diff --git a/.gitignore b/.gitignore index c6906a5..7a5cbed 100644 --- a/.gitignore +++ b/.gitignore @@ -170,4 +170,4 @@ debug_data/ !cytoplasm.tif !binary_mask_sdh.tif data/* -sample_atp_intensity_plot.png \ No newline at end of file +*intensity_plot.png \ No newline at end of file diff --git a/myoquant/commands/run_atp.py b/myoquant/commands/run_atp.py index c94082f..1bf736c 100644 --- a/myoquant/commands/run_atp.py +++ b/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: diff --git a/myoquant/src/ATP_analysis.py b/myoquant/src/ATP_analysis.py index 41386fb..cc8cbdd 100644 --- a/myoquant/src/ATP_analysis.py +++ b/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( diff --git a/myoquant/src/common_func.py b/myoquant/src/common_func.py index f6ed068..0a6080e 100644 --- a/myoquant/src/common_func.py +++ b/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) - 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