Notebook: Advanced
Passing workflows between functions¶
Demo to use workflows in functions for end to end image analysis
This is slightly advanced, where we need to define a custom function for image analysis.
When you have a custom function and want to use it in napari-lattice with the GUI or CLI, you will need to create a .py file
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import os
import pyclesperanto_prototype as cle
from napari_workflows import Workflow
from napari_workflows import _io_yaml_v1 as io_yaml
cle.get_device()
import os
import pyclesperanto_prototype as cle
from napari_workflows import Workflow
from napari_workflows import _io_yaml_v1 as io_yaml
cle.get_device()
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<Intel(R) Iris(R) Xe Graphics on Platform: Intel(R) OpenCL Graphics (1 refs)>
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#download sample data from https://zenodo.org/records/14903188
img_path = "./sample_data/RBC_tiny.czi"
#make dir if not exists
if not os.path.exists("./sample_data"):
os.makedirs("./sample_data")
import urllib.request
url = "https://zenodo.org/records/14903188/files/RBC_tiny.czi?download=1"
if not os.path.exists(img_path):
print("Downloading file")
urllib.request.urlretrieve(url, img_path)
print(f"Downloaded at : {img_path}")
else:
print(f"File already exists at : {img_path}")
#download sample data from https://zenodo.org/records/14903188
img_path = "./sample_data/RBC_tiny.czi"
#make dir if not exists
if not os.path.exists("./sample_data"):
os.makedirs("./sample_data")
import urllib.request
url = "https://zenodo.org/records/14903188/files/RBC_tiny.czi?download=1"
if not os.path.exists(img_path):
print("Downloading file")
urllib.request.urlretrieve(url, img_path)
print(f"Downloaded at : {img_path}")
else:
print(f"File already exists at : {img_path}")
File already exists at : ./sample_data/RBC_tiny.czi
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#absolute path for saving
save_path = "C:/Users/rajasekhar.p/napari_lattice/notebooks/sample_data"
#make dir if not exists
if not os.path.exists(save_path):
os.makedirs(save_path)
#absolute path for saving
save_path = "C:/Users/rajasekhar.p/napari_lattice/notebooks/sample_data"
#make dir if not exists
if not os.path.exists(save_path):
os.makedirs(save_path)
Lets apply a workflow, where we perform
- median filter
- binarise using otsu algorithm with threshold of 1000
- label each object using connected components labeling-
- measure each object using connected components
As we have multiple steps, we can define it in a function and then call this function in a workflow.
We would like to save the deskewed image, segmented image and the measurements as a csv file, so we return all 3.
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from skimage.measure import regionprops_table
import numpy as np
import pandas as pd
def image_seg_measure(input_image):
# Apply median filter
median_filtered = cle.median_sphere(input_image, radius_x=2, radius_y=2, radius_z=2)
# Apply Otsu thresholding
binary_image = cle.threshold_otsu(median_filtered)
# Apply connected components labeling
labeled_image = cle.connected_components_labeling_box(binary_image)
#convert labeled_image to np array as original image will be an opencl array
labeled_image_np = np.array(labeled_image)
#measure using regionprops_table
props = regionprops_table(labeled_image_np, input_image,
properties=['label', 'bbox','area',
'intensity_mean'])
#convert to pandas dataframe
#props_df = pd.DataFrame(props)
return (input_image, labeled_image_np, props)
from lls_core import LatticeData
image_seg_workflow = Workflow()
#Otsu thresholding
input_arg = "deskewed_image"
#define a simple gaussian filter
task_name = "measurement"
#Apply function
image_seg_workflow.set(task_name, image_seg_measure, input_arg )
print(image_seg_workflow)
from skimage.measure import regionprops_table
import numpy as np
import pandas as pd
def image_seg_measure(input_image):
# Apply median filter
median_filtered = cle.median_sphere(input_image, radius_x=2, radius_y=2, radius_z=2)
# Apply Otsu thresholding
binary_image = cle.threshold_otsu(median_filtered)
# Apply connected components labeling
labeled_image = cle.connected_components_labeling_box(binary_image)
#convert labeled_image to np array as original image will be an opencl array
labeled_image_np = np.array(labeled_image)
#measure using regionprops_table
props = regionprops_table(labeled_image_np, input_image,
properties=['label', 'bbox','area',
'intensity_mean'])
#convert to pandas dataframe
#props_df = pd.DataFrame(props)
return (input_image, labeled_image_np, props)
from lls_core import LatticeData
image_seg_workflow = Workflow()
#Otsu thresholding
input_arg = "deskewed_image"
#define a simple gaussian filter
task_name = "measurement"
#Apply function
image_seg_workflow.set(task_name, image_seg_measure, input_arg )
print(image_seg_workflow)
Workflow: measurement <- (<function image_seg_measure at 0x000001F9238FC160>, 'deskewed_image')
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#Before we run this, lets look at whats in the directory
os.listdir(save_path)
#Before we run this, lets look at whats in the directory
os.listdir(save_path)
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['gaussian_workflow.yaml', 'RBC_tiny.czi']
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params_workflow = LatticeData(
input_image=img_path,
save_dir=save_path,
workflow=image_seg_workflow,
)
params_workflow.save()
params_workflow = LatticeData(
input_image=img_path,
save_dir=save_path,
workflow=image_seg_workflow,
)
params_workflow.save()
[INFO:2025-08-29 11:48:41,214] Processing File ./sample_data/RBC_tiny.czi
Timepoints: 0%| | 0/1 [00:00<?, ?it/s][INFO:2025-08-29 11:48:42,038] Processing File <xarray.DataArray 'transpose-d511a424874269c0d63d068279594e59' (Z: 834, Y: 118,
X: 209)> Size: 41MB
dask.array<getitem, shape=(834, 118, 209), dtype=uint16, chunksize=(834, 118, 209), chunktype=numpy.ndarray>
Coordinates:
C <U17 68B 'LatticeLightsheet'
* Z (Z) float64 7kB 0.0 0.3 0.6 0.9 1.2 ... 249.0 249.3 249.6 249.9
* Y (Y) float64 944B 0.0 0.145 0.29 0.435 ... 16.53 16.67 16.82 16.96
* X (X) float64 2kB 0.0 0.145 0.29 0.435 ... 29.72 29.87 30.01 30.16
Attributes:
unprocessed: <Element 'ImageDocument' at 0x000001F9755C1440>
INFO: blockdim levels (1) < subsamp levels (3): First-level block size (4, 256, 256) will be used for all levels INFO: blockdim levels (1) < subsamp levels (3): First-level block size (4, 256, 256) will be used for all levels
Timepoints: 100%|██████████| 1/1 [00:13<00:00, 13.41s/it]
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#list files in save_path
os.listdir(save_path)
#list files in save_path
os.listdir(save_path)
Out[15]:
['gaussian_workflow.yaml', 'RBC_tiny.czi', 'RBC_tiny_deskewed.h5', 'RBC_tiny_deskewed.xml', 'RBC_tiny_deskewed_1.h5', 'RBC_tiny_deskewed_1.xml', 'RBC_tiny_deskewed_output_2.csv']
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csv_path = "C:/Users/rajasekhar.p/napari_lattice/notebooks/sample_data/RBC_tiny_deskewed_output_2.csv"
df = pd.read_csv(csv_path)
#apply series.explode
df.apply(pd.Series.explode)
csv_path = "C:/Users/rajasekhar.p/napari_lattice/notebooks/sample_data/RBC_tiny_deskewed_output_2.csv"
df = pd.read_csv(csv_path)
#apply series.explode
df.apply(pd.Series.explode)
Out[10]:
| Unnamed: 0 | time | channel | label | bbox-0 | bbox-1 | bbox-2 | bbox-3 | bbox-4 | bbox-5 | area | intensity_mean | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | T0 | C0 | [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 ... | [23 8 5 7 7 18 5 6 15 6 7 4 6 5 34 ... | [ 49 179 85 550 676 748 651 1072 1289 ... | [ 0 0 0 0 0 0 0 0 0 0 0 ... | [32 57 58 14 50 41 52 41 16 58 40 32 33 50 36 ... | [ 64 291 615 556 742 795 1118 1295 1290 ... | [ 22 38 209 5 49 9 209 186 1 35 23 ... | [ 669 47513 889527 128 48386 30... | [ 824.44012451 815.22802734 897.43670654 82... |
- RBC_tiny_deskewed.h5 is the deskewed image
- RBC_tiny_deskewed_1.h5 is the label image
- RBC_tiny_deskewed_output_2.csv is the table with measurements
Troubleshooting
To inspect the components of workflows, you can use the process_workflow function to access the data.
It returns a generator object, which can be accessed using the next function
As we were returning 2 images and a measurement table/dictionary, it will have 3 outputs
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for slice in params_workflow.process_workflow():
first_slice = slice
break
for slice in params_workflow.process_workflow():
first_slice = slice
break
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slice
slice
Out[8]:
('slices',
<generator object LatticeData.process_workflow.<locals>._generator at 0x0000020086544900>)
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data_set = next(first_slice[1])
data_set
data_set = next(first_slice[1])
data_set
Timepoints: 0%| | 0/1 [00:00<?, ?it/s][INFO:2025-08-29 11:37:29,403] Processing File <xarray.DataArray 'transpose-2f7dac7b3adef14da02a10aa2414f35b' (Z: 834, Y: 118,
X: 209)> Size: 41MB
dask.array<getitem, shape=(834, 118, 209), dtype=uint16, chunksize=(834, 118, 209), chunktype=numpy.ndarray>
Coordinates:
C <U17 68B 'LatticeLightsheet'
* Z (Z) float64 7kB 0.0 0.3 0.6 0.9 1.2 ... 249.0 249.3 249.6 249.9
* Y (Y) float64 944B 0.0 0.145 0.29 0.435 ... 16.53 16.67 16.82 16.96
* X (X) float64 2kB 0.0 0.145 0.29 0.435 ... 29.72 29.87 30.01 30.16
Attributes:
unprocessed: <Element 'ImageDocument' at 0x00000200D8185260>
Out[9]:
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measurements = data_set.data[2]#.apply(Series.explode)
measurements
measurements = data_set.data[2]#.apply(Series.explode)
measurements