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GFPGAN | GFPGAN aims at developing Practical Algorithms | Computer Vision library

 by   TencentARC Python Version: v1.3.0 License: Non-SPDX

 by   TencentARC Python Version: v1.3.0 License: Non-SPDX

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kandi X-RAY | GFPGAN Summary

GFPGAN is a Python library typically used in Artificial Intelligence, Computer Vision, Deep Learning, Pytorch applications. GFPGAN has no bugs, it has no vulnerabilities, it has build file available and it has medium support. However GFPGAN has a Non-SPDX License. You can install using 'pip install GFPGAN' or download it from GitHub, PyPI.
:rocket: Thanks for your interest in our work. You may also want to check our new updates on the tiny models for anime images and videos in Real-ESRGAN :blush:. GFPGAN aims at developing a Practical Algorithm for Real-world Face Restoration. It leverages rich and diverse priors encapsulated in a pretrained face GAN (e.g., StyleGAN2) for blind face restoration. :question: Frequently Asked Questions can be found in FAQ.md. If GFPGAN is helpful in your photos/projects, please help to :star: this repo or recommend it to your friends. Thanks:blush: Other recommended projects: :arrow_forward: Real-ESRGAN: A practical algorithm for general image restoration :arrow_forward: BasicSR: An open-source image and video restoration toolbox :arrow_forward: facexlib: A collection that provides useful face-relation functions :arrow_forward: HandyView: A PyQt5-based image viewer that is handy for view and comparison.
Support
Support
Quality
Quality
Security
Security
License
License
Reuse
Reuse

kandi-support Support

  • GFPGAN has a medium active ecosystem.
  • It has 17269 star(s) with 2595 fork(s). There are 295 watchers for this library.
  • There were 1 major release(s) in the last 12 months.
  • There are 73 open issues and 65 have been closed. On average issues are closed in 6 days. There are 3 open pull requests and 0 closed requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of GFPGAN is v1.3.0
GFPGAN Support
Best in #Computer Vision
Average in #Computer Vision
GFPGAN Support
Best in #Computer Vision
Average in #Computer Vision

quality kandi Quality

  • GFPGAN has no bugs reported.
GFPGAN Quality
Best in #Computer Vision
Average in #Computer Vision
GFPGAN Quality
Best in #Computer Vision
Average in #Computer Vision

securitySecurity

  • GFPGAN has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
GFPGAN Security
Best in #Computer Vision
Average in #Computer Vision
GFPGAN Security
Best in #Computer Vision
Average in #Computer Vision

license License

  • GFPGAN has a Non-SPDX License.
  • Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.
GFPGAN License
Best in #Computer Vision
Average in #Computer Vision
GFPGAN License
Best in #Computer Vision
Average in #Computer Vision

buildReuse

  • GFPGAN releases are available to install and integrate.
  • Deployable package is available in PyPI.
  • Build file is available. You can build the component from source.
  • Installation instructions, examples and code snippets are available.
GFPGAN Reuse
Best in #Computer Vision
Average in #Computer Vision
GFPGAN Reuse
Best in #Computer Vision
Average in #Computer Vision
Top functions reviewed by kandi - BETA

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

  • Parse command line arguments .
  • Modify a checkpoint .
  • Forward a list of styles .
  • Enhance an image .
  • return the SHA1 hash of the git repo
  • Write the version python file .
  • Initialize equal convolution .
  • 3x3d Conv2d Conv2d Conv2d .
  • Get the hash of the current working directory .
  • Read requirements file .

GFPGAN Key Features

Colab Demo for GFPGAN ; (Another Colab Demo for the original paper model)

Online demo: Huggingface (return only the cropped face)

Online demo: Replicate.ai (may need to sign in, return the whole image)

Online demo: Baseten.co (backed by GPU, returns the whole image)

We provide a clean version of GFPGAN, which can run without CUDA extensions. So that it can run in Windows or on CPU mode.

:fire::fire::white_check_mark: Add V1.3 model, which produces more natural restoration results, and better results on very low-quality / high-quality inputs. See more in Model zoo, Comparisons.md

:white_check_mark: Integrated to Huggingface Spaces with Gradio. See Gradio Web Demo.

:white_check_mark: Support enhancing non-face regions (background) with Real-ESRGAN.

:white_check_mark: We provide a clean version of GFPGAN, which does not require CUDA extensions.

:white_check_mark: We provide an updated model without colorizing faces.

Installation

copy iconCopydownload iconDownload
git clone https://github.com/TencentARC/GFPGAN.git
cd GFPGAN

:zap: Quick Inference

copy iconCopydownload iconDownload
wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth -P experiments/pretrained_models

BibTeX

copy iconCopydownload iconDownload
@InProceedings{wang2021gfpgan,
    author = {Xintao Wang and Yu Li and Honglun Zhang and Ying Shan},
    title = {Towards Real-World Blind Face Restoration with Generative Facial Prior},
    booktitle={The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
    year = {2021}
}

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 GFPGAN

We now provide a clean version of GFPGAN, which does not require customized CUDA extensions. If you want to use the original model in our paper, please see PaperModel.md for installation.
Clone repo git clone https://github.com/TencentARC/GFPGAN.git cd GFPGAN
Install dependent packages # Install basicsr - https://github.com/xinntao/BasicSR # We use BasicSR for both training and inference pip install basicsr # Install facexlib - https://github.com/xinntao/facexlib # We use face detection and face restoration helper in the facexlib package pip install facexlib pip install -r requirements.txt python setup.py develop # If you want to enhance the background (non-face) regions with Real-ESRGAN, # you also need to install the realesrgan package pip install realesrgan

Support

If you have any question, please email xintao.wang@outlook.com or xintaowang@tencent.com.

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