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Merge pull request #11 from openproblems-bio/add_watershed
watershed added
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‎src/methods_segmentation/watershed/config.vsh.yaml‎

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name: watershed
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label: "watershed Segmentation"
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summary: "Output of the segmentation method watershed"
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description: "Output of the segmentation method watershed"
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links:
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documentation: "https://github.com/openproblems-bio/task_ist_preprocessing"
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repository: "https://github.com/theislab/txsim"
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references:
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doi: "10.1109/34.87344"
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__merge__: /src/api/comp_method_segmentation.yaml
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arguments:
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- name: --normalize_func
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type: string
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default: "gamma"
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- name: --normalize_gamma
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type: integer
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default: 1
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- name: --normalize_gain
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type: integer
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default: 1
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- name: --normalize_inv
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type: boolean
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default: False
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- name: --normalize_cutoff
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type: double
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default: 0.5
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- name: --contrast_adjustment_func
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type: string
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default: "equalize_adapthist"
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- name: --contrast_adjustment_kernel_size
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type: string
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default: "None"
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- name: --contrast_adjustment_clip_limit
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type: double
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default: 0.01
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- name: --contrast_adjustment_nbins
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type: integer
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default: 256
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- name: --contrast_adjustment_mask
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type: string
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default: "None"
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- name: --contrast_adjustment_in_range
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type: string
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default: "image"
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- name: --contrast_adjustment_out_range
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type: string
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default: "dtype"
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- name: --blur_func
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type: string
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default: "gaussian"
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- name: --blur_sigma
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type: integer
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default: 1
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- name: --blur_output
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type: string
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default: "None"
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- name: --blur_mode
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type: string
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default: "nearest"
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- name: --blur_cval
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type: double
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default: 0.0
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- name: --blur_preserve_range
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type: boolean
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default: False
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- name: --blur_truncate #keep as double
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type: double
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default: 4.0
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- name: --threshold_func
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type: string
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default: "local_otsu"
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- name: --threshold_nbins
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type: integer
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default: 256
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- name: --threshold_hist
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type: string
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default: "None"
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- name: --threshold_out
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type: string
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default: "None"
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- name: --threshold_mask
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type: string
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default: "None"
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- name: --threshold_shift_x
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type: boolean
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default: False
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- name: --threshold_shift_y
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type: boolean
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default: False
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- name: --threshold_shift_z
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type: boolean
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default: False
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- name: --threshold_footprint
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type: string
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default: "square"
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- name: --threshold_footprint_size
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type: integer
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default: 50
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- name: --distance_transform_func
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type: string
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default: "distance_transform_edt"
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- name: --distance_transform_sampling
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type: string
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default: "None"
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- name: --distance_transform_return_distances
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type: boolean
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default: True
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- name: --distance_transform_return_indices
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type: boolean
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default: False
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- name: --distance_transform_distances
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type: string
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default: "None"
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- name: --distance_transform_indices
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type: string
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default: "None"
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- name: --local_maxima_func
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type: string
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default: "find_local_maxima"
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- name: --local_maxima_min_distance
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type: integer
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default: 5
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- name: --post_processing_func_1
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type: string
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default: "remove_small_objects"
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- name: --post_processing_min_size_1
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type: integer
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default: 64
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- name: --post_processing_connectivity_1
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type: integer
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default: 1
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- name: --post_processing_out_1
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type: string
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default: "None"
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- name: --post_processing_func_2
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type: string
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default: "remove_small_holes"
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- name: --post_processing_area_threshold_2
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type: integer
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default: 64
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- name: --post_processing_connectivity_2
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type: integer
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default: 1
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- name: --post_processing_out_2
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type: string
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default: "None"
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- name: --bg_intensity_filter_bg_factor
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type: double
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default: 0.3
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- name: --bg_intensity_filter_window_size
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type: integer
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default: 1000
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- name: --bg_intensity_filter_bg_size
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type: integer
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default: 2000
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resources:
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- type: python_script
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path: script.py
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engines:
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- type: docker
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image: openproblems/base_python:1.0.0
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setup:
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- type: python
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pypi: spatialdata
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__merge__:
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- /src/base/setup_txsim_partial.yaml
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- type: native
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runners:
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- type: executable
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- type: nextflow
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directives:
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label: [ midtime, lowcpu, lowmem ]
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‎src/methods_segmentation/watershed/script.py‎

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import txsim as tx
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import numpy as np
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import os
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import yaml
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import spatialdata as sd
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import anndata as ad
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import shutil
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import numpy as np
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from spatialdata.models import Labels2DModel
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import xarray as xr
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def convert_to_lower_dtype(arr):
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max_val = arr.max()
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if max_val <= np.iinfo(np.uint8).max:
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new_dtype = np.uint8
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elif max_val <= np.iinfo(np.uint16).max:
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new_dtype = np.uint16
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elif max_val <= np.iinfo(np.uint32).max:
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new_dtype = np.uint32
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else:
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new_dtype = np.uint64
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return arr.astype(new_dtype)
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## VIASH START
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par = {
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"input": "../task_ist_preprocessing/resources_test/common/2023_10x_mouse_brain_xenium/dataset.zarr",
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"output": "segmentation.zarr"
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}
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## VIASH END
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hyperparameters = par.copy()
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hyperparameters = {k:(v if v != "None" else None) for k,v in hyperparameters.items()}
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del hyperparameters['input']
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del hyperparameters['output']
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sdata = sd.read_zarr(par["input"])
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image = sdata['morphology_mip']['scale0'].image.compute().to_numpy()
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transformation = sdata['morphology_mip']['scale0'].image.transform.copy()
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sd_output = sd.SpatialData()
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image = sdata['morphology_mip']['scale0'].image.compute().to_numpy()
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transformation = sdata['morphology_mip']['scale0'].image.transform.copy()
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img_arr = tx.preprocessing.segment_watershed(image[0], hyperparameters)
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image = convert_to_lower_dtype(img_arr)
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data_array = xr.DataArray(image, name=f'segmentation', dims=('y', 'x'))
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parsed_data = Labels2DModel.parse(data_array, transformations=transformation)
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sd_output.labels['segmentation'] = parsed_data
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print("Writing output", flush=True)
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if os.path.exists(par["output"]):
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shutil.rmtree(par["output"])
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sd_output.write(par["output"])
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