f

RobinNet/13unet_mrt_threshgt.py

@@ -67,7 +67,7 @@ class mrt_unet(BaseNetwork):
         for elem in full_list:
             if elem.endswith("T1.gipl.npy"):
                 X_img_list.append(elem)
-                if self.gt_type == "ctthresh_gt" or self.gt_type == "_mr_ctunetpred":
+                if self.gt_type == "ctthresh_gt":
                     X_img_list.append(elem)
                     X_img_list.append(elem)
             elif elem.endswith(self.gt_type + ".gipl.npy"):

RobinNet/nets/BaseNetwork.py

@@ -108,7 +108,7 @@ class BaseNetwork:
                 else:
                     if elem.endswith("ct.gipl.npy"):
                         X_img_list.append(elem)
-                        if self.gt_type == "thresh" or self.gt_type == "_mr_ctunetpred":
+                        if self.gt_type == "thresh":
                             X_img_list.append(elem)
                             X_img_list.append(elem)
                     elif elem.endswith(self.gt_type+".gipl.npy"):
@@ -117,7 +117,7 @@ class BaseNetwork:
             for elem in full_list:
                 if elem.endswith("ct.gipl.npy"):
                     X_img_list.append(elem)
-                    if self.gt_type == "thresh" or self.gt_type == "_mr_ctunetpred":
+                    if self.gt_type == "thresh":
                         X_img_list.append(elem)
                         X_img_list.append(elem)
                 elif elem.endswith(self.gt_type+".gipl.npy"):

RobinNet/saveImgs_run_trained_model.py

@@ -21,7 +21,7 @@ def main():
 
     root = tk.Tk()
     root.withdraw()
-    file_path = filedialog.askopenfilename(initialdir="saves/mr_unet_cv_ctthresh/")
+    file_path = filedialog.askopenfilename(initialdir="finalResults/complete_segmr/mr_unet_cv_ctthresh")
     file_path = file_path[:-9]
     print(file_path)
 
@@ -196,12 +196,12 @@ def main():
                 y_true = onehotencode(y_true)
                 y_pred = Unet(image, weights, filter_multiplier, training=False)
 
-                if detailed_images:
+                if npys3d:
                     if y_pred3d == []:
                         y_pred3d = y_pred[:,:,:,0].numpy()
                     else:
                         y_pred3d =  np.append(y_pred[:,:,:,0].numpy(), y_pred3d, axis=0)
-                    print(np.shape(y_pred3d))
+                    #print(np.shape(y_pred3d))
 
                 # print(tf.shape(y_pred), tf.shape(y_true))
                 #y_true = onehotencode(tf.reshape(y_true, (1, 512, 512, 1)), autoencoder=True)
@@ -271,8 +271,9 @@ def main():
                                             y_pred[0, :, :, 0], cmap=plt.cm.bone)
 
             if npys3d:
-                np.save(img_savepath + "P" + str(TP_num).zfill(2) + "_pred3D", y_pred3d)
-                np.save("data/npy/" + "P" + str(TP_num).zfill(2) + "_mr_ctunetpred.gipl", y_pred3d)
+                #np.save(img_savepath + "P" + str(TP_num).zfill(2) + "_pred3D", y_pred3d)
+                np.save("data/npy_thresh/" + "P" + str(TP_num).zfill(2) + "_mr_ctunetpred.gipl", y_pred3d)
+                print("ypred shape: ", np.shape(y_pred3d))
             #plt.show()
             #print(test_loss)
             print("TestLoss Mean for P", test_patients[j], ": ", np.mean(test_loss))

RobinNet/tryOut.py

@@ -9,7 +9,14 @@ import matplotlib.pyplot as plt
 from scipy import ndimage
 from datetime import datetime
 
-mr_img = np.load("data/npy/P03_mr_T1.gipl.npy")
+
+img = np.load("data/npy_thresh/P01_mr_ctunetpred.gipl.npy")
+print(np.shape(img))
+
+plt.imshow(img[5,:, :], cmap=plt.cm.bone)
+plt.show()
+
+'''mr_img = np.load("data/npy/P03_mr_T1.gipl.npy")
 ct_img = np.load("data/npy/P03_mr_ctseg_gt.gipl.npy")
 mrseg = np.load("data/npy/P03_segmr.gipl.npy")
 
@@ -30,7 +37,7 @@ for i in range(18):
     plt.imshow(mrseg[i,:, :], cmap=plt.cm.bone)
     plt.title('MR Segmentation')
     # print(i)
-plt.show()
+plt.show()'''