# %% # %load_ext autoreload # %autoreload 2 # %% import napari import napari_omaas from napari_omaas import utils from napari.utils import nbscreenshot import numpy as np from napari_omaas.custom_exceptions import CustomException import sys import os from pathlib import Path import tempfile # %% def show_last_layer(viewer): """ Helper function to hide all layers but last one. Parameters ---------- viewer : a viewer instance """ for index, layer in enumerate(viewer.layers): if index!=(len(viewer.layers)-1): layer.visible = False # %% # viewer = napari.Viewer(show=False) viewer = napari.Viewer(show=True) o = napari_omaas.OMAAS(viewer) viewer.window.add_dock_widget(o, area='right') # %% ################## ## SupRep-SQT1 ### ################## # my_file = r"\\PC160\GroupOdening\OM_data_Bern\raw_data\2024\20241110\Videosdata\20241110_12h-06m-25" #control 4Hz 11.10.24 #7026 SupRep-SQT1 *red only* *done* # my_file = r"\\PC160\GroupOdening\OM_data_Bern\raw_data\2024\20241110\Videosdata\20241110_12h-20m-25" #carbachol 2.5Hz 11.10.24 #7026 SupRep-SQT1 *red only* *done* # %% my_file = "20241110_12h-20m-25" # %% freq_estim_condition = "2.5Hz" # freq_estim_condition = "4Hz" condition = "carbachol" # condition = "control" # illumination_type = "dual_illumination" illumination_type = "red_only" # genotype = "WT" # genotype = "Sham-SQT1" # genotype = "UT-SQT1" genotype = "SupRep-SQT1" # animal_id = str(7028) # animal_id = str(7004) # animal_id = str(7009) animal_id = str(7026) # %% ############# here we create the saving results folder ############# # results_folder_name = fr"M:\PhD students\Saranda Nimani\Optical Mapping\SQT-SupRep\APD maps\{genotype}\{freq_estim_condition}\{condition}\{illumination_type}\{animal_id}\{os.path.basename(my_file)}" # results_folder_name = fr"APD maps\{genotype}\{freq_estim_condition}\{condition}\{illumination_type}\{animal_id}\{os.path.basename(my_file)}" results_folder_path = Path(tempfile.gettempdir()) / "napari_omaas_sample_data" / "APD_maps_tutorial" / "APD_ROI_Analysis"/ genotype / freq_estim_condition / condition / illumination_type / animal_id / os.path.basename(my_file) # results_folder_path.mkdir(exist_ok=True) # results_folder_path = os.path.normpath(results_folder_name) if not results_folder_path.exists(): print(f"Creating Folder: \n'{results_folder_path}'\n*Done*.") results_folder_path.mkdir(parents=True, exist_ok=True) else: print(f"Folder: \n'{results_folder_path}'\nAlready exists") # %% ############# here we create a folder to save the mask for segmentation ############# mask_folder = results_folder_path / "mask" if not os.path.exists(mask_folder): print(f"Creating Folder: \n'{mask_folder}'\n*Done*.") mask_folder.mkdir(parents=True, exist_ok=True) else: print(f"Folder: \n'{mask_folder}'\nAlready exists") # %% # # %%capture # the %%capture command above will hide the ouptut of this cell bc can be very long (only used for the documentation) try: # viewer.open(path=my_file, plugin= "napari-omaas") viewer.open_sample('napari-omaas', 'heart_sample_single_illumination') except Exception as e: raise CustomException(e, sys) # %% nbscreenshot(viewer) # %% viewer.layers[-1].metadata # %% my_shape = [np.array([[-27.10448098, 376.83908381], [-27.10448098, 67.77374194], [272.08514108, 67.77374194], [272.08514108, 376.83908381]])] viewer.add_shapes(my_shape) o.rotate_l_crop.setChecked(True) o.crop_from_shape_btn.click() # done show_last_layer(viewer=viewer) # this is just to manipulate the viewer by changing the position of the image, zooming it and changing to the first frame viewer.camera.center = 0.0, 150, 127 viewer.camera.zoom = 2.3 viewer.dims.current_step = (0, 156, 749) # %% # # change to tab called "Shapes" # o.tabs.setCurrentIndex(1) # o.crop_all_views_and_rotate_btn.click() # show_last_layer(viewer=viewer) # %% my_shape =[np.array([[128.03402574, 142.83315215], [128.03402574, 187.82476519], [178.56426079, 187.82476519], [178.56426079, 142.83315215]])] viewer.add_shapes(my_shape, name="ROI_1") o.plot_last_generated_img(shape_indx=1) # %% nbscreenshot(viewer) # %% # you can call the current clipping values with the following command: o.double_slider_clip_trace.value() # %% clipping_values = (569.5483417085427, 4576.026331658292) #carbachol 2.5Hz 11.10.24 #7026 SupRep-SQT1 *red only* o.plot_last_generated_img(shape_indx=1) o.is_range_clicked_checkbox.setChecked(True) o.double_slider_clip_trace.setValue(clipping_values) o.clip_trace_btn.click() # %% show_last_layer(viewer) # %% nbscreenshot(viewer) # %% # %%capture # the %%capture command above will hide the ouptut of this cell bc can be very long (only used for the documentation) curr_imag = viewer.layers[-1] viewer.layers.selection.active = curr_imag contrast_kernel = 3 ref_frame = 10 o.pre_smooth_temp.setValue(1) o.pre_smooth_spat.setValue(1) o.c_kernels.setValue(contrast_kernel) o.ref_frame_val.setText(str(ref_frame)) o.apply_optimap_mot_corr_btn.click() # %% # Using slide window method for normalization to detrend the signal o.data_normalization_options.setCurrentText("Slide window") o.slide_wind_n.setValue(200) # o.data_normalization_options.setCurrentText("Local max") # Note: Use when stable traces or when "Slide window" is creating artifacts o.inv_and_norm_data_btn.click() o.plot_last_generated_img(shape_indx=1) # %% nbscreenshot(viewer) # %% o.spat_filter_types.setCurrentText("Median") o.apply_spat_filt_btn.click() # %% o.apply_temp_filt_btn.click() # %% o.plot_last_generated_img(shape_indx=1) # %% nbscreenshot(viewer) # %% show_last_layer(viewer) # %% mask_file_path = os.path.join(mask_folder, "Heart_labels_NullBckgrnd.tif") # %% # use another image layer to create mask, then use that mask to to apply segmentation to last layer viewer.layers.selection.active = viewer.layers[5] # take the ratio image normalized o.return_img_no_backg_btn.setChecked(False) # o.return_img_no_backg_btn.setChecked(True) o.apply_auto_segmentation_btn.click() viewer.layers.select_previous() o.apply_manual_segmentation_btn.click() # done # %% show_last_layer(viewer) # %% # save Mask if newly created try: print(f"Saving mask to: \n'{mask_file_path}'\n*Done*.") viewer.layers[-1].save(mask_file_path) except Exception as e: CustomException(e, sys) # %% # curr_imag = viewer.layers[-1] # try: # viewer.open(mask_file_path) # except Exception as e: # CustomException(e, sys) # viewer.layers.selection.active = curr_imag # o.apply_manual_segmentation_btn.click() # done # dont forget to change mask folder! # %% APD_ROIs = [ np.array([[69.71631372, 38.57159234], [69.71631372, 53.11573853], [85.42399161, 53.11573853], [85.42399161, 38.57159234]]), np.array([[165.70767858, 42.06218743], [165.70767858, 56.60633362], [181.41535646, 56.60633362], [181.41535646, 42.06218743]]), np.array([[230.28368766, 71.15047981], [230.28368766, 85.694626 ], [245.99136555, 85.694626 ], [245.99136555, 71.15047981]]), np.array([[ 76.11573805, 181.10422501], [ 76.11573805, 195.6483712 ], [ 91.82341593, 195.6483712 ], [ 91.82341593, 181.10422501]]), np.array([[161.63531765, 176.45009823], [161.63531765, 190.99424442], [177.34299553, 190.99424442], [177.34299553, 176.45009823]]), np.array([[232.02898521, 147.36180585], [232.02898521, 161.90595204], [247.73666309, 161.90595204], [247.73666309, 147.36180585]]) ] RV, LV = ["Right ventricle" for i in range(0,3)], ["Left ventricle" for i in range(0,3)] RV_id, LV_id = [f"RV_{i}" for i in range(0,3)], [f"LV_{i}" for i in range(0,3)] heart_view = ["Anterior" for i in range(0,6)] position = ["Base", "Middle", "Apex"] features = { 'ID':RV_id + LV_id, 'region':RV + LV, 'panView': heart_view, 'position':position + position } edge_color_cycle = ['magenta', 'blue'] text = { 'string': '{ID}: {region}\nPosition: {position}\nView: {panView}', 'anchor': 'upper_left', 'translation': [0, 0], 'size': 15, 'color': 'black', } viewer.add_shapes( APD_ROIs, text = text, features=features, edge_color='region', face_color='transparent', edge_color_cycle=edge_color_cycle, edge_width=2 ) # %% nbscreenshot(viewer) # %% o.tabs.setCurrentIndex(4) # %% o.plot_last_generated_img(shape_indx=2) # %% o.compute_APD_btn.click() # %% nbscreenshot(viewer) # %% o.APD_rslts_dir_box_text.setText(str(results_folder_path)) # uncomment this to save the files directly on the current results folder o.save_APD_rslts_btn.click()