face-average | Averages faces from the input using OpenCV and Dlib | Computer Vision library

by georgegach Python Version: Current License: MIT

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

kandi X-RAY | face-average Summary

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

Averages faces from the input using OpenCV and Dlib

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Quality

Security

License

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Support

face-average has a low active ecosystem.

It has 8 star(s) with 4 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

There are 3 open issues and 0 have been closed. On average issues are closed in 330 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of face-average is current.

Quality

face-average has no bugs reported.

Security

face-average has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

face-average 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

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

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

Installation instructions, examples and code snippets are available.

Top functions reviewed by kandi - BETA

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

Runs the image
Compute the faces of the image
Calculate Delaunay triangulation
Warp two triangles
Compute the similarity between two points
Extract features from images
Checks if the given point is within the given rectangle
Get images from path
Apply Affine transformation to src image
Load images
Convenience function to constrain a point
Save the results to file

Get all kandi verified functions for this library.

face-average Key Features

No Key Features are available at this moment for face-average.

face-average Examples and Code Snippets

No Code Snippets are available at this moment for face-average.

Community Discussions

Trending Discussions on face-average

OpenCV error: Expected cv::UMat for argument 'M'

QUESTION

OpenCV error: Expected cv::UMat for argument 'M'

Asked 2019-Mar-03 at 07:16

I'm new to Python, therefore having troubles with debugging a script. I'm trying to create an 'average face' using an opencv script to recreate with my own images. Here's the Github repo I'm using, but same goes for this one and this one.

The land mark detection part works but the average.py script throws the error I don't understand how to solve.

The first two errors I solved by replacing the xrange() function with range(). Than the estimateRigidTransform() seemed depricated and there for I swapped it with estimateAffinePartial2D() so far so good.

Now the console throws me the following error:

TypeError: Expected cv::UMat for argument 'M'

This is the scripts' code snippet:

...

ANSWER

Answered 2019-Feb-28 at 12:36

Instead of using tform = cv2.estimateAffinePartial2D(np.array([inPts]), np.array([outPts])) and returning tform, return tform[0].

Refer to the documentation for more details.

You will notice that estimateAffinePartial2D returns retVal and inliers. That's why when you are returning tform, you are getting a TypeError.

I have also created a PR to fix the code on our LearnOpenCV GitHub repository.

Vishwesh

Edit: You can check the PR here.

Source https://stackoverflow.com/questions/54909657

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

Vulnerabilities

No vulnerabilities reported

Install face-average

Move to directory and execute run.py with appropriate parameters. This script will open a debug window showing the progress of the execution with 200ms frames, generate .ff files for every image and output average face as ./results/us-mp-president.jpg.

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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