Merge pull request #12 from lambda-science/dev

Dev

Author: lambda-science

.gitignore

@@ -169,4 +169,8 @@ debug_data/
 !nuclei.tif
 !cytoplasm.tif
 !binary_mask_sdh.tif
-data/*
+data/*
+*intensity_plot.png
+*.idea
+cellpose.sh
+nohup.out

.python-version

@@ -1 +1 @@
-3.10.9
+3.10.11

.vscode/launch.json

@@ -26,7 +26,7 @@
             "request": "launch",
             "module": "myoquant",
             "justMyCode": true,
-            "args": ["atp-analysis", "sample_img/sample_atp.jpg", "--cellpose-path", "sample_img/sample_atp_cellpose_mask.tiff"],
+            "args": ["atp-analysis", "sample_img/sample_atp.jpg", "--cellpose-path", "sample_img/sample_atp_cellpose_mask.tiff", "--intensity-method", "mean", "--n-classes", "2", "--erosion"],
         }
     ]
 }

CLI_Documentation.md

@@ -10,6 +10,7 @@ $ myoquant [OPTIONS] COMMAND [ARGS]...
 
 **Options**:
 
+- `--version`
 - `--help`: Show this message and exit.
 
 **Commands**:
@@ -43,6 +44,12 @@ $ myoquant atp-analysis [OPTIONS] IMAGE_PATH
 - `--output-path PATH`: The path to the folder to save the results. Will save in the same folder as input image if not specified.
 - `--intensity-threshold INTEGER RANGE`: Fiber intensity threshold to differenciate between the two fiber types. If not specified, the analysis will try to deduce it. [1<=x<=254]
 - `--cellpose-diameter INTEGER`: Approximative single cell diameter in pixel for CellPose detection. If not specified, Cellpose will try to deduce it.
+- `--channel INTEGER`: Image channel to use for the analysis. If not specified, the analysis will be performed on all three channels.
+- `--channel-first / --no-channel-first`: If the channel is the first dimension of the image, set this to True. False by default. [default: no-channel-first]
+- `--rescale-exposure / --no-rescale-exposure`: Rescale the image exposure if your image is not in the 0 255 forma, False by default. [default: no-rescale-exposure]
+- `--n-classes INTEGER RANGE`: The number of classes of cell to detect. If not specified this is defaulted to two classes. [default: 2; x<=10]
+- `--intensity-method TEXT`: The method to use to compute the intensity of the cell. Can be either 'median' or 'mean'. [default: median]
+- `--erosion INTEGER RANGE`: Perform an erosion on the cells images to remove signal in the cell membrane (usefull for fluo). Expressed in percentage of the cell radius [default: False; x<=45]
 - `--export-map / --no-export-map`: Export the original image with cells painted by classification label. [default: export-map]
 - `--export-stats / --no-export-stats`: Export per fiber and per nuclei stat table. [default: export-stats]
 - `--help`: Show this message and exit.

myoquant/__main__.py

@@ -1,11 +1,27 @@
 import typer
 from rich.console import Console
+import pkg_resources
+
+__version__ = pkg_resources.get_distribution("myoquant").version
+__version_cellpose__ = pkg_resources.get_distribution("cellpose").version
+__version_stardist__ = pkg_resources.get_distribution("stardist").version
+__version_torch__ = pkg_resources.get_distribution("torch").version
+__version_tensorflow__ = pkg_resources.get_distribution("tensorflow").version
 
 from .commands.docs import app as docs_app
 from .commands import run_sdh, run_he, run_atp
 
 console = Console()
 
+
+def version_callback(value: bool):
+    if value:
+        print(
+            f"MyoQuant Version: {__version__} \nCellpose Version: {__version_cellpose__} \nStardist Version: {__version_stardist__} \nTorch Version: {__version_torch__} \nTensorflow Version: {__version_tensorflow__}"
+        )
+        raise typer.Exit()
+
+
 app = typer.Typer(
     name="MyoQuant",
     add_completion=False,
@@ -15,6 +31,17 @@
 app.add_typer(docs_app, name="docs", help="Generate documentation")
 
 
+@app.callback()
+def main(
+    version: bool = typer.Option(
+        None, "--version", callback=version_callback, is_eager=True
+    )
+):
+    """
+    MyoQuant Analysis Command Line Interface
+    """
+
+
 app.registered_commands += (
     run_sdh.app.registered_commands
     + run_he.app.registered_commands

myoquant/commands/run_atp.py

@@ -59,6 +59,32 @@ def atp_analysis(
         None,
         help="Approximative single cell diameter in pixel for CellPose detection. If not specified, Cellpose will try to deduce it.",
     ),
+    channel: int = typer.Option(
+        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,
+        help="The number of classes of cell to detect. If not specified this is defaulted to two classes.",
+    ),
+    intensity_method: str = typer.Option(
+        "median",
+        help="The method to use to compute the intensity of the cell. Can be either 'median' or 'mean'.",
+    ),
+    erosion: int = typer.Option(
+        False,
+        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,
         help="Export the original image with cells painted by classification label.",
@@ -110,6 +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
@@ -135,7 +162,17 @@ def atp_analysis(
     ) as progress:
         progress.add_task(description="Reading all inputs...", total=None)
         image_ndarray = imread(image_path)
-
+        if channel is not None:
+            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:
@@ -200,8 +237,13 @@ def atp_analysis(
         transient=False,
     ) as progress:
         progress.add_task(description="Detecting fiber types...", total=None)
-        result_df, full_label_map, df_cellpose_details = run_atp_analysis(
-            image_ndarray, mask_cellpose, intensity_threshold
+        result_df, full_label_map, df_cellpose_details, fig = run_atp_analysis(
+            image_ndarray,
+            mask_cellpose,
+            intensity_threshold,
+            n_classes,
+            intensity_method,
+            erosion,
         )
     if export_map:
         with Progress(
@@ -214,7 +256,12 @@ def atp_analysis(
                 description="Blending label and original image together...", total=None
             )
             labelRGB_map = label2rgb(image_ndarray, full_label_map)
-            overlay_img = blend_image_with_label(image_ndarray, labelRGB_map)
+            if channel is not None:
+                overlay_img = blend_image_with_label(
+                    image_ndarray, labelRGB_map, fluo=True
+                )
+            else:
+                overlay_img = blend_image_with_label(image_ndarray, labelRGB_map)
             overlay_filename = image_path.stem + "_label_blend.tiff"
             overlay_img.save(output_path / overlay_filename)
 
@@ -239,6 +286,11 @@ def atp_analysis(
         f"💾 OUTPUT: Summary Table saved as {output_path/csv_name}",
         style="green",
     )
+    plot_name = image_path.stem + "_intensity_plot.png"
+    fig.savefig(output_path / plot_name)
+    console.print(
+        f"💾 OUTPUT: Intensity Plot saved as {output_path/plot_name}", style="green"
+    )
     if export_map:
         console.print(
             f"💾 OUTPUT: Overlay image saved as {output_path/overlay_filename}",