selectivesearch | Selective Search Implementation for Python | Search Engine library

by AlpacaDB Python Version: 0.4 License: MIT

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

kandi X-RAY | selectivesearch Summary

selectivesearch is a Python library typically used in Database, Search Engine applications. selectivesearch 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 selectivesearch' or download it from GitHub, PyPI.

Selective Search Implementation for Python

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Quality

Security

License

Reuse

Support

selectivesearch has a low active ecosystem.

It has 692 star(s) with 248 fork(s). There are 39 watchers for this library.

It had no major release in the last 12 months.

There are 19 open issues and 7 have been closed. On average issues are closed in 216 days. There are 2 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of selectivesearch is 0.4

Quality

selectivesearch has 0 bugs and 0 code smells.

Security

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

selectivesearch code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

selectivesearch 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

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

Deployable package is available in PyPI.

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

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

It has 199 lines of code, 15 functions and 4 files.

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

Top functions reviewed by kandi - BETA

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

Perform selective search
Extract regions from an image
Use selective search .
calculate colour histogram
Calculate a texture histogram .
Given a list of regions extract all the regions that intersect them .
Generate the segments of the image .
Calculate texture gradient
Merge two regions .
Compute the similarity between two images

Get all kandi verified functions for this library.

selectivesearch Key Features

No Key Features are available at this moment for selectivesearch.

selectivesearch Examples and Code Snippets

No Code Snippets are available at this moment for selectivesearch.

Community Discussions

Trending Discussions on selectivesearch

Special function on feature maps of convolutional layer

Extracting image from bounding box - selective search

QUESTION

Special function on feature maps of convolutional layer

Asked 2019-Jan-25 at 17:02

In Short:

How do I pass feature maps from convolutional layer defined in Keras to a special function (region proposer) which is then passed to other Keras layers (e.g Softmax classifier)?

Long:

I'm trying to implement something like Fast R-CNN (not Faster R-CNN) in Keras. The reason for this is because I'm trying to implement a custom architecture as seen in the figure below:

Here's the code for the figure above (excluding candidates input):

...

ANSWER

Answered 2019-Jan-25 at 17:02

To my best understanding, selective-search take an input and return n no of patches of different (H,W). So in your case, feature-map is of dims (164,164,96), you can assume (164,164) as the input for selective-search and it will give you n number of patch, for exp as (H1,W1), (H2,W2),.... So you can now append all the channel as it is, to that patch, so it becomes as of dims (H1,W1,96),(H2,W2,96),.....

Note: But there is downside of doing this too. Selective-Search algorithm use the strategy in which it breaks the image in grids and then re-join those patch as per the heatmap of the object. You would not be able to do that on feature-map. But you can use random search method on that and it can be useful.

Source https://stackoverflow.com/questions/54366838

QUESTION

Extracting image from bounding box - selective search

Asked 2018-Feb-14 at 20:12

I am learning how to properly use a selective search algorithm to create bounding boxes around an image, extract the smaller images within the bounding box and then run further analysis on the smaller images.

I am able to obtain the bounding boxes through the following, but how do I save/extract/export the images within each bounding box?

...

ANSWER

Answered 2017-Aug-15 at 10:35

You know how to get each rectangle using the lines

Source https://stackoverflow.com/questions/45666888

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

Vulnerabilities

No vulnerabilities reported

Install selectivesearch

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