d-noise | AI denoising pipeline between Blender and NVIDIA 's OptiX AI | GPU library

by grantwilk Python Version: v1.1 License: GPL-3.0

X-Ray Key Features Code Snippets Community Discussions(4)Vulnerabilities Install Support

kandi X-RAY | d-noise Summary

d-noise is a Python library typically used in Hardware, GPU, Deep Learning, Pytorch applications. d-noise has no bugs, it has no vulnerabilities, it has a Strong Copyleft License and it has low support. However d-noise build file is not available. You can download it from GitHub.

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Quality

Security

License

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Support

d-noise has a low active ecosystem.

It has 298 star(s) with 34 fork(s). There are 21 watchers for this library.

It had no major release in the last 12 months.

There are 3 open issues and 65 have been closed. On average issues are closed in 320 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of d-noise is v1.1

Quality

d-noise has 0 bugs and 0 code smells.

Security

d-noise has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

d-noise code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

d-noise is licensed under the GPL-3.0 License. This license is Strong Copyleft.

Strong Copyleft licenses enforce sharing, and you can use them when creating open source projects.

Reuse

d-noise releases are available to install and integrate.

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

d-noise saves you 233 person hours of effort in developing the same functionality from scratch.

It has 568 lines of code, 58 functions and 4 files.

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

Top functions reviewed by kandi - BETA

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

Register application handlers
Recursively cleans all files in directory
Load image from directory
Remove files from directory
Run D - NOISE
Denoises an image
Trim the trailing slash
Force an UI update
Denoise an image
Return absolute path
Sanitize a file path
Get the most recent render files
Denoise the render result
Set image context
Get file extension
Save an image
Unregister application handlers
Enable pass - diffs
Disable bypasses
Add nodes to the scene
Renders the dialog
Return the progress indicator
Load DNOISE settings
Add new render nodes
Remove the script directory
Download the script

Get all kandi verified functions for this library.

d-noise Key Features

No Key Features are available at this moment for d-noise.

d-noise Examples and Code Snippets

No Code Snippets are available at this moment for d-noise.

Community Discussions

Trending Discussions on d-noise

Add various noise types to image when using tf.data.dataset

OpenCV: Remove background noise and increase signal strength

How should I remove noise from this thresholded image in OpenCV?

How to create noisy images for data augmentation

QUESTION

Add various noise types to image when using tf.data.dataset

Asked 2022-Feb-06 at 15:48

I have been using the function mentioned here to add different types of noise (Gauss, salt and pepper, etc) to an image.

However, I am trying to build an input pipeline using tf.data.Dataset. I think I have figured out how to add Gaussian and Poisson noise:

...

ANSWER

Answered 2022-Feb-06 at 14:54

Here's a way to do the random salt and pepper augmentation:

Source https://stackoverflow.com/questions/71006591

QUESTION

OpenCV: Remove background noise and increase signal strength

Asked 2020-Nov-01 at 23:24

I'm new to OpenCV and tried multiple things but still have some problems. I have images like this:

In the center there is a cluster (hard to see). I want to find those clusters and count them. I use cv2.findContours for this and this already works very good for images where this clusters have a good brightness and the background noise is not too strong.

With images like this, where the cluster is very dark or images where the background noise is very strong and looks very similar to the actual cluster, i have problems detecting them.

So what I would like to do now is remove the background noise, so that only the clusters are left. Then I could increase the brightness of the image and (I think) it should be easier to identify those clusters.

The background noise can very a lot! The image above is a sample, where there is not that much background noise, but it can be a lot worse. I have images where I know, that there are no clusters in it (negative control). Everything in there is just the background-noise. So my idea was to find the dominant color in this negative control:

...

ANSWER

Answered 2020-Oct-28 at 21:15

Here is one way in Python/OpenCV/Skimage

Source https://stackoverflow.com/questions/64578790

QUESTION

How should I remove noise from this thresholded image in OpenCV?

Asked 2020-Apr-14 at 20:41

I would like to remove anything that is not part of the letters and numbers in the image. The input image is as such:

I have tried to apply canny edge detection, but it is susceptible to noise, and the noise contours are quite big. Due to this reason, morphological operations have also been unsuccessful. I tried cv2.MORPH_CLOSE but the noise areas got bigger.

My code is here, but it's completely useless as of now in removing noise:

...

ANSWER

Answered 2020-Apr-14 at 20:41

The image you have posted is very challenging.
The solution I am posting is too specific for the image you have posted.
I tried to keep it as general as I could, but I don't expect it to work very well on other images.
You may use it for getting ideas for more options for removing noise.

The solution is mainly based on finding connected components and removing the smaller components - considered to be noise.

I used pytesseract OCR for checking if the result is clean enough for OCR.

Here is the code (please read the comments):

Source https://stackoverflow.com/questions/61212063

QUESTION

How to create noisy images for data augmentation

Asked 2020-Jan-14 at 22:40

I followed the most upvoted answer to a question regarding adding noise to an image. However it doesn't work for me. I just want to observe different noise effects on image while using Python How to add noise (Gaussian/salt and pepper etc) to image in Python with OpenCV

From what I know, images are something of uint8 type? I'm not certain if this type can take decimals.

The salt and pepper part don't work either

...

ANSWER

Answered 2020-Jan-14 at 22:40

Here's a vectorized approach using OpenCV + skimage.util.random_noise. You can experiment with noise modes such as localvar, pepper, s&p, and speckle to obtain the desired result. You can set the proportion of noise with the amount parameter. Here's an example using s&p with amount=0.011:

Input image

Result

With amount=0.051:

Source https://stackoverflow.com/questions/59735866

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

Vulnerabilities

No vulnerabilities reported

Install d-noise

You can download it from GitHub.
You can use d-noise 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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