grayscaleSphere.py

This code demonstrates how to generate a grayscale sphere in a 3D volume.

This code demonstrates how to generate a grayscale sphere in a 3D volume. All voxels inside the sphere have value = 255, and others are 0.

import iolink
 
import math
from array import array
 
 
def grayscaleSphere(shape: iolink.VectorXu64):
 
    # first we can check if given shape has exactly 3 dimensions
    if shape.size() != 3:
        raise ValueError("Shape must have 3 dimensions ")
 
    # To create a grayscale ImageView, the datatype of the image must only have 1 components
    dt = iolink.DataTypeId.UINT8
    image = iolink.ImageViewFactory.allocate(shape, dt)
 
    # indicate the created image is a VOLUME with Grayscale color interpretation
    image.axes_interpretation = iolink.AxesInterpretationId.VOLUME
    image.image_interpretation = iolink.ImageInterpretation.GRAYSCALE
 
    # determine the radius of the volume
    # we use the smallest axis length
    center = shape / 2
    radius = min(*center)
 
    # draw the sphere voxel by voxel
    for i in range(0, shape[0]):
        print("line {}".format(i))
        for j in range(0, shape[1]):
            for k in range(0, shape[2]):
                voxPos = iolink.Vector3d(i, j, k)
                voxPosInt = iolink.VectorXu64(i, j, k)
                delta = voxPos - center
                if  delta.length() > float(radius):
                    image.write(voxPosInt, 0)
                else:
                    image.write(voxPosInt, 255)
 
    return image
 
 
image = grayscaleSphere(iolink.VectorXu64([100, 100, 100]))
 
print("Generated image:", str(image))
 
# Since it is not easy to visualize a volume, we can at least visualize a slice from it
# Here we extract the 50th slice on Z axis to obtain a 2D image.
slice = iolink.ImageViewFactory.extract_adjusted_region(image, iolink.RegionXu64((0, 0, 50), (100, 100, 1)))
# we put this slice in memory to be able to export it as numpy
slice = iolink.ImageViewFactory.copy_in_memory(slice)
 
print("Slice extracted:", str(slice))
 
 
 
#import numpy as np
#from matplotlib import pyplot as plt
 
 
 
#plt.imshow(npy, interpolation='nearest')
#plt.show()
 
print("SUCCESS")