Slicing 3D Objects for a Laser Cutter with OpenSCAD

Recently, my makerspace got a new laser cutter that can fit 4×2 foot plywood sheets. This means I can redo our arcade’s control panel with a design in OpenSCAD. Here’s the mockup:


The design is based on the old X-Men 6-player cabinet, though the final build got cut down to 4-players for space reasons. The red areas you see on the bottom are keepout sections for the structure underneath. There’s also a cutout in back, which is for a built-in keyboard. It’s 3/8 inch thick, and intended to be made from two layers of 1/8 inch plywood plus 1/8 inch acrylic on top.

Now, how do you go from an OpenSCAD file to a laser cutter?

In theory, you can have OpenSCAD generate an STL file, and then import that into a 3D printer slicer. A slicer’s job is to take a 3D model, break it into 2D slices, and then generate the printer head’s path for each slice. So you might think that you can set the slicer to have a layer height of 1/8 inch, and then have it output some kind of vector file format for each slice. Slic3r does indeed have an SVG output, which gives you hope.

Your hope will soon be dashed as you run into impedance mismatches. First, Slic3r wants the nozzle diameter to be larger than the layer height. We’re not going to be dealing with a physical nozzle here, but we have to set it to a ridiculous size (like 10mm) just to make Slic3r happy. Once we get past that, we get our SVG output in a single file with multiple layers. Slic3r’s SVG output is meant for resin 3D printers, and isn’t going to work well for laser cutters. What we want is one file per layer with simple paths, not filled-in.

It turns out we can hack our own slicer with OpenSCAD’s projection() command, combined with importing an STL. It works like this:

The translate() brings the object’s Z coord down by -0.1mm. The projection() command then creates a 2D slice of whatever intersects at Z axis zero. Note that you need the cut = true here. Without it, OpenSCAD creates the projection as a kind of shadow of the entire object, rather than whatever is on the Z axis.

That gets us a vector image like this:


If we loaded the code above into OpenSCAD, compiled it, and exported to a vector file like SVG or DXF, we would get the slice of the 0.1 layer. Then we’d have to do -0.1 - 3.175 (3.175mm is about 1/8 inch), compile and export, and then again for the third layer. This is too much manual effort, even for just the 3 layers I need for the control panel. I would never want to do it for something like a terrain piece.

Fortunately, OpenSCAD has a command line interface for compiling and exporting models. What we can do, then, is programmatically create the OpenSCAD projection code, run the command line, and get each layer in turn.

Which is exactly what Laser Slicer does. Here’s how to run it:

We put in a start height of 0.1 (you might get weird results if you try to start with zero), then go up 3.175mm each step, ending whenever we go above 7mm. It then generates 3 dxf files, one for each layer. It’s probably slower per layer than 3D printer slicers, but since the layers are thicker, it doesn’t need to generate as many of them.

All code on Github now.

Games::Chipmunk now at v0.5, can do useful things

Games::Chipmunk, the Perl bindings for the Chipmunk 2D graphics library, are now at version 0.5.

There are no changes to the bindings themselves in this version, but there is an example Pachniko program:


You click the mouse to drop a ball in that spot, and away it goes. Things seem to be at the point of doing real games with it.

SQL::Functional Cookbook–Updates

Updates are easy, too.

UPDATE takes a table to update, followed by SET and WHERE. In SET, we’re using the op function, which is actually an alias for match. Calling it op is more readable here, since we’re not matching anything. Note that the data there is still handled as placeholders.

The WHERE part works just like it does in SELECT.

SQL::Functional Cookbook: Inserts

We can build an insert statement easily, too.

INTO takes a table, which it feeds to INSERT. The INSERT function likewise takes a list of table columns (in an array ref), followed by VALUES, which itself takes an arrayref of the data you’re going to put into the columns. All the scalars in that arrayref are passed back in @sql_params and filled in as placeholders.

Inserting with a subselect is supported, which we will cover later.

SQL::Functional Cookbook: ANDs and ORs

It seems like it should be easy, but I was always disappointed with how other solutions handle arbitrarily nested ANDs and ORs. Most SQL creation libraries seem to start by adding support for a list of AND statements. At some point, the authors realize they need ORs, so they slap that in. Much later, they realize they need to mix ANDs and ORs, and then all sorts of convolutions get written.

With SQL::Functional‘s approach, nesting ANDs and ORs together is as natural as straight SQL. First, we’ll back up a few steps and demonstrate the ANDs:

Notice that unlike SQL, the AND is a prefix rather than infix. This might take some getting used to, but it does mean you can pass it an arbitrary number of statements:

In the final SQL, all of these will be joined together with AND. The OR statement works the same way:

If we need to mix the two together, we just do that:

Thus, the nesting falls naturally out of the system, just like it should be.

SQL::Functional Cookbook: Simple Select

SQL::Functional now has reasonably complete converge of common SQL statements. There’s always going to be something else to add, but the main thing it needs now is a cookbook of standard cases. Along those lines, I thought I’d start up a series of blog posts that can be compiled into a complete cookbook.

We’ll start with a basic SELECT statement.

One of SQL::Functional‘s strong points is in making easy jobs easy. Its other strong point, making hard jobs possible, will be covered later.

Let's break down what’s actually going on. The SELECT function takes a scalar for all the fields, so we have to pass it as an arrayref. FROM takes a list of tables, which we might do for joins, but we'll just pass it one for now. WHERE takes a list of clauses.

Ideally, we could say something like:

But that would require deep Perl voodoo (e.g. source code filters), so we content ourselves with the match function to build the same thing. It also helps us here by having the matched data passed as a placeholder.

In the end, $sql will contain:

With @sql_params containing the data for all the placeholders. We can run this through DBI like any other statement:


Callback Functions Between C and Perl

Everything you ever want to do with xs is documented somewhere in perlxs, perlguts, perlapi, perlxstypemap, and perlcall. Figuring out where it’s documented, and how it relates to everything else, is the hard part.

In my case, the part I wanted was in perlcall, in the section “Strategies for Storing Callback Context Information”. What I wanted to do was to glue together the callbacks for the Perl interface in Games::Chipmunk to the Chipmunk 2D physics library.

From Perl, we want to be able to say things like this:

The C version of cpBodySetVelocityUpdateFunc() does not know what a Perl sub is. It sees an SV, which happens to contain a bunch of things that the Perl interpreter can execute as a Perl sub. What we need to do is hand off a C function to the callback, and then use that to grab our SV and use call_sv() to call it.

If C supported closures, this would be easy. C does not support closures.

Some C libraries with callbacks have a parameter that will pass whatever data you give it directly into the callback later on. Chipmunk has this on a few functions, but not consistently.

One thing the Chipmunk libraries do give us is a pseduo-Object Oriented interface, where we pass the associated datastructure in as the first parameter on every function (Object Oriented languages move this parameter to the left of the function call). This gives us something we can grab on to for getting the SV we need that stores our Perl sub.

The “object” is a pointer, and pointers are just numbers. Numbers can be looked up in a hash. So we’ll make a bunch of global hashes, one for each set of callbacks, and use the address as the lookup key and the SV as the value.

The examples below use Perl’s context macros, which means those global hashes are still thread-safe. You can read about them in perlxs, under the section “Safely Storing Static Data in XS”. Their use won’t be detailed here.

Using cpBodySetVelocityUpdateFunc as an example, we start with the xs declaration:

We’re taking an SV* and assuming it holds a reference to the sub. We store it in the HV* bodyVelocityFuncs, which is initialized elsewhere. Using (char*)&body (C is a true bastion of type safety), we convert the address of the cpBody* into a char pointer, which the hash can use as a key. Lastly, we call the actual cpBodySetVelocityUpdateFunc() in the C library, and pass it our own C function as a callback.

That C function looks like this:

This goes at the top of your xs file, before any PACKAGE declarations, up with #include "EXTERN.h" and such. The perlxs doc doesn’t show this part in its examples very well, but you need to put the dTHX; call here to declare the context for a bunch of Perl macros that come later. Otherwise, you’ll get a bunch of cryptic compiler errors and spend a few hours scratching your head, until you finally come across the section in perlguts entitled “How multiple interpreters and concurrency are supported”. Like I said, everything is documented, you just won’t know where.

Going past the Perl macros, we get to hv_fetch(). This function should have gotten the same cpBody* as we got earlier, so we once again torture the type system and pretend it’s a pointer to a char array for the sake of a hash lookup key. The person who wrote this part of the interface was a two-star C programmer, so we better check that we actually got the SV we wanted by checking perl_func == (SV**) NULL.

Now we’ll need to grow the stack so we can push the subref’s arguments there. In this case, the callback received four arguments, and we want to pass all four to the subref. In this particular case, the last two arguments are easy. They’re double precision floats, which we can pass directly into a Perl SV that contains a number.

The first two are the tricky ones. They’re pointers to complex structs. In my typemap, I made cpBody* into a T_PTROBJ. This means it converts the ‘*’ into ‘Ptr’, collapses any whitespace, and uses the resulting name as the Perl class. You can make an SV contain a pointer to this C object and then carry it around in Perl. The cpVect* comes out the same way (since we take a pointer to the gravity struct).

Finally, we can call call_sv() to call the actual subref.

The good news is that Games::Chipmunk v0.3 has most of the callbacks implemented. There’s some in cpSpatialIndex that aren’t there because they don’t follow the same conventions. Still, this should be good enough to write real physics systems for games in Perl.

Adventures in Code Generation — Graphics::GVG

Vector graphic games, like Battlezone or Asteroids, are old favorites of mine, and I’ve been wanting to make a game with that same style. Partially, that’s because I’m not that artistic, and it’s easy to make the style look cool. Just make everything come together at hard angles and let it go.

I considered SVG for the job, and leaned towards it for a while just for the sake of not falling into Not Invented Here. The problem is that SVG is incredibly complicated, especially for rendering. In a game where I’d likely be writing my own rendering of the SVG standard, I just didn’t want to do it. What’s more, for any kind of complicated effect, I’d probably have to use CSS, which is a second really complicated standard to implement.

So I went off to do it myself. When I inevitably end up having to reimplement some feature of SVG, I’ll just live with that.

Anyway, this led me to make Graphics::GVG. It uses a simple scripting language (parsed by Marpa::R2) to define how to draw your vector art:

The drawing commands inside the glow { ... } block will be rendered with a glow effect. Exactly what that means is up to the renderer.

There’s an included OpenGL renderer, which is where the real fun starts. The script above would be parsed into an Abstract Syntax Tree (AST), and then renderers compile that into the system of their choice. In the OpenGL case, it compiles the AST into a Perl package, which has a draw() method that does a series of OpenGL functions.

For example, a simple rectangle GVG script:

Gets turned into the Perl code below by the OpenGL renderer:

Which isn’t going to win any formatting awards, but it’s not meant to be edited by humans, anyway.

The above gets returned as a string, so you can compile it right into the running program using eval(STRING) (see Dynamic Code Loading for why you shouldn’t be afraid of this sort of thing). Alternatively, you could save it as a .pm file and load it up that way.

Either way, you get yourself an object from that package with new(), and then call draw() on it for each frame.

The generated code could be improved, for certain. For performance, it’ll probably move to vertex buffers. There should also be a way to make predictable package names rather than the UUID. If the overhead of calling all those OpenGL functions ends up being an issue, it could be compiled to a C function that can be called from XS.

In the future, there will be other renderers, which I hope can combine to output one set of package code. Meaning you would call $obj->draw_opengl for the OpenGL renderer, or $obj->init_chipmunk to setup the geometry for the Chipmunk2D physics library.

New module: SQL::Functional

If you have an application with simple database needs, writing the SQL strings directly in the program works fine. If it’s bigger, an Object-Relational Mapper like DBIx::Class starts to make sense. I find the additional boilerplate of an ORM isn’t worth the effort for simple needs, but it doesn’t take much more for the overhead to be worthwhile.

Then there’s another point beyond that where the ORM doesn’t know how to effectively and efficiently run a request. When you get there, it’s time to write raw SQL again, perhaps giving the result set back to your ORM so you can continue on as if nothing happened.

How do you write that SQL? Probably with strings again, or a helper library like SQL::Abstract.

The problem with raw strings is brought up in Epigrams in Programming:

  • It’s difficult to extract sense from strings, but they’re the only communication coin we can count on.
  • The string is a stark data structure and everywhere it is passed there is much duplication of process. It is a perfect vehicle for hiding information.

The nature of our database problem is that we’ve hit on a very complicated case, and now we’re stuck using the most ad-hoc way of structuring data. Not a good combination!

SQL::Abstract tries to address this by providing a structured way of building a string. We still have to output a string (” . . . they’re the only communication coin we can count on”), but at least we don’t have to deal with it directly. The problem is that, again, we’re getting to the most complicated cases of SQL, and SQL::Abstract abuses syntax tricks for these cases. Consider this case I pulled out of its docs:

Why are we taking references to scalars? And references to arrayrefs, for that matter? Could you copy-and-paste this to a DBA and explain what’s going on?

When you drill down a bit, the reason for these issues is that SQL::Abstract uses an Object Oriented interface to build this string, and OO is just not a good fit for this problem.

On the other hand, the problem can be solved more cleanly by combining functions that look vaguely like SQL:

This is exactly what SQL::Functional does. Version 0.1 is now up on CPAN.

New Module: Linux::IRPulses

I’ve been playing around with Infrared Remote data lately. I wanted to use LIRC to parse the data, but the problem was that I was working with a lot of custom IR protocols, and I didn’t want to use the usual LIRC data flow of mapping codes to buttons to executable programs. If you want the usual way, try Lirc::Client.

Instead, I decided to parse the pulse/space data right out of the “mode2″ program. This is what’s done by Linux::IRPulses.

This will probably change at some point to parsing data directly from /dev/lirc*, but that will require a few C bindings.