hdrnet | An implementation of 'Deep Bilateral Learning for Real-Time | Computer Vision library

by google Python Version: Current License: Apache-2.0

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

kandi X-RAY | hdrnet Summary

hdrnet is a Python library typically used in Artificial Intelligence, Computer Vision, Deep Learning, Pytorch applications. hdrnet has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However hdrnet build file is not available. You can download it from GitHub.

An implementation of 'Deep Bilateral Learning for Real-Time Image Enhancement', SIGGRAPH 2017

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Quality

Security

License

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Support

hdrnet has a low active ecosystem.

It has 730 star(s) with 306 fork(s). There are 35 watchers for this library.

It had no major release in the last 6 months.

There are 15 open issues and 1 have been closed. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of hdrnet is current.

Quality

hdrnet has 0 bugs and 0 code smells.

Security

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

hdrnet code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

hdrnet is licensed under the Apache-2.0 License. This license is Permissive.

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

Reuse

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

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

Installation instructions, examples and code snippets are available.

hdrnet saves you 1170 person hours of effort in developing the same functionality from scratch.

It has 2640 lines of code, 136 functions and 21 files.

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

Top functions reviewed by kandi - BETA

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

Apply sliced image
Runs the inference
Implements the convolution layer
Convolutional convolution layer
A fully connected layer
Get list of input files
Inverse inference function
Compute output layer
Bilateral slicing
Calculate the slice of an image
Generate a list of HDNetPoints
Construct a guidemap
Calculate the vertical slice for a given region
Computes the range weights for a given guide
Bilateral scaffolds for a single slice
Pad image with symmetric padding
Save data to file

Get all kandi verified functions for this library.

hdrnet Key Features

No Key Features are available at this moment for hdrnet.

hdrnet Examples and Code Snippets

No Code Snippets are available at this moment for hdrnet.

Community Discussions

Trending Discussions on hdrnet

Statically link custom op from .a file in tensorflow serving

QUESTION

Statically link custom op from .a file in tensorflow serving

Asked 2020-Feb-28 at 14:00

I have a custom op implemented CUDA and built using Makefile like this hdrnet. I can build .so and import in tensorflow. For tf-serving statically linking .a file is required but all tutorials reference bazel build process for custom op instead of directly linking compiled op from .a file. Do I have to write build process as referenced by examples or I can build tf-serving with .so/.a files directly?

...

ANSWER

Answered 2020-Feb-28 at 14:00

I ended up compile tensorflow-serving from source with op-linked in. Tensorflow tutorials are not complete for this and additional dependency was missing which I resolved in this issue issue.

Source https://stackoverflow.com/questions/60297072

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

Vulnerabilities

No vulnerabilities reported

Install hdrnet

Our network requires a custom Tensorflow operator to "slice" in the bilateral grid. To build it, run:.
We provide a set of pretrained models. One of these is included in the repo (see pretrained_models/local_laplacian_sample). To download the rest of them run:.

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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