kleine aanpassingen voor weergave

scripts/1.U-net_chris.py

@@ -6,10 +6,11 @@ from datetime import datetime
 import sys 
 # sys.path.append('./../code')
 # from utils_quintin import * 
-# from sfransen.utils_quintin import *
+from sfransen.utils_quintin import *
 # sys.path.append('./../code/DWI_exp')    
 # from callbacks import IntermediateImages, dice_coef
 # from callbacks import RocCallback
+from sfransen.utils_quintin import *
 from sfransen.DWI_exp import IntermediateImages, dice_coef
 from sfransen.DWI_exp.preprocessing_function import preprocess
 import yaml

scripts/4.frocs.py

@@ -35,6 +35,7 @@ EXPERIMENT = args.experiment
 
 MODEL_PATH = f'./../train_output/{EXPERIMENT}_{series_}/models/{EXPERIMENT}_{series_}.h5'
 YAML_DIR = f'./../train_output/{EXPERIMENT}_{series_}'
+IMAGE_DIR = f'./../train_output/{EXPERIMENT}_{series_}'
 
 DATA_DIR = "./../data/Nijmegen paths/"
 TARGET_SPACING = (0.5, 0.5, 3) 
@@ -42,10 +43,11 @@ INPUT_SHAPE = (192, 192, 24, len(SERIES))
 IMAGE_SHAPE = INPUT_SHAPE[:3]
 
 DATA_SPLIT_INDEX = read_yaml_to_dict('./../data/Nijmegen paths/train_val_test_idxs.yml')
-TEST_INDEX = DATA_SPLIT_INDEX['test_set0']
+TEST_INDEX = DATA_SPLIT_INDEX['val_set0']
 
 N_CPUS = 12
 
+
 ########## load images in parrallel ##############
 print_(f"> Loading images into RAM...")
 
@@ -116,8 +118,8 @@ predictions = [preprocess_softmax(pred, threshold="dynamic")[0] for pred in pred
 
 # Remove outer edges 
 zeros = np.zeros(np.shape(predictions))
-test = np.squeeze(predictions)[:,:,2:190,2:190]
-zeros[:,:,2:190,2:190] = test
+test = np.squeeze(predictions)[:,2:-2,2:190,2:190]
+zeros[:,2:-2,2:190,2:190] = test
 predictions = zeros
 
 # perform Froc
@@ -127,7 +129,6 @@ dump_dict_to_yaml(metrics, YAML_DIR, "froc_metrics", verbose=True)
 
 ############## save image as example #################
 # save image nmr 3
-IMAGE_DIR = f'./../train_output/train_10h_{series_}'
 img_s = sitk.GetImageFromArray(predictions_blur[3].squeeze())
 sitk.WriteImage(img_s, f"{IMAGE_DIR}/predictions_blur_001.nii.gz")
 

scripts/5.Visualize_frocs.py

@@ -1,6 +1,4 @@
-import sys
-sys.path.append('./../code')
-from utils_quintin import *
+from sfransen.utils_quintin import *
 import matplotlib.pyplot as plt
 import argparse
 

scripts/6.saliency_map.py

@@ -44,10 +44,10 @@ froc_metrics = read_yaml_to_dict(f'{YAML_DIR}/froc_metrics.yml')
 top_10_idx = np.argsort(froc_metrics['roc_pred'])[-1    :]
 
 DATA_SPLIT_INDEX = read_yaml_to_dict('./../data/Nijmegen paths/train_val_test_idxs.yml')
-TEST_INDEX = DATA_SPLIT_INDEX['test_set0']
+TEST_INDEX = DATA_SPLIT_INDEX['val_set0']
 
 TEST_INDEX_top10 = [TEST_INDEX[i] for i in top_10_idx]
-
+TEST_INDEX_image = [371]
 N_CPUS = 12
 
 ########## load images in parrallel ##############
@@ -77,7 +77,7 @@ partial_seg = partial(load_images_parrallel,
 images = []
 for s in SERIES:
     image_paths_seq = image_paths[s]
-    image_paths_index = np.asarray(image_paths_seq)[TEST_INDEX_top10]
+    image_paths_index = np.asarray(image_paths_seq)[TEST_INDEX_image]
     data_list = pool.map(partial_images,image_paths_index)
     data = np.stack(data_list, axis=0)
     images.append(data)
@@ -85,7 +85,7 @@ for s in SERIES:
 images_list = np.transpose(images, (1, 2, 3, 4, 0))
 
 #load segmentations
-seg_paths_index = np.asarray(seg_paths)[TEST_INDEX_top10]
+seg_paths_index = np.asarray(seg_paths)[TEST_INDEX_image]
 data_list = pool.map(partial_seg,seg_paths_index)
 segmentations = np.stack(data_list, axis=0)
  

scripts/7.Visualize_saliency.py

@@ -1,7 +1,6 @@
 import argparse
 import numpy as np 
 import matplotlib.pyplot as plt
-# import matplotlib.cm as cm
 
 parser = argparse.ArgumentParser(
     description='Calculate the froc metrics and store in froc_metrics.yml')
@@ -19,6 +18,7 @@ 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'
+SLIDE = 10
 
 ########## load saliency map ############
 heatmap = np.load(SALIENCY_DIR)
@@ -38,13 +38,13 @@ fig, axes = plt.subplots(2,len(SERIES))
 print(np.shape(axes))
 print(np.shape(heatmap))
 print(np.shape(images_list))
-max_value = np.amax(heatmap)
-min_value = np.amin(heatmap)
+max_value = np.amax(heatmap[:,:,SLIDE,:])
+min_value = np.amin(heatmap[:,:,SLIDE,:])
 
 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].imshow(np.transpose(images_list[:,:,SLIDE,indx]),cmap='gray')
+    im = axes[1,indx].imshow(np.transpose(np.squeeze(heatmap[:,:,SLIDE,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()

scripts/8.Visualize_training.py

@@ -28,10 +28,10 @@ df = pd.read_csv(f'{folder_input}')
 
 # read csv file
 for metric in df:
-    # if not metric == 'epoch':
-    if metric == 'loss' or metric == 'val_loss': 
+    if not metric == 'epoch':
+    # if metric == 'loss' or metric == 'val_loss': 
         plt.plot(df['epoch'], df[metric], label=metric)
-        plt.ylim(ymin=0,ymax=0.01)
+        # plt.ylim(ymin=0,ymax=0.01)
 
 
 

scripts/scroll_trough.py

@@ -1,13 +1,16 @@
-
-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)
+####### dir ############
+SALIENCY_DIR = f'./../train_output/train_n0.001_run4_t2_b50_b400_b800_b1400_adc/saliency.npy'
+
+########## load saliency map ############
+heatmap = np.load(SALIENCY_DIR)
+heatmap = np.squeeze(heatmap)
+#take one image out
+heatmap = np.squeeze(abs(heatmap))
+max_value = np.amax(heatmap)
+min_value = np.amin(heatmap)
 
 
 class IndexTracker:
@@ -19,7 +22,7 @@ class IndexTracker:
         rows, cols, self.slices = X.shape
         self.ind = self.slices//2
 
-        self.im = ax.imshow(self.X[:, :, self.ind], cmap='jet')
+        self.im = ax.imshow(self.X[:, :, self.ind], cmap='jet',vmin=min_value, vmax=max_value)
         self.update()
 
     def on_scroll(self, event):
@@ -35,14 +38,14 @@ class IndexTracker:
         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()
+# fig, ax = plt.subplots(1, 1)
+# tracker = IndexTracker(ax, heatmap[:,:,:,0])
+# fig.canvas.mpl_connect('scroll_event', tracker.on_scroll)
+# plt.show()
 
-plt.figure(1)
+plt.figure()
 fig, ax = plt.subplots(1, 1)
-tracker = IndexTracker(ax, heatmap[:,:,:,3])
+tracker = IndexTracker(ax, heatmap[:,:,:,4])
 fig.canvas.mpl_connect('scroll_event', tracker.on_scroll)
 plt.show()