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Tag: numpy

How to apply an operation to every PIXEL (not every rgb component!) of a python image (either using numpy, opencv or PIL)?

I’m trying to apply a function over all pixels of an image (in my specific case I want to do some colour approximation per pixel, but I don’t think this is relevant for the problem) in an efficient way. The thing is that I’ve found different approaches to do so, but all of them apply a function over each component

Insert a matrix in matplolib

I want to import a numpy matrix in a matplotlib plot. Let’s say that I have this matrix: In a scale from 0 to 3 of the x axis, I want to plot the points This is the code I use: and this is the outcome: Yet, if I have to use a large array of let’s say (1000×1000) elements

positive weights and negative weights [closed]

Closed. This question needs to be more focused. It is not currently accepting answers. Want to improve this question? Update the question so it focuses on one problem only by editing this post. Closed 2 years ago. Improve this question I have an array named as weights – I am trying for hours to get an array where the output

Which numpy index is copy and which is view?

Question Regarding Numpy Indexing which can either return copy or view, please confirm if my understandings are correct. If not, please provide explanations and provide pointers to the related specifications. Q: Basic slicing The Numpy Indexing documentation has Basic Slicing and Indexing section. I believe this section talks only about basic slicing as mentioned one of three ways in Indexing.

How to get the last index of model’s prediction?

I am new to PyTorch. I have a variable pred which has a list of a tensor. So I wanted to access the last element which is the class. I did that by first converting the list into a tensor. Now, how do I access the last element or is there any better/efficient way of doing this? EDIT: For further

How to use np.unique on big arrays?

I work with geospatial images in tif format. Thanks to the rasterio lib I can exploit these images as numpy arrays of dimension (nb_bands, x, y). Here I manipulate an image that contains patches of unique values that I would like to count. (they were generated with the scipy.ndimage.label function). My idea was to use the unique method of numpy

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