Face_Detection_Recognition
kandi X-RAY | Face_Detection_Recognition Summary
kandi X-RAY | Face_Detection_Recognition Summary
Face_Detection_Recognition is a Python library. Face_Detection_Recognition has no bugs, it has no vulnerabilities and it has low support. However Face_Detection_Recognition build file is not available. You can download it from GitHub.
Face_Detection_Recognition
Face_Detection_Recognition
Support
Quality
Security
License
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Support
Face_Detection_Recognition has a low active ecosystem.
It has 338 star(s) with 152 fork(s). There are 7 watchers for this library.
It had no major release in the last 6 months.
There are 5 open issues and 0 have been closed. On average issues are closed in 338 days. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of Face_Detection_Recognition is current.
Quality
Face_Detection_Recognition has 0 bugs and 0 code smells.
Security
Face_Detection_Recognition has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
Face_Detection_Recognition code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.
License
Face_Detection_Recognition 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
Face_Detection_Recognition releases are not available. You will need to build from source code and install.
Face_Detection_Recognition has no build file. You will be need to create the build yourself to build the component from source.
Face_Detection_Recognition saves you 843 person hours of effort in developing the same functionality from scratch.
It has 1932 lines of code, 134 functions and 14 files.
It has high code complexity. Code complexity directly impacts maintainability of the code.
Top functions reviewed by kandi - BETA
kandi has reviewed Face_Detection_Recognition and discovered the below as its top functions. This is intended to give you an instant insight into Face_Detection_Recognition implemented functionality, and help decide if they suit your requirements.
- Bulk detect face detection
- Compute bounding box
- Generate a bounding box for an image
- Computes the NMS of the given boxes
- Read a ROI image
- Extracts the image from the given rectangle
- Compute the intersection of two bounding boxes
- Convert a list of rects to bboxes
- Generate the list of file names
- Setup the module
- Create TMNN layer
- Creates a face embedding for a BTL image
- Use face recognition
- Visualize a list of landmarks
- Loads a model
- Calculate the value for a given threshold
- Detect the face of the image
- Train the model
- Performs the test validation
- Read image from file
- Create input pipeline
- Read an image file
- Calculate ROC curve
- Read data from file
- Store the revision info
- Decorator to add a layer to the layer
Get all kandi verified functions for this library.
Face_Detection_Recognition Key Features
No Key Features are available at this moment for Face_Detection_Recognition.
Face_Detection_Recognition Examples and Code Snippets
No Code Snippets are available at this moment for Face_Detection_Recognition.
Community Discussions
No Community Discussions are available at this moment for Face_Detection_Recognition.Refer to stack overflow page for discussions.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install Face_Detection_Recognition
You can download it from GitHub.
You can use Face_Detection_Recognition 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.
You can use Face_Detection_Recognition 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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