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nuc_morph_analysis/analyses/density/draw_nucleus_contours_on_watershed.py
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| #%% | ||
| from nuc_morph_analysis.lib.preprocessing.twoD_zMIP_area import watershed_workflow, pseudo_cell_helper, pseudo_cell_testing_helper | ||
| from pathlib import Path | ||
| import pandas as pd | ||
| from nuc_morph_analysis.lib.preprocessing import global_dataset_filtering | ||
| from nuc_morph_analysis.lib.preprocessing import filter_data | ||
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| import numpy as np | ||
| import matplotlib.pyplot as plt | ||
| import matplotlib.cm as cm | ||
| from nuc_morph_analysis.analyses.dataset_images_for_figures.figure_helper import return_glasbey_on_dark | ||
| from skimage.measure import find_contours | ||
| from nuc_morph_analysis.lib.visualization.plotting_tools import colorize_image | ||
| from nuc_morph_analysis.analyses.density.watershed_validate import get_contours_from_pair_of_2d_seg_image, draw_contours_on_image | ||
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| def run_validation_and_plot(TIMEPOINT=48,colony='medium',RESOLUTION_LEVEL=1,plot_everything=False, testing=False): | ||
| """ | ||
| run an image through the watershed based pseudo cell segmentation and examine the outputs | ||
| optionally, run a test image through the same pipeline | ||
| Parameters | ||
| ---------- | ||
| TIMEPOINT : int, optional | ||
| The timepoint to analyze, by default 48 | ||
| colony : str, optional | ||
| The colony to analyze, by default 'medium' | ||
| RESOLUTION_LEVEL : int, optional | ||
| The resolution level to analyze, by default 1 | ||
| plot_everything : bool, optional | ||
| Whether to plot a large set of extra images colored by features and with contours, by default True | ||
| testing : bool, optional | ||
| Whether to run a test image through the pipeline, by default False | ||
| Returns | ||
| ------- | ||
| pd.DataFrame | ||
| The dataframe containing the pseudo cell segmentation results | ||
| if plot_everything is False | ||
| if plot_everything is True, returns the full dataframe (after merging with the tracking dataset) | ||
| """ | ||
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| # perform watershed based pseudo cell segmentation | ||
| if testing: # on test data | ||
| labeled_nucleus_image = pseudo_cell_testing_helper.make_nucleus_image_array() | ||
| df_2d, img_dict = pseudo_cell_helper.get_pseudo_cell_boundaries(labeled_nucleus_image, return_img_dict=True) | ||
| colony = 'test' | ||
| else: # or on real data | ||
| df_2d, img_dict = watershed_workflow.get_image_and_run(colony, TIMEPOINT, RESOLUTION_LEVEL, return_img_dict=True) | ||
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| # now display all of the intermediates of the | ||
| # watershed based pseudo cell segmentation | ||
| # 'cyto_distance':(cyto_distance, "Distance Transform restricted\nto Cytoplasmic Segmentation"), | ||
| # 'nucleus_edge_img':(nucleus_edge_img, "Nucleus Edge"), | ||
| cmapstr = 'viridis' | ||
| nuc_mip = img_dict['mip_of_labeled_image'][0] | ||
| cell_mip = img_dict['pseudo_cells_img'][0] | ||
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| nuc_edge = img_dict['nucleus_edge_img'][0] | ||
| cytp_dist = img_dict['cyto_distance'][0] | ||
| for full_crop, sizes in [('crop',(500,200,500,500)),('full',(0,0,nuc_edge.shape[1],nuc_edge.shape[0]))]: | ||
| x1,y1,w,h = sizes | ||
| crop_exp = np.index_exp[y1:y1+h,x1:x1+w] | ||
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| cimg = cytp_dist | ||
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| # define the colormap for the image | ||
| cmap = cm.get_cmap(cmapstr) | ||
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| fig, axlist = plt.subplots(1, 1, figsize=(6, 4)) | ||
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| vmin,vmax = (None,None) | ||
| mappable = axlist.imshow(cimg, interpolation='nearest',cmap=cmap, | ||
| vmin=vmin,vmax=vmax, | ||
| ) | ||
| cbar = plt.colorbar(mappable,ax=axlist,label='cyto_distance') | ||
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| # create the contours | ||
| contour_list = get_contours_from_pair_of_2d_seg_image(nuc_mip,cell_mip) | ||
| # remove cell contours | ||
| contour_list2 = [] | ||
| for contour in contour_list: | ||
| contour_item = list(contour) | ||
| contour_item[2] = [] # empty list | ||
| contour_list2.append(contour_item) | ||
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| draw_contours_on_image(axlist,contour_list2,new_color=np.asarray((200,0,200))) | ||
| # remove the axis | ||
| plt.axis('off') | ||
| plt.xlim([x1,x1+w]) | ||
| plt.ylim([y1,y1+h]) | ||
| titlestr = f"edges of nuclei overlaid on cytoplasmic distance transform\n{colony}_{TIMEPOINT}_{full_crop}_res{RESOLUTION_LEVEL}" | ||
| plt.title(f'{titlestr}') | ||
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| # now save the figure | ||
| savedir = Path(__file__).parent / 'figures' / 'validating nucleus edge' | ||
| savedir.mkdir(exist_ok=True,parents=True) | ||
| savename = f'{colony}_{TIMEPOINT}_{full_crop}_res{RESOLUTION_LEVEL}.png' | ||
| savepath = savedir / savename | ||
| plt.savefig(savepath, | ||
| dpi=300, | ||
| bbox_inches='tight') | ||
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| if __name__ == '__main__': | ||
| # set the details | ||
| dft_test = run_validation_and_plot(testing=True) | ||
| dft0 = run_validation_and_plot(plot_everything=True) |
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