Smile_Detector | test Smile-Detector Machine Learning models | Machine Learning library

by coneypo Python Version: Current License: No License

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

kandi X-RAY | Smile_Detector Summary

Smile_Detector is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow applications. Smile_Detector has no bugs, it has no vulnerabilities and it has low support. However Smile_Detector build file is not available. You can download it from GitHub.

Smile_Detector

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Quality

Security

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Support

Smile_Detector has a low active ecosystem.

It has 27 star(s) with 20 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

Smile_Detector has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of Smile_Detector is current.

Quality

Smile_Detector has 0 bugs and 0 code smells.

Security

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

Smile_Detector code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

Smile_Detector 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

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

Smile_Detector has no build file. You will be need to create the build yourself to build the component from source.

Smile_Detector saves you 83 person hours of effort in developing the same functionality from scratch.

It has 214 lines of code, 8 functions and 5 files.

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

Top functions reviewed by kandi - BETA

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

Write to csv file
Get features from image
Linear regression model
Read data from csv
Extract features from image
Wraps MLPC classifier
Linear regression
Wraps SGDC classifier

Get all kandi verified functions for this library.

Smile_Detector Key Features

No Key Features are available at this moment for Smile_Detector.

Smile_Detector Examples and Code Snippets

No Code Snippets are available at this moment for Smile_Detector.

Community Discussions

Trending Discussions on Smile_Detector

How to create cv::Mat from dlib::rectangle on dlib's array2d image?

QUESTION

How to create cv::Mat from dlib::rectangle on dlib's array2d image?

Asked 2019-Aug-24 at 13:09

Im using dlib's hog detector because it finds faces much better than opencv haarcascades. But it can not detect emotions on faces (or can???). I need extract a "sub-image" from dlib::rectangle with face, and create cv::Mat from it to call cv::detectMultiScale() with preloaded "haarcascade_smile.xml".

How to perform this extraction/conversion?

Code sample below...

...

ANSWER

Answered 2019-Aug-24 at 13:09

There are two ways you can achieve this, either through the extract_image_chips functionality in dlib (docs) or by extracting the sub-image from a wrapping cv::Mat using the corresponding OpenCV API. Which one you will use depends on how convenient the choice is for the rest of your processing pipeline.

Concluding from your sample it seems that the OpenCV path is the most convenient (but again, please review the design and API options):

Source https://stackoverflow.com/questions/57595274

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

Vulnerabilities

No vulnerabilities reported

Install Smile_Detector

You can download it from GitHub.
You can use Smile_Detector 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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