G'MIC VS NumPy
Compare G'MIC VS NumPy and see what are their differences
LibHunt tracks mentions of software libraries on relevant social networks. Based on that data, you can find the most popular projects and their alternatives.
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G'MIC
ImageMagick
GraphicsMagick
GIMP
Inkscape
GenFavicon
Adobe Photoshop
Ordinatore
NumPy
Pandas
OpenCV
Scikit-learn
Dataiku
Exploratory
htm.java
WEKA
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Based on our record, NumPy should be more popular than G'MIC. It has been mentiond 108 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
But I would use G'MIC as you can scale the grain, control opacity Filters > G'MIC_Qt, a window opens Degradations > Add Grain > https://i.imgur.com/FHXJ6CF.jpg. Source: 6 months ago
G'MIC will do it, On the GIMP top menu go to Filters > G'MIC_Qt, do your color correction and then at the bottom on the input select "All" or "All visible" or whatnot (multiple option). Source: 12 months ago
This is just GMIC filters which are an awesome free filter suite for Photoshop/Gimp/Krita. Source: about 1 year ago
You do not need to abandon the ship, with 2 plugins (one is G'MIC, the other one is to export layers as image and it does way more as well), and a one line code in terminal, you will be able to do it with GIMP (although I think it's the perfect job for ImageMagick, but I don't master it). Source: about 1 year ago
With a plugin, GMIC you can also produce the average layer, so that spares you setting all the opacities. You still have to load them in Gimp (not too likely to have hem all fit and display). You can also use GMIC directly in a command line (but again, a command line with 75000 files is not obvious, so you may also have to divide and conquer). Source: about 1 year ago
Below is an example of a code cell. We'll visualize some simple data using two popular packages in Python. We'll use NumPy to create some random data, and Matplotlib to visualize it. - Source: dev.to / 9 months ago
In NumPy with * or multiply(). ` or multiply()` can multiply 0D or more D arrays by element-wise multiplication. - Source: dev.to / 3 months ago
Data science projects often use numpy. However, numpy objects are not JSON-serializable and therefore require conversion to standard python objects in order to be saved:. - Source: dev.to / 3 months ago
Numpy: A library for scientific computing in Python. - Source: dev.to / 6 months ago
Python has become a preferred language for data analysis due to its simplicity and robust library ecosystem. Among these, NumPy stands out with its efficient handling of numerical data. Let’s say you’re working with numbers for large data sets—something Python’s native data structures may find challenging. That’s where NumPy arrays come into play, making numerical computations seamless and speedy. - Source: dev.to / 7 months ago
ImageMagick - ImageMagick is a software suite to create, edit, and compose bitmap images.
Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
GraphicsMagick - GraphicsMagick is the swiss army knife of image processing.
OpenCV - OpenCV is the world's biggest computer vision library
GIMP - GIMP is a multiplatform photo manipulation tool.
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.