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3DDFA_V2 | official PyTorch implementation of Towards Fast | Computer Vision library

 by   cleardusk Python Version: v0.12 License: MIT

 by   cleardusk Python Version: v0.12 License: MIT

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kandi X-RAY | 3DDFA_V2 Summary

3DDFA_V2 is a Python library typically used in Artificial Intelligence, Computer Vision, Deep Learning, Pytorch applications. 3DDFA_V2 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.
This work extends 3DDFA, named 3DDFA_V2, titled Towards Fast, Accurate and Stable 3D Dense Face Alignment, accepted by ECCV 2020. The supplementary material is here. The gif above shows a webcam demo of the tracking result, in the scenario of my lab. This repo is the official implementation of 3DDFA_V2. Compared to 3DDFA, 3DDFA_V2 achieves better performance and stability. Besides, 3DDFA_V2 incorporates the fast face detector FaceBoxes instead of Dlib. A simple 3D render written by c++ and cython is also included. This repo supports the onnxruntime, and the latency of regressing 3DMM parameters using the default backbone is about 1.35ms/image on CPU with a single image as input. If you are interested in this repo, just try it on this google colab! Welcome for valuable issues, PRs and discussions 😄.
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Support
Quality
Quality
Security
Security
License
License
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kandi-support Support

  • 3DDFA_V2 has a medium active ecosystem.
  • It has 1801 star(s) with 293 fork(s). There are 55 watchers for this library.
  • It had no major release in the last 12 months.
  • There are 51 open issues and 49 have been closed. On average issues are closed in 23 days. There are no pull requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of 3DDFA_V2 is v0.12
3DDFA_V2 Support
Best in #Computer Vision
Average in #Computer Vision
3DDFA_V2 Support
Best in #Computer Vision
Average in #Computer Vision

quality kandi Quality

  • 3DDFA_V2 has 0 bugs and 0 code smells.
3DDFA_V2 Quality
Best in #Computer Vision
Average in #Computer Vision
3DDFA_V2 Quality
Best in #Computer Vision
Average in #Computer Vision

securitySecurity

  • 3DDFA_V2 has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
  • 3DDFA_V2 code analysis shows 0 unresolved vulnerabilities.
  • There are 0 security hotspots that need review.
3DDFA_V2 Security
Best in #Computer Vision
Average in #Computer Vision
3DDFA_V2 Security
Best in #Computer Vision
Average in #Computer Vision

license License

  • 3DDFA_V2 is licensed under the MIT License. This license is Permissive.
  • Permissive licenses have the least restrictions, and you can use them in most projects.
3DDFA_V2 License
Best in #Computer Vision
Average in #Computer Vision
3DDFA_V2 License
Best in #Computer Vision
Average in #Computer Vision

buildReuse

  • 3DDFA_V2 releases are available to install and integrate.
  • Build file is available. You can build the component from source.
  • Installation instructions are not available. Examples and code snippets are available.
  • 3DDFA_V2 saves you 1085 person hours of effort in developing the same functionality from scratch.
  • It has 2457 lines of code, 151 functions and 47 files.
  • It has medium code complexity. Code complexity directly impacts maintainability of the code.
3DDFA_V2 Reuse
Best in #Computer Vision
Average in #Computer Vision
3DDFA_V2 Reuse
Best in #Computer Vision
Average in #Computer Vision
Top functions reviewed by kandi - BETA

kandi has reviewed 3DDFA_V2 and discovered the below as its top functions. This is intended to give you an instant insight into 3DDFA_V2 implemented functionality, and help decide if they suit your requirements.

  • Compute the nms of the given boxes .
  • Calculate the jaccard similarity .
  • Initialize the network .
  • Draw landmarks on an image .
  • Crop image
  • Write vertices to a file
  • Crop an image .
  • Compute the NMS for a given threshold
  • Plot a box .
  • Compute overlap between vertices .

3DDFA_V2 Key Features

The official PyTorch implementation of Towards Fast, Accurate and Stable 3D Dense Face Alignment, ECCV 2020.

Usage

copy iconCopydownload iconDownload
git clone https://github.com/cleardusk/3DDFA_V2.git
cd 3DDFA_V2

Citation

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@inproceedings{guo2020towards,
    title =        {Towards Fast, Accurate and Stable 3D Dense Face Alignment},
    author =       {Guo, Jianzhu and Zhu, Xiangyu and Yang, Yang and Yang, Fan and Lei, Zhen and Li, Stan Z},
    booktitle =    {Proceedings of the European Conference on Computer Vision (ECCV)},
    year =         {2020}
}

@misc{3ddfa_cleardusk,
    author =       {Guo, Jianzhu and Zhu, Xiangyu and Lei, Zhen},
    title =        {3DDFA},
    howpublished = {\url{https://github.com/cleardusk/3DDFA}},
    year =         {2018}
}

Community Discussions

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Trending Discussions on Computer Vision

QUESTION

Image similarity in swift

Asked 2022-Mar-25 at 11:42

The swift vision similarity feature is able to assign a number to the variance between 2 images. Where 0 variance between the images, means the images are the same. As the number increases this that there is more and more variance between the images.

What I am trying to do is turn this into a percentage of similarity. So one image is for example 80% similar to the other image. Any ideas how I could arrange the logic to accomplish this:

import UIKit
import Vision
func featureprintObservationForImage(atURL url: URL) -> VNFeaturePrintObservation? {
let requestHandler = VNImageRequestHandler(url: url, options: [:])
let request = VNGenerateImageFeaturePrintRequest()
do {
  try requestHandler.perform([request])
  return request.results?.first as? VNFeaturePrintObservation
} catch {
  print("Vision error: \(error)")
  return nil
}
  }
 let apple1 = featureprintObservationForImage(atURL: Bundle.main.url(forResource:"apple1", withExtension: "jpg")!)
let apple2 = featureprintObservationForImage(atURL: Bundle.main.url(forResource:"apple2", withExtension: "jpg")!)
let pear = featureprintObservationForImage(atURL: Bundle.main.url(forResource:"pear", withExtension: "jpg")!)
var distance = Float(0)
try apple1!.computeDistance(&distance, to: apple2!)
var distance2 = Float(0)
try apple1!.computeDistance(&distance2, to: pear!)

ANSWER

Answered 2022-Mar-25 at 10:26

It depends on how you want to scale it. If you just want the percentage you could just use Float.greatestFiniteMagnitude as the maximum value.

1-(distance/Float.greatestFiniteMagnitude)*100

A better solution would probably be to set a lower ceiling and everything above that ceiling would just be 0% similarity.

1-(min(distance, 10)/10)*100

Here the artificial ceiling would be 10, but it can be any arbitrary number.

Source https://stackoverflow.com/questions/71615277

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

Vulnerabilities

No vulnerabilities reported

Install 3DDFA_V2

You can download it from GitHub.
You can use 3DDFA_V2 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

Jianzhu Guo (郭建珠) [Homepage, Google Scholar]: guojianzhu1994@foxmail.com or guojianzhu1994@gmail.com or jianzhu.guo@nlpr.ia.ac.cn (this email will be invalid soon).

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