I was wondering if there are any performance increases when changing from OpenCV2.1 to OpenCV2.4?
Also, I've read a little about GPU development with OpenCV. Can someone recommend any beginner guides/primers? Is it as simple as redifining the matrix as cv::gpu::GpuMat?
There is a set of performance tests in the full source build - but generally each release of openCV tries to improve performance where possible
Yes using the GPU is essentially as straight forward as use cv::gpu::Mat although not all code is ported to CUDA and of course not all code will benefit, see http://docs.opencv.org/modules/gpu/doc/gpu.html
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I was wondering whether I could leverage the modularity drake gives to test Visual SLAM algorithms on realtime data. I would like to create 3 blocks that output acceleration, angular speed, and RGBD data. The blocks should pull information from a real sensor. Another block would process the data and produce the current transform of the camera and a global map. Effectively, I would like to cast my problem into a "Systems" framework so I can easily add filters where I need them.
My question is: Given other people's experience with this library, is Drake the right tool for the job for this usecase? Specifically, can I use this library to process real time information in a production setting?
Visual SLAM is not a use case I've implemented myself, but I believe the Drake Systems framework should be up to the task, depending on what you mean by "realtime".
We definitely ship RGBD data through the framework often.
We haven't made any attempt to support running Drake in hard realtime, but certainly can run at high rates. If you were to hit a performance bottleneck, we tend to be pretty responsive and would welcome PRs.
As for the "production-level", it is certainly our intention for the code / process to be mature enough for that setting, and numerous teams do already.
I've finished an algorithm aimed to foreground extraction based on video recently, but it processes too slowly per frame. There is an algorithm based on Mixed Gaussian Model named BackgroundSubtractorMOG2 in OpenCV3.0 and I find it processes quickly as nearly 15 times as mine per frame. I just wonder is it accelerated by OpenCL on GPU ? Or it is just run on CPU? p.s. I've seen some source codes of it and noticed there are OpenCL blocks but I'm not sure since I'm fresh. I will be very appreciated if anyone could help me figure it out!
If you look at the API page here You will find the line:
The function implements a sparse iterative version of the Lucas-Kanade optical flow in pyramids. See [Bouguet00]. The function is parallelized with the TBB library.
The TBB library is a parallization library and is used to "write parallel C++ programs that take full advantage of multicore performance" - this means that it is using more than just one CPU at a time, a much quicker way of processing. This can be seen on lines like this (Line 566):
parallel_for_(Range(0, image.rows),
MOG2Invoker(image, fgmask,
(GMM*)bgmodel.data,
(float*)(bgmodel.data + sizeof(GMM)*nmixtures*image.rows*image.cols),
bgmodelUsedModes.data, nmixtures, (float)learningRate,
(float)varThreshold,
backgroundRatio, varThresholdGen,
fVarInit, fVarMin, fVarMax, float(-learningRate*fCT), fTau,
bShadowDetection, nShadowDetection));
I am going to train my Haar classifier for flowers(which I am highly skeptical about). I have been following the CodingRobin Tut for everything.
http://coding-robin.de/2013/07/22/train-your-own-opencv-haar-classifier.html
Now, it has been emphasized that I use GPU support, multithreading etc. otherwise the training is gonna take days. I am going to use pre-built libraries and therefore the pre-built opencv_traincascade utility.
I want to ask beforehand, Will I be able to leverage GPU support if I use the pre-built libs, given that I install CUDA?
Where does TBB fit in the whole picture?
Do you recommend me building the whole library from scratch with TBB and CUDA support checked, or that would be a waste?
Note: I am using OpenCV 2.4.11. And I am a complete beginner to OpenCV.
I need to develop an image processing program for my project in which I have to count the number of cars on the road. I am using GPU programming. Should I go for OpenCV program with GPU processing feature or should I develop my entire program on CUDA without any OpenCV library?
The algorithms which I am using for counting the number of cars is background subtraction, segmentation and edge detection.
You can use GPU functions in OpenCV.
First visit the introduction about this : http://docs.opencv.org/modules/gpu/doc/introduction.html
Secondly, I think above mentioned processes are already implemented in OpenCV optimized for GPU. So It will be much easier to develop with OpenCV.
Canny Edge Detection : http://docs.opencv.org/modules/gpu/doc/image_processing.html#gpu-canny
PerElement Operations (including subtraction): http://docs.opencv.org/modules/gpu/doc/per_element_operations.html#per-element-operations
For other functions, visit OpenCV docs.
OpenCV, no doubt, has the biggest collection of Image processing functionality and recently they've started porting functions to CUDA as well. There's a new GPU module in latest OpenCV with few functions ported to CUDA.
Being said that, OpenCV is not the best option to build a CUDA based application as there are many dedicated CUDA libraries like CUVI that beat OpenCV in Performance. If you're looking for an optimized solution, you should also give them a try.
I'm working on robot vision system and its main purpose is to detect objects, i want to choose one of these libraries (CImg , OpenCV) and I have knowledge about both of them.
The robot I'm using has Linux , 1GHz CPU and 1G ram and I'm using C++ the size of image is 320p.
I want to have a real-time image processing near 20 out of 25 frames per seconds.
In your opinion which library is more powerful l although I have tested both and they have the same process time, open cv is slightly better and I think that's because I use pointers with open cv codes.
Please share your idea and your reason.
thanks.
I think you can possibly get best performance when you integrated - OpenCV with IPP.
See this reference, http://software.intel.com/en-us/articles/intel-integrated-performance-primitives-intel-ipp-open-source-computer-vision-library-opencv-faq/
Here is another reference http://experienceopencv.blogspot.com/2011/07/speed-up-with-intel-integrated.html
Further, if you freeze the algorithm that works perfectly, usually you can isolate your algorithm and work your way towards doing serious optimization (such as memory optimization, porting to assembly etc.) which might not be ready to use.
It really depends on what you want to do (what kind of objects you want to detect, accuracy, what algorithm you are using etc..) and how much time you have got. If it is for generic computer vision/image processing, I would stick with OpenCV. As Dipan said, do consider further optimization. In my experience with optimization for Computer Vision, the bottleneck usually is in memory interconnect bandwidth (or memory itself) and so you might have to trade in cycles (computation) to save on communication. Do understand the algorithm really well to further optimize the algorithm (which at times can give huge improvements as compared to compilers).