I'm trying to make a radial checkerboard stimulus using psychopy's radialstim where the width of each "ring" increases as eccentricity increases. It's very easy to make a radial checkerboard with rings of equal width. I simply make a radialStim object and use the default tex = 'sqrXsqr'. That creates something that looks like this:
rings of equal width
But I want to display something that looks like this (ignoring the color and the fact that it's a wedge):
rings of expanding width
From reading the psychopy documentation and forum, It seems as though in order to get the radial checkerboard I want, I will need to create a texture from scratch representing it. The texture is defined as an NXN array of 1s, 0s, and -1s. 1s represent white patches, -1s represent black patches, and 0s represent gray (non-checkerboard) patches. What's the best way to create this texture? I'm finding several how-to's for creating an "equal-ring-width" checkerboard texture from scratch, but I haven't yet found an online resource for creating the texture I want to make.
I appreciate any help with this, thanks.
Related
I made a mesh from a Digital Elevation Map that spanned 1x1 degree box of geography, but when I scale the mesh up to 11139m in blender I get these visible jagged shadows on the peaks of the mesh. I'd prefer to not scale everything down but I suppose I can, it just seems like a strange issue I want to better understand.
My goal is to use the landscape in a WebVR application, but when I put this mesh into an Aframe scene it also has this issue. Thanks for any tips!
Quick answer:
I think this may be caused by the clipping start/end values. Also called near/far clipping planes. Adjusting them may fix the issue but also limit the rendering distance.
Longer explanation:
Take a look at this:
It's a simple grayscale, but imagine it is scaled across your entire scene depth (Z depth buffer). The range of this buffer is set by the start/stop clipping (near/far) camera setting.
By default Blender has its start/stop (near/far) clipping set to 0.01 - 1000.
While A-Frame has it like 0.005 - 10000. You may find more information here: A-Frame camera #properties
That means the renderer has to somehow fit every single point in that range somewhere on the grayscale. That may cause overlapping or Z-fighting because it is simply lacking precision to distinguish the details. And that is mainly visible at edges/peaks because the polygons are connected there at acute angles and the program has to round up the Z-values. That causes overlapping visible as darker shadows (most likely the backside of the polygon behind).
You may also want to read more about Z-fighting because it is somewhat related.
Example
I've been searching around the web about how to do this and I know that it needs to be done with OpenCV. The problem is that all the tutorials and examples that I find are for separated shapes detection or template matching.
What I need is a way to detect the contents between 3 circles (which can be a photo or something else). From what I searched, its not to difficult to find the circles with the camera using contours but, how do I extract what is between them? The circles work like a pattern on the image to grab what is "inside the pattern".
Do I need to use the contours of each circle and measure the distance between them to grab my contents? If so, what if the image is a bit rotated/distorted on the camera?
I'm using Xamarin.iOS for this but from what I already saw, I believe I need to go native for this and any Objective C example is welcome too.
EDIT
Imagining that the image captured by the camera is this:
What I want is to match the 3 circles and get the following part of the image as result:
Since the images come from the camera, they can be rotated or scaled up/down.
The warpAffine function will let you map the desired area of the source image to a destination image, performing cropping, rotation and scaling in a single go.
Talking about rotation and scaling seem to indicate that you want to extract a rectangle of a given aspect ratio, hence perform a similarity transform. To define such a transform, three points are too much, two suffice. The construction of the affine matrix is a little tricky.
I have a problem with detection of chessboard-like pattern. The image is very noisy because it is registered with the use of laser scanner.
The only thing I have managed to achieve is detection of big rectangle:
Now I have no idea how to detect those small squares. I tried all sorts of different algorithms, but the contrast in the squares seems too low. Does anybody have any ideas?
Other pattern images: https://dl.dropboxusercontent.com/u/3681534/kalibrator/6.png https://dl.dropboxusercontent.com/u/3681534/kalibrator/8.png
A way to progress would be to determine the grayvalue level at the inner border of the rectangle, then:
Adjust the average brightness inside the rectangle border.
With that knowledge it is possible to adjust the average brightness inside the rectangle to one value (the small square will still be a bit lighter than the rest)
Increase the contrast a lot
Find the lines that run along the edges of the squares
Either access the line crossings directly or paint white and black
Calculate your calibration data
I'm attempting to calculate vertex normals for various game assets. The normals I calculate are used for "inflating" the model (to draw behind the real model producing a thick outline).
I currently compute the normal for each face and average all of them (several other questions on Stack Overflow suggest this approach). However, this doesn't work for sharp corners like this one (adjacent faces' normals marked in orange, the normal I'm trying to calculate is outlined in green).
The object looks like a small pedestal and we're looking at the front-left corner. There are three adjoining faces (the bottom face isn't visible; its normal points straight down).
Blender computes an excellent normal that lies squarely in the middle of the three faces' normals; it seems like it somehow calculates a normal that has minimum rotation to each of the three face normals. Blender's normal also doesn't change when the quads are triangulated differently.
Averaging the faces' normals gives me a different normal that points slightly upward in the Z-axis (-0.45, -0.89, +0.08). Inflating my model this way doesn't produce a good outline because the bottom face of the outline is shifted up and doesn't enclose the original model.
I attempted to look at the Blender source code but couldn't find what I was looking for. If anyone can point me to the algorithm in the Blender source, I'd accept that also.
Weight the surface normals by the angle of the faces where they join. It is a common practice in surface rendering (see discussion here: http://www.bytehazard.com/code/vertnorm.html), and will ensure that your bottom face is weighted stronger than the two slanted side faces. I don't know if Blender does it differently, but you should give it a try.
I'm creating heightmaps using Fractal Brownian Motion. I'm then coloring it based on the heights and mapping it to a sphere. My problem is that the heightmap doesn't wrap seamlessly. I've used the Diamond Square algorithm and it's pretty easy to make things seamless using it, but I can't seem to figure out how to do it with fBm and I seem to be having trouble finding an explanation for it on the web.
To clarify, by "seamless", I mean that when I map it to a sphere, it creates a seamless map on the sphere.
Instead of calculating the heightmap per pixel on the heightmap, calculate the heightmap in 3D space based on each point on the sphere and then map that to an image pixel. You're going to have trouble wrapping a 2D, rectangular heightmap like that onto a sphere without getting ugly results at the poles unless you start your calculations from the sphere.
fBM generalizes to 3 dimensions, so given a point on the sphere you can get the height at that point, and then you can do the math to map that value to where it should be stored in the heightmap image.
Or you could use one of the traditional map projections. A cylindrical projection (x, y)->(x, sin y) would give you a seam of just one meridian, which you could rotate to the back. Or you could "antialias" the edge by one or another means.
With a stereographic projection (x,y,z)->(x/(z+1),y/(z+1)), there's only one sour point (the projection point itself).