If I have the vertex normals of a normal scene showing up as colours in a texture in world space is there a way to calculate edges efficiently or is it mathematically impossible? I know it's possible to calculate edges if you have the normals in view space but I'm not sure if it is possible to do so if you have the normals in world space (I've been trying to figure out a way for the past hour..)
I'm using DirectX with HLSL.
if ( normalA dot normalB > cos( maxAngleDiff )
then you have an edge. It won't be perfect but it will definitely find edges that other methods won't.
Or am i misunderstanding the problem?
Edit: how about, simply, high pass filtering the image?
I assume you are trying to make cartoon style edges for a cell shader?
If so, simply make a dot product of the world space normal with the world space pixel position minus camera position. As long as your operands are all in the same space you should be ok.
float edgy = dot(world_space_normal, pixel_world_pos - camera_world_pos);
If edgy is near 0, it's an edge.
If you want a screen space sized edge you will need to render additional object id information on another surface and post process the differences to the color surface.
It will depend on how many colors your image contain, and how they merge: sharp edges, dithered, blended,...
Since you say you have the vertex normals I am assuming that you can access the color-information on a single plane.
I have used two techniques with varying success:
I searched the image for local areas of the same color (RGB) and then used the highest of R, G or B to find the 'edge' - that is where the selected R,G or B is no longer the highest value;
the second method I used is to reduce the image to 16 colors internally, and it is easy to find the outlines in this case.
To construct vectors would then depend on how fine you want the granularity of your 'wireframe'-image to be.
Related
I'm trying to blindly detect signals in a spectra.
one way that came to my mind is to detect rectangles in the waterfall (a 2D matrix that can be interpret as an image) .
Is there any fast way (in the order of 0.1 second) to find center and width of all of the horizontal rectangles in an image? (heights of rectangles are not considered for me).
an example image will be uploaded (Note I know that all rectangles are horizontal.
I would appreciate it if you give me any other suggestion for this purpose.
e.g. I want the algorithm to give me 9 center and 9 coordinates for the above image.
Since the rectangle are aligned, you can do that quite easily and efficiently (this is not the case with unaligned rectangles since they are not clearly separated). The idea is first to compute the average color of each line and for each column. You should get something like that:
Then, you can subtract the background color (blue), compute the luminance and then compute a threshold. You can remove some artefact using a median/blur before.
Then, you can just scan the resulting 1D array filled with binary values so to locate where each rectangle start/stop. The center of each rectangle is ((x_start+x_end)/2, (y_start+y_end)/2).
I have an array of data from a grayscale image that I have segmented sets of contiguous points of a certain intensity value from.
Currently I am doing a naive bounding box routine where I find the minimum and maximum (x,y) [row, col] points. This obviously does not provide the smallest possible box that contains the set of points which is demonstrable by simply rotating a rectangle so the longest axis is no longer aligned with a principal axis.
What I wish to do is find the minimum sized oriented bounding box. This seems to be possible using an algorithm known as rotating calipers, however the implementations of this algorithm seem to rely on the idea that you have a set of vertices to begin with. Some details on this algorithm: https://www.geometrictools.com/Documentation/MinimumAreaRectangle.pdf
My main issue is in finding the vertices within the data that I currently have. I believe I need to at least find candidate vertices in order to reduce the amount of iterations I am performing, since the amount of points is relatively large and treating the interior points as if they are vertices is unnecessary if I can figure out a way to not include them.
Here is some example data that I am working with:
Here's the segmented scene using the naive algorithm, where it segments out the central objects relatively well due to the objects mostly being aligned with the image axes:
.
In red, you can see the current bounding boxes that I am drawing utilizing 2 vertices: top-left and bottom-right corners of the groups of points I have found.
The rotation part is where my current approach fails, as I am only defining the bounding box using two points, anything that is rotated and not axis-aligned will occupy much more area than necessary to encapsulate the points.
Here's an example with rotated objects in the scene:
Here's the current naive segmentation's performance on that scene, which is drawing larger than necessary boxes around the rotated objects:
Ideally the result would be bounding boxes aligned with the longest axis of the points that are being segmented, which is what I am having trouble implementing.
Here's an image roughly showing what I am really looking to accomplish:
You can also notice unnecessary segmentation done in the image around the borders as well as some small segments, which should be removed with some further heuristics that I have yet to develop. I would also be open to alternative segmentation algorithm suggestions that provide a more robust detection of the objects I am interested in.
I am not sure if this question will be completely clear, therefore I will try my best to clarify if it is not obvious what I am asking.
It's late, but that might still help. This is what you need to do:
expand pixels to make small segments connect larger bodies
find connected bodies
select a sample of pixels from each body
find the MBR ([oriented] minimum bounding rectangle) for selected set
For first step you can perform dilation. It's somehow like DBSCAN clustering. For step 3 you can simply select random pixels from a uniform distribution. Obviously the more pixels you keep, the more accurate the MBR will be. I tested this in MATLAB:
% import image as a matrix of 0s and 1s
oI = ~im2bw(rgb2gray(imread('vSb2r.png'))); % original image
% expand pixels
dI = imdilate(oI,strel('disk',4)); % dilated
% find connected bodies of pixels
CC = bwconncomp(dI);
L = labelmatrix(CC) .* uint8(oI); % labeled
% mark some random pixels
rI = rand(size(oI))<0.3;
sI = L.* uint8(rI) .* uint8(oI); % sampled
% find MBR for a set of connected pixels
for i=1:CC.NumObjects
[Y,X] = find(sI == i);
mbr(i) = getMBR( X, Y );
end
You can also remove some ineffective pixels using some more processing and morphological operations:
remove holes
find boundaries
find skeleton
In MATLAB:
I = imfill(I, 'holes');
I = bwmorph(I,'remove');
I = bwmorph(I,'skel');
Light Field captures the scene from slightly different points. This means I would have two images of the same scene with a slight shift, as shown in the following figure:
Assuming the red squares in the images above are pixels. I know that the spatial difference between those two pixels is a shift. Nevertheless, what other information do these two pixels give us in terms of scene radiance? I mean is there a way to find (or compute) the difference in image irradiance values between those two points?
Look for color space representations other than RGB. Some of them have explicit channel(s) carrying luminance information of a pixel.
A varaiant of the same idea is to convert to a Black and White image and examine the pixel values.
Given two rectangles, and we know the position of four corners, widths, heights, angles.
How to compute the overlapping ratio of these two rectangles?
Can you please help me out?
A convenient way is by the Sutherland-Hodgman polygon clipping algorithm. It works by clipping one of the polygons with the four supporting lines (half-planes) of the other. In the end you get the intersection polygon (at worst an octagon) and find its area by the polygon area formula.
You'll make clipping easier by counter-rotating the polygons around the origin so that one of them becomes axis parallel. This won't change the area.
Note that this approach generalizes easily to two general convex polygons, taking O(N.M) operations. G.T. Toussaint, using the Rotating Caliper principle, reduced the workload to O(N+M), and B. Chazelle & D. P. Dobkin showed that a nonempty intersection can be detected in O(Log(N+M)) operations. This shows that there is probably a little room for improvement for the S-H clipping approach, even though N=M=4 is a tiny problem.
Use rotatedRectangleIntersection function to get contour and use contourArea function to get area and find the ratios
https://docs.opencv.org/3.0-beta/modules/imgproc/doc/structural_analysis_and_shape_descriptors.html#rotatedrectangleintersection
Lets say you have rectangle A and B the you can use the operation:
intersection_area = (A & B).area();
from this area you can calculate de respective ratio towards one of the rectangles. there will be harder more dynamic ways to do this as well.
I got a problem with texture coordinates. First I would like to describe what I want to do then I will ask the question.
I want to have a mesh that has more textures using only one big texture. The big texture merges all textures the mesh is using in it. I made a routine that merges textures, that is no problem, but I still have to modify the texture coordinates, so the mesh that now uses only one texture instead of many has everything placed well.
See the picture:
On the upper left corner I got one of the textures (let's call it A) I merged into a big texture, the right one (B). A's top left is 0,0 and bottom right is 1,1. For easy use let's say that B.width = A.width * 2 and so for the height too. So on B the mini texture (M what is the A originally) bottom-right should be 0.5,0.5.
I got no problems understanding these so far and I hope I understood it ok. But the problem here is, that there are texture coordinates that are:
above 1
negative
on the original A. What should these be on M?
Let's say, A has -0.1,0 - is that -0.05,0 on M inside B?
What about those numbers that are outside 0..1 region? Is -3.2,0 on A -1.6 or -3.1 on B? So I clip of the part that is %1 and divide by 2 (because I stated above that width is double) or should I divide the whole number by 2? As far I understand so far, numbers outside this region are about mirroring the texture. How do I manage this, so the output does not contain the orange texture from B?
If my question is not clear enough (I am not much skilled in English), please ask and I will edit/answer, just help me clear my confusion :)
Thanks in advance:
Péter
A single texture has coordinates in [0-1,0-1] range
The new texture has coordinates in [0-1,0-1] range
In your new texture composed by four single textures, your algoritm has to translate texture coordinates this way.
Blue single square texture will have new coordinates in [0-0.5,
0-0.5] range
Orange single square texture will have new coordinates
in [0.5-1, 0-0.5] range