d

RobinNet/rerunModels.py

+import numpy as np
+import tensorflow as tf
+import matplotlib.pyplot as plt
+import tkinter as tk
+import os
+import matplotlib
+from tkinter import filedialog
+from skimage import transform
+import SimpleITK as sitk
+import argparse
+#import os
+#import pydot
+#from graphviz import Digraph
+#import shutil
+#from tensorflow.keras import layers, models
+#from utils.dataLoader import *
+from utils.other_functions import *
+from nets.Unet import *
+
+def main():
+    models = ["12", "34", "56", "78", "910", "1112", "1314", "1516"]
+    folder_path = "finalResults/complete_seg/ae_class_cv_seg/"
+    #modeltype = "Cluster_"
+    modeltype = "Class_"
+    #modeltype = "Cluster_class_"
+    #modeltype = "Unet_"
+
+    ##################### U-Net #####################
+    for fold in models:
+
+        file_path = folder_path+"TPs"+fold+modeltype
+
+        ### read out files ###
+        weights = np.load(file_path+"model.npy", allow_pickle=True)
+        [test_patients,
+         val_patients,
+         number_patients,
+         img_path,
+         shrink_data,
+         newSize,
+         lr,
+         batch_size,
+         num_epochs,
+         e,
+         augment,
+         save_path,
+         gt_type,
+         filter_multiplier] = np.load(file_path+"params.npy", allow_pickle=True)
+        #  autoencoder_model__e100_switchclass1024_nohiddenclusternet needs to comment out gt_type and val_patients
+        print('Training Parameters:')
+        print('-----------------')
+        print('Number of Patients: ', number_patients)
+        print('Number of epochs: ', num_epochs)
+        print('Test Patient number: ', test_patients)
+        print('Image Size: ', newSize)
+        print('Filter Multiplier: ', filter_multiplier)
+        print('Data Augmentation: ', augment)
+        print('Learning rate: ', lr)
+        print('Image Path: ', img_path)
+        print('Save Path: ', save_path)
+        print('GT Type:', gt_type)
+
+        ### Load test patient
+        if gt_type == "thresh" or gt_type == "ctthresh_gt":
+            img_path = "data/npy_thresh/"
+        else:
+            img_path = "data/npy/"
+        full_list = os.listdir(img_path)
+        seg_list = os.listdir("data/npy/")
+
+        X_img_list = []
+        GT_img_list = []
+        ytrue_img_list = []
+        # thresh_img_list = []
+        if "mr" in save_path:
+            for elem in full_list:
+                if elem.endswith("T1.gipl.npy") and (elem.startswith('P' + str(test_patients[0]).zfill(2)) or elem.startswith('P' + str(test_patients[1]).zfill(2))):
+                    X_img_list.append(elem)
+                    if gt_type == "ctthresh_gt":
+                        X_img_list.append(elem)
+                        X_img_list.append(elem)
+
+                elif elem.endswith(gt_type + ".gipl.npy") and (elem.startswith('P' + str(test_patients[0]).zfill(2)) or elem.startswith('P' + str(test_patients[1]).zfill(2))):
+                    GT_img_list.append(elem)
+            for elem in seg_list:
+                if elem.endswith("segmr.gipl.npy") and (elem.startswith('P' + str(test_patients[0]).zfill(2)) or elem.startswith('P' + str(test_patients[1]).zfill(2))):
+                    ytrue_img_list.append(elem)
+                    if gt_type == "ctthresh_gt":
+                        ytrue_img_list.append(elem)
+                        ytrue_img_list.append(elem)
+
+        else:
+            for elem in full_list:
+                if elem.endswith("ct.gipl.npy") and (elem.startswith('P' + str(test_patients[0]).zfill(2)) or elem.startswith('P' + str(test_patients[1]).zfill(2))):
+                    X_img_list.append(elem)
+                    if gt_type == "thresh":
+                        X_img_list.append(elem)
+                        X_img_list.append(elem)
+
+                elif elem.endswith(gt_type+".gipl.npy") and (elem.startswith('P' + str(test_patients[0]).zfill(2)) or elem.startswith('P' + str(test_patients[1]).zfill(2))):
+                    GT_img_list.append(elem)
+            for elem in seg_list:
+                if elem.endswith("seg.gipl.npy") and (elem.startswith('P' + str(test_patients[0]).zfill(2)) or elem.startswith('P' + str(test_patients[1]).zfill(2))):
+                    ytrue_img_list.append(elem)
+                    if gt_type == "thresh":
+                        ytrue_img_list.append(elem)
+                        ytrue_img_list.append(elem)
+        list.sort(X_img_list)
+        list.sort(GT_img_list)
+        list.sort(ytrue_img_list)
+        print("Input Image List", X_img_list)
+        print("GT Image List", GT_img_list)
+        print("True Segmentation Image List", ytrue_img_list)
+
+
+        for j in range(2):
+            if gt_type == "thresh" or gt_type == "ctthresh_gt":
+                X_img_npys = np.load(img_path + X_img_list[j*3])
+                GT_img_npys = np.load(img_path + GT_img_list[j*3])
+                ytrue_img_npys = np.load(img_path + ytrue_img_list[j*3])
+                print(GT_img_list[j*3])
+                print(GT_img_list[j * 3+1])
+                print(GT_img_list[j * 3+2])
+
+                X_img_npys = np.append(X_img_npys, np.load(img_path + X_img_list[j * 3+1]), axis=0)
+                GT_img_npys = np.append(GT_img_npys, np.load(img_path + GT_img_list[j * 3+1]), axis=0)
+                ytrue_img_npys = np.append(ytrue_img_npys, np.load(img_path + ytrue_img_list[j * 3+1]), axis=0)
+
+                X_img_npys = np.append(X_img_npys, np.load(img_path + X_img_list[j * 3+2]), axis=0)
+                GT_img_npys = np.append(GT_img_npys, np.load(img_path + GT_img_list[j * 3+2]), axis=0)
+                ytrue_img_npys = np.append(ytrue_img_npys, np.load(img_path + ytrue_img_list[j * 3+2]), axis=0)
+
+                print("Input shape: ", np.shape(X_img_npys))
+                print("GT shape: ", np.shape(GT_img_npys))
+                print("True Segm shape: ", np.shape(ytrue_img_npys))
+
+            else:
+                X_img_npys = np.load(img_path + X_img_list[j])
+                GT_img_npys = np.load(img_path + GT_img_list[j])
+                ytrue_img_npys = np.load(img_path + ytrue_img_list[j])
+
+                print("Input shape: ", np.shape(X_img_npys))
+                print("GT shape: ", np.shape(GT_img_npys))
+                print("True Segm shape: ", np.shape(ytrue_img_npys))
+
+
+
+            X_img_npys = transform.resize(X_img_npys, (X_img_npys.shape[0], newSize[0], newSize[1]), order=0,
+                                          preserve_range=True, mode='constant', anti_aliasing=False,
+                                          anti_aliasing_sigma=None)
+            GT_img_npys = transform.resize(GT_img_npys, (GT_img_npys.shape[0], newSize[0], newSize[1]), order=0,
+                                           preserve_range=True, mode='constant', anti_aliasing=False,
+                                           anti_aliasing_sigma=None)
+            ytrue_img_npys = transform.resize(ytrue_img_npys, (ytrue_img_npys.shape[0], newSize[0], newSize[1]),
+                                              order=0,
+                                              preserve_range=True, mode='constant', anti_aliasing=False,
+                                              anti_aliasing_sigma=None)
+            X_test = np.reshape(X_img_npys, (X_img_npys.shape[0], X_img_npys.shape[1], X_img_npys.shape[2], 1))
+            GT_test = np.reshape(GT_img_npys, (GT_img_npys.shape[0], GT_img_npys.shape[1], GT_img_npys.shape[2], 1))
+            ytrue = np.reshape(ytrue_img_npys,
+                               (ytrue_img_npys.shape[0], ytrue_img_npys.shape[1], ytrue_img_npys.shape[2], 1))
+
+
+            test_dataset = tf.data.Dataset.from_tensor_slices((X_test, ytrue))
+            test_dataset = test_dataset.batch(batch_size=1)
+            print(test_patients)
+            TP_num = test_patients[j]
+
+            ###################################################################################
+
+            detailed_images = False
+            npys3d          = True
+
+            ###################################################################################
+
+
+
+            test_loss = []
+            test_loss_hdd = []
+            test_loss_hdd2 = []
+            y_pred3d = []
+            for features in test_dataset:
+                image, y_true = features
+                y_true = onehotencode(y_true)
+                y_pred = Unet(image, weights, filter_multiplier, training=False)
+
+                loss = dice_loss(y_pred, y_true)
+                loss = tf.make_ndarray(tf.make_tensor_proto(loss))
+                test_loss.append(loss)
+                #print(test_loss)
+                try:
+                    y_true_np = np.squeeze(y_true[0, :, :, 0].numpy() > 0.5)
+                    y_true_np = y_true_np.astype(np.float_)
+                    pred_np = np.squeeze(y_pred[0, :, :, 0].numpy() > 0.5)
+                    pred_np = pred_np.astype(np.float_)
+                    hausdorff_distance_filter = sitk.HausdorffDistanceImageFilter()
+                    hausdorff_distance_filter.Execute(sitk.GetImageFromArray(y_true_np), sitk.GetImageFromArray(pred_np))
+                    test_loss_hdd.append(hausdorff_distance_filter.GetHausdorffDistance())
+
+                    hausdorff_distance_filter2 = sitk.HausdorffDistanceImageFilter()
+                    hausdorff_distance_filter2.Execute(sitk.GetImageFromArray(pred_np), sitk.GetImageFromArray(y_true_np))
+                    test_loss_hdd2.append(hausdorff_distance_filter2.GetHausdorffDistance())
+                except:
+                    pass
+
+            #plt.show()
+            #print(test_loss)
+            print("TestLoss Mean for P", test_patients[j], ": ", np.mean(test_loss))
+
+            #print(test_loss_hdd)
+            print("Hausdorff-Distance for P", test_patients[j],":", np.mean(test_loss_hdd))
+            print("Hausdorff-Distance2 for P", test_patients[j], ":", np.mean(test_loss_hdd2))
+      #####
+
+if __name__ == "__main__":
+    main()
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