tf-lift | Tensorflow port of LIFT , with training code | 3D Printing library

by cvlab-epfl Python Version: v1.0.0 License: No License

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | tf-lift Summary

tf-lift is a Python library typically used in Modeling, 3D Printing, Deep Learning, Tensorflow applications. tf-lift has no bugs, it has no vulnerabilities and it has low support. However tf-lift build file is not available. You can download it from GitHub.

Tensorflow port of LIFT (ECCV 2016), with training code.

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Quality

Security

License

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Support

tf-lift has a low active ecosystem.

It has 180 star(s) with 57 fork(s). There are 19 watchers for this library.

It had no major release in the last 12 months.

There are 5 open issues and 44 have been closed. On average issues are closed in 39 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of tf-lift is v1.0.0

Quality

tf-lift has 0 bugs and 0 code smells.

Security

tf-lift has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

tf-lift code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

tf-lift 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.

Reuse

tf-lift releases are available to install and integrate.

tf-lift 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.

tf-lift saves you 1875 person hours of effort in developing the same functionality from scratch.

It has 4136 lines of code, 140 functions and 35 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

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

Load training data for a given set .
Load patches .
Build the graph .
Calculate the XYZS for a set of scores .
Generate spatial transformer .
Create the pairwise pairs for the given task .
Randommine the image using a 3D bottleneck .
1D convolutional network .
Set up training .
get subpixel coordinates

Get all kandi verified functions for this library.

tf-lift Key Features

No Key Features are available at this moment for tf-lift.

tf-lift Examples and Code Snippets

No Code Snippets are available at this moment for tf-lift.

Community Discussions

Trending Discussions on tf-lift

How to read the .mat file from Visual SFM in Python Code?

QUESTION

How to read the .mat file from Visual SFM in Python Code?

Asked 2020-Jul-10 at 11:10

Can someone help me with the Python code to read the .mat file generated from Visual SFM? You can download the .mat file from the link:

https://github.com/cvlab-epfl/tf-lift/tree/master/example

You can get a .mat file in the zip in the link and the file is what I am asking for help. It seems to be an ASCII file. I do not know how to read the data in the file. I tried to load the data in the .mat file with scipy.io.loadmat() but an error occurred as:

...

ANSWER

Answered 2020-Jul-10 at 11:10

If you mean this VisualSFM (http://ccwu.me/vsfm/doc.html), then the .mat file isn't a MATLAB .mat file, but a 'match' file.

From the website:

[name].sift stores all the detected SIFT features, and [name].mat stores the feature matches.

It seems there is C++ code for reading this file (http://ccwu.me/vsfm/MatchFile.zip) which you could use to write a python parser.

Additionally, it seems like there is a python socket interface to VSFM, which may allow you to do what you want https://github.com/nrhine1/vsfm_util

Source https://stackoverflow.com/questions/62815242

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

Vulnerabilities

No vulnerabilities reported

Install tf-lift

You can download it from GitHub.
You can use tf-lift 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

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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