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Resolve compile errors in volume labeling #275

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ctrueden merged 1 commit into from
Sep 12, 2024
Merged

Resolve compile errors in volume labeling #275

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55 changes: 29 additions & 26 deletions docs/ops/doc/examples/volume_labeling.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -112,20 +112,24 @@ In addition to the result tables, the label imdage (also known as an *index imag
from net.imglib2.roi.labeling import LabelRegions
from net.imglib2.type.logic import BitType
from net.imglib2.type.numeric.real import FloatType

from org.scijava.table import DefaultGenericTable

from jarray import array

def extract_channel(image, ch):
def extract_channel(image, axis, ch):
"""Extract a channel from the input image.

Extract the given channel from the input image.

:param image:

Input Img.


Input image.

:param axis:

Integer corresponding to the Channel axis.

:param ch:

Channel number to extract.
Expand All @@ -134,12 +138,10 @@ In addition to the result tables, the label imdage (also known as an *index imag

A view of the extracted channel.
"""
# find C and Z axis indicies
c_idx = find_axis_index(image, "Channel")

return ops.op("transform.hyperSliceView").input(image, c_idx, ch - 1).apply()



return ops.op("transform.hyperSliceView").input(image, axis, ch - 1).apply()


def extract_inside_mask(mask_a, mask_b):
"""Extract the mask "A" data from regions inside mask "B".

Expand Down Expand Up @@ -193,43 +195,44 @@ In addition to the result tables, the label imdage (also known as an *index imag
return i
else:
continue

return None


def gaussian_subtraction(image, sigma):
"""Perform a Gaussian subtraction on an image.

Apply a Gaussian blur and subtract from input image.

:param image:

Input Img.

:param sigma:

Sigma value.

:return:

Gaussian blur subtracted image.
"""
blur = ops.op("filter.gauss").input(image, sigma).apply()
out = ops.op("create.img").input(image, FloatType()).apply()
ops.op("math.sub").input(image, blur).output(out).compute()

return out

# crop the input data to a 450 x 450 patch
min_arr = array([370, 136, 0, 0], "l")
max_arr = array([819, 585, 2, 59], "l")
img_crop = ops.op("transform.intervalView").input(img, min_arr, max_arr).apply()
img_crop = Views.dropSingletonDimensions(img_crop)
img_crop = ops.op("transform.dropSingletonDimensionsView").input(img_crop).apply()
img_crop = ops.op("transform.offsetView").input(img_crop, array([370, 136, 0, 0], "l")).apply()

# extract channels
ch_a_img = extract_channel(img_crop, ch_a)
ch_b_img = extract_channel(img_crop, ch_b)
c_idx = find_axis_index(img, "Channel")
ch_a_img = extract_channel(img_crop, c_idx, ch_a)
ch_b_img = extract_channel(img_crop, c_idx, ch_b)

# customize the following sections below for your own data
# clean up channel "A" and create a mask
Expand Down