Is there a way within ol3 to scale up/down the tiles within a given zoomlevel?
What I would like to achieve:
Some maps are really hard to read on small devices - especially if you have a little greater viewing distance. So it could be helpful to be able to apply a moderate scale (e.g. 2) so that a tile of 256x256 would be rendered at 512x512 (more ore less what ol3 is doing if we allow for higher zoom then our source provides).
A made a simple example that scales the container - see at http://wellenvogel.github.io/ol3-scaletest/ - but this has great disadvantages:
it would break ol3 pointer coordinate detection
it would scale all vector layers - not very usefull
So I'm wondering if there is any chance to achieve this - e.g. by setting different resolutions at the view/tilegrid/source.
Related
There was a gif on the internet where someone used some sort of CAD and drew multiple vector pictures in it. On the first frame they zoom-in on a tiny dot, revealing there a whole new different vector picture just on a different scale, and then they proceed to zoom-in further on another tiny dot, revealing another detailed picture, repeating several times. here is the link to the gif
Or another similar example: imagine you have a time-series with a granularity of a millisecond per sample and you zoom out to reveal years-worth of data.
My questions are: how such a fine-detailed data, in the end, gets rendered, when a huge amount of data ends up getting aliased into a single pixel.
Do you have to go through the whole dataset to render that pixel (i.e. in case of time-series: go through million records to just average them out into 1 line or in case of CAD render whole vector picture and blur it into tiny dot), or there are certain level-of-detail optimizations that can be applied so that you don't have to do this?
If so, how do they work and where one can learn about it?
This is a very well known problem in games development. In the following I am assuming you are using a scene graph, a node-based tree of objects.
Typical solutions involve a mix of these techniques:
Level Of Detail (LOD): multiple resolutions of the same model, which are shown or hidden so that only one is "visible" at any time. When to hide and show is usually determined by the distance between camera and object, but you could also include the scale of the object as a factor. Modern 3d/CAD software will sometimes offer you automatic "simplification" of models, which can be used as the low res LOD models.
At the lowest level, you could even just use the object's bounding
box. Checking whether a bounding box is in view is only around 1-7 point checks depending on how you check. And you can utilise object parenting for transitive bounding boxes.
Clipping: if a polygon is not rendered in the view port at all, no need to render it. In the GIF you posted, when the camera zooms in on a new scene, what is left from the larger model is a single polygon in the background.
Re-scaling of world coordinates: as you zoom in, the coordinates for vertices become sub-zero floating point numbers. Given you want all coordinates as precise as possible and given modern CPUs can only handle floats with 64 bits precision (and often use only 32 for better performance), it's a good idea to reset the scaling of the visible objects. What I mean by that is that as your camera zooms in to say 1/1000 of the previous view, you can scale up the bigger objects by a factor of 1000, and at the same time adjust the camera position and focal length. Any newly attached small model would use its original scale, thus preserving its precision.
This transition would be invisible to the viewer, but allows you to stay within well-defined 3d coordinates while being able to zoom in infinitely.
On a higher level: As you zoom into something and the camera gets closer to an object, it appears as if the world grows bigger relative to the view. While normally the camera space is moving and the world gets multiplied by the camera's matrix, the same effect can be achieved by changing the world coordinates instead of the camera.
First, you can use caching. With tiles, like it's done in cartography. You'll still need to go over all the points, but after that you'll be able zoom-in/zoom-out quite rapidly.
But if you don't have extra memory for cache (not so much actually, much less than the data itself), or don't have time to go over all the points you can use probabilistic approach.
It can be as simple as peeking only every other point (or every 10th point or whatever suits you). It yields decent results for some data. Again in cartography it works quite well for shorelines, but not so well for houses or administrative boarders - anything with a lot of straight lines.
Or you can take a more hardcore probabilistic approach: randomly peek some points, and if, for example, there're 100 data points that hit pixel one and only 50 hit pixel two, then you can more or less safely assume that if you'll continue to peek points still pixel one will be twice as likely to be hit that pixel two. So you can just give up and draw pixel one with a twice more heavy color.
Also consider how much data you can and want to put in a pixel. If you'll draw a pixel in black and white, then there're only 256 variants of color. And you don't need to be more precise. Or if you're going to draw a pixel in full color then you still need to ask yourself: will anyone notice the difference between something like rgb(123,12,54) and rgb(123,11,54)?
I have an problem: My 3d plot gets distorted, if I rotate or pan the plot. I know this behaviour is intended to show the user the most at all times, but it looks silly, so I want an orthogonal view or Axes which are equally long.
Earth distorted.
I have an WPF Application, where the ILN Form/Control is hosted via WindowsFormsHost.
I tried every possibility regarding Plotcube.Projection, have set Plotcube.Limits, changed ILN_Panel autosize etc. My next idea was that maybe I need to configure the WPF and/or Forms window itself.
Thanks!
I think that is not possible. At least not easy within PlotCube. If you do not need PlotCube just put the object into the Camera node and set Projection to Orthogonal. And you are done.
With PlotCube it is much more complicated. You must make sure that all containers have equal aspect ratio (width / height) WinformsHostControl, PlotCube, PlotCube.ScreenRect, PlotCube.DataScreenRect, PlotCube.Plots (data group), and its Limits. If you want to rotate everything free you must make sure to have equal aspect ration on all 3 dimensions wehere applicable.
I am new to the field of medical imaging - and trying to solve this (potentially basic problem). For a machine learning purpose, I am trying to standardize and normalize a library of DICOM images, to ensure that all images have the same rotation and are at the same scale (e.g. in mm). I have been playing around with the Mango viewer, and understand that one can create transformation matrices that might be helpful in this regard. I have however the following basic questions:
I would have thought that a scaling of the image would have changed the pixel spacing in the image header. Does this tag not provide the distance between pixels, and should this not change as a result of scaling?
What is the easiest way to standardize a library of images (ideally in python)? Is it possible and should one extract a mean pixel spacing across all images, and then scaling all images to match that mean? or is there a smarter way to ensure consistency in scaling and rotation?
Many thanks in advance, W
Does this tag not provide the distance between pixels, and should this
not change as a result of scaling?
Think of the image voxels as fixed units of space, which are sampling your image. When you apply your transform, you are translating/rotating/scaling your image around within these fixed units of space. That is, the size and shape of the voxels doesn't change. They just sample different parts of your image.
You can resample your image by making your voxels bigger or smaller or changing their shape (pixel spacing), but this can be independent of the transform you are applying to the image.
What is the easiest way to standardize a library of images (ideally in
python)?
One option is FSL-FLIRT, although it only accepts data in NIFTI format, so you'd have to convert your DICOMs to NIFTI. There is also this Python interface to FSL.
Is it possible and should one extract a mean pixel spacing across all
images, and then scaling all images to match that mean? or is there a
smarter way to ensure consistency in scaling and rotation?
I think you'd just to have pick a reference image to register all your other images too. There's no right answer: picking the highest resolution image/voxel dimensions or an average or some resampling into some other set of dimensions all sound reasonable.
I create a big image stitched out of many single microscope images.
Suddenly, (after several month of working properly) the stitched overview images became blurry and they are containing strange structural artefacts like askew lines (not the rectangulars, they are because of not perfect stitching)
If I open any particular tile in full size, they are not blurry and the artefacts are hardly observable. (Consider, the image below is already 4x scaled)
The overview image is created manually by scaling each tile using QImage::scaled and copying all of them to the corresponding region in the big image. I'm not using opencv's stitching.
I assume, this happens because of image contents, because most if the overview images are ok.
The question is, how can I avoid such hardly observable artefacts to become very clearly visible after scaling? Is there some means in OpenCV or QImage?
Is there any algorithms to find out, if image content could lead to such effect for defined scale-factor?
Many thanks in advance!
Are you sure the camera is calibrated properly? That the lightning is uniform? Is the lens clear? Do you have electrical components that interfere with the camera connection?
If you add image frames of photos on a uniform material (or non-uniform material, moved randomly for significant time), the resultant integrated image should be completely uniform.
If your produced image is not uniform, especially if you get systematic noise (like the apparent sinusoidal noise in the provided pictures), write a calibration function that transforms image -> calibrated image.
Filtering in Fourier space is another way to filter out the noise but considering that the image is rotated you will lose precision, and you'll be cutting off components of the real signal, too. The following empiric method will reduce the noise in your particular case significantly:
ground_output: composite image with per-pixel sum of >10 frames (more is better) over uniform material (e.g. excited slab of phosphorus)
ground_input: the average(or sqrt(sum of px^2)) in ground_output
calib_image: ground_input /(per px) ground_output. Saved for the session, or persistent in a file (important: ensure no lossy compression! (jpeg)).
work_input: the images to work on
work_output = work_input *(per px) calib_image: images calibrated for systematic noise.
If you can't create a perfect ground_input target such as having a uniform material on hand, do not worry too much. If you move any material uniformly (or randomly) for enough time, it will act as a uniform material in this case (think of a blurred photo).
This method has the added advantage of calibrating solitary faulty pixels that ccd cameras have (eg NormalPixel.value(signal)).
If you want to have more fun you can always fit the calibration function to something more complex than a zero-intercept line (steps 3. and 5.).
I suggest scaling the image with some other software to verify if the artifacts are in fact caused by Qt or are inherent in the image you've captured.
The askew lines look a lot like analog tv interference, or CCTV noise induced by 50 or 60 Hz power lines running alongside the signal cable or some other electrical interference on the signal.
If the image distortion is caused by signal interference then you can try to mitigate it by moving the signal lines away from whatever could be the source of the problem, or fit something to try to filter the noise (baluns for example).
I've seen many mandelbrot image generator drawing a low resolution fractal of the mandelbrot and then continuously improve the fractal. Is this a tiling algorithm? Here is an example: http://neave.com/fractal/
Update: I've found this about recursively subdivide and calculate the mandelbrot: http://www.metabit.org/~rfigura/figura-fractal/math.html. Maybe it's possible to use a kd-tree to subdivide the image?
Update 2: http://randomascii.wordpress.com/2011/08/13/faster-fractals-through-algebra/
Update 3: http://www.fractalforums.com/programming/mandelbrot-exterior-optimization/15/
Author of Fractal eXtreme and the randomascii blog post linked in the question here.
Fractal eXtreme does a few things to give a gradually improving fractal image:
Start from the middle, not from the top. This is a trivial change that many early fractal programs ignored. The center should be the area the user cares the most about. This can either be starting with a center line, or spiraling out. Spiraling out has more overhead so I only use it on computationally intense images.
Do an initial low-res pass with 8x8 blocks (calculating one pixel out of 64). This gives a coarse initial view that is gradually refined at 4x4, 2x2, then 1x1 resolutions. Note that each pass does three times as many pixels as all previous passes -- don't recalculate the original points. Subsequent passes also start at the center, because that is more important.
A multi-pass method lends itself well to guessing. If four pixels in two rows have the same value then the pixels in-between probably have the same value, so don't calculate them. This works extremely well on some images. A cleanup pass at the end to look for pixels that were miscalculated is necessary and usually finds a few errors, but I've never seen visible errors after the cleanup pass, and this can give a 10x+ speedup. This feature can be disabled. The success of this feature (guess percentage) can be viewed in the status window.
When zooming in (double-click to double the magnification) the previously calculated pixels can be used as a starting point so that only three quarters of the pixels need calculating. This doesn't work when the required precision increases but these discontinuities are rare.
More sophisticated algorithms are definitely possible. Curve following, for instances.
Having fast math also helps. The high-precision routines in FX are fully unwound assembly language (generated by C# code) that uses 64-bit multiplies.
FX also has a couple of checks for points within the two biggest bulbs, to avoid calculating them at all. It also watches for cycles in calculations -- if the exact same point shows up then the calculations will repeat.
To see this in action visit http://www.cygnus-software.com/
I think that site is not as clever as you give it credit for. I think what happens on a zoom is this:
Take the previous image, scale it up using a standard interpolation method. This gives you the 'blurry' zoomed in image. Click the zoom in button several times to see this best
Then, in concentric circles starting from the central point, recalculate squares of the image in full resolution for the new zoom level. This 'sharpens' the image progressively from the centre outwards. Because you're probably looking at the centre, you see the improvement straight away.
You can more clearly see what it's doing by zooming far in, then dragging the image in a diagonal direction, so that almost all the screen is undrawn. When you release the drag, you will see the image rendered progressively in squares, in concentric circles from the new centre.
I haven't checked, but I don't think it's doing anything clever to treat in-set points differently - it's just that because an entirely-in-set square will be black both before and after rerendering, you can't see a difference.
The oldschool Mandelbrot rendering algorithm is the one that begins calculating pixels at the top-left position, goes right until it reaches the end of the screen then moves to the beginning of next line, like an ordinary typewriter machine (visually).
The linked algorithm is just calculating pixels in a different order, and when it calculates one, it quickly makes assumption about certain neighboring pixels and later goes back to properly redraw them. That's when you see improvement, think of it as displaying a progressive JPEG. If you zoom into the set, certain pixel values will remain the same (they don't need to be recalculated) the interim pixels will be guessed, quickly drawn and later recalculated.
A continuously improving Mandelbrot is just for your eyes, it will never finish earlier than a properly calculating per-pixel algorithm which can detect "islands".