I'm trying to save a 2D slice of a 3D array that I'm slicing with the following code:
import nibabel as nib
import numpy as np
from nibabel.testing import data_path
import os
vol1= np.load("teste01.npy")
zSlice= (vol1[1, :, :]).squeeze()
print (zSlice.shape)
np.save(zSlice, os.path.join("D:/Volumes convertidos LIDC/slice01.npy"))
I'm getting an error: TypeError: expected str, bytes or os.PathLike object, not ndarray
Is there any way to fix this? I need 2D arrays in order to be able to insert my images into an automatic lung vessel segmentation model but I only have 3D images, is there any way to obtain all the slices from said 3D image instead of slicing it manually (like I'm trying to do?
You just mixed up the parameters for numpy.save. Use the filename as the first parameter and the data as the second:
np.save(os.path.join("D:/Volumes convertidos LIDC/slice01.npy"), zSlice)
Related
I'm using the following code to slice a 3D image:
import nibabel as nib
import numpy as np
from nibabel.testing import data_path
import os
vol1= np.load("teste01.npy")
zSlice= (vol1[200, :, :]).squeeze()
print (zSlice.shape)
np.save("D:/Volumes convertidos LIDC/slice200.npy", zSlice)
The problem is that I need to do it manually, I need to obtain all slices and there are just to many for it to be possible to keep doing it like that. Is there any alternative?
If I understand correctly what you want to do, the following should work:
for i, s in enumerate(vol1):
np.save(f"D:/Volumes convertidos LIDC/slice{i}.npy", s)
This will save each 2-dimensional slice taken along the 0-th axis in a separate file (which can mean a lot of files)
I'm moving my first steps into vtk and I'm quite struggling given the lack of documentation.
I've got a .vtk file which is a vtkDataSet type object I haven't created. I would need to export the content of it and convert it to a 3D numpy matrix, customise it and its tensor and write everything in a vtkDataSet object and .vtk file.
What I've ended up so far is save the coordinates, which is not what I need, of the points into a numpy array using vtk.util.numpy_support vtk_to_numpy. However, I'd need a 3D numpy matrix representing the volume rendering of it.
Speaking about the tensor, I figured out how and where to save my 9-elements tensor into the file. I'm just not sure on how to set it properly to be related to the points.
The last step, which is 3D numpy array to vtk, looks feasible using numpy.ravel and numpy_to_vtk from vtk.util.numpy_support.
Here's some code I'm using as a test:
# reader for mrtrix vtk file
reader = vtk.vtkDataSetReader()
file_name = 'my_file.vtk'
reader.SetFileName(file_name)
reader.Update()
# get the vtkDataArray
data_set = reader.GetOutput()
# these are the coordinates of the points
# I'd need the 3D numpy volume rendering matrix instead
point_array = data_set.GetPoints().GetData()
# test tensor
# I'd need to save a tensor for every element of the 3D numpy matrix
tensor = numpy_to_vtk(np.zeros([data_set.GetNumberOfPoints(), 9]))
tensor.SetName('Tensors_')
point_data = data_set.GetPointData()
point_data.SetAttribute(tensor, 4)
This may be useful in your case:
https://github.com/marcomusy/vedo/blob/master/vedo/examples/volumetric/numpy2volume1.py
and retrieve the numpy object with e.g.
print('numpy array from Volume:', vol.getPointArray().shape)
How to load pixels of multiple images in a directory in a numpy array . I have loaded a single image in a numpy array . But can not figure out how to load multiple images from a directory . Here what i have done so far
image = Image.open('bn4.bmp')
nparray=np.array(image)
This loads a 32*32 matrices . I want to load 100 of the images in a numpy array . I want to make 100*32*32 size numpy array . How can i do that ? I know that the structure would look something like this
for filename in listdir("BengaliBMPConvert"):
if filename.endswith(".bmp"):
-----------------
else:
continue
But can not find out how to load the images in numpy array
Getting a list of BMP files
To get a list of BMP files from the directory BengaliBMPConvert, use:
import glob
filelist = glob.glob('BengaliBMPConvert/*.bmp')
On the other hand, if you know the file names already, just put them in a sequence:
filelist = 'file1.bmp', 'file2.bmp', 'file3.bmp'
Combining all the images into one numpy array
To combine all the images into one array:
x = np.array([np.array(Image.open(fname)) for fname in filelist])
Pickling a numpy array
To save a numpy array to file using pickle:
import pickle
pickle.dump( x, filehandle, protocol=2 )
where x is the numpy array to be save, filehandle is the handle for the pickle file, such as open('filename.p', 'wb'), and protocol=2 tells pickle to use its current format rather than some ancient out-of-date format.
Alternatively, numpy arrays can be pickled using methods supplied by numpy (hat tip: tegan). To dump array x in file file.npy, use:
x.dump('file.npy')
To load array x back in from file:
x = np.load('file.npy')
For more information, see the numpy docs for dump and load.
Use OpenCV's imread() function together with os.listdir(), like
import numpy as np
import cv2
import os
instances = []
# Load in the images
for filepath in os.listdir('images/'):
instances.append(cv2.imread('images/{0}'.format(filepath),0))
print(type(instances[0]))
class 'numpy.ndarray'
This returns you a list (==instances) in which all the greyscale values of the images are stored. For colour images simply set .format(filepath),1.
I just would like to share two sites where one can split a dataset into train, test and validation sets: split_folder
and create numpy arrays out of images residing in respective folders code snippet from medium by muskulpesent
I have converted set of images to ndarray and stored it, now i have to convert them back to images without saving it to disk. I tried with " toimage() " function, but it is displaying only 1 image.
toimage(resizedlist.values()[0]).show()
resizedlist.values contains the ndarray of 49 images. Is there any way to display images randomly??
Thanks in advance!
To plot an ndarray as an image you can use matplotlib:
import numpy as np
import matplotlib.pyplot as plt
random = np.random.normal(0,1,size=[100,100])
plt.imshow(random,aspect="auto")
plt.show()
If your image data is stored RGBA, imshow will plot the image with the correct colours etc.
For reference, all this information can be found here:
http://matplotlib.org/1.3.1/users/image_tutorial.html
How can I create a huge numpy array using pytables. I tried this but gives me the "ValueError: array is too big." error:
import numpy as np
import tables as tb
ndim = 60000
h5file = tb.openFile('test.h5', mode='w', title="Test Array")
root = h5file.root
h5file.createArray(root, "test", np.zeros((ndim,ndim), dtype=float))
h5file.close()
Piggybacking off of #b1r3k's response, to create an array that you are not going to access all at once (i.e. bring the whole thing into memory), you want to use a CArray (Chunked Array). The idea is that you would then fill and access it incrementally:
import numpy as np
import tables as tb
ndim = 60000
h5file = tb.openFile('test.h5', mode='w', title="Test Array")
root = h5file.root
x = h5file.createCArray(root,'x',tb.Float64Atom(),shape=(ndim,ndim))
x[:100,:100] = np.random.random(size=(100,100)) # Now put in some data
h5file.close()
You could try to use tables.CArray class as it supports compression but...
I think questions is more about numpy than pytables because you are creating array using numpy before storing it with pytables.
In that way you need a lot of ram to execute np.zeros((ndim,ndim) - and this is probably the place where exception: "ValueError: array is too big." is raised.
If matrix/array is not dense then you could use sparse matrix representation available in scipy: http://docs.scipy.org/doc/scipy/reference/sparse.html
Another solution is to try to access your array via chunks if it you don't need whole array at once - check out this thread: Very large matrices using Python and NumPy