Simple SDF mesh generation in Python

Overview

sdf

Generate 3D meshes based on SDFs (signed distance functions) with a dirt simple Python API.

Special thanks to Inigo Quilez for his excellent documentation on signed distance functions:

Example

Here is a complete example that generates the model shown. This is the canonical Constructive Solid Geometry example. Note the use of operators for union, intersection, and difference.

from sdf import *

f = sphere(1) & box(1.5)

c = cylinder(0.5)
f -= c.orient(X) | c.orient(Y) | c.orient(Z)

f.save('out.stl')

Yes, that's really the entire code! You can 3D print that model or use it in a 3D application.

More Examples

Have a cool example? Submit a PR!

gearlike.py knurling.py blobby.py weave.py
gearlike knurling blobby weave
gearlike knurling blobby weave

Requirements

Note that the dependencies will be automatically installed by setup.py when following the directions below.

  • Python 3
  • numpy
  • Pillow
  • scikit-image
  • scipy

Installation

Use the commands below to clone the repository and install the sdf library in a Python virtualenv.

git clone https://github.com/fogleman/sdf.git
cd sdf
virtualenv env
. env/bin/activate
pip install -e .

Confirm that it works:

python examples/example.py # should generate a file named out.stl

You can skip the installation if you always run scripts that import sdf from the root folder.

Viewing the Mesh

Find and install a 3D mesh viewer for your platform, such as MeshLab.

I have developed and use my own cross-platform mesh viewer called meshview (see screenshot). Installation is easy if you have Go and glfw installed:

$ brew install go glfw # on macOS with homebrew
$ go get -u github.com/fogleman/meshview/cmd/meshview

Then you can view any mesh from the command line with:

$ meshview your-mesh.stl

See the meshview README for more complete installation instructions.

On macOS you can just use the built-in Quick Look (press spacebar after selecting the STL file in Finder) in a pinch.

API

In all of the below examples, f is any 3D SDF, such as:

f = sphere()

Bounds

The bounding box of the SDF is automatically estimated. Inexact SDFs such as non-uniform scaling may cause issues with this process. In that case you can specify the bounds to sample manually:

f.save('out.stl', bounds=((-1, -1, -1), (1, 1, 1)))

Resolution

The resolution of the mesh is also computed automatically. There are two ways to specify the resolution. You can set the resolution directly with step:

f.save('out.stl', step=0.01)
f.save('out.stl', step=(0.01, 0.02, 0.03)) # non-uniform resolution

Or you can specify approximately how many points to sample:

f.save('out.stl', samples=2**24) # sample about 16M points

By default, samples=2**22 is used.

Tip: Use the default resolution while developing your SDF. Then when you're done, crank up the resolution for your final output.

Batches

The SDF is sampled in batches. By default the batches have 32**3 = 32768 points each. This batch size can be overridden:

f.save('out.stl', batch_size=64) # instead of 32

The code attempts to skip any batches that are far away from the surface of the mesh. Inexact SDFs such as non-uniform scaling may cause issues with this process, resulting in holes in the output mesh (where batches were skipped when they shouldn't have been). To avoid this, you can disable sparse sampling:

f.save('out.stl', sparse=False) # force all batches to be completely sampled

Worker Threads

The SDF is sampled in batches using worker threads. By default, multiprocessing.cpu_count() worker threads are used. This can be overridden:

f.save('out.stl', workers=1) # only use one worker thread

Without Saving

You can of course generate a mesh without writing it to an STL file:

points = f.generate() # takes the same optional arguments as `save`
print(len(points)) # print number of points (3x the number of triangles)
print(points[:3]) # print the vertices of the first triangle

If you want to save an STL after generate, just use:

write_binary_stl(path, points)

Visualizing the SDF

You can plot a visualization of a 2D slice of the SDF using matplotlib. This can be useful for debugging purposes.

f.show_slice(z=0)
f.show_slice(z=0, abs=True) # show abs(f)

You can specify a slice plane at any X, Y, or Z coordinate. You can also specify the bounds to plot.

Note that matplotlib is only imported if this function is called, so it isn't strictly required as a dependency.


How it Works

The code simply uses the Marching Cubes algorithm to generate a mesh from the Signed Distance Function.

This would normally be abysmally slow in Python. However, numpy is used to evaluate the SDF on entire batches of points simultaneously. Furthermore, multiple threads are used to process batches in parallel. The result is surprisingly fast (for marching cubes). Meshes of adequate detail can still be quite large in terms of number of triangles.

The core "engine" of the sdf library is very small and can be found in mesh.py.

In short, there is nothing algorithmically revolutionary here. The goal is to provide a simple, fun, and easy-to-use API for generating 3D models in our favorite language Python.

Files

  • sdf/d2.py: 2D signed distance functions
  • sdf/d3.py: 3D signed distance functions
  • sdf/dn.py: Dimension-agnostic signed distance functions
  • sdf/ease.py: Easing functions that operate on numpy arrays. Some SDFs take an easing function as a parameter.
  • sdf/mesh.py: The core mesh-generation engine. Also includes code for estimating the bounding box of an SDF and for plotting a 2D slice of an SDF with matplotlib.
  • sdf/progress.py: A console progress bar.
  • sdf/stl.py: Code for writing a binary STL file.
  • sdf/text.py: Generate 2D SDFs for text (which can then be extruded)
  • sdf/util.py: Utility constants and functions.

SDF Implementation

It is reasonable to write your own SDFs beyond those provided by the built-in library. Browse the SDF implementations to understand how they are implemented. Here are some simple examples:

@sdf3
def sphere(radius=1, center=ORIGIN):
    def f(p):
        return np.linalg.norm(p - center, axis=1) - radius
    return f

An SDF is simply a function that takes a numpy array of points with shape (N, 3) for 3D SDFs or shape (N, 2) for 2D SDFs and returns the signed distance for each of those points as an array of shape (N, 1). They are wrapped with the @sdf3 decorator (or @sdf2 for 2D SDFs) which make boolean operators work, add the save method, add the operators like translate, etc.

@op3
def translate(other, offset):
    def f(p):
        return other(p - offset)
    return f

An SDF that operates on another SDF (like the above translate) should use the @op3 decorator instead. This will register the function such that SDFs can be chained together like:

f = sphere(1).translate((1, 2, 3))

Instead of what would otherwise be required:

f = translate(sphere(1), (1, 2, 3))

Remember, it's Python!

Remember, this is Python, so it's fully programmable. You can and should split up your model into parameterized sub-components, for example. You can use for loops and conditionals wherever applicable. The sky is the limit!

See the customizable box example for some starting ideas.


Function Reference

3D Primitives

sphere

sphere(radius=1, center=ORIGIN)

f = sphere() # unit sphere
f = sphere(2) # specify radius
f = sphere(1, (1, 2, 3)) # translated sphere

box

box(size=1, center=ORIGIN, a=None, b=None)

f = box(1) # all side lengths = 1
f = box((1, 2, 3)) # different side lengths
f = box(a=(-1, -1, -1), b=(3, 4, 5)) # specified by bounds

rounded_box

rounded_box(size, radius)

f = rounded_box((1, 2, 3), 0.25)

wireframe_box

wireframe_box(size, thickness)

f = wireframe_box((1, 2, 3), 0.05)

torus

torus(r1, r2)

f = torus(1, 0.25)

capsule

capsule(a, b, radius)

f = capsule(-Z, Z, 0.5)

capped_cylinder

capped_cylinder(a, b, radius)

f = capped_cylinder(-Z, Z, 0.5)

rounded_cylinder

rounded_cylinder(ra, rb, h)

f = rounded_cylinder(0.5, 0.1, 2)

capped_cone

capped_cone(a, b, ra, rb)

f = capped_cone(-Z, Z, 1, 0.5)

rounded_cone

rounded_cone(r1, r2, h)

f = rounded_cone(0.75, 0.25, 2)

ellipsoid

ellipsoid(size)

f = ellipsoid((1, 2, 3))

pyramid

pyramid(h)

f = pyramid(1)

Platonic Solids

tetrahedron

tetrahedron(r)

f = tetrahedron(1)

octahedron

octahedron(r)

f = octahedron(1)

dodecahedron

dodecahedron(r)

f = dodecahedron(1)

icosahedron

icosahedron(r)

f = icosahedron(1)

Infinite 3D Primitives

The following SDFs extend to infinity in some or all axes. They can only effectively be used in combination with other shapes, as shown in the examples below.

plane

plane(normal=UP, point=ORIGIN)

plane is an infinite plane, with one side being positive (outside) and one side being negative (inside).

f = sphere() & plane()

slab

slab(x0=None, y0=None, z0=None, x1=None, y1=None, z1=None, k=None)

slab is useful for cutting a shape on one or more axis-aligned planes.

f = sphere() & slab(z0=-0.5, z1=0.5, x0=0)

cylinder

cylinder(radius)

cylinder is an infinite cylinder along the Z axis.

f = sphere() - cylinder(0.5)

Text

Yes, even text is supported!

Text

text(name, text, width=None, height=None, texture_point_size=512)

FONT = 'Arial'
TEXT = 'Hello, world!'

w, h = measure_text(FONT, TEXT)

f = rounded_box((w + 1, h + 1, 0.2), 0.1)
f -= text(FONT, TEXT).extrude(1)

Positioning

translate

translate(other, offset)

f = sphere().translate((0, 0, 2))

scale

scale(other, factor)

Note that non-uniform scaling is an inexact SDF.

f = sphere().scale(2)
f = sphere().scale((1, 2, 3)) # non-uniform scaling

rotate

rotate(other, angle, vector=Z)

f = capped_cylinder(-Z, Z, 0.5).rotate(pi / 4, X)

orient

orient(other, axis)

orient rotates the shape such that whatever was pointing in the +Z direction is now pointing in the specified direction.

c = capped_cylinder(-Z, Z, 0.25)
f = c.orient(X) | c.orient(Y) | c.orient(Z)

Boolean Operations

The following primitives a and b are used in all of the following boolean operations.

a = box((3, 3, 0.5))
b = sphere()

The named versions (union, difference, intersection) can all take one or more SDFs as input. They all take an optional k parameter to define the amount of smoothing to apply. When using operators (|, -, &) the smoothing can still be applied via the .k(...) function.

union

f = a | b
f = union(a, b) # equivalent

difference

f = a - b
f = difference(a, b) # equivalent

intersection

f = a & b
f = intersection(a, b) # equivalent

smooth_union

f = a | b.k(0.25)
f = union(a, b, k=0.25) # equivalent

smooth_difference

f = a - b.k(0.25)
f = difference(a, b, k=0.25) # equivalent

smooth_intersection

f = a & b.k(0.25)
f = intersection(a, b, k=0.25) # equivalent

Repetition

repeat

repeat(other, spacing, count=None, padding=0)

repeat can repeat the underlying SDF infinitely or a finite number of times. If finite, the number of repetitions must be odd, because the count specifies the number of copies to make on each side of the origin. If the repeated elements overlap or come close together, you made need to specify a padding greater than zero to compute a correct SDF.

f = sphere().repeat(3, (1, 1, 0))

circular_array

circular_array(other, count, offset)

circular_array makes count copies of the underlying SDF, arranged in a circle around the Z axis. offset specifies how far to translate the shape in X before arraying it. The underlying SDF is only evaluated twice (instead of count times), so this is more performant than instantiating count copies of a shape.

f = capped_cylinder(-Z, Z, 0.5).circular_array(8, 4)

Miscellaneous

blend

blend(a, *bs, k=0.5)

f = sphere().blend(box())

dilate

dilate(other, r)

f = example.dilate(0.1)

erode

erode(other, r)

f = example.erode(0.1)

shell

shell(other, thickness)

f = sphere().shell(0.05) & plane(-Z)

elongate

elongate(other, size)

f = example.elongate((0.25, 0.5, 0.75))

twist

twist(other, k)

f = box().twist(pi / 2)

bend

bend(other, k)

f = box().bend(1)

bend_linear

bend_linear(other, p0, p1, v, e=ease.linear)

f = capsule(-Z * 2, Z * 2, 0.25).bend_linear(-Z, Z, X, ease.in_out_quad)

bend_radial

bend_radial(other, r0, r1, dz, e=ease.linear)

f = box((5, 5, 0.25)).bend_radial(1, 2, -1, ease.in_out_quad)

transition_linear

transition_linear(f0, f1, p0=-Z, p1=Z, e=ease.linear)

f = box().transition_linear(sphere(), e=ease.in_out_quad)

transition_radial

transition_radial(f0, f1, r0=0, r1=1, e=ease.linear)

f = box().transition_radial(sphere(), e=ease.in_out_quad)

wrap_around

wrap_around(other, x0, x1, r=None, e=ease.linear)

FONT = 'Arial'
TEXT = ' wrap_around ' * 3
w, h = measure_text(FONT, TEXT)
f = text(FONT, TEXT).extrude(0.1).orient(Y).wrap_around(-w / 2, w / 2)

2D to 3D Operations

extrude

extrude(other, h)

f = hexagon(1).extrude(1)

extrude_to

extrude_to(a, b, h, e=ease.linear)

f = rectangle(2).extrude_to(circle(1), 2, ease.in_out_quad)

revolve

revolve(other, offset=0)

f = hexagon(1).revolve(3)

3D to 2D Operations

slice

slice(other)

f = example.translate((0, 0, 0.55)).slice().extrude(0.1)

2D Primitives

circle

line

rectangle

rounded_rectangle

equilateral_triangle

hexagon

rounded_x

polygon

Owner
Michael Fogleman
Software Engineer at Formlabs
Michael Fogleman
Machine Leaning applied to denoise images to improve OCR Accuracy

Machine Learning to Denoise Images for Better OCR Accuracy This project is an adaptation of this tutorial and used only for learning purposes: https:/

Antonio Bri Pérez 2 Nov 16, 2022
APS 6º Semestre - UNIP (2021)

UNIP - Universidade Paulista Ciência da Computação (CC) DESENVOLVIMENTO DE UM SISTEMA COMPUTACIONAL PARA ANÁLISE E CLASSIFICAÇÃO DE FORMAS Link do git

Eduardo Talarico 5 Mar 09, 2022
Textboxes : Image Text Detection Model : python package (tensorflow)

shinTB Abstract A python package for use Textboxes : Image Text Detection Model implemented by tensorflow, cv2 Textboxes Paper Review in Korean (My Bl

Jayne Shin (신재인) 91 Dec 15, 2022
Opencv-image-filters - A camera to capture videos in real time by placing filters using Python with the help of the Tkinter and OpenCV libraries

Opencv-image-filters - A camera to capture videos in real time by placing filters using Python with the help of the Tkinter and OpenCV libraries

Sergio Díaz Fernández 1 Jan 13, 2022
OCR of Chicago 1909 Renumbering Plan

Requirements: Python 3 (probably at least 3.4) pipenv (pip3 install pipenv) tesseract (brew install tesseract, at least if you have a mac and homebrew

ted whalen 2 Nov 21, 2021
This repository contains codes on how to handle mouse event using OpenCV

Handling-Mouse-Click-Events-Using-OpenCV This repository contains codes on how t

Happy N. Monday 3 Feb 15, 2022
pulse2percept: A Python-based simulation framework for bionic vision

pulse2percept: A Python-based simulation framework for bionic vision Retinal degenerative diseases such as retinitis pigmentosa and macular degenerati

67 Dec 29, 2022
Automatically download multiple papers by keywords in CVPR

CVFPaperHelper Automatically download multiple papers by keywords in CVPR Install mkdir PapersToRead cd PaperToRead pip install requests tqdm git clon

46 Jun 08, 2022
Apply different text recognition services to images of handwritten documents.

Handprint The Handwritten Page Recognition Test is a command-line program that invokes HTR (handwritten text recognition) services on images of docume

Caltech Library 117 Jan 02, 2023
a deep learning model for page layout analysis / segmentation.

OCR Segmentation a deep learning model for page layout analysis / segmentation. dependencies tensorflow1.8 python3 dataset: uw3-framed-lines-degraded-

99 Dec 12, 2022
Binarize document images

Binarization Binarization for document images Examples Introduction This tool performs document image binarization (i.e. transform colour/grayscale to

QURATOR-SPK 48 Jan 02, 2023
SRA's seminar on Introduction to Computer Vision Fundamentals

Introduction to Computer Vision This repository includes basics to : Python Numpy: A python library Git Computer Vision. The aim of this repository is

Society of Robotics and Automation 147 Dec 04, 2022
Rubik's Cube in pygame with OpenGL

Rubik Rubik's Cube in pygame with OpenGL The script show on the screen a Rubik Cube buit with OpenGL. Then I have also implemented all the possible mo

Gabro 2 Apr 15, 2022
Generate a list of papers with publicly available source code in the daily arxiv

2021-06-08 paper code optimal network slicing for service-oriented networks with flexible routing and guaranteed e2e latency networkslicing multi-moda

79 Jan 03, 2023
BoxToolBox is a simple python application built around the openCV library

BoxToolBox is a simple python application built around the openCV library. It is not a full featured application to guide you through the w

František Horínek 1 Nov 12, 2021
[EMNLP 2021] Improving and Simplifying Pattern Exploiting Training

ADAPET This repository contains the official code for the paper: "Improving and Simplifying Pattern Exploiting Training". The model improves and simpl

Rakesh R Menon 138 Dec 26, 2022
Using python libraries to track hands

Python-HandTracking Using python libraries to track hands on a camera Uses cv2 and mediapipe libraries custom hand tracking module PyCharm IDE Final E

Martin Matsudaira 1 Dec 17, 2021
The Open Source Framework for Machine Vision

SimpleCV Quick Links: About Installation [Docker] (#docker) Ubuntu Virtual Environment Arch Linux Fedora MacOS Windows Raspberry Pi SimpleCV Shell Vid

Sight Machine 2.6k Dec 31, 2022
零样本学习测评基准,中文版

ZeroCLUE 零样本学习测评基准,中文版 零样本学习是AI识别方法之一。 简单来说就是识别从未见过的数据类别,即训练的分类器不仅仅能够识别出训练集中已有的数据类别, 还可以对于来自未见过的类别的数据进行区分。 这是一个很有用的功能,使得计算机能够具有知识迁移的能力,并无需任何训练数据, 很符合现

CLUE benchmark 27 Dec 10, 2022
基于Paddle框架的PSENet复现

PSENet-Paddle 基于Paddle框架的PSENet复现 本项目基于paddlepaddle框架复现PSENet,并参加百度第三届论文复现赛,将在2021年5月15日比赛完后提供AIStudio链接~敬请期待 AIStudio链接 参考项目: whai362-PSENet 环境配置 本项目

QuanHao Guo 4 Apr 24, 2022