better atp options and treatment

.gitignore

@@ -170,4 +170,4 @@ debug_data/
 !cytoplasm.tif
 !binary_mask_sdh.tif
 data/*
-sample_atp_intensity_plot.png
+*intensity_plot.png

myoquant/commands/run_atp.py

@@ -63,6 +63,14 @@ def atp_analysis(
         None,
         help="Image channel to use for the analysis. If not specified, the analysis will be performed on all three channels.",
     ),
+    channel_first: bool = typer.Option(
+        False,
+        help="If the channel is the first dimension of the image, set this to True. False by default.",
+    ),
+    rescale_exposure: bool = typer.Option(
+        False,
+        help="Rescale the image exposure if your image is not in the 0 255 forma, False by default.",
+    ),
     n_classes: int = typer.Option(
         2,
         max=10,
@@ -72,9 +80,10 @@ def atp_analysis(
         "median",
         help="The method to use to compute the intensity of the cell. Can be either 'median' or 'mean'.",
     ),
-    erosion: bool = typer.Option(
+    erosion: int = typer.Option(
         False,
-        help="Perform an erosion on the cells images to remove signal in the cell membrane (usefull for fluo)",
+        max=45,
+        help="Perform an erosion on the cells images to remove signal in the cell membrane (usefull for fluo). Expressed in percentage of the cell radius",
     ),
     export_map: bool = typer.Option(
         True,
@@ -127,6 +136,7 @@ def atp_analysis(
         from ..src.ATP_analysis import run_atp_analysis
         import numpy as np
         from PIL import Image
+        from skimage.exposure import rescale_intensity
 
         try:
             from imageio.v2 import imread
@@ -153,8 +163,16 @@ def atp_analysis(
         progress.add_task(description="Reading all inputs...", total=None)
         image_ndarray = imread(image_path)
         if channel is not None:
-            image_ndarray = image_ndarray[:, :, channel]
-
+            if channel_first:
+                # Put the channel as third dimension instead of first
+                image_ndarray = np.moveaxis(image_ndarray, 0, -1)
+        image_ndarray = image_ndarray[:, :, channel]
+        if rescale_exposure:
+            image_ndarray = rescale_intensity(
+                image_ndarray,
+                in_range=(np.amin(image_ndarray), np.amax(image_ndarray)),
+                out_range=np.uint8,
+            )
         if mask_path is not None:
             mask_ndarray = imread(mask_path)
             if np.unique(mask_ndarray).shape[0] != 2:

myoquant/src/ATP_analysis.py

@@ -14,7 +14,7 @@
 
 
 def get_all_intensity(
-    image_array, df_cellpose, intensity_method="median", erosion=False
+    image_array, df_cellpose, intensity_method="median", erosion=None
 ):
     all_cell_median_intensity = []
     for index in range(len(df_cellpose)):
@@ -97,7 +97,7 @@ def predict_all_cells(
     intensity_threshold,
     n_classes=2,
     intensity_method="median",
-    erosion=False,
+    erosion=None,
 ):
     all_cell_median_intensity = get_all_intensity(
         histo_img, cellpose_df, intensity_method, erosion
@@ -146,7 +146,7 @@ def run_atp_analysis(
     intensity_threshold=None,
     n_classes=2,
     intensity_method="median",
-    erosion=False,
+    erosion=None,
 ):
     df_cellpose = df_from_cellpose_mask(mask_cellpose)
     class_predicted_all, intensity_all, intensity_threshold = predict_all_cells(

myoquant/src/common_func.py

@@ -173,17 +173,19 @@ def df_from_stardist_mask(mask, intensity_image=None):
     return df_stardist
 
 
-def extract_single_image(raw_image, df_props, index, erosion=False):
+def extract_single_image(raw_image, df_props, index, erosion=None):
     single_entity_img = raw_image[
         df_props.iloc[index, 5] : df_props.iloc[index, 7],
         df_props.iloc[index, 6] : df_props.iloc[index, 8],
     ].copy()
     surface_area = df_props.iloc[index, 1]
     cell_radius = math.sqrt(surface_area / math.pi)
-    single_entity_mask = df_props.iloc[index, 9]
-    erosion_size = int(cell_radius / 5)  # 20% of the cell
-    if erosion:
-        for i in range(erosion_size):
+    single_entity_mask = df_props.iloc[index, 9].copy()
+    erosion_size = cell_radius * (
+        erosion / 100
+    )  # Erosion in percentage of the cell radius
+    if erosion is not None:
+        for i in range(int(erosion_size)):
             single_entity_mask = binary_erosion(
                 single_entity_mask, out=single_entity_mask
             )