Develop Reference

Iterate Images through Folder

i have a folder of gray-scale images from 0 to 9 and they are around 2400, i need to load them in python so as to have all Zeors together as an array , then Ones together as an array, etc...
i used below code to load one image as an array but i don`t know how to load all images and group each number together.
i thought about iteration through the folder.
Do anyone know how to do it or is there any other idea?
import imageio
im = imageio.imread('Train/1.jpg')

You can do it in the following manner:
import imageio
for i in range(9):
im = imageio.imread('Train/'+str(i)+'.jpg')
You can also create a 3D variable whose third dimension is equal to the number of files to read the complete data in a single 3D array.

Numpy fromfile function does not work properly

So i'm working on a code where i need to write and read files. I'm using python and numpy, but the numpy fromfile function does not seem to work propperly. First i create an array with 500 elements, and save it with the savetxt function. I check the file and it is all right, just how i wanted.
import numpy as np
w = np.zeros(500, float)
np.savetxt("weights.txt", weight, '%.100f', )
print(weight[2])
But after i change the line where i create the array with the one where i read it from a file a problem accours. The zeros turn into really small numbers. I can't guess why. Here is the line where i read from file:
weight = np.fromfile("weights.txt", float, -1)
Should i write a custom function that turns files into arrays or is there a way to make it work?

third dimension must be 3 or 4 in matplotlib while i want to save 2

I have an image converted from rgb to L*a*b space.
I can save L part separately but not ab part together.
plt.imsave(path,ab_part) gives error. The last dimension should be 3 or 4
where in my case: ab_part.ndim=3 and ab_part.shape=a x b x 2 one dimension is saved as L part.
So how can I save it? I have also tried to save as savefig after doing plt.figure.fromarray('.')
So how can I save it as image or if not then a flattened array would be ok. any suggestion?
p.s. Please suggest only scipy, numpy, matplotlib but not cv2

What you're trying to save is not an image (at least, not one with a common file format), it's just data. Use np.save, or even just the builtin pickle module, to save arrays.

Creating a new Image file in astropy with specified dimensions of another image file

I'm having a problem wit fits file manipulation in the astropy package, and I'm in need of some help.
I essentially want to take an image I have in fits file format, and create a new file I need to start inputing correction factors to and a new image which can then be used with the correction factors and the original image to produce a correction image. Each of these will have the same dimensions.
Starting with this:
from astropy.io import fits
# Compute the size of the images (you can also do this manually rather than calling these keywords from the header):
#URL: /Users/UCL_Astronomy/Documents/UCL/PHASG199/M33_UVOT_sum/UVOTIMSUM/M33_sum_epoch1_um2_norm.img
nxpix_um2_ext1 = fits.open('...')[1]['NAXIS1']
nypix_um2_ext1 = fits.open('...')[1]['NAXIS2']
#nxpix_um2_ext1 = 4071 #hima_sk_um2[1].header['NAXIS1'] # IDL: nxpix_uw1_ext1 = sxpar(hima_sk_uw1_ext1,'NAXIS1')
#nypix_um2_ext1 = 4321 #hima_sk_um2[1].header['NAXIS2'] # IDL: nypix_uw1_ext1 = sxpar(hima_sk_uw1_ext1,'NAXIS2')
# Make a new image file with the same dimensions (and headers, etc) to save the correction factors:
coicorr_um2_ext1 = ??[nxpix_um2_ext1,nypix_um2_ext1]
# Make a new image file with the same dimensions (and headers, etc) to save the corrected image:
ima_sk_coicorr_um2_ext1 = ??[nxpix_um2_ext1,nypix_um2_ext1]
Can anyone give me the obvious knowledge I am missing to do this...the last two lines are just there to outline what is missing. I have included ?? to perhaps signal I need something else there perhaps fits.writeto() or something similar...

The astropy documentation takes you though this task step by step: create an array with size (NAXIS1,NAXIS2), put the data in the primary HDU, make an HDUlist and write it to disk:
import numpy as np
from astropy.io import fits
data = np.zeros((NAXIS2,NAXIS1))
hdu = fits.PrimaryHDU(data)
hdulist = fits.HDUList([hdu])
hdulist.writeto('new.fits')

I think #VincePs answer is correct but I'll add some more information because I think you are not using the capabilities of astropy well here.
First of all Python is zero-based so the primary extension has the number 0. Maybe you got that wrong, maybe you don't - but it's uncommon to access the second HDU so I thought I better mention it.
hdu_num = 0 # Or use = 1 if you really want the second hdu.
First you do not need to open the same file twice, you can open it once and close it after extracting the relevant values:
with fits.open('...') as hdus:
nxpix_um2_ext1 = hdus[hdu_num]['NAXIS1']
nxpix_um2_ext1 = hdus[hdu_num]['NAXIS2']
# Continue without indentation and the file will be closed again.
or if you want to keep the whole header (for saving it later) and the data you can use:
with fits.open('...') as hdus:
hdr = hdus[hdu_num].header
data = hdus[hdu_num].data # I'll also take the data for comparison.
I'll continue with the second approach because I think it's a lot cleaner and you'll have all the data and header values ready.
new_data = np.zeros((hdr['NAXIS2'], hdr['NAXIS1']))
Please note that Python interprets the axis different than IRAF (and I think IDL, but I'm not sure) so you need axis2 as first and axis1 as second element.
So do a quick check that the shapes are the same:
print(new_data.shape)
print(data.shape)
If they are not equal I got confused about the axis in Python (again) but I don't think so. But instead of creating a new array based on the header values you can also create a new array by just using the old shape:
new_data_2 = np.zeros(data.shape)
That will ensure the dimensions and shape is identical. Now you have an empty image. If you rather like a copy then you can, but do not need to, explicitly copy the data (except if you opened the file explicitly in write/append/update mode then you should always copy it but that's not the default.)
new_data = data # or = data.copy() for explicitly copying.
Do your operations on it and if you want to save it again you can use what #VinceP suggested:
hdu = fits.PrimaryHDU(new_data, header=hdr) # This ensures the same header is written to the new file
hdulist = fits.HDUList([hdu])
hdulist.writeto('new.fits')
Please note that you don't have to alter the shape-related header keywords even if you changed the data's shape because during writeto astropy will update these (by default)

Extract and save a slice from a volume with numpy

I'm trying to extract a slice from a volume using numpy.
The volume is 512x512x132 and I want the slice number 66.
Each voxel is an unsigned 16-bit integer.
My code is:
import numpy
original = numpy.fromfile(filepath, dtype=numpy.uint16)
original = numpy.reshape(original, (512,512,132))
slice = original[:,:,66]
f = open('test.rawl', 'w')
slice.tofile(f)
f.close()
The code complete cleanly, but when I open the slice with an external program, it is not the slice data but garbage.
What I am doing wrong?
Thanks

Your first problem is that you have your axes wrong. Assuming you have 132 layers of 512x512 images you want to use:
original = numpy.fromfile(filepath, dtype=numpy.uint16).reshape((132, 512, 512))
Then for the slice take:
slc = original[66]
Also, binary data such as Numpy arrays use:
f = open('test.raw', 'wb')
The 'b' in the mode is for binary. Otherwise Python will assume you're trying to write text and do things like convert newlines to the appropriate format for the system, among other things.
By the way, the ndarray.tofile() method also takes a filename, so unless you have a particular reason to it's not necessary to create a file handle first. You can just use
arr.tofile('test.raw')
One final note: Try not to use slice as a variable. That's a builtin name in Python and you could run into trouble by shadowing it with something else.