Merge pull request #10 from openproblems-bio/segm_m_binning

binning method

src/methods_segmentation/binning/config.vsh.yaml

@@ -0,0 +1,36 @@
+name: binning
+label: "Binning Segmentation"
+summary: "Segment the spatial data into equidistant square bins"
+description: "The binning method for segmentation serves as a baseline of a poor segmentation"
+links:
+  documentation: "https://github.com/openproblems-bio/task_ist_preprocessing"
+  repository: "https://github.com/openproblems-bio/task_ist_preprocessing"
+references:
+  doi: "10.1101/2023.02.13.528102"
+
+__merge__: /src/api/comp_method_segmentation.yaml 
+
+arguments:
+  - name: --bin_size
+    type: integer
+    default: 30
+
+resources:
+  - type: python_script
+    path: script.py
+
+engines:
+  - type: docker
+    image: openproblems/base_python:1.0.0
+    setup:
+      - type: python
+        pypi: spatialdata
+    __merge__: 
+      - /src/base/setup_txsim_partial.yaml
+  - type: native
+
+runners:
+  - type: executable
+  - type: nextflow
+    directives:
+      label: [ midtime, lowcpu, lowmem ]

src/methods_segmentation/binning/script.py

@@ -0,0 +1,58 @@
+import txsim as tx
+import numpy as np
+import os
+import yaml
+import spatialdata as sd
+import anndata as ad
+import shutil
+import numpy as np
+from spatialdata.models import Labels2DModel
+import xarray as xr
+
+
+
+def convert_to_lower_dtype(arr):
+    max_val = arr.max()
+    if max_val <= np.iinfo(np.uint8).max:
+        new_dtype = np.uint8
+    elif max_val <= np.iinfo(np.uint16).max:
+        new_dtype = np.uint16
+    elif max_val <= np.iinfo(np.uint32).max:
+        new_dtype = np.uint32
+    else:
+        new_dtype = np.uint64
+
+    return arr.astype(new_dtype)
+
+## VIASH START
+par = {
+  "input": "../task_ist_preprocessing/resources_test/common/2023_10x_mouse_brain_xenium/dataset.zarr",
+  "output": "segmentation.zarr"
+}
+
+## VIASH END
+
+hyperparameters = par.copy()
+
+hyperparameters = {k:(v if v != "None" else None) for k,v in hyperparameters.items()}
+del hyperparameters['input']
+del hyperparameters['output']
+
+sdata = sd.read_zarr(par["input"])
+image = sdata['morphology_mip']['scale0'].image.compute().to_numpy()
+transformation = sdata['morphology_mip']['scale0'].image.transform.copy()
+
+sd_output = sd.SpatialData()
+image = sdata['morphology_mip']['scale0'].image.compute().to_numpy()
+transformation = sdata['morphology_mip']['scale0'].image.transform.copy()
+img_arr = tx.preprocessing.segment_binning(image[0], hyperparameters['bin_size'])   ### TOdo find the optimal bin_size
+image = convert_to_lower_dtype(img_arr)
+data_array = xr.DataArray(image, name=f'segmentation', dims=('y', 'x'))
+parsed_data = Labels2DModel.parse(data_array, transformations=transformation)
+sd_output.labels['segmentation'] = parsed_data
+
+print("Writing output", flush=True)
+if os.path.exists(par["output"]):
+  shutil.rmtree(par["output"])
+sd_output.write(par["output"])
+