DeblurGAN | Image Deblurring using Generative Adversarial Networks | Machine Learning library

by KupynOrest Python Version: Current License: Non-SPDX

X-Ray Key Features Code Snippets(3)Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | DeblurGAN Summary

DeblurGAN is a Python library typically used in Institutions, Learning, Education, Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Generative adversarial networks applications. DeblurGAN has no bugs, it has no vulnerabilities and it has medium support. However DeblurGAN build file is not available and it has a Non-SPDX License. You can download it from GitHub.

Pytorch implementation of the paper DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks.

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DeblurGAN has a medium active ecosystem.

It has 2324 star(s) with 503 fork(s). There are 60 watchers for this library.

It had no major release in the last 6 months.

There are 137 open issues and 77 have been closed. On average issues are closed in 111 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of DeblurGAN is current.

Quality

DeblurGAN has 0 bugs and 0 code smells.

Security

DeblurGAN has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

DeblurGAN code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

DeblurGAN 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.

Reuse

DeblurGAN releases are not available. You will need to build from source code and install.

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

Installation instructions are not available. Examples and code snippets are available.

DeblurGAN saves you 1210 person hours of effort in developing the same functionality from scratch.

It has 2726 lines of code, 150 functions and 37 files.

It has medium code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

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

Train the model
Compute the PSNR between two images
Display the current visual results
Add images
Fits the model
Plot the input canvas
Blur the image
Plots the canvas
Fits the PSF
Plot the PSFs
Create a dataset
Save the document
Create directories
Create a custom data loader
Create a dataset from a directory
Optimizes the parameters
Calculate the loss of the network
Create a model instance
Save visual images
Define the layer D
Computes the SSIM between two images
Get the transform
Define the netG
Parse command line arguments
Download data
Test the network

Get all kandi verified functions for this library.

DeblurGAN Key Features

No Key Features are available at this moment for DeblurGAN.

DeblurGAN Examples and Code Snippets

DeblurGAN : Blind Motion Deblurring Using Conditional Adversarial Networks,Deblur your own images

Python

Lines of Code : 3

License : No License

Copy

tar -cvf DeblurGAN_model.tar

python main.py --mode test_only --pre_trained_model ./path/to/model --test_Blur_path ./path/to/own/images

python main.py --mode test_only --pre_trained_model ./path/to/model --test_Blur_path ./path/to/own/images --in_me

DeblurGAN : Blind Motion Deblurring Using Conditional Adversarial Networks,Train using GOPRO dataset

Python

Lines of Code : 2

License : No License

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python GOPRO_preprocess.py --GOPRO_path ./GOPRO/data/path --output_path ./data/output/path

python main.py --train_Sharp_path ./GOPRO/path/sharp --train_Blur_path ./GOPRO/path/blur

Watch out! Motion is Blurring the Vision of Your Deep Neural Networks,Requirements

Python

Lines of Code : 1

License : No License

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pip install -r requirements.txt

Community Discussions

Trending Discussions on DeblurGAN

How to generate blurry image dataset

QUESTION

How to generate blurry image dataset

Asked 2021-Aug-03 at 01:06

I've got barcode dataset including 2-d barcodes as well as 1-d ones. i have trouble in decoding barcodes out of focus when testing decoding program using a camera.

I'm thinking DeblurGAN might help and I need blurry barcode dataset.

will it be the only way to apply blur operation with opencv to the dataset? was wondering if there is any real blur image generator or barcode out of focus dataset?

Thx

...

ANSWER

Answered 2021-Aug-03 at 01:06

DeblurGAN itself seems to have code for blurring: https://github.com/KupynOrest/DeblurGAN/tree/master/motion_blur

By the way, training an end-to-end neural network that directly decodes blurry barcodes (instead of a two-step approach of deblurring followed by decoding, not jointly trained) seems worth trying. (Especially if you use "tricks" (correct consideration of the blurring process) from state-of-the-art deblurring methods.)

Note that you can generate synthetic data if you need more data.

Source https://stackoverflow.com/questions/68615931

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

Vulnerabilities

No vulnerabilities reported

Install DeblurGAN

You can download it from GitHub.
You can use DeblurGAN 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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