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
RRD: Rotation-Sensitive Regression for Oriented Scene Text Detection

RRD: Rotation-Sensitive Regression for Oriented Scene Text Detection For more details, please refer to our paper. Citing Please cite the related works

Minghui Liao 102 Jun 29, 2022
Random maze generator and solver

Maze Generator and Solver I wrote a maze generator that works with two commonly known algorithms: Depth First Search and Randomized Prims. Both of the

Daniel Pérez 10 Sep 23, 2022
第一届西安交通大学人工智能实践大赛(2018AI实践大赛--图片文字识别)第一名;仅采用densenet识别图中文字

OCR 第一届西安交通大学人工智能实践大赛(2018AI实践大赛--图片文字识别)冠军 模型结果 该比赛计算每一个条目的f1score,取所有条目的平均,具体计算方式在这里。这里的计算方式不对一句话里的相同文字重复计算,故f1score比提交的最终结果低: - train val f1score 0

尹畅 441 Dec 22, 2022
Tools for manipulating and evaluating the hOCR format for representing multi-lingual OCR results by embedding them into HTML.

hocr-tools About About the code Installation System-wide with pip System-wide from source virtualenv Available Programs hocr-check -- check the hOCR f

OCRopus 285 Dec 08, 2022
Use Convolutional Recurrent Neural Network to recognize the Handwritten line text image without pre segmentation into words or characters. Use CTC loss Function to train.

Handwritten Line Text Recognition using Deep Learning with Tensorflow Description Use Convolutional Recurrent Neural Network to recognize the Handwrit

sushant097 224 Jan 07, 2023
Simple app for visual editing of Page XML files

Name nw-page-editor - Simple app for visual editing of Page XML files. Version: 2021.02.22 Description nw-page-editor is an application for viewing/ed

Mauricio Villegas 27 Jun 20, 2022
Official implementation of Character Region Awareness for Text Detection (CRAFT)

CRAFT: Character-Region Awareness For Text detection Official Pytorch implementation of CRAFT text detector | Paper | Pretrained Model | Supplementary

Clova AI Research 2.5k Jan 03, 2023
ScanTailor Advanced is the version that merges the features of the ScanTailor Featured and ScanTailor Enhanced versions, brings new ones and fixes.

ScanTailor Advanced The ScanTailor version that merges the features of the ScanTailor Featured and ScanTailor Enhanced versions, brings new ones and f

952 Dec 31, 2022
Single Shot Text Detector with Regional Attention

Single Shot Text Detector with Regional Attention Introduction SSTD is initially described in our ICCV 2017 spotlight paper. A third-party implementat

Pan He 215 Dec 07, 2022
A simple OCR API server, seriously easy to be deployed by Docker, on Heroku as well

ocrserver Simple OCR server, as a small working sample for gosseract. Try now here https://ocr-example.herokuapp.com/, and deploy your own now. Deploy

Hiromu OCHIAI 541 Dec 28, 2022
Fast image augmentation library and easy to use wrapper around other libraries. Documentation: https://albumentations.ai/docs/ Paper about library: https://www.mdpi.com/2078-2489/11/2/125

Albumentations Albumentations is a Python library for image augmentation. Image augmentation is used in deep learning and computer vision tasks to inc

11.4k Jan 02, 2023
Autonomous Driving project for Euro Truck Simulator 2

hope-autonomous-driving Autonomous Driving project for Euro Truck Simulator 2 Video: How is it working ? In this video, the program processes the imag

Umut Görkem Kocabaş 36 Nov 06, 2022
Automatically remove the mosaics in images and videos, or add mosaics to them.

Automatically remove the mosaics in images and videos, or add mosaics to them.

Hypo 1.4k Dec 30, 2022
A simple document layout analysis using Python-OpenCV

Run the application: python main.py *Note: For first time running the application, create a folder named "output". The application is a simple documen

Roinand Aguila 109 Dec 12, 2022
7th place solution

SIIM-FISABIO-RSNA-COVID-19-Detection 7th place solution Validation: We used iterative-stratification with 5 folds (https://github.com/trent-b/iterativ

11 Jul 17, 2022
Geometric Augmentation for Text Image

Text Image Augmentation A general geometric augmentation tool for text images in the CVPR 2020 paper "Learn to Augment: Joint Data Augmentation and Ne

Canjie Luo 440 Jan 05, 2023
Um RPG de texto orientado a objetos.

RPG de texto Um RPG de texto orientado a objetos, sem história. Um RPG (Role-playing game) baseado em texto em que você pode viajar para alguns locais

Vinicius 3 Oct 05, 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
Awesome multilingual OCR toolkits based on PaddlePaddle (practical ultra lightweight OCR system, provide data annotation and synthesis tools, support training and deployment among server, mobile, embedded and IoT devices)

English | 简体中文 Introduction PaddleOCR aims to create multilingual, awesome, leading, and practical OCR tools that help users train better models and a

27.5k Jan 08, 2023
Code for the "Sensing leg movement enhances wearable monitoring of energy expenditure" paper.

EnergyExpenditure Code for the "Sensing leg movement enhances wearable monitoring of energy expenditure" paper. Additional data for replicating this s

Patrick S 42 Oct 26, 2022