I am trying to model animal movement in a large raster layer of 4113 rows and 6085 columns and a resolution of 220m. Memory does not seem to be an issue as of now; we have about 100MB RAM available and I've increased the RAM usage of NetLogo. However, when I try resizing the NetLogo world to the dimensions of the raster layer, I get this exception:
"Dimensions (width = 1129238, height=763785) are too large."
I am unable to understand how this width and height corresponds to the number of rows and columns of the raster layer. Does it have something to do with the patch size in NetLogo? Resizing patches/increasing resolution of the raster layer did not help.
So my questions are:
1. Are there any workarounds to this problem
2. How do raster dimensions correspond to dimensions in the exception message
3. What are the maximum dimensions that the NetLogo world can handle.
We could split this large layer into several smaller tiles, but it would be useful to understand the maximum dimensions that NetLogo can handle without crashing.
Thanks.
Related
The project is about measurement of different objects under the Kinect 2. The image acquisition code sample from the SDK is adapted in a way to save the depth information at the whole range and without limiting the values at 0-255.
The Kinect is mounted on a beam hoist and moved in a straight line over the model. At every stop, multiple images are taken, the mean calculated and the error correction applied. Afterwards the images are put together to have a big depth map instead of multiple depth images.
Due to the reduced image size (to limit the influence of the noisy edges), every image has a size of 350x300 pixels. For the moment, the test is done with three images to be put together. As with the final program, I know in which direction the images are taken. Due to the beam hoist, there is no rotation, only translation.
In Matlab the images are saved as matrices with the depth values going from 0 to 8000. As I could only find ideas on how to treat images, the depth maps are transformed into images with a colorbar. Then only the color part is saved and put into the stitching script, i.e. not the axes and the grey part around the image.
The stitching algorithm doesn’t work. It seems to me that the grayscale-images don’t have enough contrast to be treated by the algorithms. Maybe I am just searching in the wrong direction?
Any ideas on how to treat this challenge?
Trying to understand how they relate to the final mesh.
As I understand it,
Terrain width and length is simply the shape/size and within that the resolution determine the vector points/wireframe and how many of them?
So, if i have a width of 500 and length of 500. and height map resolution of 257, then there will be a vector every 2m squared ?
Now, finally, I assume detail resolution and detail resolution per patch have no effect on this at all? but simply the rending process and how the mesh is split up into small chucks?
So, if i have a width of 500 and length of 500. and height map
resolution of 257, then there will be a vector every 2m squared ?
Yes, you guessed it right. Width and length is the width and length in units of the terrain, whereas heightmap resolution is in pixels. So essentially one pixel on the heightmap will map to ~4 square units (2^2) on the terrain.
Now, finally, I assume detail resolution and detail resolution per
patch have no effect on this at all?
The detail resolution is the resolution of the splat map which contains the data for placement of detail objects like grass, plants, rocks, etc. and is in no way related to the shape of the terrain. Detail patches are just for optimization and LODGroups of detail objects.
I'm trying to fit two data sets. Those contain the results of measuring the same object with two different measurement devices (x-ray vs. µct).
I did manage to reconstruct the image data and fit the orientation and offset of the stacks. It looks like this (one image from a stack of about 500 images):
The whole point of this is to compare several denoising algorithms on the x-ray data (left). It is assumed that the data from µCT (right) is close to the real signal without any noise. So, I want to compare the denoised x-ray data from each of the algorithms to the "pure" signal from µCT to see which algorithm produces the lowest RMS-error. Therefore, I need to somehow fit the grayvalues from the left part to those of the right part without manipulating the noise too much.
The gray values in the right are in the range of 0 to 100 whereas the x-ray data ranges from about 4000 to 30000. The "bubbles" are in a range of about 8000 to 11000. (those are not real bubbles but an artificial phantom with holes out of a 3D printer)
What I tried to do is (kind of) band pass those bubbles and map them to ~100 while shifting everything else towards 4 (which is the value for the background on the µCT data).
That's the code for this:
zwst = zwsr;
zwsr(zwst<=8000)=round(zwst(zwst<=6500)*4/8000);
zwsr(zwst<=11000 & zwst>8000)= round(zwst(zwst<=11000 & zwst>8000)/9500*100);
zwsr(zwst>11000)=round(zwst(zwst>11000)*4/30000);
The results look like this:
Some of those bubbles look distorted and the noise part in the background is gone completely. Is there any better way to fit those gray values while maintaining the noisy part?
EDIT: To clarify things: The µCT data is assumed to be noise free while the x-ray data is assumed to be noisy. In other words, µCT = signal while x-ray = signal + noise. To quantize the quality of my denoising methods, I want to calculate x-ray - µCT = noise.
Too long for a comment, and I believe a reasonable answer:
There is a huge subfield of image processing/ signal processing called image fusion. There is even a specific Matlab library for that using wavelets (http://uk.mathworks.com/help/wavelet/gs/image-fusion.html).
The idea behind image fusion is: given 2 images of the same thing but with very different resolution/data, how can we create a single image containing the information of both?
Stitching both images "by hand" does not give very good result generally so there are a big amount of techniques to do it mathematically. Waveletes are very common here.
Generally this techniques are widely used in medical imaging , as (like in your case) different imaging techniques give different information, and doctors want all of them together:
Example (top row: images pasted together, bottom row: image fusion techniques)
Have a look to some papers, some matlab tutorials, and probably you'll get there with the easy-to-use matlab code, without any fancy state of the art programming.
Good luck!
Is it possible for the Unity TerrainData structure to take absolute elevations? I have a terrain generator that generates absolute elevations, but they are huge. The perlin octave with the highest amplitude is the one that decides what altitude the entire map is at, with an amplitude of 2500 and wavelength 10000. In order for my map to tile properly and transition between altitudes seamlessly, I need to be able to use this system of absolute altitude. I would scale down my generator's output to fit in the limited space (between 0 and 1), and stretch the y scale of the TerrainData, but it will lose too much precision.
What can I do? Is there a way I can use elevations that may vary by as much as 2500 meters?
One thing that might be important is that there will never be that much variation in the space of a single Terrain object, but across many, many Terrain objects, it is possible for the player to traverse that kind of altitude.
I've tested changing different variables, and I've reached the following conclusion...
Heightmap Resolution does not mean precision of data (some people I asked believed it determined the number of possible height values). It means the number of samples per row and column. This, along with size determines how far apart samples are, and effectively how large the polygons of the terrain are. It's my impression that there is no way to improve precision, although I now know how to increase the height of the terrain object. Instead, since I will never have 2500 meters of elevation difference in the same terrain object, each piece of terrain generated by my generator I will put in a terrain object that is positioned and sized to contain all of the data in that square. The data will also have to be converted so that it will fit, but other than that, I see no drawbacks to this method.
Important note: Resolution must be 2^n + 1 where n is any number. If you provide a different value for resolution, the next permitted value down will be selected (always the one below your choice).
I am having a problem transferring the position of some objects in still image (RGB image ) into 2D view of the room where the image had been taken.I have the coordinates of about 3 objects in the image (i mean X,y coordinate ) as well as the distance between them and I want to transfer the position of these 3 objects into the plan view .
Any help is much appreciated
You will probably need to clarify your question, but if I'm reading it the right way, it coult be as simple as taking the ratio from one object to another.
For example, if your sensor is 640px wide, and that covers a horizontal length of 10 meters, then you know that every 64 pixels represents one meter in the real world.
Bare in mind that this assumes the objects in the real world are at in the same plane, orthogonal to the lens vector. If objects are in different planes (depths), then you have a bigger problem in your hands.