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and cmake finds it, but buildlink3 hides it. Reported by Aymeric.
XXX: should be made an option instead
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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
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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.
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2.1
(April, 2010)
General Modifications
- The whole OpenCV is now using exceptions instead of the old
libc-style mechanism.
* That is, instead of checking error code with cvGetErrStatus()
(which currently always returns 0) you can now just call OpenCV
functions inside C++ try-catch statements, cv::Exception is now
derived from std::exception.
- All the parallel loops in OpenCV have been converted from OpenMP
* to Intel TBB (http://www.threadingbuildingblocks.org/). Thus
parallel version of OpenCV can now be built using MSVC 2008
Express Edition or using earlier than 4.2 versions of GCC.
- SWIG-based Python wrappers are still included,
* but they are not built by default and it's generally preferable
to use the new wrappers. The python samples have been rewritten
by James Bowman to use the new-style Python wrappers, which have
been also created by James.
- OpenCV can now be built and run in 64-bit mode on MacOSX 10.6 and
Windows (see HighGUI and known problems below).
* On Windows both MSVC 2008 and mingw64 are known to work.
- In theory OpenCV is now able to determine the host CPU on-fly and
make use of SSE/SSE2/... instructions,
* if they are available. That is, it should be more safe to use
WITH_SSE* flags in CMake. However, if you want maximum
portability, it's recommended to turn on just WITH_SSE and
WITH_SSE2 and leave other SSE* turned off, as we found that using
WITH_SSE3, WITH_SSSE3 and WITH_SSE4_1 can yield the code
incompatible with Intel's pre-Penryn or AMD chips.
- Experimental "static" OpenCV configuration in CMake was contributed
by Jose Luis Blanco.
* Pass "BUILD_SHARED_LIBS=OFF" to CMake to build OpenCV statically.
New Functionality, Features
* - cxcore, cv, cvaux:
* Grabcut (http://en.wikipedia.org/wiki/GrabCut) image segmentation
algorithm has been implemented.
* See opencv/samples/c/grabcut.cpp
* new improved version of one-way descriptor is added. See
opencv/samples/c/one_way_sample.cpp
* modified version of H. Hirschmuller semi-global stereo matching
algorithm that we call SGBM
* (semi-global block matching) has been created. It is much
faster than Kolmogorov's graph cuts-based algorithm and yet
it's usually better than the block matching StereoBM
algorithm. See opencv/samples/c/stereo_matching.cpp.
* existing StereoBM stereo correspondence algorithm by K. Konolige
was noticeably improved:
* added the optional left-right consistency check and speckle
filtering, improved performance (by ~20%).
* User can now control the image areas visible after the stereo
rectification
* (see the extended stereoRectify/cvStereoRectify), and also
limit the region where the disparity is computed (see
CvStereoBMState::roi1, roi2; getValidDisparityROI).
* Mixture-of-Gaussian based background subtraction algorithm has
been rewritten for better performance
* and better accuracy. Alternative C++ interface
BackgroundSubtractor has been provided, along with the
possibility to use the trained background model to segment
the foreground without updating the model. See
opencv/samples/c/bgfg_segm.cpp.
- highgui:
* MacOSX: OpenCV now includes Cocoa and QTKit backends, in addition
to Carbon and Quicktime.
* Therefore you can build OpenCV as 64-bit library. Thanks to
Andre Cohen and Nicolas Butko, which components Note however
that the backend are now in the alpha state, they can crash
or leak memory, so for anything more serious than quick
experiments you may prefer to use Carbon and Quicktime. To
do that, pass USE_CARBON=ON and USE_QUICKTIME=ON to CMake
and build OpenCV in 32-bit mode (i.e. select i386
architecture in Xcode).
* Windows. OpenCV can now be built in 64-bit mode with MSVC 2008
and also mingw64.
* Fullscreen has been added (thanks to Yannick Verdie).
* Call cvSetWindowProperty(window_name, CV_WINDOW_FULLSCREEN,
1) to make the particular window to fill the whole screen.
This feature is not supported in the Cocoa bindings yet.
* gstreamer backend has been improved a lot (thanks to Stefano
Fabri)
Bug Fixes
* - about 200 bugs have been fixed
2.0
(September, 2009)
New functionality, features:
* - General:
* New Python interface officially in.
- MLL:
* The new-style class aliases (e.g. cv::SVM ~ CvSVM) and the
train/predict methods, taking cv::Mat in addition to CvMat, have
been added. So now MLL can be used more seamlesly with the rest
of the restyled OpenCV.
2.0 beta
(September, 2009)
New functionality, features:
* General:
* The brand-new C++ interface for most of OpenCV functionality
(cxcore, cv, highgui) has been introduced. Generally it means
that you will need to do less coding to achieve the same results;
it brings automatic memory management and many other advantages.
* See the C++ Reference section in opencv/doc/opencv.pdf and
opencv/include/opencv/*.hpp.
* The previous interface is retained and still supported.
* The source directory structure has been reorganized; now all the
external headers are placed in the single directory on all
platforms.
* The primary build system is CMake,
* CXCORE, CV, CVAUX:
* CXCORE now uses Lapack (CLapack 3.1.1.1 in OpenCV 2.0) in its
various linear algebra functions (such as solve, invert, SVD,
determinant, eigen etc.) and the corresponding old-style
functions (cvSolve, cvInvert etc.
* Lots of new feature and object detectors and descriptors have
been added (there is no documentation on them yet), see cv.hpp
and cvaux.hpp:
* FAST - the fast corner detector, submitted by Edward Rosten
* MSER - maximally stable extremal regions, submitted by Liu
Liu
* LDetector - fast circle-based feature detector
* by V. Lepetit (a.k.a. YAPE)
* Fern-based point classifier and the planar object detector -
* based on the works by M. Ozuysal and V. Lepetit
* One-way descriptor - a powerful PCA-based feature
descriptor,
* S. Hinterstoisser, O. Kutter, N. Navab, P. Fua, and V.
Lepetit, "Real-Time Learning of Accurate Patch
Rectification". Contributed by Victor Eruhimov
* Spin Images 3D feature descriptor
* based on the A. Johnson PhD thesis; implemented by
Anatoly Baksheev
* Self-similarity features - contributed by Rainer Leinhar
* HOG people and object detector - the reimplementation of
Navneet Dalal framework
* (http://pascal.inrialpes.fr/soft/olt/). Currently, only
the detection part is ported, but it is fully
compatible with the original training code.
* See cvaux.hpp and opencv/samples/c/peopledetect.cpp.
* LBP (Local Binary Pattern) features
* Extended variant of the Haar feature-based object detector -
implemented by Maria Dimashova. It now supports Haar
features and LBPs, other features can be added in the same
way.
* Adaptive skin detector and the fuzzy meanshift tracker -
contributed by Farhad Dadgostar, see cvaux.hpp and
opencv/samples/c/adaptiveskindetector.cpp
* The new traincascade application complementing the new-style
HAAR+LBP object detector has been added. See
opencv/apps/traincascade.
* The powerful library for approximate nearest neighbor search
FLANN by Marius Muja is now shipped with OpenCV, and the
OpenCV-style interface to the library is included into cxcore.
See cxcore.hpp and opencv/samples/c/find_obj.cpp
* The bundle adjustment engine has been contributed by PhaseSpace;
see cvaux.hp
* Added dense optical flow estimation function based on the paper
* "Two-Frame Motion Estimation Based on Polynomial Expansion"
by G. Farnerback.
* See cv::calcOpticalFlowFarneback and the C++ documentation
* Image warping operations (resize, remap, warpAffine,
warpPerspective) now all support bicubic and Lanczos
interpolation.
* Most of the new linear and non-linear filtering operations
(filter2D, sepFilter2D, erode, dilate ...) support arbitrary
border modes and can use the valid image pixels outside of the
ROI (i.e. the ROIs are not "isolated" anymore), see the C++
documentation.
* The data can now be saved to and loaded from GZIP-compressed
XML/YML files, e.g.: cvSave("a.xml.gz", my_huge_matrix);
* MLL:
* Added the Extremely Random Trees that train super-fast, comparing
to Boosting or Random Trees (by Maria Dimashova).
* The decision tree engine and based on it classes (Decision Tree
itself, Boost, Random Trees) have been reworked and now:
* they consume much less memory (up to 200% savings)
* the training can be run in multiple threads (when OpenCV is
built with OpenMP support)
* the boosting classification on numerical variables is
especially fast because of the specialized low-overhead
branch.
* mltest has been added. While far from being complete, it contains
correctness tests for some of the MLL classes.
* HighGUI:
* [Linux] The support for stereo cameras (currently Videre only)
has been added.
* There is now uniform interface for capturing video from two-,
three- ... n-head cameras.
* Images can now be compressed to or decompressed from buffers in
the memory, see the C++ HighGUI reference manual
* Documentation:
* The reference manual has been converted from HTML to LaTeX (by
James Bowman and Caroline Pantofaru)
* Samples, misc.:
* Better eye detector has been contributed by Shiqi Yu, see
opencv/data/haarcascades/*[lefteye|righteye]*.xml
* sample LBP (Local Binary Pattern) cascade for the frontal face
detection has been created by Maria Dimashova, see
opencv/data/lbpcascades/lbpcascade_frontalface.xml
* Several high-quality body parts and facial feature detectors have
been
* contributed by Modesto Castrillon-Santana,
* see opencv/data/haarcascades/haarcascade_mcs*.xml
Optimization:
* Many of the basic functions and the image processing operations(like
arithmetic operations, geometric image transformations, filtering
etc.) have got SSE2 optimization, so they are several times faster.
* The model of IPP support has been changed. Now IPP is supposed to be
detected by CMake at the configuration stage and linked against
OpenCV. (In the beta it is not implemented yet though).
* PNG encoder performance improved by factor of 4 by tuning the
parameters
1.1pre1
(October, 2008)
New functionality/features:
* General:
* Octave bindings have been added. See interfaces/swig/octave (for
now, Linux only)
* CXCORE, CV, CVAUX:
* Speeded-up Robust Features (SURF), contributed by Liu Liu. see
samples/c/find_obj.cpp and the documentation opencvref_cv.htm
* Many improvements in camera calibration:
* Added stereo camera calibration: cvStereoCalibrate,
cvStereoRectify etc.
* Single camera calibration now uses Levenberg-Marquardt
method and supports extra flags to switch on/off
optimization of individual camera parameters
* The optional 3rd radial distortion parameter (k3*r^6) is now
supported in every calibration-related function
* 2 stereo correspondence algorithms:
* very fast block matching method by Kurt Konolige (processes
the Tsukuba stereo pair in <10ms on Core2Duo laptop)
* slow but more accurate graph-cut based algorithm by
Kolmogorov and Zabin
* Better homography estimation algorithms (RANSAC and LMEDs)
* new C++ template image classes contributed by Daniel Filip
(Google inc.). see opencv/cxcore/include/cvwimage.h
* Fast approximate nearest neighbor search (by Xavier Delacour)
* Codebook method for background/foreground segmentation (by Gary
Bradski)
* Sort function (contributed by Shiqi Yu)
* [OpenCV+IPP] Face Detection (cvHaarDetectObjects) now runs much
faster (up to 2x faster) when using IPP 5.3 or higher.
* Much faster (~4x faster) fixed-point variant of cvRemap has been
added
* MLL:
* Python bindings for MLL have been added. There are no samples
yet.
* HighGUI:
* [Windows, 32bit] Added support for videoInput library. Hence,
cvcam is [almost] not needed anymore
* [Windows, 32bit] FFMPEG can now be used for video
decoding/encoding via ffopencv*.dll
* [Linux] Added unicap support
* Improved internal video capturing and video encoding APIs
* Documentation:
* OpenCV book has been published (sold separately :) see
docs/index.htm)
* New samples (opencv/samples):
* Many Octave samples
* find_obj.cpp (SURF), bgfg_codebook.cpp (Codebook BG/FG
segmentation),
* stereo_calib.cpp (Stereo calibration and stereo
correspondence)
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version, and bump all depends.
Per discussion on pkgsrc-changes.
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Also add some patches to remove use of deprecated symbols and fix other
problems when looking for or compiling against libpng-1.4.x.
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a dependency.
PKGREVISION++
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package maintainer who sent PR pkg/34655.
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OpenCV means Intel(R) Open Source Computer Vision Library. It is a
collection of C functions and a few C++ classes that implement many
popular Image Processing and Computer Vision algorithms.
OpenCV provides cross-platform middle-to-high level API that includes
about 300 C functions and a few C++ classes. Also there are Python
bindings to OpenCV. OpenCV has no strict dependencies on external
libraries, though it can use some (such as libjpeg, ffmpeg, GTK+ etc.)
OpenCV provides transparent interface to Intel(R) Integrated Performance
Primitives (IPP). That is, it loads automatically IPP libraries optimized
for specific processor at runtime, if they are available.
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