I am developing a sample camera and I am able to control image sensor directly. The sensors gives out Bayer image and I need to do show images as live view.
I looked at debayering codes and also white balancing. Is there any library in C/C++ that can help me in this process?
Since I need to have live view, I need to do these things very fast and hence I need algorithms that are very fast.
For example, I can change the RGB gains on sensor and hence I need an algorithm that act at that level, instead of acting on generated image.
Is there any library that help to save images in raw format?
simplecv has a function for white balance control:
simplecv project web site
Related
I'm really interested in the Google Street View mobile application, which integrates a method to create a fully functional spherical panorama using only your smartphone camera. (Here's the procedure for anyone interested: https://www.youtube.com/watch?v=NPs3eIiWRaw)
What strikes me the most is that it always manages to create the full sphere, even when stitching a feature-less near monochrome blue sky or ceiling ; which gets me to thinking that they're not using feature based matching.
Is it possible to get a decent quality full spherical mosaic without using feature based matching and only using sensor data? Are smartphone sensors precise enough? What library would be usable to do this? OpenCV? Something else?
Thanks!
The features are needed for registration. In the app the clever UI makes sure they already know where each photo is relative to the sphere so in the extreme case all the have to do is reproject/warp and blend. No additional geometry processing needed.
I would assume that they do try to do some small corrections to improve the registration, but even if these fail, you can fallback onto the sensor based ones acquired at capture time.
This is a case where a clever UI makes the vision problem significantly easier.
While I have been researching best practices and experimenting multiple options for an ongoing project(i.e. Unity3D iOS project in Vuforia with native integration, extracting frames with AVFoundation then passing the image through cloud-based image recognition), I have come to the conclusion that I would like to use ARkit, Vision Framework, and CoreML; let me explain.
I am wondering how I would be able to capture ARFrames, use the Vision Framework to detect and track a given object using a CoreML model.
Additionally, it would be nice to have a bounding box once the object is recognized with the ability to add an AR object upon a gesture touch but this is something that could be implemented after getting the solid project down.
This is undoubtedly possible, but I am unsure of how to pass the ARFrames to CoreML via Vision for processing.
Any ideas?
Update: Apple now has a sample code project that does some of these steps. Read on for those you still need to figure out yourself...
Just about all of the pieces are there for what you want to do... you mostly just need to put them together.
You obtain ARFrames either by periodically polling the ARSession for its currentFrame or by having them pushed to your session delegate. (If you're building your own renderer, that's ARSessionDelegate; if you're working with ARSCNView or ARSKView, their delegate callbacks refer to the view, so you can work back from there to the session to get the currentFrame that led to the callback.)
ARFrame provides the current capturedImage in the form of a CVPixelBuffer.
You pass images to Vision for processing using either the VNImageRequestHandler or VNSequenceRequestHandler class, both of which have methods that take a CVPixelBuffer as an input image to process.
You use the image request handler if you want to perform a request that uses a single image — like finding rectangles or QR codes or faces, or using a Core ML model to identify the image.
You use the sequence request handler to perform requests that involve analyzing changes between multiple images, like tracking an object's movement after you've identified it.
You can find general code for passing images to Vision + Core ML attached to the WWDC17 session on Vision, and if you watch that session the live demos also include passing CVPixelBuffers to Vision. (They get pixel buffers from AVCapture in that demo, but if you're getting buffers from ARKit the Vision part is the same.)
One sticking point you're likely to have is identifying/locating objects. Most "object recognition" models people use with Core ML + Vision (including those that Apple provides pre-converted versions of on their ML developer page) are scene classifiers. That is, they look at an image and say, "this is a picture of a (thing)," not something like "there is a (thing) in this picture, located at (bounding box)".
Vision provides easy API for dealing with classifiers — your request's results array is filled in with VNClassificationObservation objects that tell you what the scene is (or "probably is", with a confidence rating).
If you find or train a model that both identifies and locates objects — and for that part, I must stress, the ball is in your court — using Vision with it will result in VNCoreMLFeatureValueObservation objects. Those are sort of like arbitrary key-value pairs, so exactly how you identify an object from those depends on how you structure and label the outputs from your model.
If you're dealing with something that Vision already knows how to recognize, instead of using your own model — stuff like faces and QR codes — you can get the locations of those in the image frame with Vision's API.
If after locating an object in the 2D image, you want to display 3D content associated with it in AR (or display 2D content, but with said content positioned in 3D with ARKit), you'll need to hit test those 2D image points against the 3D world.
Once you get to this step, placing AR content with a hit test is something that's already pretty well covered elsewhere, both by Apple and the community.
I am working on a project with gesture recognition. Now I want to prepare a presentation in which I can only show images. I have a series of images defining a gesture, and I want to show them in a single image just like motion history images are shown in literature.
My question is simple, which functions in opencv can I use to make a motion history image using lets say 10 or more images defining the motion of hand.
As an example I have the following image, and I want to show hand's location (opacity directly dependent on time reference).
I tried using GIMP to merge layers with different opacity to do the same thing, however the output is not good.
You could use cv::updateMotionHistory
Actually OpenCV also demonstrates the usage in samples/c/motempl.c
I need to write an application that uses image processing functionality to identify the type of lightning in an image. The lightning types that it has to identify are the cloud to ground and the intracloud lightning which are shown in the pictures below. The cloud to ground lightning has these features: it hits the ground and has flashes branching downwards and the features of the intracloud lightning are that: it has no contact with the ground. Are there any image processing algorithms that you guys know which i can use to identify these features in the image such that the application will be able to identify the lightning type? I want to implement this in C++ using the CImg library.
Thanking you in advance
!!Since I cant upload photos because am a new user, i posted the links to the images!!
http://wvlightning.com/types.shtml
Wow, this seems like a fun algorithm. If you had a large set of images for each type you might be able to use HAAR training (http://note.sonots.com/SciSoftware/haartraining.html) but I'm not sure that would work because of the form of lightning. Maybe HAAR in combination with your own algorithm. For instance it should be very straightforward to know whether the lightning goes to the ground. You could use some OpenCV image analysis to do that - http://www.cs.iit.edu/~agam/cs512/lect-notes/opencv-intro/
I'm building a web cam application as my C++ project in my college. I am integrating QT (for GUI) and OpenCV (for image processing). My application will be a simple web cam app that will access the web cam, show/record videos, capture images and other stuffs.
Well, I also want to put in a feature to add cliparts to captured images, or the streaming video. While on my research, I found out that there is no way we can overlay two images using OpenCV. The best alternative I was able to find was to reconfigure the whole image to add the clipart into the original image making it a single image. You see, that's not going to work for me as I have to be able to move the clipart and resize or rotate the clipart in my canvas.
So, I was wondering if anybody could tell me how to achieve the effect I want most efficiently.
I would really appreciate your help. The deadline for the project submission is closing in and its a huge bump on the road to completion. PLEEEASE... RELP!!
If you just want to stick a logo onto the openCV image then you simply define a region of interest (roi) on the destination video image and copy the source image to this (the details vary with each version of opencv)
If you want the logo to be semi transparent - like a TV channel ID - then you can copy the image but loop over the pixels writing a destination that is source_pixel/2 + dest_pixel/2;