EPNet | Enhancing Point Features with Image Semantics | Image Editing library

by happinesslz Python Version: Current License: MIT

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

kandi X-RAY | EPNet Summary

EPNet is a Python library typically used in Media, Image Editing applications. EPNet has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

EPNet: Enhancing Point Features with Image Semantics for 3D Object Detection (ECCV 2020). Paper is now available in EPNet, and the code is based on PointRCNN.

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Quality

Security

License

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Support

EPNet has a low active ecosystem.

It has 184 star(s) with 35 fork(s). There are 7 watchers for this library.

It had no major release in the last 6 months.

There are 18 open issues and 24 have been closed. On average issues are closed in 6 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of EPNet is current.

Quality

EPNet has 0 bugs and 0 code smells.

Security

EPNet has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

EPNet code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

EPNet is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

EPNet releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

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

EPNet saves you 3336 person hours of effort in developing the same functionality from scratch.

It has 7159 lines of code, 347 functions and 41 files.

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

Top functions reviewed by kandi - BETA

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

Decorator for the joint function decorator
Make a list
Create a new optimizer
Create an optimizer
Augment the ROI into a single ROI
Transform cartesian coordinates
Generate a random augmenting box
Performs the augmentation of the roi
Calculate the iouv4 iouv
Forward computation
Evaluate single CKPT
Forward a pointcloud
Remove kitti annotations from an annotation
Generate gt database
Compute the focal point loss
Create a scheduler
Convert object to BeVox box
Rotate the kernel for the kernel
Compute the intersection of two line segments
CPU wrapper for roipool_pc
Evaluate the CKPTN
Forward the proposal target
Get kitti image info
Calculate the reg loss for the reg loss
Generate image for gt
Generate RPNR features

Get all kandi verified functions for this library.

EPNet Key Features

No Key Features are available at this moment for EPNet.

EPNet Examples and Code Snippets

No Code Snippets are available at this moment for EPNet.

Community Discussions

Trending Discussions on EPNet

What's the difference between these two regular expressions?

QUESTION

What's the difference between these two regular expressions?

Asked 2020-Jun-06 at 23:57

The short and immediate version of the question is: Why are these two regex different? i.e.,

href=(['"]).+?\1

href=(['"]).+?['"] or href=(['"]).+?(['"])

I am practicing regex on this site and I am trying to solve this level

http://play.inginf.units.it/#/level/6

I am posting the entire content here in case the site goes down in future.

...

ANSWER

Answered 2020-Jun-06 at 23:34

The backreference has to match the same thing that the capture group matched. So the first regexp will match

Source https://stackoverflow.com/questions/62239175

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

Vulnerabilities

No vulnerabilities reported

Install EPNet

You can download it from GitHub.
You can use EPNet 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

This is Pytorch implementation for EPNet on KITTI dataset, which is mainly achieved by Liu Zhe and Huang Tengteng. Some parts also benefit from Chen Xiwu.

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