60 lines
2.3 KiB
Python
Executable File
60 lines
2.3 KiB
Python
Executable File
import argparse
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from email.mime import image
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import numpy as np
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import matplotlib.pyplot as plt
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import SimpleITK as sitk
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parser = argparse.ArgumentParser(
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description='Calculate the froc metrics and store in froc_metrics.yml')
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parser.add_argument('-experiment',
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help='Title of experiment')
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parser.add_argument('--series', '-s',
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metavar='[series_name]', required=True, nargs='+',
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help='List of series to include')
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args = parser.parse_args()
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########## constants #################
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SERIES = args.series
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series_ = '_'.join(args.series)
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EXPERIMENT = args.experiment
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fold = 0
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SALIENCY_DIR = f'./../train_output/{EXPERIMENT}_{series_}_{fold}/saliency.npy'
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IMAGES_DIR = f'./../train_output/{EXPERIMENT}_{series_}_{fold}/images_list.npy'
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SEGMENTATION_DIR = f'./../train_output/{EXPERIMENT}_{series_}_{fold}/segmentations.npy'
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SLIDE = 10
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########## load saliency map ############
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heatmap = np.load(SALIENCY_DIR)
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heatmap = np.squeeze(heatmap)
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######### load images and segmentations ###########
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images_list = np.load(IMAGES_DIR)
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images_list = np.squeeze(images_list)
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segmentations = np.load(SEGMENTATION_DIR)
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######## take average ##########
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# len(heatmap) is smaller then maximum number of images
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# if len(heatmap) < 100:
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# heatmap = np.mean(abs(heatmap),axis=0)
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heatmap = abs(heatmap)
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fig, axes = plt.subplots(2,len(SERIES))
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print(np.shape(axes))
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print(np.shape(heatmap))
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print(np.shape(images_list))
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max_value = np.amax(heatmap[:,:,SLIDE,:])
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min_value = np.amin(heatmap[:,:,SLIDE,:])
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for indx in range(len(SERIES)):
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print(indx)
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heatmap_i = np.transpose(np.squeeze(heatmap[:,:,SLIDE,indx]))
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im = axes[0,indx].imshow(np.transpose(images_list[:,:,SLIDE,indx]),cmap='gray')
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axes[0,indx].imshow(np.ma.masked_where(heatmap_i < 0.10, heatmap_i),vmin=min_value, vmax=max_value*0.5, alpha=0.25, cmap='jet')
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axes[0,indx].set_title(SERIES[indx])
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axes[0,indx].set_axis_off()
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axes[1,indx].set_axis_off()
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cbar = fig.colorbar(im, ax=axes.ravel().tolist(), shrink=0.5, orientation='horizontal')
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cbar.set_ticks([min_value,max_value])
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cbar.set_ticklabels(['less important', 'important'])
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fig.suptitle('Saliency map', fontsize=16)
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plt.savefig(f'./../train_output/{EXPERIMENT}_{series_}_{fold}/saliency_map.png', dpi=300) |