updating bug pr with ark_brain_2024 fixes

src/ark/segmentation/ez_seg/ez_seg_display.py

@@ -47,7 +47,7 @@ def display_channel_image(
     base_image: np.ndarray = imread(image_path, as_gray=True)
 
     # convert image to presentable RGB
-    base_image = base_image / 255
+    base_image_scaled = base_image / 255
 
     # Plot
     fig: Figure = plt.figure(dpi=300, figsize=(6, 6))
@@ -104,8 +104,8 @@ def overlay_mask_outlines(
     channel_image: np.ndarray = imread(channel_image_path, as_gray=True)
     mask_image: np.ndarray = imread(mask_image_path, as_gray=True)
 
-    # Auto-scale the base image
-    channel_image_scaled = img_as_ubyte(channel_image)
+    # convert image to presentable RGB
+    channel_image_scaled = channel_image / 255
 
     # Apply Canny edge detection to extract outlines
     edges: np.ndarray = feature.canny(

src/ark/segmentation/ez_seg/merge_masks.py

@@ -4,7 +4,7 @@
 import numpy as np
 import os
 from skimage.io import imread
-from scipy.ndimage import label
+from skimage.morphology import label
 from alpineer import load_utils, image_utils
 from ark.segmentation.ez_seg.ez_seg_utils import log_creator
 
@@ -14,6 +14,7 @@ def merge_masks_seq(
     object_list: List[str],
     object_mask_dir: Union[pathlib.Path, str],
     cell_mask_path: Union[pathlib.Path, str],
+    cell_mask_suffix: str,
     overlap_percent_threshold: int,
     save_path: Union[pathlib.Path, str],
     log_dir: Union[pathlib.Path, str]
@@ -28,6 +29,7 @@ def merge_masks_seq(
         object_list (List[str]): A list of names representing previously generated object masks. Note, order matters.
         object_mask_dir (Union[pathlib.Path, str]): Directory where object (ez) segmented masks are located
         cell_mask_path (Union[str, pathlib.Path]): Path to where the original cell masks are located.
+        cell_mask_suffix (str): Name of the cell type you are merging. Usually "whole_cell".
         overlap_percent_threshold (int): Percent overlap of total pixel area needed fo object to be merged to a cell.
         save_path (Union[str, pathlib.Path]): The directory where merged masks and remaining cell mask will be saved.
         log_dir (Union[str, pathlib.Path]): The directory to save log information to.
@@ -43,7 +45,7 @@ def merge_masks_seq(
     # for each fov, import cell and object masks (multiple mask types into single xr.DataArray)
     for fov in fov_list:
         curr_cell_mask = imread(fname=os.path.join(
-            cell_mask_path, '_'.join([f'{fov}', 'whole_cell.tiff']))
+            cell_mask_path, '_'.join([f'{fov}', f'{cell_mask_suffix}.tiff']))
         )
 
         fov_object_names = [f'{fov}_' + obj + '.tiff' for obj in object_list]
@@ -69,14 +71,15 @@ def merge_masks_seq(
             curr_cell_mask = remaining_cells
 
         # save the unmerged cells as a tiff.
-        image_utils.save_image(fname=save_path / (fov + "_final_cells_remaining.tiff"), data=curr_cell_mask.astype(np.int32))
+        image_utils.save_image(fname=save_path / (fov + f"_final_{cell_mask_suffix}_remaining.tiff"), data=curr_cell_mask.astype(np.int32))
 
     # Write a log saving mask merging info
     variables_to_log = {
         "fov_list": fov_list,
         "object_list": object_list,
         "object_mask_dir": object_mask_dir,
         "cell_mask_path": cell_mask_path,
+        "cell_mask_suffix": cell_mask_suffix,
         "overlap_percent_threshold": overlap_percent_threshold,
         "save_path": save_path
     }
@@ -111,8 +114,8 @@ def merge_masks_single(
         raise ValueError("Both masks must have the same shape")
 
     # Relabel cell, object masks
-    cell_labels, num_cell_labels = label(cell_mask)
-    object_labels, num_object_labels = label(object_mask)
+    cell_labels, num_cell_labels = label(cell_mask, return_num=True)
+    object_labels, num_object_labels = label(object_mask, return_num=True)
 
     # Instantiate new array for merging
     merged_mask = object_labels.copy()

src/ark/segmentation/marker_quantification.py

@@ -1,6 +1,7 @@
 import copy
 import warnings
 from typing import List
+import re
 
 import numpy as np
 import pandas as pd
@@ -596,16 +597,25 @@ def process_lists(fov_names: List[str], mask_names: List[str]) -> List[str]:
     Function to strip prefixes from list: fov_names, strip '.tiff' suffix from list: mask names,
     and remove underscore prefixes, returning unique mask values (i.e. categories of masks).
 
-    Args:
-        fov_names (List[str]): list of fov names. Matching fov names in mask names will be returned without fov prefix.
-        mask_names (List[str]): list of mask names. Mask names will be returned without tif suffix.
+        Args:
+            fov_names (List[str]): list of fov names. Matching fov names in mask names will be returned without fov prefix.
+            mask_names (List[str]): list of mask names. Mask names will be returned without tif suffix.
 
-    Returns:
-        List[str]: Unique mask names (i.e. categories of masks)
+        Returns:
+            List[str]: Unique mask names (i.e. categories of masks)
     """
     stripped_mask_names = io_utils.remove_file_extensions(mask_names)
-    result = [itemB[len(prefix):] for itemB in stripped_mask_names for prefix in fov_names if itemB.startswith(prefix)]
+
+    # break fov names into tokens, compare against mask names and return only those mask names that also contain the fov
+    result = []
+    for prefix in fov_names:
+        prefix_tokens = list(filter(bool, re.split("[^a-zA-Z0-9]", prefix)))
+        for itemB in stripped_mask_names:
+            itemB_tokens = list(filter(bool, re.split("[^a-zA-Z0-9]", itemB)))
+            if set(prefix_tokens).issubset(itemB_tokens):
+                result.append(itemB[len(prefix):])
+
     # Remove underscore prefixes and return unique values
     cleaned_result = [item.lstrip('_') for item in result]
     unique_result = list(set(cleaned_result))
-    return unique_result
+    return unique_result

templates/ez_segmenter.ipynb

@@ -20,7 +20,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {
     "tags": [
      "import"
@@ -564,6 +564,7 @@
     "* `merge_masks_list`: list of object masks to merge to the base (cell) `image.List` of object masks to merge to the base (cell) image.\n",
     "* `percent_overlap`: percent threshold required for a cell mask to be merged into an object mask\n",
     "* `cell_dir`: the final mask directory\n",
+    "* `cell_mask_suffix`: Suffix name of the cell mask files. Usually \"whole_cell\"\n",
     "* `merged_masks_dir`: the directory to store the merged masks"
    ]
   },
@@ -582,6 +583,7 @@
     "\n",
     "# Overwrite if different from above\n",
     "cell_dir = os.path.join(segmentation_dir, \"deepcell_output\")\n",
+    "cell_mask_suffix = \"whole_cell\"\n",
     "merged_masks_dir = os.path.join(segmentation_dir, \"merged_masks_dir\")"
    ]
   },
@@ -629,6 +631,7 @@
     "    object_list=merge_masks_list,\n",
     "    object_mask_dir=ez_masks_dir,\n",
     "    cell_mask_path=cell_dir,\n",
+    "    cell_mask_suffix=cell_mask_suffix,\n",
     "    overlap_percent_threshold=percent_overlap,\n",
     "    save_path=merged_masks_dir,\n",
     "    log_dir=log_dir\n",