matplotlib
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matplotlib | OpenCV | |
---|---|---|
36 | 195 | |
19,056 | 74,965 | |
1.4% | 1.5% | |
10.0 | 9.9 | |
7 days ago | about 7 hours ago | |
Python | C++ | |
Python License 2.0 | Apache License 2.0 |
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.
matplotlib
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How and where is matplotlib package making use of PySide?
However, when I look up the matplotlib source, I can't find pyside used anywhere in dependency list. Even a repo search for the term "pyside" gives mentions in the issue tracker but no actual use in the code.
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Top 10 growing data visualization libraries in Python in 2023
Github: https://github.com/matplotlib/matplotlib
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The Python Packages That Gave Me Nightmares: A Guide to Overcoming Common Challenges
Matplotlib: Matplotlib is a 2D plotting library that allows you to create visualizations of your data. It's a powerful tool for data analysis, but the syntax can be complex and the customization options can be overwhelming. GitHub - https://github.com/matplotlib/matplotlib
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Where to find a dynamic charge density animation/simulation?
I will think more about what I want to say next, but for now, I would like to say that I need the super-particles and PIC methods as I think that is the way forward for me. Are there ways to implement these methods in matplotlib, Visit or Paraview? Do I take existing code and import it into those programs to visualize it? Or can I directly program/simulate something in those visualizion tools without needing to import any code?
Your choices are an n-body simulation (e.g., LAMMPS) with Coulomb interactions or, if your electrons are sufficiently sparse, a particle-in-cell (e.g., Starfish). Your best bets for visualization are going to be matplotlib or something more user-friendly like Visit or Paraview. Without a neutralizing background, however, your electrons are just going to repel each other, hit the walls, and disappear - there's not going to be much interesting to visualize. What are you actually trying to simulate? With more information maybe you could receive some more targeted advice.
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How to model the hanging chain PDE using numerical methods in Python?
There are plenty of data visualization tools in python, but probably the easiest to get started with is Matplotlib
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Best way to learn ML/AI hands-on as a developer?
An example of how I would do this is to just plot your data on a line graph (https://matplotlib.org/) . Are there any repeating trends? Next try splitting your data into day of the week, day of the month, months, etc. Look for any kind of seasonality (we're trying to use the past to predict the future, so if the future is not like the past our models will fail).
- Matplotlib - Visualization with Python
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SerpApi Demo Project: Walmart Coffee Exploratory Data Analysis
Install libraries and tell matplotlib to plot inline (inside notebook) with the help of % magic functions which sets the backend of matplotlib to the inline backend:
OpenCV
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Exploring Open-Source Alternatives to Landing AI for Robust MLOps
Data analysis involves scrutinizing datasets for class imbalances or protected features and understanding their correlations and representations. A classical tool like pandas would be my obvious choice for most of the analysis, and I would use OpenCV or Scikit-Image for image-related tasks.
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Image segmentation in huggingface
You'll need to plot the predictions. There are a few open source tools to do that, supervision is one you can use (https://github.com/roboflow/supervision) and opencv is another common option (https://github.com/opencv/opencv)
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NodeJS: Blurring Human Faces in Photos
The OpenCV4NodeJs A.I. library provides an interface for calling OpenCV routines in NodeJS.
- NodeJS - Ofuscando rostos humanos em fotos
- SIMD Everywhere Optimization from ARM Neon to RISC-V Vector Extensions
- VidCutter: A program for lossless video cutting
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Looking to recreate a cool AI assistant project with free tools
- [ OpenCV](https://opencv.org/) instead of YoloV8 for computer vision and object detection
I came across a very interesting [project]( (4) Mckay Wrigley on Twitter: "My goal is to (hopefully!) add my house to the dataset over time so that I have an indoor assistant with knowledge of my surroundings. It’s basically just a slow process of building a good enough dataset. I hacked this together for 2 reasons: 1) It was fun, and I wanted to…" / X ) made by Mckay Wrigley and I was wondering what's the easiest way to implement it using free, open-source software. Here's what he used originally, followed by some open source candidates I'm considering but would love feedback and advice before starting: Original Tools: - YoloV8 does the heavy lifting with the object detection - OpenAI Whisper handles voice - GPT-4 handles the “AI” - Google Custom Search Engine handles web browsing - MacOS/iOS handles streaming the video from my iPhone to my Mac - Python for the rest Open Source Alternatives: - [ OpenCV](https://opencv.org/) instead of YoloV8 for computer vision and object detection - Replacing GPT-4 is still a challenge as I know there are some good open-source LLms like Llama 2, but I don't know how to apply this in the code perhaps in the form of api - [DeepSpeech](https://github.com/mozilla/DeepSpeech) rather than Whisper for offline speech-to-text - [Coqui TTS](https://github.com/coqui-ai/TTS) instead of Whisper for text-to-speech - Browser automation with [Selenium](https://www.selenium.dev/) instead of Google Custom Search - Stream video from phone via RTSP instead of iOS integration - Python for rest of code I'm new to working with tools like OpenCV, DeepSpeech, etc so would love any advice on the best way to replicate the original project in an open source way before I dive in. Are there any good guides or better resources out there? What are some pitfalls to avoid? Any help is much appreciated!
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[Question] I'd like to find out about how the x, y, w, h values retrieved by detectMultiScale() (for the rectangle boundary during face detection) and how it is calculated in the Haar Cascade OpenCV library. Does anyone know where I can find the code?
Glancing at the code, I think it's detectMultiScaleNoGrouping and then the operator() of CascadeClassifierInvoker gets called. It will probably help you to put a breakpoint and step through that bit of the code.
On GitHub https://github.com/opencv/opencv
What are some alternatives?
PyQtGraph - Fast data visualization and GUI tools for scientific / engineering applications
plotly - The interactive graphing library for Python :sparkles: This project now includes Plotly Express!
pygal - PYthon svg GrAph plotting Library
libvips - A fast image processing library with low memory needs.
bqplot - Plotting library for IPython/Jupyter notebooks
VTK - Mirror of Visualization Toolkit repository
yolov5 - YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite
bokeh - Interactive Data Visualization in the browser, from Python
CImg - The CImg Library is a small and open-source C++ toolkit for image processing
EasyOCR - Ready-to-use OCR with 80+ supported languages and all popular writing scripts including Latin, Chinese, Arabic, Devanagari, Cyrillic and etc.
Boost.GIL - Boost.GIL - Generic Image Library | Requires C++14 since Boost 1.80
plotnine - A Grammar of Graphics for Python