pycpd | Pure Numpy Implementation of the Coherent Point Drift | Image Editing library

by siavashk Python Version: 2.0.0 License: MIT

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

kandi X-RAY | pycpd Summary

pycpd is a Python library typically used in Media, Image Editing, Numpy applications. pycpd has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can install using 'pip install pycpd' or download it from GitHub, PyPI.

Pure Numpy Implementation of the Coherent Point Drift Algorithm

Support

Quality

Security

License

Reuse

Support

pycpd has a low active ecosystem.

It has 407 star(s) with 109 fork(s). There are 15 watchers for this library.

It had no major release in the last 12 months.

There are 3 open issues and 46 have been closed. On average issues are closed in 83 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of pycpd is 2.0.0

Quality

pycpd has 0 bugs and 79 code smells.

Security

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

pycpd code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

pycpd 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

pycpd releases are available to install and integrate.

Deployable package is available in PyPI.

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

pycpd saves you 234 person hours of effort in developing the same functionality from scratch.

It has 704 lines of code, 55 functions and 19 files.

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

Top functions reviewed by kandi - BETA

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

Performs the registration algorithm
Compute the expectation
Perform the iteration
Maximize the model
Returns the registration parameters
Transform the source point cloud
Update transform parameters
Updates the variance of the mixture model
Transform a point cloud into a point cloud
Gaussian kernel
Return the README rst file

Get all kandi verified functions for this library.

pycpd Key Features

No Key Features are available at this moment for pycpd.

pycpd Examples and Code Snippets

No Code Snippets are available at this moment for pycpd.

Community Discussions

Trending Discussions on pycpd

How to find the matched SIFT features that are spatially consistent?

QUESTION

How to find the matched SIFT features that are spatially consistent?

Asked 2018-Mar-26 at 22:05

I have extracted DenseSIFT from the query and database image and quantized by kmeans using VLFeat. The challenge is to find those SIFT features that quantized to the same visual words and be spatially consistent (have a similar position to object centers). I have tried few techniques:

using FLANN() on the SIFT (normal SIFT) coordinates on both query and database image and find the nearest neighbor and then comparing the visual words (NOTE: this gave few points that did not work).
Using Coherent-Point-Drift (CPD) on SIFT coordinates to find the matched points (I am not sure about this whether it is a right solution or not).

I am struggling with it for many days, and I hope experts can guide me with this. What are the possible solutions or algorithms that I can use for solving this?

...

ANSWER

Answered 2018-Mar-26 at 22:05

Neither of those two methods you mentioned achieve what you want do. The answer depends on the object in your pictures. If it has mostly flat faces, then you can rely on estimating the homography, see this tutorial.

If that's not case then can use the epipolar constraint to remove outliers / get geometrically consistent matches, see this tutorial. There are some other ways to achieve this if the speed is of importance in your application.

Source https://stackoverflow.com/questions/49476357

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

Vulnerabilities

No vulnerabilities reported

Install pycpd

You can install using 'pip install pycpd' or download it from GitHub, PyPI.
You can use pycpd 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 .

Find more information at: