Alright, I’m toying around with converting a PIL image object back and forth to a numpy array so I can do some faster pixel by pixel transformations than PIL’s PixelAccess object would allow. I’ve figured out how to place the pixel information in a useful 3D numpy array by way of:
When calling
I’m facing an issue with allocating huge arrays in numpy on Ubuntu 18 while not facing the same issue on MacOS.
Can you suggest a module function from numpy/scipy that can find local maxima/minima in a 1D numpy array? Obviously the simplest approach ever is to have a look at the nearest neighbours, but I would like to have an accepted solution that is part of the numpy distro.
I have a matrix in the type of a Numpy array. How would I write it to disk it as an image? Any format works (png, jpeg, bmp…). One important constraint is that PIL is not present.
Suppose I have a matrix A with some arbitrary values:
I have a 4-D numpy array, with the first dimension representing the number of images in a data set, the second and third being the (equal) width and height, and the 4th being the number of channels (3). For example let’s say I have 4 color images that are 28*28, so my image data looks like this:
Is there a way to dump a NumPy array into a CSV file? I have a 2D NumPy array and need to dump it in human-readable format.
Here is my code to generate a dataframe:
Suppose I have a large in memory numpy array, I have a function func that takes in this giant array as input (together with some other parameters). func with different parameters can be run in parallel. For example: