sketch-rnn | Multilayer LSTM and Mixture Density Network | Machine Learning library

by hardmaru Python Version: Current License: No License

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kandi X-RAY | sketch-rnn Summary

sketch-rnn is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow applications. sketch-rnn has no bugs, it has no vulnerabilities and it has medium support. However sketch-rnn build file is not available. You can download it from GitHub.

Multilayer LSTM and Mixture Density Network for modelling path-level SVG Vector Graphics data in TensorFlow

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sketch-rnn has a medium active ecosystem.

It has 788 star(s) with 127 fork(s). There are 33 watchers for this library.

It had no major release in the last 6 months.

There are 6 open issues and 1 have been closed. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of sketch-rnn is current.

Quality

sketch-rnn has 0 bugs and 0 code smells.

Security

sketch-rnn has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

sketch-rnn code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

sketch-rnn does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

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sketch-rnn releases are not available. You will need to build from source code and install.

sketch-rnn has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions are not available. Examples and code snippets are available.

Top functions reviewed by kandi - BETA

kandi has reviewed sketch-rnn and discovered the below as its top functions. This is intended to give you an instant insight into sketch-rnn implemented functionality, and help decide if they suit your requirements.

Set whether this segment is closed
Return True if this segment is closer than the last segment

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sketch-rnn Key Features

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sketch-rnn Examples and Code Snippets

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Community Discussions

Trending Discussions on sketch-rnn

How to convert SVG polylines to quickdraw stroke-3 numpy format?

Cannot feed value of shape in Tensorflow

QUESTION

How to convert SVG polylines to quickdraw stroke-3 numpy format?

Asked 2021-Feb-06 at 19:28

I would like to convert a basic SVG file containing polylines into the stroke-3 format used by sketch-rnn (and the quickdraw dataset).

To my understanding, each polyline point in stroke-3 format would be:

stored as [delta_x, delta_y, pen_up], where
delta_x, delta_y represent the coordinates relative to the previous point and
pen_up is a bit that is 1 when the pen is up (e.g. move_to operation a-la turtle graphics) or 0 when the pen is down (e.g. line_to operation a-la turtle graphics).

I've attempted to write the function and convert an SVG, but I when I render a test of the stroke-3 format I get an extra line.

My input SVG looks like this:

...

ANSWER

Answered 2021-Feb-06 at 16:20

Your conversion is correct, the bug is in the rendering code. It must be is_down = data[i][2] == 0 instead of is_down = data[i-1][2] == 0 in draw_stroke3.

This error didn't show up with the other paths as in all but two cases the new path starts at the end of the previous path. In the other case where you really move to a new start point the additional line coincided with a line already drawn.

UPDATE AND CORRECTION:
I noticed that I mis-interpreted the meaning of the pen-up bit: in fact it shows that the pen is to be lifted after drawing the current stroke, not for the current stroke as I though at first. Therefore your rendering code appears to be OK and the bug is in the stroke3 file generation.
I guess you can do it much simpler by recording the end points for each operation along with the op code (1 = move, 0 = draw) for the current operation. After conversion to a numpy array we can easily convert these absolute positions the relative displacements by do the difference of the first two columns and then shift the third column with the op codes backwards by one position:

Source https://stackoverflow.com/questions/65985435

QUESTION

Cannot feed value of shape in Tensorflow

Asked 2020-May-22 at 18:59

I am attempting to train a CNN model following along with a tutorial, using the exact code specified and am receiving an error. Below is the code I am using.

...

ANSWER

Answered 2020-May-22 at 18:59

Thank you jdehesa, I was able to simply put in a static parameter for the height and the program seems to run successfully.

Source https://stackoverflow.com/questions/61959486

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install sketch-rnn

You can download it from GitHub.
You can use sketch-rnn like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

Alex Graves' paper on text sequence and handwriting generation. Karpathy's char-rnn tool, motivation for creating sketch-rnn. KanjiVG. Fantastic Database of Kanji Stroke Order. Very clean TensorFlow implementation of char-rnn, written by Sherjil Ozair, where I based the skeleton of this code off of. svg.path. I used this well written tool to help convert path data into line data. CASIA Online and Offline Chinese Handwriting Databases. Download stroke data for written cursive Simplifed Chinese. How Do Humans Sketch Objects? TU Berlin Sketch Database. Would be interesting to extend this work and generate random vector art of real life stuff. Doraemon in SVG format. Potrace. Beautiful looking tool to convert raster bitmapped drawings into SVG for potentially scaling up resolution of drawings. Could potentially apply this to generate large amounts of training data. Rendering Belzier Curve Codes. I used this very useful code to convert Belzier curves into line segments.

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