How to convert tensor from 2D to 4D

I’m currently working with DICOM files and the TensorFlow IO library for DICOM files seems to throw some errors. So what I was originally doing was this:

    # read file bytes
    image_bytes = tf.io.read_file(image_path)

    # Convert to a tensor
    image_as_tensor = tfio.image.decode_dicom_image(image_bytes, dtype=IMAGE_TYPE)

    print(image_as_tensor.get_shape())

(1, 519, 519, 1)

Anyways, I instead decided to load the DICOM files with pydicom, which seems to work loading the data into a numpy array. Yet, when I create a tensor from the data, I can’t seem to get it in the correct dimensions:

        # read into dicom file
        ds = pydicom.dcmread(image_path)
        print(ds.pixel_array.shape)

        # take pixel array, and lets create a tensor
        image_as_tensor = tf.convert_to_tensor(ds.pixel_array, dtype=IMAGE_TYPE)
        print(image_as_tensor.get_shape())

(519, 519)

Ultimately, I do want the (Z, X, Y, D) format for some later parts of the program, but not sure how to get the 2D tensor into that version.

Thanks!

Answer

You could just use numpy.reshape(), e.g.:

import numpy as np

arr = np.zeros((20, 30))
shape = arr.shape

print(shape)
# (20, 30)

arr = arr.reshape(1, *shape, 1)
print(arr.shape)
# (1, 20, 30, 1)