stereo-matching | A simple wrapper for stereo matching | Computer Vision library

by eborboihuc Python Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | stereo-matching Summary

stereo-matching is a Python library typically used in Artificial Intelligence, Computer Vision applications. stereo-matching has no bugs, it has no vulnerabilities and it has low support. However stereo-matching build file is not available. You can download it from GitHub.

Keywords : Semi-Global Matching, StereoSGBM_create.

Support

Quality

Security

License

Reuse

Support

stereo-matching has a low active ecosystem.

It has 7 star(s) with 1 fork(s). There are no watchers for this library.

It had no major release in the last 6 months.

stereo-matching has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of stereo-matching is current.

Quality

stereo-matching has 0 bugs and 0 code smells.

Security

stereo-matching has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

stereo-matching code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

stereo-matching 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

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

stereo-matching 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.

It has 421 lines of code, 28 functions and 3 files.

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

Top functions reviewed by kandi - BETA

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

Visualize the camera
Visualize parameter
Rotate an array
Project the points of the ellipsoid
Visualize the projected image
Calculate the projected image
Translate an array
Visualize the model
Compute the left matcher
Normalize the image
Display the image
Calculate a point cloud from dispersion map
Convert value to RGB
Remove invalid interpolations
Calculate triangulation
Reads the depth image
Read an RGB image
Parse command line arguments
Read image from path
Show the current parameters

Get all kandi verified functions for this library.

stereo-matching Key Features

No Key Features are available at this moment for stereo-matching.

stereo-matching Examples and Code Snippets

No Code Snippets are available at this moment for stereo-matching.

Community Discussions

Trending Discussions on stereo-matching

How to ensure end of immutable borrow after function call in order to enable mutable borrow?

(Tensorflow) What is the correct NN needed for this task?

QUESTION

How to ensure end of immutable borrow after function call in order to enable mutable borrow?

Asked 2019-Aug-05 at 06:35

I have come across a borrowchecker problem using Rust 2018 that I cannot find the solution to. Basically, I have a function that takes a mutable reference to a vec, and as the first part of its execution passes that same vec into another function as an immutable reference. The latter function returns a new owned value - or at least I intend it to. The problem for me is that the compiler seems to regard the immutable borrow for the function call as lasting until the end of the outer function.

Unfortunately, this isn't a problem that is solved simply by putting braces around things (it shouldn't be anyway since I'm using Rust 2018). Moreover, while I have found a number of SO questions that appear to touch on similar matters (e.g. this, this, this and this), I haven't been able to find anything else that directly addresses this problem. Or at least, nothing where I have been able to work out what I should do from it. Crucially, most other similar questions either seem to involve a reference as the return type or were only an issue before non-lexical lifetimes.

I have created an executable MVE in the Rust Playground, and the full program in case it helps. I post the code below, for reference:

...

ANSWER

Answered 2019-Aug-05 at 06:25

The Solution I found is not to use std::iter::Sum and rewrite the sum call using fold:

Source https://stackoverflow.com/questions/57352413

QUESTION

(Tensorflow) What is the correct NN needed for this task?

Asked 2018-Apr-16 at 15:34

I'm about to start developing a neural net here with Tensorflow, but before I get into it too deep, I was hoping I could get some feedback on exactly what type of neural net I will need for this (If a net is the right way to go about this at all)

I need the NN to input an image, and output another image. This will be used for path-mapping on a robot I'm working on. The input image will be a disparity map, and the output will be a "driveable map" (an image that displays what in the scene can be driven on, and what can't)

I have built a dataset using Unity 3d. Here is an example from the set:

disparity map

driveable map:

As you can probably see, white represents the area where my robot can drive and black is where it can't. I will need the NN to take a disparity map, and give me back a "driveable map". Can this be done? Thanks!

...

ANSWER

Answered 2018-Apr-16 at 15:34

Sorry I'm not an expert. Since there hasn't been a response on this and if you are still looking, the vocabulary I would use to describe this type of problem is disparity networks and segmentation. Your best bet may be a specific type of disparity network: U-net

Source https://stackoverflow.com/questions/47871915

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

Vulnerabilities

No vulnerabilities reported

Install stereo-matching

You can download it from GitHub.
You can use stereo-matching 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: