opschonen van scripts. Update van saliency visualisatie.

scripts/7.Visualize_saliency.py

@@ -1,90 +1,57 @@
+import argparse
 import numpy as np 
 import matplotlib.pyplot as plt
-import matplotlib.cm as cm
+# import matplotlib.cm as cm
 
-heatmap = np.load('saliency.npy')
-print(np.shape(heatmap))
+parser = argparse.ArgumentParser(
+    description='Calculate the froc metrics and store in froc_metrics.yml')
+parser.add_argument('-experiment',
+    help='Title of experiment')                
+parser.add_argument('--series', '-s', 
+    metavar='[series_name]', required=True, nargs='+',
+    help='List of series to include')
+args = parser.parse_args()
+
+########## constants #################
+SERIES = args.series
+series_ = '_'.join(args.series)
+EXPERIMENT = args.experiment
+SALIENCY_DIR = f'./../train_output/{EXPERIMENT}_{series_}/saliency.npy'
+IMAGES_DIR = f'./../train_output/{EXPERIMENT}_{series_}/images_list.npy'
+SEGMENTATION_DIR = f'./../train_output/{EXPERIMENT}_{series_}/segmentations.npy'
+
+########## load saliency map ############
+heatmap = np.load(SALIENCY_DIR)
 heatmap = np.squeeze(heatmap)
+
+######### load images and segmentations ###########
+images_list = np.load(IMAGES_DIR)
+images_list = np.squeeze(images_list)
+segmentations = np.load(SEGMENTATION_DIR)
+######## take average ##########
+# len(heatmap) is smaller then maximum number of images
+# if len(heatmap) < 100:
+    # heatmap = np.mean(abs(heatmap),axis=0)
+heatmap = abs(heatmap)
+
+fig, axes = plt.subplots(2,len(SERIES))
+print(np.shape(axes))
 print(np.shape(heatmap))
-
-
-### take average over 5 #########
-heatmap = np.mean(abs(heatmap),axis=0)
-print(np.shape(heatmap))
-
-SERIES = ['t2','b50','b400','b800','b1400','adc']
-fig, axes = plt.subplots(1,6)
+print(np.shape(images_list))
 max_value = np.amax(heatmap)
-pri
 min_value = np.amin(heatmap)
-# vmin vmax van hele heatmap voor scaling in imshow
-# cmap naar grey 
 
-im = axes[0].imshow(np.squeeze(heatmap[:,:,12,0]))
-axes[1].imshow(np.squeeze(heatmap[:,:,12,1]), vmin=min_value, vmax=max_value)
-axes[2].imshow(np.squeeze(heatmap[:,:,12,2]), vmin=min_value, vmax=max_value)
-axes[3].imshow(np.squeeze(heatmap[:,:,12,3]), vmin=min_value, vmax=max_value)
-axes[4].imshow(np.squeeze(heatmap[:,:,12,4]), vmin=min_value, vmax=max_value)
-axes[5].imshow(np.squeeze(heatmap[:,:,12,5]), vmin=min_value, vmax=max_value)
-
-axes[0].set_title("t2")
-axes[1].set_title("b50")
-axes[2].set_title("b400")
-axes[3].set_title("b800")
-axes[4].set_title("b1400")
-axes[5].set_title("adc")
+for indx in range(len(SERIES)):
+    print(indx)
+    axes[0,indx].imshow(images_list[:,:,12,indx],cmap='gray')
+    im = axes[1,indx].imshow(np.squeeze(heatmap[:,:,12,indx]),vmin=min_value, vmax=max_value)
+    axes[0,indx].set_title(SERIES[indx])
+    axes[0,indx].set_axis_off()
+    axes[1,indx].set_axis_off()
 
 cbar = fig.colorbar(im, ax=axes.ravel().tolist(), shrink=0.5, orientation='horizontal')
-cbar.set_ticks([-0.1,0,0.1])
-cbar.set_ticklabels(['less importance', '0', 'important'])
-fig.suptitle('Average saliency maps over the 5 highest predictions', fontsize=16)
-plt.show()
+cbar.set_ticks([min_value,max_value])
+cbar.set_ticklabels(['less important', 'important'])
+fig.suptitle('Saliency map', fontsize=16)
+plt.savefig(f'./../train_output/{EXPERIMENT}_{series_}/saliency_map.png', dpi=300)
 
-quit()
-
-#take one image out
-heatmap = np.squeeze(heatmap[0])
-
-import numpy as np
-import matplotlib.pyplot as plt
-
-# Fixing random state for reproducibility
-np.random.seed(19680801)
-
-
-class IndexTracker:
-    def __init__(self, ax, X):
-        self.ax = ax
-        ax.set_title('use scroll wheel to navigate images')
-
-        self.X = X
-        rows, cols, self.slices = X.shape
-        self.ind = self.slices//2
-
-        self.im = ax.imshow(self.X[:, :, self.ind], cmap='jet')
-        self.update()
-
-    def on_scroll(self, event):
-        print("%s %s" % (event.button, event.step))
-        if event.button == 'up':
-            self.ind = (self.ind + 1) % self.slices
-        else:
-            self.ind = (self.ind - 1) % self.slices
-        self.update()
-
-    def update(self):
-        self.im.set_data(self.X[:, :, self.ind])
-        self.ax.set_ylabel('slice %s' % self.ind)
-        self.im.axes.figure.canvas.draw()
-
-plt.figure(0)
-fig, ax = plt.subplots(1, 1)
-tracker = IndexTracker(ax, heatmap[:,:,:,5])
-fig.canvas.mpl_connect('scroll_event', tracker.on_scroll)
-plt.show()
-
-plt.figure(1)
-fig, ax = plt.subplots(1, 1)
-tracker = IndexTracker(ax, heatmap[:,:,:,3])
-fig.canvas.mpl_connect('scroll_event', tracker.on_scroll)
-plt.show()