head-pose-estimation | Realtime human head pose estimation with ONNXRuntime | Machine Learning library

by yinguobing Python Version: Current License: MIT

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

kandi X-RAY | head-pose-estimation Summary

head-pose-estimation is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow, OpenCV applications. head-pose-estimation has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. You can download it from GitHub.

Real time human head pose estimation using TensorFlow and OpenCV.

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head-pose-estimation has a medium active ecosystem.

It has 1025 star(s) with 270 fork(s). There are 23 watchers for this library.

It had no major release in the last 6 months.

There are 25 open issues and 22 have been closed. On average issues are closed in 112 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of head-pose-estimation is current.

Quality

head-pose-estimation has 0 bugs and 0 code smells.

Security

head-pose-estimation has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

head-pose-estimation code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

head-pose-estimation is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

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head-pose-estimation releases are not available. You will need to build from source code and install.

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

Installation instructions, examples and code snippets are available.

Top functions reviewed by kandi - BETA

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

Draws an annotation box
Solve the pose of an image
Detect the landmarks of the given image
Return the face of a given image
Make a square box
Get face boxes from image
Move a box around a given offset
Check if a box is inside an image

Get all kandi verified functions for this library.

head-pose-estimation Key Features

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head-pose-estimation Examples and Code Snippets

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Community Discussions

Trending Discussions on head-pose-estimation

Recreating Global 3D Points, from Local 3D Points and Global 2D Points; SolvePnP

How does Elevation of a Head Pose in Python-OpenCV work?

QUESTION

Recreating Global 3D Points, from Local 3D Points and Global 2D Points; SolvePnP

Asked 2022-Mar-11 at 08:38

Aloha, i have a list of 2D Keypoints which are located in the global scope/frame (image points), and a list of corresponding 3D Keypoints in the local scope (often called texture or object points). The image points are ranging from x[0-1920]y[0,1080] and the object points are withing the range of x[-1,1]y[-1,1]. I have followed the approach described in this paper on page 6 with the tutorial from here, but the output of my 3D points is not correct at all, the movement of the points is all over the place. Below is my approach using SolvePnP. Am I on the wrong track here, since SolvePnP is normally used for detecting the camera movement (open for other suggestions!) or is my method wrong?

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ANSWER

Answered 2022-Mar-09 at 13:13

yes, solvePnP is okay to use
yes, your math is wrong

I'll assume that you get your points from a face landmark detector, so they have a fixed order. I'll also assume that your 3D model points are given in the same order and their values are consistent and somewhat similar to the face you look at. You should exclude points that denote flesh and mandible (as opposed to skull bone). You actually want to track the skull, not the position of lips and jaws that move all over the place.

rvec is an axis-angle encoding. Its length is the amount of rotation (expected between 0 and 3.14=pi) and its direction is the axis of rotation.

Use cv.Rodrigues to turn the rvec into a 3x3 rotation matrix.

In fact, just build yourself some functions that take rvec and tvec and build a 4x4 matrix. Extending all points to be (x,y,z,1) is a hassle but only once.

And make sure you use @ for matrix multiplication (or np.dot, np.matmul, ...) because * is element-wise multiplication.

Source https://stackoverflow.com/questions/71408443

QUESTION

How does Elevation of a Head Pose in Python-OpenCV work?

Asked 2020-Nov-05 at 16:35

I am trying to estimate the head pose of single images mostly following this guide: https://towardsdatascience.com/real-time-head-pose-estimation-in-python-e52db1bc606a

The detection of the face works fine - if i plot the image and the detected landmarks they line up nicely.

I am estimating the camera matrix from the image, and assume no lens distortion:

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ANSWER

Answered 2020-Nov-05 at 16:35

Ok so it seems i have found a solution - the model points (which i have found in several blogs on the topic) seem to be wrong. The code seems to work with this combination of model and image points (no idea why it was trial and error):

Source https://stackoverflow.com/questions/64679950

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

Vulnerabilities

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Install head-pose-estimation

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