SDH export stats

Author: Corentin

myoquant/SDH_analysis.py

@@ -92,7 +92,8 @@ def run_sdh_analysis(image_array, model_SDH, mask_cellpose):
     class_predicted_all, proba_predicted_all = predict_all_cells(
         image_array, df_cellpose, model_SDH
     )
-
+    df_cellpose["class_predicted"] = class_predicted_all
+    df_cellpose["proba_predicted"] = proba_predicted_all
     count_per_label = np.unique(class_predicted_all, return_counts=True)
     class_and_proba_df = pd.DataFrame(
         list(zip(class_predicted_all, proba_predicted_all)),
@@ -114,4 +115,4 @@ def run_sdh_analysis(image_array, model_SDH, mask_cellpose):
     result_df = pd.DataFrame(columns=headers, data=data)
     # Paint The Full Image
     full_label_map = paint_full_image(image_array, df_cellpose, class_predicted_all)
-    return result_df, full_label_map
+    return result_df, full_label_map, df_cellpose

myoquant/__main__.py

@@ -152,7 +152,7 @@ def sdh_analysis(
 
     model_SDH = load_sdh_model(model_path)
     console.print("SDH Model loaded !", style="blue")
-    result_df, full_label_map = run_sdh_analysis(
+    result_df, full_label_map, df_cellpose_details = run_sdh_analysis(
         image_ndarray_sdh, model_SDH, mask_cellpose
     )
     console.print("Analysis completed ! ", style="green")
@@ -174,6 +174,16 @@ def sdh_analysis(
     console.print(
         f"Table saved as a .csv file named {output_path/csv_name}", style="green"
     )
+    if export_stats:
+        cell_details_name = image_path.stem + "_cell_details.csv"
+        df_cellpose_details.drop("image", axis=1).to_csv(
+            output_path / cell_details_name,
+            index=False,
+        )
+        console.print(
+            f"Cell Table saved as a .csv file named {output_path/cell_details_name}",
+            style="green",
+        )
     label_map_name = image_path.stem + "_label_map.tiff"
     Image.fromarray(full_label_map).save(output_path / label_map_name)
     console.print(