Compute distance field

This example shows how to segment an image and compute the distance field of several phases

import matplotlib.pyplot as plt
import spam.datasets
import spam.filters

import spam.mesh

Read the data from datasets and convert

im = spam.datasets.loadConcreteNe()
# tifffile is reading images in the z,y,x order
sizeZ = im.shape[2]
print("Encoded type: {} -> max={}, min={}".format(im[0, 0, 0].dtype, im.max(), im.min()))
plt.figure()
plt.imshow(im[sizeZ // 2, :, :], cmap="Greys")

Encoded type: uint16 -> max=65535, min=2866

<matplotlib.image.AxesImage object at 0x7f11a78481c0>

Cylindrical mask

In order to ignore the ouside of the specimen, a cylindrical mask is created.

cyl = spam.mesh.createCylindricalMask(im.shape, 48.5)
plt.figure()
plt.imshow(cyl[sizeZ // 2, :, :], cmap="Greys")

<matplotlib.image.AxesImage object at 0x7f11a7d228f0>

Threshold

With a threshold of 36000 it should only keep the inclusions. The outside of the specimen is ignored thanks to the cylindrical mask.

binIm = (im < 36000) * cyl
plt.figure()
plt.imshow(binIm[sizeZ // 2, :, :], cmap="Greys")

<matplotlib.image.AxesImage object at 0x7f11a7d22140>

Distance field

The distance field generates by default a field of floats between -1 and 1 with positive values inside the inclusions, negative values outside and 0 at the border.

dist = spam.filters.distanceField(binIm)
plt.figure()
plt.imshow(dist[sizeZ // 2, :, :])
plt.colorbar()
plt.show()