I'm trying to simulate a shaky cam in a static video. I could choose a couple of points randomly and then pan/zoom/warp using easing, but I was wondering if there's a better, more standard way.
A shaky camera will usually not include zooming. The image rotation component would also be very small, and can probably be ignored. You can probably get sufficient results with 2D translation only.
What you should probably do is define your shake path in time - the amount of image motion from the original static video for each frame - and then shift each frame by this amount.
You might want to crop your video a bit to hide any blank parts near the image border, remaining blank regions may be filled using in-painting. This path should be relatively smooth
and not completely random jitter since you are simulating physical hand motion.
To make the effect more convincing, you should also add motion-blur.
The direction of this blur is the same as the shake-path, and the amount is based on the current shake speed.
Related
I am trying the stereo_calib example and it fails with garbage output. For instance:
However, it is finding corners in my images...
My xml file and images are all here:
https://drive.google.com/open?id=12-5jBN7FK-LO6SLb4r3YYkrOnP7f_xmG
What am I doing wrong? I first tried printing a pattern on a sheet of paper, then thought ok that must be too wavy or something, so had this printed on foam board. But no dice.
(we chatted on a side channel, so this is to the benefit of the rest of the world)
tl;dr: hold the board very still or get a camera with global shutter.
Rolling shutter (see here and there), an attribute of most webcam sensors, many camcorder sensors, and some industrial image sensors, will distort objects that are moving. If you've moved the board even just a little during a frame capture (visible in files right19/right20), it will be captured with distortion. That will affect everything you do with the picture, starting with intrinsic calibration.
To give a sense of scale for the distortions: assuming a 30 FPS video stream, the worst case rolling shutter lag is 33 ms. A pedestrian travels 40-50 mm in that time. If your hands are moving slightly, you can maybe expect a tenth of that, which is still a lot in proportion to the square sizes most people use.
Another source of trouble is printers. If you've printed your checkerboard pattern, make sure to measure the width and height of your squares. they might be slightly rectangular. It's also a good idea to make sure the pattern is quite flat, not bent.
I'm doing some work with a camera and video stabilization with OpenCV.
Let's suppose I know exactly (in meters) how much my camera has moved from one frame to another and I want to use this to return the second frame where it should be.
I'm sure I have to do some math with this number before I make the translation matrix, but i'm a little lost with that... Any help?
Thanks.
EDIT:Ok I'll try to explain it better:
I want to remove from a video the movement (shaking) of the camera and I know how much the camera has moved (and the direction) from one frame to another.
So what I want to do is to move back the second frame where it should be using that information I have.
I have to make a traslation matrix for each two frames and apply it to the second frame.
But here is when I doubt: As the info I have is en meters and is the movement of the camera, and now I'm working with a image and pixels, I think I have to do some operations so the traslation is correct, but I'm not sure what they are exactly
Knowing how much the camera has moved is not enough for creating a synthesized frame. For that you'll need the 3D model of the world as well, which I assume you don't have.
To demonstrate that assume the camera movement is pure translation and you are looking at two objects, one is very far - a few kilometers away and the other is very close - a few centimeters away. The very far object will hardly move in the new frame, while the very close one can move dramatically or even disappear from the field of view of the second frame, you need to know how much the viewing angle has changed for each point and for that you need the 3D model.
Having sensor information may help in the case of rotation but it is not as useful for translations.
I have a video of soccer in which the players are relatively far away from the camera and thus represent small portions of the image. I'm using background subtraction to detect the players and the results are fine but I have been asked to try detecting using Hog.
I tried using the detect MultiScale using the default descriptors presented on opencv but i cant get any detection. I dont really understand how can I make it work on this case, because on other sequences where the people are closer to the camera, the detector works fine.
Here is a sample image link
Thanks.
The descriptor you use with HOG determines the minimum size of person you can detect: with the DefaultPeopleDetector the detection window is 128 pixels high x 64 wide, so you can detect people around 90px high. With the Daimler descriptor the size you can detect is a bit smaller.
Your pedestrians are still too small for this, so you may need to magnify the whole image, or just the parts which show up as foreground using background segmentation.
Have a look at the function definition for detectMultiscale http://docs.opencv.org/modules/objdetect/doc/cascade_classification.html#cascadeclassifier-detectmultiscale
It might be that you need to reduced the value of minsize so as to detect smaller people or the people might just be too far away.
I have been looking for a bit and know that people are able to track faces with core image and openGL. However I am not that sure where to start the process of tracking a colored ball with the iOS camera.
Once I have a lead to being able to track the ball. I hope to create something to detect. when the ball changes directions.
Sorry I don't have source code, but I am unsure where to even start.
The key point is image preprocessing and filtering. You can use the Camera API-s to get the video stream from the camera. Take a snapshot picture from it, then you should use a Gaussian-blur on it (spatial enhance), then a Luminance Average Threshold Filter (to make black and white image). After that a morphological preprocessing should be wise (opening, closing operators), to hide the small noises. Then an Edge detection algorithm (with for example a Prewitt-operator). After these processes only the edges remain, your ball should be a circle (when the recording environment was ideal) After that you can use a Hough-transform to find the center of the ball. You should record the ball position and in the next frame, the small part of the picture can be processed (around the ball only).
Other keyword could be: blob detection
A fast library for image processing (on GPU with openGL) is Brad Larsons: GPUImage library https://github.com/BradLarson/GPUImage
It implements all the needed filter (except Hough-transformation)
The tracking process can be defined as following:
Having the initial coordinate and dimensions of an object with a given visual characteristics (image features)
In the next video frame, find the same visual characteristics near the coordinate of the last frame.
Near means considering basic transformations related to the last frame:
translation in each direction;
scale;
rotation;
The variation of these tranformations are strictly related with the frame rate. Higher the frame rate, nearest the position will be in the next frame.
Marvin Framework provides plug-ins and examples to perform this task. It's not compatible with iOs yet. However, it is open source and I think you can port the source code easily.
This video demonstrates some tracking features, starting at 1:10.
I've 2 pictures of the same scene from an uncalibrated camera. The pics are from a slightly different angle and scale(zoom) and I'd like to superpose them, rejecting any kind of shake. In other words, I should transform them so the shake becomes imperceptible, do a Motion Compensation.
I've already tried using a simple SURF (feature) detector along with Homography but sometimes the result isn't satisfactory. So I am thinking about trying Image Rectification to compensate the motion.
- Would it work with slight changes, such as user shake?
- Would it really work to reject shake for these 2 frames? And for a bigger buffer of pictures (10 maybe)?
- Anyone knows if it would fix scale disparity (different zoom in the images)?
- What the algorithm really do? Will it transform both pictures into a third orientation?
If there is a better solution, I would be glad to know =)
EDIT
I don't aim to compensate blur motion but the displacement itself. For example, in this file the author compensates the angle difference between two cameras by Image Rectification. How does it actually work? Does it always create an intermediate picture orientation or can I specify that one of the pictures shall remains still??
Also, would I be able to apply this to many frames or it would always find an intermediate orientation for each two frames I put in?
Cheers,
I'm not sure how well superimposing the images would work. Another way to remove blur (including motion blur which should dominate in handheld camera devices) from an image is by blind deconvolution. It is basically a method of finding the inverse of the blur filter that was physically applied (camera shaken) to the real image. There's plenty of techniques out on the web. I've specifically had good results using a modified version of the algorithm in this paper: http://www.cse.cuhk.edu.hk/~leojia/all_final_papers/motion_deblur_cvpr07.pdf
It also comes with an executable file somewhere around the web so you can see if it's fit for your purpose.
Good luck out there!