I'm working in Objective-C, and storing my vertices points in an NSArray.
I have an array of 2D vertices with their bounding box, which I call "polygon".
I would like to distort the whole polygon according to how I move the corners points of the bounding box.
I guess I'll have to calculate some proportions depending on which corner I'm dragging. But, I don't know from where to begin for the calculations, and if there's a simple and fast algorithm for this. I don't want to use an external library, adn perform the calculations directly in my program.
The image here is produced by using an image software "distort" function, that does exactly what I want. If it is possible on a bitmap image, I suppose it is possible on any matrix of points.
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i am quite a beginner in Gmsh and am trying to create a mesh for hydrodynamic simulation from coastlines. I used splines for the complex coastline for simplicity, but the produced mesh crossed over the coastlines. What should i do to make the mesh not cross over the bounding curves?
Image for reference
Your mesh is simply to coarse in the moment. The points of each Triangle in the mesh lie on the real geometry/coastline but the edges are linearly connected and do not care about the geometry.
In order to refine the mesh you might try to press Mesh->Refine by Splitting a couple of times and see split the few current cells. The mesh should get finer and should not violate the geometry boarder by as much as right now.
BUT by this you'll only make the "issue" less obvious to see. On a smaller scale you will always see mesh cells that are partly "outside" the geometry borders. You cannot prevent this with concave meshes like the one you have here. If you have s.th. convex like a circle all elements will strictly lie inside the geometry border.
So as a first step, make a finer mesh until you are satisfied with the match between geometry and mesh.
I am attempting to come up with a quick and efficient means of translating a 3d mesh into a projected AABB. In the end, I would like to accomplish something similar to figure 1 wherein only the area of the screen covered by the cube is located inside the bounding box highlighted in red. ((if it is at all possible, getting the area as small as possible, highlighted in blue, would increase efficiency down the road.))
Figure 1. https://i.imgur.com/pd0E20C.png
Currently, I have tried:
Calculating the point position on the screen using camera.unproject_position(). this failed largely due to my inability to wrap my head around the pixel positions trending towards infinity. I understand it has something to do with Tan, but frankly, it is too late for my brain to function anymore.
Getting the area of collision between the view frustum and the AABB of the mesh instance. This method seems convoluted, and to get it in a usable format I would need to project the result into 2d coordinates again.
Using the MeshInstance VisualInstance to create a texture wherein a pixel is white if it contains the mesh instance, and black otherwise. Visual instances in general just baffle me, and I did not think it would be efficient to have another viewport just to output this texture.
What I am looking for:
An output that can be passed to a shader informing where to complete certain calculations. Right now this is set up to use a bounding box, but it could easily be rewritten to also use a texture. It also could be rewritten to use polygons, but I am trying to keep calculations to a minimum in the shader.
Certain solutions I have tried before have worked, slightly, but this must be robust. The camera interfacing with the 3d object will be able to move completely around and through it, meaning at times the view will be completely surrounded by the 3d model with points both in front, and behind.
Thank you for any help you can provide.
I will try my best to update this post with information if needed.
I am trying to use MATLAB's camera calibrator to calibrate an infrared camera. I was able to get the intrinsic matrix by just feeding around 100 images to the calibrator. But I'm struggling with how to get the extrinsic matrix [R|t].
Because the extrinsic matrix is used to map the world frame with the camera frame, so in theory, when the camera(object) is moving, there will be many extrinsic matrices.
In the picture below, if the intrinsic matrix is determined using 50 images, then there are 50 extrinsic matrices correspond to each image. Am I correct?
You are right. Usually, a by-product of an intrinsic calibration is the extrinsic matrix for each pattern observed; this is mostly used to draw the patterns with respect to the camera as in the picture you posted.
What you usually do afterwards is to define some external reference frame that makes sense for you application, also known as the 'world' reference frame, and compute the pose of the camera with respect to it. That's the extrinsic matrix you always hear about.
For this, you:
Define the reference frame and take some points with known 3D coordinates on it; this can be a grid drawn on the floor, for example.
Take a picture of the 3D points with the calibrated camera and get a list of the correspondent 2D (image) coordinates of the points.
Use a pose estimation function that takes: the camera intrinsic parameters, the 3D points and the correspondent 2D image points. I am more familiar with OpenCV, but the Matlab function that seems to do the job is: https://www.mathworks.com/help/vision/ref/estimateworldcamerapose.html
I have created an experimental fast rectangular object tracking system; it will be used for headtracking and controllling objects in 3D engine (Ogre3D).
For now I am able to show to the webcam any kind of bright colored rectangle (text markers are good objects) and system registers basic properties of this object (hue/value/lightness and initial width and height in 0 degrees rotation).
After I have registered the trackable object, I do some simple frame processing to create grayscale probabilty map.
So now I have 2 known things:
1) 4 corners for the last object position (it's always a rectangle but it may be rotated)
2) a pretty rectangular (but still far from perfect) blob which is the brightest in the frame. I can get coordinates of any point of the blob without problems, point detection is stable enough.
I can find a bounding rectangle of the object without problems, but I have a problem with detecting the object corners themselves.
I need the simplest possible (quick&dirty would be great) algorithm to scan the image starting with some known coordinates (a point inside the blob) and detect new 4 x,y coordinates of a "blobish" rectangle corners (not corners of a bounding box but corners of the rectangular blob itself).
Ready-to-use C++ function would be awesome, but somehow google doesn't like me today :(
I think that it would be overkill to use some complicated function form OpenCV library just to extract 4 points of a single rectanglular blob. But if you know a quick and efficient way how to do it using OpenCV (it must be real-time and light on CPU because I'll run the 3D engine at the same time) then I would be really grateful.
You can apply Hough transform on segmented image to detect lines. Using detected lines you can calculate their intersection to find the corner coordinates of the blob.
I'm using a 3d engine and need to translate between 3d world space and 2d screen space using perspective projection, so I can place 2d text labels on items in 3d space.
I've seen a few posts of various answers to this problem but they seem to use components I don't have.
I have a Camera object, and can only set it's current position and lookat position, it cannot roll. The camera is moving along a path and certain target object may appear in it's view then disappear.
I have only the following values
lookat position
position
vertical FOV
Z far
Z near
and obviously the position of the target object.
Can anyone please give me an algorithm that will do this using just these components?
Many thanks.
all graphics engines use matrices to transform between different coordinats systems. Indeed OpenGL and DirectX uses them, because they are the standard way.
Cameras usually construct the matrices using the parameters you have:
view matrix (transform the world to position in a way you look at it from the camera position), it uses lookat position and camera position (also the up vector which usually is 0,1,0)
projection matrix (transforms from 3D coordinates to 2D Coordinates), it uses the fov, near, far and aspect.
You could find information of how to construct the matrices in internet searching for the opengl functions that create them:
gluLookat creates a viewmatrix
gluPerspective: creates the projection matrix
But I cant imagine an engine that doesnt allow you to get these matrices, because I can ensure you they are somewhere, the engine is using it.
Once you have those matrices, you multiply them, to get the viewprojeciton matrix. This matrix transform from World coordinates to Screen Coordinates. So just multiply the matrix with the position you want to know (in vector 4 format, being the 4ยบ component 1.0).
But wait, the result will be in homogeneous coordinates, you need to divide X,Y,Z of the resulting vector by W, and then you have the position in Normalized screen coordinates (0 means the center, 1 means right, -1 means left, etc).
From here it is easy to transform multiplying by width and height.
I have some slides explaining all this here: https://docs.google.com/presentation/d/13crrSCPonJcxAjGaS5HJOat3MpE0lmEtqxeVr4tVLDs/present?slide=id.i0
Good luck :)
P.S: when you work with 3D it is really important to understand the three matrices (model, view and projection), otherwise you will stumble every time.
so I can place 2d text labels on items
in 3d space
Have you looked up "billboard" techniques? Sometimes just knowing the right term to search under is all you need. This refers to polygons (typically rectangles) that always face the camera, regardless of camera position or orientation.