/O
* opencv_ml - statistical machine learning models (SVM, Decision
Trees, Boosting etc.)
* opencv_features2d - 2D feature detectors and descriptors (SURF,
FAST etc.,
* including the new feature detectors-descriptor-matcher
framework)
* opencv_video - motion analysis and object tracking (optical flow,
motion templates, background subtraction)
* opencv_objdetect - object detection in images (Haar & LBP face
detectors, HOG people detector etc.)
* opencv_calib3d - camera calibration, stereo correspondence and
elements of 3D data processing
* opencv_flann - the Fast Library for Approximate Nearest Neighbors
(FLANN 1.5) and the OpenCV wrappers
* opencv_contrib - contributed code that is not mature enough
* opencv_legacy - obsolete code, preserved for backward
compatibility
* opencv_gpu - acceleration of some OpenCV functionality using CUDA
(relatively unstable, yet very actively developed part of OpenCV)
* If you detected OpenCV and configured your make scripts using CMake or
pkg-config tool, your code will likely build fine without any changes.
Otherwise, you will need to modify linker parameters (change the
library names) and update the include paths.
* It is still possible to use #include <cv.h> etc. but the recommended
notation is:
* #include "opencv2/imgproc/imgproc.hpp"
* ..
* Please, check the new C and C++ samples
(https://code.ros.org/svn/opencv/trunk/opencv/samples), which now
include the new-style headers.
* The new-style wrappers now cover much more of OpenCV 2.x API. The
documentation and samples are to be added later. You will need numpy
in order to use the extra added functionality.
* SWIG-based Python wrappers are not included anymore.
* OpenCV can now be built for Android (GSoC 2010 project), thanks to
Ethan Rublee; and there are some samples too. Please, check
http://opencv.willowgarage.com/wiki/Android
* The completely new opencv_gpu acceleration module has been created
with support by NVidia. See below for details.
New Functionality, Features
* core:
* The new cv::Matx<T, m, n> type for fixed-type fixed-size matrices
has been added. Vec<T, n> is now derived from Matx<T, n, 1>. The
class can be used for very small matrices, where cv::Mat use
implies too much overhead. The operators to convert Matx to Mat
and backwards are available.
* cv::Mat and cv::MatND are made the same type: typedef cv::Mat
cv::MatND. Note that many functions do not check the matrix
dimensionality yet, so be careful when processing 3-, 4- ...
dimensional matrices using OpenCV.
* Experimental support for Eigen 2.x/3.x is added (WITH_EIGEN2
option in CMake). Again, there are convertors from Eigen2
matrices to cv::Mat and backwards. See
modules/core/include/opencv2/core/eigen.hpp.
* cv::Mat can now be print with "<<" operator. See
opencv/samples/cpp/cout_mat.cpp.
* cv::exp and cv::log are now much faster thanks to SSE2
optimization.
* imgproc:
* color conversion functions have been rewritten;
* RGB->Lab & RGB->Luv performance has been noticeably
improved. Now the functions assume sRGB input color space
(e.g. gamma=2.2). If you want the original linear RGB->L**
conversion (i.e. with gamma=1), use CV_LBGR2LAB etc.
* VNG algorithm for Bayer->RGB conversion has been added. It's
much slower than the simple interpolation algorithm, but
returns significantly more detailed images
* The new flavors of RGB->HSV/HLS conversion functions have
been added for 8-bit images. They use the whole 0..255 range
for the H channel instead of 0..179. The conversion codes
are CV_RGB2HSV_FULL etc.
* special variant of initUndistortRectifyMap for wide-angle cameras
has been added: initWideAngleProjMap()
* features2d:
* the unified framework for keypoint extraction, computing the
descriptors and matching them has been introduced. The previously
available and some new detectors and descriptors, like SURF,
Fast, StarDetector etc. have been wrapped to be used through the
framework. The key advantage of the new framework (besides the
uniform API for different detectors and descriptors) is that it
also provides high-level tools for image matching and textured
object detection. Please, see documentation
http://opencv.willowgarage.com/documentation/cpp/features2d_common_interfaces_of_feature_detectors.html
* and the C++ samples:
* descriptor_extractor_matcher.cpp - finding object in a
scene using keypoints and their descriptors.
* generic_descriptor_matcher.cpp - variation of the above
sample where the descriptors do not have to be computed
explicitly.
* bagofwords_classification.cpp - example of extending
the framework and using it to process data from the VOC
databases:
* http://pascallin.ecs.soton.ac.uk/challenges/VOC/
* the newest super-fast keypoint descriptor BRIEF by Michael
Calonder has been integrated by Ethan Rublee. See the sample
opencv/samples/cpp/video_homography.cpp
* SURF keypoint detector has been parallelized using TBB (the patch
is by imahon and yvo2m)
* objdetect:
* LatentSVM object detector, implementing P. Felzenszwalb
algorithm, has been contributed by Nizhniy Novgorod State
University (NNSU) team. See
* opencv/samples/c/latentsvmdetect.cpp
* calib3d:
* The new rational distortion model:
* x' = x*(1 + k1*r2 + k2*r4 + k3*r6)/(1 + k4*r2 + k5*r4 +
k6*r6) + <tangential_distortion for x>,
y' = y*(1 + k1*r2 + k2*r4 + k3*r6)/(1 + k4*r2 + k5*r4 +
k6*r6) + <tangential_distortion for y>
* has been introduced. It is useful for calibration of cameras
with wide-angle lenses. Because of the increased number of
parameters to optimize you need to supply more data to
robustly estimate all of them. Or, simply initialize the
distortion vectors with zeros and pass
CV_CALIB_RATIONAL_MODEL to enable the new model +
CV_CALIB_FIX_K3 + CV_CALIB_FIX_K4 + CV_CALIB_FIX_K5 or other
such combinations to selectively enable or disable certain
coefficients.
* rectification of trinocular camera setup, where all 3 heads are
on the same line, is added. see samples/cpp/3calibration.cpp
* ml:
* Gradient boosting trees model has been contributed by NNSU team.
* highgui:
* Experimental Qt backend for OpenCV has been added as a result of
GSoC 2010 project, completed by Yannick Verdie. The backend has a
few extra features, not present in the other backends, like text
rendering using TTF fonts, separate "control panel" with sliders,
push-buttons, checkboxes and radio buttons, interactive zooming,
panning of the images displayed in highgui windows, "save as"
etc. Please, check the youtube videos where Yannick demonstrates
the new features: http://www.youtube.com/user/MrFrenchCookie#p/u
* The new API is described here:
http://opencv.willowgarage.com/documentation/cpp/highgui_qt_new_functions.html
To make use of the new API, you need to have Qt SDK (or
libqt4 with development packages) installed on your machine,
and build OpenCV with Qt support (pass -DWITH_QT=ON to
CMake; watch the output, make sure Qt is used as GUI
backend)
* 16-bit and LZW-compressed TIFFs are now supported.
* You can now set the mode for IEEE1394 cameras on Linux.
* contrib:
* Chamfer matching algorithm has been contributed by Marius Muja,
Antonella Cascitelli, Marco Di Stefano and Stefano Fabri. See
samples/cpp/chamfer.cpp
* gpu:
* This is completely new part of OpenCV, created with the support
by NVidia. Note that the package is at alpha, probably early beta
state, so use it with care and check OpenCV SVN for updates.
In order to use it, you need to have the latest NVidia CUDA SDK
installed, and build OpenCV with CUDA support (-DWITH_CUDA=ON
CMake flag). All the functionality is put to cv::gpu namespace.
The full list of functions and classes can be found at
opencv/modules/gpu/include/opencv2/gpu/gpu.hpp, and here are some
major components of the API:
* image arithmetics, filtering operations, morphology,
geometrical transformations, histograms
* 3 stereo correspondence algorithms: Block Matching, Belief
Propagation and Constant-Space Belief Propagation.
* HOG-based object detector. It runs more than order of
magnitude faster than the CPU version!
* See opencv/samples/cpp/
* python bindings:
* A lot more of OpenCV 2.x functionality is now covered by Python
bindings.
Documentation, Samples
* Links to wiki pages (mostly empty) have been added to each function
description, see http://opencv.willowgarage.com
* All the samples have been documented; most samples have been converted
to C++ to use the new OpenCV API.
Bug Fixes
* Over 300 issues have been resolved. Most of the issues (closed and
still open) are listed at https://code.ros.org/trac/opencv/report/6.
