Merge pull request #706 from NEUBIAS/batch_processing

Updates for batch processing

_includes/batch_processing/batch_measure_nuclei_shape.py

@@ -3,34 +3,40 @@
 
 # %%
 # Import python modules
-from OpenIJTIFF import open_ij_tiff
+from OpenIJTIFF import open_ij_tiff, save_ij_tiff
 from skimage.measure import label, regionprops_table
 from skimage.filters import threshold_otsu
-from skimage.io import imsave
 import pandas as pd
 import pathlib
 from pathlib import Path
+from napari import Viewer
 
 # %%
 # Create a function that analyses one image
 # Below, this function will be called several times, for all images
 def analyse(image_path, output_folder):
+
+    # This prints which image is currently analysed
+    print("Analyzing:", image_path)
+
     image, axes, scales, units = open_ij_tiff(image_path)
 
     # Binarize the image using auto-thresholding
     threshold = threshold_otsu(image)
     print("Threshold:", threshold)
     binary_image = image > threshold
-    
+
     # Perform connected components analysis (i.e create labels)
-    label_image = label(binary_image)
-
+    # Note that label returns 32 bit data which save_ij_tif below can't handle.
+    # We can safely convert to 16 bit as we know that we don't have too many objects
+    label_image = label(binary_image).astype('uint16')
+
     # Measure calibrated (scaled) nuclei shapes
-    df = pd.DataFrame( regionprops_table(
+    df = pd.DataFrame(regionprops_table(
         label_image,
-        properties = {'label', 'area'},
-        spacing = scales ) )
-    
+        properties={'label', 'area'},
+        spacing=scales))
+
     # Round all measurements to 2 decimal places.
     # This increases the readability a lot,
     # but depending on your scientific question,
@@ -44,8 +50,8 @@ def analyse(image_path, output_folder):
     image_path = pathlib.Path(image_path)
 
     # Save the labels
-    label_image_path = output_folder / f"{image_path.stem}_labels.tif" 
-    imsave(label_image_path, label_image)
+    label_image_path = output_folder / f"{image_path.stem}_labels.tif"
+    save_ij_tiff(label_image_path, label_image, axes, scales, units)
 
     # Save the measurements table
     # to a tab delimited text file (sep='\t')
@@ -57,13 +63,21 @@ def analyse(image_path, output_folder):
 # %%
 # Assign an output folder 
 # Note: This uses your current working directory; you may want to change this to another folder on your computer
-output_folder = Path.cwd()
+output_dir = Path.cwd()
 
 # %%
 # Create a list of the paths to all data
 image_paths = ["https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_8bit__mitocheck_incenp_t1.tif", 
                "https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_8bit__mitocheck_incenp_t70.tif"]
 
 for image_path in image_paths:
-    print("Analyzing:", image_path)
-    analyse(image_path, output_folder)
+    analyse(image_path, output_dir)
+
+# %%
+# Plot the first output image to check if the pipeline worked
+image1, *_ = open_ij_tiff(image_paths[0])
+labels1, *_ = open_ij_tiff('xy_8bit__mitocheck_incenp_t1_labels.tif')
+
+viewer = Viewer()
+viewer.add_image(image1)
+viewer.add_labels(labels1)

_includes/batch_processing/batch_measure_nuclei_shapes.md

@@ -2,3 +2,16 @@
 
 - In a previous module [there is a workflow to measure the shapes of nuclei in one image](https://neubias.github.io/training-resources/workflow_segment_2d_nuclei_measure_shape/index.html#2dnuclei)
 - Adapt this workflow for automated batch analysis of many images
+- Start by building the skeleton of the workflow without filling in the functionality;
+
+  Note that the code below runs fine, but does not produce any results:
+
+```
+def analyse(image_path, output_folder):
+    print("Analyzing:", image_path)
+    
+for image_path in image_paths:
+    analyse(image_path, output_dir)
+```
+
+ - Make sure the loop with the (almost) empty analyse function runs without error before filling in the image analysis steps