I am writing C++ class for managing multiple cameras and reading frames from them. Let's say it is wrapper for OpenCV. Currently I am finding cameras by trying to create devices from 0-10 range and If there is output I know that I've found working camera. I can always save internal IDs of those cameras to distinguish them but what If another camera is plugged in? It may break the order of IDs. So is there any way to distinguish OpenCV cameras for example by getting their hardware IDs?
I know this doesn't help you much, but the short answer is "No, OpenCV doesn't currently provide that capability."
According to the doc, any hardware ids are not properties you can retrieve using the get method or any other.
Having said that, if you're very intent on using OpenCV, I would still test the behavior of OpenCV 2.4.10 on various platforms and using various middleware and see how it behaves. If you get a consistent behavior, then you can run with it, but be somewhat ready for it to break in the future. What would work for you is that OpenCV is using various middleware in the backend, such as V4L, Qt, etc., and these are well-maintained and more-or-less consistent.
In retrospect, I would stay away from OpenCV's video interface altogether right now for commercial software, unless you're okay with the situation I described. Beware that OpenCV 3.0 videoio library is unstable at this point and has open bug reports.
Related
I'm wondering whether there is OpenCV documentation on VideoCapture lists compatible sensor configurations e.g. Resolution < 640x480 || FPS < 1e100. Basically I am wondering whether the class is compatible with large resolutions such as 1960x1080 or even 2304x1536, and whether there are run-time implications of this even if the height/width fields can be set.
OpenCV provides a simple cross-platform video input API. It does not attempt to provide a comprehensive feature list, and even (some of) the properties it does provide, do not alwyas work with all hardware and platforms.
The whole highgui module is mostly aim at rapid prototyping and not for bulletproof video I/O. If your platform provides more advanced SDKs for image capture and configuration, you can use those SDKs and simply pass the resulting image buffers to be wrapped with cv::Mat().
I am using OpenCV to show image on the projector. But it seems the cv::imshow is not fast enough or maybe the data transfer is slow from my CPU to GPU then to projector, so I wonder if there is a faster way to display than OpenCV?
I considered OpenGL, since OpenGL directly uses GPU, the command may be faster than from CPU which is used by OpenCV. Correct me if I am wrong.
OpenCV already supports OpenGL for image output by itself. No need to write this yourself!
See the documentation:
http://docs.opencv.org/modules/highgui/doc/user_interface.html#imshow
http://docs.opencv.org/modules/highgui/doc/user_interface.html#namedwindow
Create the window first with namedWindow, where you can pass the WINDOW_OPENGL flag.
Then you can even use OpenGL buffers or GPU matrices as input to imshow (the data never leaves the GPU). But it will also use OpenGL to show regular matrix data.
Please note:
To enable OpenGL support, configure OpenCV using CMake with
WITH_OPENGL=ON . Currently OpenGL is supported only with WIN32, GTK
and Qt backends on Windows and Linux (MacOS and Android are not
supported). For GTK backend gtkglext-1.0 library is required.
Note that this is OpenCV 2.4.8 and this functionality has changed quite recently. I know there was OpenGL support in earlier versions in conjunction with the Qt backend, but I don't remember when it was introduced.
About the performance: It is a quite popular optimization in the CV community to output images using OpenGL, especially when outputting video sequences.
OpenGL is optimised for rendering images, so it's likely faster. It really depends if the OpenCV implementation uses any GPU acceleration AND if the bottleneck is on rendering side of things.
Have you tried GPU accelerated OpenCV? - http://opencv.org/platforms/cuda.html
How big is the image you are displaying? How long does it take to display the image using cv::imshow now?
I know it's an old question, but I happened to have exactly the same problem. And from my observations I've concluded that the root of the problem is the projector's own latency, especially if one is using an older model.
How have I concluded it?
I displayed the same video sequence with cv::imshow() on the laptop monitor and on the projector. Then I waved my hand. It was obvious, that projector introduces significant latency.
To double-check, I've opended a webcam video, waved my hand in front of it and observed the difference on the monitor and on the projector. Webcam does no processing, no opencv operations, so in my understanding the only thing that would explain the latency would be the projector itself.
I recently saw the virtual mirror concept on you tube, I tried it out and researched about it. It seems that the creators have used augmented reality so that people can see the output on their screens. On researching I found out that we identify a pattern on which a 3D image is superimposed.
Question 1:How are they able to superimpose the jewellery and track the face of the person without identifying any pattern?
I also tried to check various libraries that I can use to make a program similar to the one they show. Seems to me that a lot of people are using Android phones and iPhones and making apps that use augmented reality.
Question 2:Is there any way that I can use c++ and try to make a program that uses augmented reality?
Oh, and the most important thing, the link to the application is provided below:
http://www.boutiqueaccessories.com.au/virtual-mirror/w1/i1001664/
Do try it out. Its a good experience. :D
I'm not able to actually try the live demo, but the linked video suggests that they either use some simplified pattern recognition (get the person's outline), or they simply track you based on the initial image (with your position/texture being determined by the outline being shown.
Following the video, it's easy to see that there's no real/advanced AR behind this. The images are simply overlayed or hidden (e.g. in case it's missing track of one ear due to you looking to the side) and they're not transformed (no perspective or resizing happening). They definitely seem to track the head (or features like ears, neck, etc.). depending on your background and surroundings that's actually a rather trivial task.
Question 2: Sure! There are lots of premade toolsets out there, but you could as well use some general image processing library such as OpenCV to do the math. Augmented reality usually uses some kind of pattern (e.g. a card or page with a known pattern) to determine the correct position and transformation for the contents to be added to the image. There are also approaches using the device's orientation and perspective changes in camera images to determine depth/position (I really like this demo).
I was trying to get RGBDemo(mostly reconstructor) working with 2 logitech stereo cameras, but I did not figure out how to do it.
I noticed that there is a opencv grabber in nestk library and its header file is included in the reconstructor.cpp. Yet, when I try "rgbd-viewer --camera-id 0", it keeps looking for kinect.
My questions:
1. Is RGBDemo only working with kinect so far?
2. If RGBDemo can work with non-kinect stereo cameras, how do I do that?
3. If I need to write my own implementation for non-kinect stereo cameras, any suggestion on how to start?
Thanks in advance.
if you want to do it with non-kinect cameras. You don't even need stereo. There are algorithms now that are able to determine whether two images' viewpoints are sufficiently different that they can be used as if they were taken by a stereo camera. In fact, they use images from different cameras that are found on the internet and reconstruct 3D models of famous places. I can write you a tutorial on how to get it working. I've been meaning to do so. The software is called Bundler. Along with Bundler, people often also use CMVS and PMVS. CMVS preprocesses the images for PMVS. PMVS generates dense clouds.
BUT! I highly recommend that you don't go this route. It makes a lot of mistakes because there is so much less information in 2D images. It makes it very hard to reconstruct the 3D model. So, it ends up making a lot of mistakes, or not working. Although Bundler and PMVS are awesome compared to previous software, the stuff you can do with kinect is on a whole other level.
To use kinect will only cost you $80 for the kinect off of ebay or $99 off of amazon and another $5 for the power adapter off of amazon. So, I'd highly recommend this route. Kinect provides much more information for the algorithm to work with than 2D images do, making it much more effective, reliable and fast. In fact, it could take hours to process images with Bundler and PMVS. Whereas with kinect, I made a model of my desk in just a few seconds! It truly rocks!
i intend to interact with a pc camera using c plus plus. Are there any libraries you can recommend or ways to go about ? The idea is to take shots with a Samsung wireless cam then get the images transferred unto a pc with cam and on the pc show images as per the face detection using the cam . any ideas where to start ?
Well, there's a wide range of ways to do it. Professional cameras have accompanying SDKs. If you camera is supported by it (I believe most webcams are) you should try OpenCV for a start.
Googling for "opencv face recognition" will yield plenty of hits, so you have a lot of reference material.
Try this reference (Face and Eyes Detection Using OpenCV) for a start.
After you start your project, you can specific questions on StackOverflow or on the OpenCV Yahoo! group.
Other options are:
1) openFrameworks.
Quoting from their website:
Openframeworks is a c++ library designed to assist the creative process by providing a simple and intuitive framework for experimentation.
The library is designed to work as a general purpose glue, and wraps together several commonly used libraries under a tidy interface: openGL for graphics, rtAudio for audio input and output, freeType for fonts,freeImage for image input and output, quicktime for video playing and sequence grabbing.
2) Qt
If you decide Qt, see this related question: Displaying WebCam video with Qt