I tried searching a lot about developing 360 camera like Google Street View but still not able to reach through the solution.
I tried with the this panoramagl-android but this is not what i am looking for.
So can any one please give me idea or suggest anything to create spherical camera application.
360 images and videos are generally created with dedicated cameras or groups of regular cameras, and the result then 'stitched' together to produce the 360 representation.
The usual way to represent a 360 image or video at this time time is an equi-rectangular projection, similar to the technique used to depict the spherical globe on flat maps of the world.
If you are trying to do this with a regular phone you face the issue that you only have one camera, so you won't get the an image from multiple cameras at the same time to stitch together. This is maybe easier to understand visually - this is an example of a set up to capture multiple views:
You then need software to 'stitch' the different videos together. There are quite a few options, many being proprietary, VideoStitch is probably the best known at this time: http://www.video-stitch.com/.
Note that this is processing intensive so it nearly always done on relatively high powered servers rather than on mobile devices.
Related
I'm working on a robot that is controller via the VR headset and sends a real-time video feed to the headset.
I've chosen to go the native way on Android and now have everything I need to receive the video stream and encode it (using GStreamer) and also to send the control data to the robot via UDP.
The last thing to do (and the one I most struggle with as I nave no prior experience with computer graphics) is to draw the image (encoded camera feed) to the screen. In the last few days, I've been reading stuff about how Vulkan and OpenGL works, I've also went through the examples provided in Oculus Mobile SDK (mainly VRCubeWorld_SurfaceView) but that's way to complex for what I need, I've tried to simplify it so I could just draw two images, but then I thought.
Do I even need any of that? And this question might sound stupid, but I really don't have any prior experience doing this.
I mean, the example is using OpenGL to basically compute all the layers of the 3D scene, apply colors and then fuse them together to get a final frame that is passed to VR_API via the function:
vrapi_SubmitFrame2(appState.Ovr, &frameDesc);
Can I just take those images, and somehow force them into the frameDesc structure to skip the whole OpenGL pipeline? If so, can anyone knowledgeable enough point me to a working solution?
I don't need any kind of panning over the images, just to render them. Later I'll be using head sensor data, but it won't actually do anything with the "scene".
I'm building an Android app that has to identify, in realtime, a mark/pattern which will be on the four corners of a visiting card. I'm using a preview stream of the rear camera of the phone as input.
I want to overlay a small circle on the screen where the mark is present. This is similar to how reference dots will be shown on screen by a QR reader at the corner points of the QR code preview.
I'm aware about how to get the frames from camera using native Android SDK, but I have no clue about the processing which needs to be done and optimization for real time detection. I tried messing around with OpenCV and there seems to be a bit of lag in its preview frames.
So I'm trying to write a native algorithm usint raw pixel values from the frame. Is this advisable? The mark/pattern will always be the same in my case. Please guide me with the algorithm to use to find the pattern.
The below image shows my pattern along with some details (ratios) about the same (same as the one used in QR, but I'm having it at 4 corners instead of 3)
I think one approach is to find black and white pixels in the ratio mentioned below to detect the mark and find coordinates of its center, but I have no idea how to code it in Android. I looking forward for an optimized approach for real-time recognition and display.
Any help is much appreciated! Thanks
Detecting patterns on four corners of a visiting card:
Assuming background is white, you can simply try this method.
Needs to be done and optimization for real time detection:
Yes, you need OpenCV
Here is an example of real-time marker detection on Google Glass using OpenCV
In this example, image showing in tablet has delay (blutooth), Google Glass preview is much faster than that of tablet. But, still have lag.
I have a client who works on styling cars, he needs an app that lets the user take several pictures of their car and render 1 3d image he can use to look around the car. Is there any way to do this? I have been searching for methods but can't find a solution.
I'm working on a project to recognize insects from user inputted images. I think that OpenCV is the route I'd like to take since I've worked with it before for facial recognition. I'm not using the camera feed and am instead using images provided by the user. For early development I plan to build in some sample images to ensure the concept is working before moving on to other features.
I would like to use 4-5 template images for each insect and have that be robust enough to detect the insect from the input image. If there are multiple insects I would like for them all to be detected and have their own rectangle drawn around them.
With that brief explanation, I am wondering what the best way to complete this task is. I know that OpenCV has template recognition, but the template size matters and I don't want to make the user ensure their insect is a certain amount of pixels in their image. Is there a way to work around this, possibly by rotating the template images or using variously sized templates? Or is there a better approach than template recognition for this project?
Unfortunately without some form of constraints, you are essentially asking if computer vision has been solved! You have several unresolved, but very interesting research problems.
Lets reduce the problem to just classifying a sample insect in a fixed pose with controlled lighting as of belonging to one of 100k insects categories; that would be tough.
Lets reduce the problem to recognizing a single insect instance in an arbitrary pose in 3d space; that would be tough.
Lets reduce the problem to recognizing a single insect instance in the same pose under arbitrary lighting conditions viewed with arbitrary optical sensors, that would be tough.
Successful computer-vision in the wild, is all about cleverly constraining the operating conditions, otherwise you are in research land. If your are in research land, then a cool thing to do is to try and exploit 3D CAD models to capture the huge variety in poses, here's a nice one on recognizing chairs,
http://www.di.ens.fr/willow/research/seeing3Dchairs/,
If not conducting research and, say your building a app, then you need to consider how you can guide the user, train the user, trick the user, into providing the best operating conditions for the recognition system.
(This was to big to put in comments)
i just need some guide on how to detect a marker and make an output text.. for ex: a marker with an image of a dog , when detected, i have an output text "DOG" in a textfield .. can someone help me with my idea? oh, btw which one is more effective to use nyartoolkit or andar for my idea?thanks:) need help..!
What you're looking for isn't augmented reality, it's object recognition. AR is chiefly concerned with presenting data overlaid on the the real world, so computation is devoted each frame to determining the position relative to the camera of the object. If you don't intent to use this data, AR libraries may be an inefficient. That said...
AR marker tracking libraries usually find markers by prominent features like corners, and can distinguish markers by binary patters encoded inside the marker, or in the marker's borders. If you're happy with having the "dog" part encoded in the border of a marker, there are libraries you can use like Qualcomm's AR development kit. This library, and Metaio's Unifeye mobile can also do natural feature tracking on pre-defined images. If you're happy with being able to recognize one specific image or images of dogs that you have defined in advance, either of these should be ok. You might have to manipulate your dog images to get good features they can identify and track. Natural objects can be problematic.
General object recognition (being able to recognize a picture of any dog, not known beforehand) is still a research topic. There are approaches, but they're mostly very computationally intensive, and most mobile solutions involve offloading the serious computation to a server. Recognition of simple outline sketches however is more tractable, there's a great paper called "Shape recognition and pose estimation for mobile augmented reality" (I can't find a copy online, but the IEEE link is here) that uses contours to identify objects - this is light enough to run on a mobile (and it's pure genius).