Linefit add-on

As a companion add-on to PlaneFit I put together a small add-on that can add a single edge (two connected vertices) that best fits a collection of selected vertices.
After installtion it will be available in the 3d-view menu Add → Fit line to selected and the result of applying it to a vaguely cylindrical point cloud is shown below:

Availability

As usual the add-on is available from my GitHub repository (right-click on the link and Save As ...)

Extra information

There are many ways to fit a line to a collection of points but here we use the same eigen decomposition we use for fitting a plane. Instead of selecting the eigen vector with the smallest eigen value as the normal of a plane, we now select the eigen vector with the largest eigen value. This vector accounts for most of the variance in the positions of all the vertices, and is therefore the best fit. (There are other metrics we could use and this explanation is may be a bit too much hand-waiving for real mathematicians but it works :-)
The relevant code is shown below:
import numpy as np

def lineFit(points):
    ctr = points.mean(axis=0)
    x = points - ctr
    M = np.cov(x.T)
    eigenvalues,eigenvectors = np.linalg.eig(M)
    direction = eigenvectors[:,eigenvalues.argmax()]
    return ctr,direction

PlaneFit: Blender add-on, tiny improvement

The PlaneFit add-on wasn't very robust yet so I added a check for less than 3 selected vertices so a plane will only be fitted when it is sensible to do so. Gives a proper warning as well and the menu (In the 3d view, Add->Mesh in edit mode) now has a more readable label.

Availability

I have updated the add-on in my GitHub repository. (right-click on the link and select Save As .. to store the .py file somewhere where you can find it again )

PlaneFit: Blender add-on to fit a plane through a set of vertices

After all the updates to my BlenderMarket add-ons, it is time to spend some time on other add-ons again.

Fitting a plane

Fitting a plane through a collection of selected vertices might be useful in all sort of scenarios. Currently you can snap a plane to a face with Blender's snap tools but if we want to fit a plane to a large set of vertices, we need to resort to basic linear algebra to do this in a fast and robust manner. And because linear algebra is not everybody's favorite subject I created a small add-on.

After installation the add-on is available from the Add menu if you have a mesh in edit mode. Clicking on it will create a square plane that is fitted through all selected vertices. A size option lets you scale this new plane interactively. The result might look something like this:
Note that currently we not check if the minimum of 3 vertices are selected, you get a an error if you try.

Availability

As usual the add-on is available from my GitHub repository. (right-click on the link and select Save As .. to store the .py file somewhere where you can find it again )

Source code

Both the linear algebra and the code to add a plane to an exiting mesh isn't completely trivial, so let me highlight the interesting bits:

Fitting a plane

The fitting code is quite short and straight forward:
import numpy as np

def planeFit(points):
    ctr = points.mean(axis=0)
    x = points - ctr
    M = np.cov(x.T)
    eigenvalues,eigenvectors = np.linalg.eig(M)
    normal = eigenvectors[:,eigenvalues.argmin()]
    return ctr,normal
Any book on linear algebra can give you a better explanation but with a fair bit of hand-waving it can be explained as follows: points is a list of 3d-vectors. ctr will be the midpoint of the plane which is the mean of each of the x, y and z-components.

In line 5 - 7 we calculate the eigen vectors of the point cloud. It is a 3d cloud so we will get 3 eigen vectors and 3 corresponding eigenvalues. Each combination of eigen value and eigen vector can be interpreted as a direction vector and a measure of how well it explains the spread of the points. This means that if the points lie roughly in a plane, the two biggest eigen vectors lie in the best fit plane while the smallest one will be the normal (all eigen vectors are perpendicular). And indeed this smallest one is the one we get in line 8.

Adding a plane an existing mesh

Now adding a plane consisting of four vertices in a square would be quite simple, yes? Ehh, no: the Mesh object has a from_pydata() function but it only works correctly when adding to an initially empty mesh. So lines 15 - 27 essentially replicate what that function does: create 4 vertices, and 4 loops, compose a polygon out of it and add it to the mesh as well. We could have worked with a BMesh representation but then we would not have an efficient way to retrieve all vertex coordinates, something we really need when working with thousands of vertices.

The code in lines 7 - 12 is a very efficient way to get the coordinates of selected vertices into a Numpy array: we create Numpy arrays to hold all the vertex coordinates and the selected status, then get all of them with the fast built-in function foreach_get(). verts[selected] then leaves us with an array of just the coordinates of selected vertices, which we pass to our planeFit() function we saw earlier.

We then create two vectors perpendicular to our normal and use them to construct four vertex coordinates.

         def execute(self, context):
  bpy.ops.object.editmode_toggle()
  me = context.active_object.data
  count = len(me.vertices)
  if count > 0:  # degenerate mesh, but better safe than sorry
   shape = (count, 3)
   verts = np.empty(count*3, dtype=np.float32)
   selected = np.empty(count, dtype=np.bool)
   me.vertices.foreach_get('co', verts)
   me.vertices.foreach_get('select', selected)
   verts.shape = shape
   ctr, normal = planeFit(verts[selected])
   dx, dy = orthopoints(normal)  # definition of orthopoints() not shown
   # can't use mesh.from_pydata here because that won't let us ADD to a mesh
   me.vertices.add(4)
   me.vertices[count  ].co = ctr+dx*self.size
   me.vertices[count+1].co = ctr+dy*self.size
   me.vertices[count+2].co = ctr-dx*self.size
   me.vertices[count+3].co = ctr-dy*self.size
   lcount = len(me.loops)
   me.loops.add(4)
   pcount = len(me.polygons)
   me.polygons.add(1)
   me.polygons[pcount].loop_total = 4
   me.polygons[pcount].loop_start = lcount
   me.polygons[pcount].vertices = [count,count+1,count+2,count+3]
   me.update(calc_edges=True)

  bpy.ops.object.editmode_toggle()
  return {'FINISHED'}

IDMapper: December 2017 release available on Blender Market

I have released an updated version of the IDMapper add-on to Blender Market.

New features

It features some cosmetic changes but more importantly it hosts a ton of new features developed based on user feedback, including:
Baking
you can now directly bake a vertex color layer to an image without the cumbersome route of setting up materials, emission shaders and dummy images necessary in the regular way. Just 1-click and a couple of seconds later you'ĺl have you color id-map as an image in the UV-image editor
Vertex groups to vertex colors
there is now an option to assign unique vertex colors based on vertex group membership of vertices. This 1-click option lets you quickly convert vertex groups to vertex colors
Create materials based on vertex colors
this option creates a material for each unique vertex color and assigns the vertices with this color to the material.
Better color list management
If you use a predefined list of colors to use in id-maps across different models the color list lets you manage these colors. It is now possible to add items to this list for any vertex color nor yet present, simplifying the maintenance of those lists
And of course there are lots of smaller improvements too.

The new features are highlighted in this video:

This video is part of a small playlist with additional tutorials:

Availability

IDMapper is available in my Blender Market shop:
https://blendermarket.com/creators/varkenvarken

New version of WeightLifter introduces baking and animation

A new version of WeightLifter is available on BlenderMarket.

WeightLifter is an add-on that can calculate all sorts of information and store this into vertex groups or vertex color layers. It can for example determine the visibility of vertices for a certain camera or the distance to some light source and much more, information that can for example be used as a density map in particle systems.

New features

The newest version introduces some new modes like calculating a flow map or the mesh deformation but the most exiting new feature is the ability to bake any weight map for each frame in an animation. This way can quickly regenerate a vertex group for each frame while rendering, effectively animating a weight map, something which is not possible otherwise.

To illustrate how to do this, I have created a short video that shows the workflow for creating an animated density map that lets the proximity to a lamp drive the emission of particles.

Nodeset: tiny enhancement

A new version of nodeset is available that includes .jpg in the default list of extensions.

Availability

The current version (201712020811) is available for download (right click and save as ...) from my GitHub repository.

Previous nodeset articles

I wrote several articles on this tiny add-on that you might want to read. They are (newest first):