image-similarity-measures
piqa
| image-similarity-measures | piqa | |
|---|---|---|
| 3 | 4 | |
| 518 | 381 | |
| 2.1% | - | |
| 4.4 | 5.7 | |
| 20 days ago | 6 months ago | |
| Python | Python | |
| MIT License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
image-similarity-measures
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Using VAE for image compression
Speaking of math, using this library -- https://github.com/up42/image-similarity-measures -- I computed the following for these images vs the original image:
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I matched 400+ images to create illusion of motion [epilepsy]
The easiest place to start is using the classical approaches such as implemented here. For the kind of qualitative assessments you're performing, you'd probably need to use some deep learning techniques but these generally require significant technical background to implement.
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I made a website that tracks Forsen's Jump King progress and can notify you above chosen percentage.
I use https://github.com/up42/image-similarity-measures for image similarity.
piqa
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What's the best strategy to get contributors and stars on a repository?
One good solution is to find someone with a problem (e.g. on Stack Overflow, Stack Exchange, Reddit, or even GitHub issues) that your project solves. That is what I did for a Python library I implemented (piqa), and people seemed to like it.
- PIQA, my first open-source project, reached 100 stars on GitHub today !
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What are some of your gold standard Python open source repos you discovered here or elsewhere that have very high quality, commented and understandable code that use best practices?
I'm currently developing piqa a Python package for Image Quality Assessment and I really try to make the code concise, readable and understandable. Have a look 😉
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🏆 A ranked list of awesome machine learning Python libraries. Updated weekly.
I see there is no image quality library (IQA) library in your list. I've made one for PyTorch - piqa - recently. It is still an early project, but there are already a few very useful metrics.
What are some alternatives?
ignite - High-level library to help with training and evaluating neural networks in PyTorch flexibly and transparently.
best-of-ml-python - 🏆 A ranked list of awesome machine learning Python libraries. Updated weekly.
OCTIS - OCTIS: Comparing Topic Models is Simple! A python package to optimize and evaluate topic models (accepted at EACL2021 demo track)
video-quality-metrics - Test specified presets/CRF values for the x264 or x265 encoder. Compares VMAF/SSIM/PSNR numerically & via graphs.
PyTorch-NLP - Basic Utilities for PyTorch Natural Language Processing (NLP)
jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
generative-evaluation-prdc - Code base for the precision, recall, density, and coverage metrics for generative models. ICML 2020.
colorama - Simple cross-platform colored terminal text in Python
COMET - A Neural Framework for MT Evaluation
rich - Rich is a Python library for rich text and beautiful formatting in the terminal.
cppdep - C/C++ Dependency Analyzer: a rewrite of John Lakos' dep_utils (adep/cdep/ldep) from "Large-Scale C++ Software Design"
youtube-dl - Command-line program to download videos from YouTube.com and other video sites