I have a problems with dimensions of the picture. It is very important because it represents a velocity structure, and so the picture I got is somehow deformed.
figure
load('newvel.dat')
v=reshape(newvel,106,14)
vt=transpose(v)
imagesc(vt) ;
I would like my picture to have ratio 106/14:7.57:1. Should I setup the axes or what? Is it possible and how?
Related
I've seen a few libraries that pixelate images, some of them even feature non-square shapes such as The Pixelator's circle and diamond shapes.
I'm looking however to make a particular shape, I want a "pixel" that is 19x27 px. Essentially, the image would still look pixelated but it would use tallish rectangle shapes as the pixel base.
Are there any libraries out there that do this, if not, what alterations to existing algorithms/functions would I need to make to accomplish this?
Unless I am not understanding your question, the algorithm you need is quite simple!
Just break your image up into a grid of rectangles the size you want (in this case 19x27). Loop over each section of the grid and take the average color of the pixels inside (you can simply take the average of each channel in RGB independently). Then set all of the pixels contained inside to the average color.
This would give you an image that is the same size as your input. You could of course resize your image first to a more appropriate output size.
You might want to look up convolution matrices.
In a shader, you would use your current pixel location to grab a set of nearby pixels from the original image to render to a pixel in a new buffer image.
It is actually just a slight variation of the Box Blur image processing algorithm except that instead of grabbing from the nearby pixels you would grab by the divisions of the original image relative to the 19x27 divisions of the resulting image.
I'm using the following code to add white Gaussian noise to a 3D synthetic image I created. (100*100*100)
sigma = sqrt(10.0^(-snr/10.0));
r=x+sigma*randn(size(x));
I found that different ways of adding the noise need different range of SNR value.
eg. if I add noise stack by stack (x=image stack), the original image become invisible until SNR goes down to -50.
but when I try to add noise straight away in 3D, (x=3D image). the original image become invisible until SNR goes down to -5.
I've looked up everywhere but couldn't found solution of what causing this...could anyone please tell me if it's normal to have a 3D noisy image with SNR=-50dB or even -70dB? Or is there any way to know the true SNR of my noisy images?
Basically I was trying to achieve this: impose an arbitrary image to a pre-defined uneven surface. (See examples below).
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I do not have a lot of experience with image processing or 3D algorithms, so here is the best method I can think of so far:
Predefine a set of coordinates (say if we have a 10x10 grid, we have 100 coordinates that starts with (0,0), (0,10), (0,20), ... etc. There will be 9x9 = 81 grids.
Record the transformations for each individual coordinate on the t-shirt image e.g. (0,0) becomes (51,31), (0, 10) becomes (51, 35), etc.
Triangulate the original image into 81x2=162 triangles (with 2 triangles for each grid). Transform each triangle of the image based on the coordinate transformations obtained in Step 2 and draw it on the t-shirt image.
Problems/questions I have:
I don't know how to smooth out each triangle so that the image on t-shirt does not look ragged.
Is there a better way to do it? I want to make sure I'm not reinventing the wheels here before I proceed with an implementation.
Thanks!
This is called digital image warping. There was a popular graphics text on it in the 1990s (probably from somebody's thesis). You can also find an article on it from Dr. Dobb's Journal.
Your process is essentially correct. If you work pixel by pixel, rather than trying to use triangles, you'll avoid some of the problems you're facing. Scan across the pixels in target bitmap, and apply the local transformation based on the cell you're in to determine the coordinate of the corresponding pixel in the source bitmap. Copy that pixel over.
For a smoother result, you do your coordinate transformations in floating point and interpolate the pixel values from the source image using something like bilinear interpolation.
It's not really a solution for the problem, it's just a workaround :
If you have the 3D model that represents the T-Shirt.
you can use directX\OpenGL and put your image as a texture of the t-shirt.
Then you can ask it to render the picture you want from any point of view.
Is it possible to display an image in multiple subplot axes, such that the image appears at the desired scale?
subplot(3,3,[1 4 7]);
%# image scaled down to fit 1 set of axes
imshow(img);
subplot(3,3,2);
plot(relevantData);
%# And so on with 5 other plots
I want to have the image scaled to either a fixed size or to fit the axes available to it, rather than to the size of a single axes.
My use case is to show a video alongside plots derived from the video, such that the plots are progressively drawn in step with the video. Once the display is correct I can save each image and combine them into a video.
Clarification
I am asking if it is possible to produce a figure as described without specifying the position of every element in absolute terms. Though one can make arbitrary figures that way (and in fact I have done so for this project), it is very tedious.
Edit:
For changing the size of the subplot:
In help subplot they mention that you can set parameters on the selected "axes" (that's what they call a plotting area in Matlab).
Using that, you can set the 'position', as seen in help axes. This property takes takes as argument:
[left, bottom, width, height]
As pointed out by #reve_etrange, one should use absolute positioning for axes 'Position'and 'OuterPosition' parameters. they can be in normalized coordinates, though.
For changing the size of the image in the subplot:
I think there are 2 useful things for you in the help imshow output:
'InitialMagnification': setting the magnification of the image.
'Parent': determines which parent imshow will use to put the image in (never tried using imshow with subplots).
I have to scale down image of any dimension to a fixed dimension of 135x135, most imp thing I have to maintain good quality of scaled down image. I'm not much familiar with Image Processing algos. Can you guys suggest me any algorithm.
Unless the input image is square, say 1000x1000, you will first have to crop it to a square aspect ratio (1:1) then scale it down to 135x135 pixels.
Firstly answer if you want to crop the image or deforming the image to fit in the box
Apply a 2d Sinc filter with the right size for the current scale factor.
Scan the new image and pick up pixels from the old one by just dividing.