I need to know how to plot a 3D plane using MATLAB knowing its Horizontal and Vertical extents, Surface area, Center Point, and Dip angle and Dip Direction.
All I can find is how to generate an infinite 3D plane !!!
In my case, I know the horizontal and vertical extensions of the plane, and I know its surface of the bounding rectangle. So I want to write a script in MATLAB to plot this Finite plane?
Can you kindly advise?
Regards
Related
I have image of robot with yellow markers as shown
The yellow points shown are the markers. There are two cameras used to view placed at an offset of 90 degrees. The robot bends in between the cameras. The crude schematic of the setup can be referred.
https://i.stack.imgur.com/aVyDq.png
Using the two cameras I am able to get its 3d co-ordinates of the yellow markers. But, I need to find the 3d-co-oridnates of the central point of the robot as shown.
I need to find the 3d position of the red marker points which is inside the cylindrical robot. Firstly, is it even feasible? If yes, what is the method I can use to achieve this?
As a bonus, is there any literature where they find the 3d location of such internal points which I can refer to (I searched, but could not find anything similar to my ask).
I am welcome to a theoretical solution as well(as long as it assures to find the central point within a reasonable error), which I can later translate to code.
If you know the actual dimensions, or at least, shape (e.g. perfect circle) of the white bands, then yes, it is feasible and possible.
You need to do the following steps, which are quite non trivial to do, and I won't do them here:
Optional but extremely suggested: calibrate your camera, and
undistort it.
find the equation of the projection of a 3D circle into a 2D camera, for any given rotation. You can simplify this by assuming the white line will be completely horizontal. You want some function that takes the parameters that make a circle and a rotation.
Find all white bands in the image, segment them, and make them horizontal (rotate them)
Fit points in the corrected white circle to the equation in (1). That should give you the parameters of the circle in 3d (radious, angle), if you wrote the equation right.
Now that you have an analytic equation of the actual circle (equation from 1 with parameters from 3), you can map any point from this circle (e.g. its center) to the image location. Remember to uncorrect for the rotations in step 2.
This requires understanding of curve fitting, some geometric analytical maths, and decent code skills. Not trivial, but this will provide a solution that is highly accurate.
For an inaccurate solution:
Find end points of white circles
Make line connecting endpoints
Chose center as mid point of this line.
This will be inaccurate because: choosing end points will have more error than fitting an equation with all points, ignores cone shape of view of the camera, ignores geometry.
But it may be good enough for what you want.
I have been able to extract the midpoint by fitting an ellipse to the arc visible to the camera. The centroid of the ellipse is the required midpoint.
There will be wrong ellipses as well, which can be ignored. The steps to extract the ellipse were:
Extract the markers
Binarise and skeletonise
Fit ellipse to the arc (found a matlab function for this)
Get the centroid of the ellipse
hsv_img=rgb2hsv(im);
bin=new_hsv_img(:,:,3)>marker_th; %was chosen 0.35
%skeletonise
skel=bwskel(bin);
%use regionprops to get the pixelID list
stats=regionprops(skel,'all');
for i=1:numel(stats)
el = fit_ellipse(stats(i).PixelList(:,1),stats(i).PixelList(:,2));
ellipse_draw(el.a, el.b, -el.phi, el.X0_in, el.Y0_in, 'g');
The link for fit_ellipse function
Link for ellipse_draw function
I have an RGB image and a point cloud acquired by LIDAR.
In the RGB image I detect a feature, let's say a circle.
I want to use this circle as a ROI in my 3d point cloud.
How can I do that? I was thinking to produce a 3d point cloud from the RGB image through the camera parameters and then match the 2 with icp algorithm.
The problem's that on the moment I produce the point cloud from the 2D image, my coordinates system change, so I don't know anymore the position of my circle.
To perform 3d reconstruction I use triangulateMultiview function
I was thinking to produce a 3d point cloud from the RGB image through the camera parameters and then match the 2 with icp algorithm.
-> this would not work and not efficient.
Actually, there is a much better way. Assuming that you know the extrinsic between the camera and lidar, any circle(or ellipse) on the image can be extended into a 3d cone using the camera intrinsic and by selecting the points within the cone you can do the ROI operation.
Let's say you can define an ellipse on your image plane by detecting and finding the parameters of an ellipse equation. The ellipse equation can be extended into the quadric(cone) equation which representing the 3D cone. Now the only thing left is testing if your 3d point is within the cone by putting the cone equation.
This is a mathematically little bit complicated problem if you are not comfortable with camera model or quadric equation.
For 2D images- Gx and Gy gives the information on vertical and horizontal edge infromation respectively. Angle of the gradient direction vector can be calculated from inverse of tan(Gy/Gx) and edge direction will be perpendicular to the gradient direction vector.
I have 3D Z-stacked image datset so each pixel will be represented by (x,y,z) co-ordinate and with respective intensity value as well. I have used 3D image gradient link for initial reference.
(1) What if I want to derive whole volume/wireframe model just with the information of 3D image gradient magnitude and direction?
(2) Do I need X, Y and Z-stacked image data seperately in order to generate whole volume/wireframe model out of it?
In matlab, I can also develop whole volume just with 2D z-stacked masks using isosurface command. Here I am exploring other possibilities to generate wireframe volume/3D model.
Thanking you in anticipation.
I wanted to change the position of z-axis in a 3d graph. I tried to do using graph properties but it does not work, Matlab has this option in 2D plot in axis properties window in the graph, but it does not work in 3d plots. Currently,the plot is at z=0 and I wanted to the position to z=6. Attached is the sketch where I need to change the position of the curve plot (red) from z=0 to z6. I appreciate if there is any solution/suggestion regarding this issue. Thank you.
sketch
Regards,
Alishah
A very simple solution for this question is that convert z in your formula to z-6. You know it from mathematics, It will shift a curve, 6 unit .
If you want to change in right or left, you plus or minus.
I am doing some work related to eye images.
I did edge detection to it. The edge is like a curve and not continuous. I have to assume it to be continuous and find normals to that curve. How do I find the normals to it using MATLAB?
you can see the image below.
I want to find the normals to the upper curve.
I hope that I was clear enough.
Even though it seems unintuitive, the edge direction at every pixel is a pretty good estimate of the normal. This would be the simplest solution, because it doesn't involve any curve fitting.
In MATLAB, you can find pixel-wise edge directions using the Sobel filter:
[BW,thresh,gv,gh] = edge(I,'sobel');
edgeDir = atan2(gv, gh);
This gives you the edge directions as angles in radians.
You may want to consider curve fitting (MSE based or some other criteria) to the data. I believe a second order will do good for the upper curve, and once you have a model you can can calculate the tangent and normal at each point.
As Zaphod recommended the normal is perpendicular to the edge. You don't need to do curve fitting, you can use back projection to identify the focal point of the curve.
Start at each edge point along the curve and draw a line from curve in the direction of the normal. Draw the line by incrementing the value of each pixel the line passes through. Once you do this for all the edges you would hope to find two pixels with higher values then the rest, one for each of your curves. You should then know by there locations which is the focal point for each curve.