Alpha b

Interface/CLI/Data/CLI_main.py

@@ -432,7 +432,7 @@ def CI_pwai(Auto: bool = False):
                                                             model, 'top_activation',
                                                             second_last_conv_layer_name = 'top_conv',
                                                             sensitivity_map = 2, pred_index=tf.argmax(model_prediction_ORG[0])) 
-                    Grad_cam_heatmap = cv2.resize(Grad_cam_heatmap, (img_array.shape[1], img_array.shape[2]))
+                    Grad_cam_heatmap = cv2.resize(np.clip(Grad_cam_heatmap, 0, 1), (img_array.shape[1], img_array.shape[2]))
                     Grad_cam_heatmap = np.uint8(255 * Grad_cam_heatmap)
                     Grad_cam_heatmap = cv2.applyColorMap(Grad_cam_heatmap, cv2.COLORMAP_VIRIDIS)
                     Grad_cam_heatmap = np.clip(np.uint8((Grad_cam_heatmap * 0.3) + ((img_array * 255) * 0.7)), 0, 255)

Interface/CLI/Data/Utils/Grad_cam.py

@@ -1,7 +1,9 @@
+import os
 import glob
 import numpy as np
 import tensorflow as tf
 # Other
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
 tf.get_logger().setLevel('ERROR')
 physical_devices = tf.config.list_physical_devices('GPU')
 for gpu_instance in physical_devices:
@@ -37,7 +39,7 @@ def make_gradcam_heatmap(img_array,
                          model,
                          last_conv_layer_name,
                          second_last_conv_layer_name=None,
-                         pred_index=None, threshold=0,
+                         pred_index=None,
                          sensitivity_map=1.0):
     """
     Function to compute the Grad-CAM heatmap for a specific class, given an input image.
@@ -48,17 +50,11 @@ def make_gradcam_heatmap(img_array,
 
     # Compute heatmap for the last convolutional layer
     heatmap = _compute_heatmap(model, img_array, last_conv_layer_name, pred_index)
-
-    # Apply threshold and adjust sensitivity
-    heatmap = np.where(heatmap > threshold, heatmap, 0)
     heatmap = heatmap ** sensitivity_map
 
     if second_last_conv_layer_name is not None:
         # Compute heatmap for the second last convolutional layer
         heatmap_second = _compute_heatmap(model, img_array, second_last_conv_layer_name, pred_index)
-
-        # Apply threshold and adjust sensitivity
-        heatmap_second = np.where(heatmap_second > threshold, heatmap_second, 0)
         heatmap_second = heatmap_second ** sensitivity_map
 
         # Average the two heatmaps

Interface/CLI/Run_CLI.py

@@ -0,0 +1,33 @@
+import subprocess
+import traceback
+import sys
+import os
+# Other
+from Data.Utils.print_color_V1_OLD import print_Color
+def run_program(file_path):
+    while True:
+        try:
+            try:
+                # Run the other Python program using subprocess
+                subprocess.run(["python", file_path], check=True)
+            except subprocess.CalledProcessError as ERROR_Py:
+                print_Color("~*An error occurred: \nERROR: ~*" + str(ERROR_Py), ['yellow', 'red'], advanced_mode=True)
+                print_Color('~*Do you want to see the detailed error message? ~*[~*Y~*/~*n~*]: ',
+                        ['yellow', 'normal', 'green', 'normal', 'red', 'normal'],
+                        advanced_mode = True,
+                        print_END='')
+                show_detailed_error = input('')
+                if show_detailed_error.lower() == 'y':
+                    print_Color('detailed error message:', ['yellow'])
+                    #print_Color('1th ERROR (FILE ERROR) [!MAIN!]:', ['red'])
+                    print_Color('2th ERROR (subprocess.run ERROR):', ['red'])
+                    traceback.print_exc()
+                choice = input("Do you want to restart the program? (y/n): ")
+                if choice.lower() != "y":
+                    break
+                os.system('cls' if os.name == 'nt' else 'clear')
+            else:
+                break
+        except OSError:
+            break
+run_program('Data\CLI_main.py')

Utils/Grad_cam.py

@@ -1,7 +1,9 @@
+import os
 import glob
 import numpy as np
 import tensorflow as tf
 # Other
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
 tf.get_logger().setLevel('ERROR')
 physical_devices = tf.config.list_physical_devices('GPU')
 for gpu_instance in physical_devices:
@@ -37,7 +39,7 @@ def make_gradcam_heatmap(img_array,
                          model,
                          last_conv_layer_name,
                          second_last_conv_layer_name=None,
-                         pred_index=None, threshold=0,
+                         pred_index=None,
                          sensitivity_map=1.0):
     """
     Function to compute the Grad-CAM heatmap for a specific class, given an input image.
@@ -48,17 +50,11 @@ def make_gradcam_heatmap(img_array,
 
     # Compute heatmap for the last convolutional layer
     heatmap = _compute_heatmap(model, img_array, last_conv_layer_name, pred_index)
-
-    # Apply threshold and adjust sensitivity
-    heatmap = np.where(heatmap > threshold, heatmap, 0)
     heatmap = heatmap ** sensitivity_map
 
     if second_last_conv_layer_name is not None:
         # Compute heatmap for the second last convolutional layer
         heatmap_second = _compute_heatmap(model, img_array, second_last_conv_layer_name, pred_index)
-
-        # Apply threshold and adjust sensitivity
-        heatmap_second = np.where(heatmap_second > threshold, heatmap_second, 0)
         heatmap_second = heatmap_second ** sensitivity_map
 
         # Average the two heatmaps

requirements.txt

@@ -1,11 +1,10 @@
-tensorflow==2.10.1
-keras==2.10.0
 absl-py==1.4.0
 adabelief-tf==0.2.1
 efficientnet==1.1.1
 gpu-control==1.0.0
 hyperas==0.4.1
 imbalanced-learn==0.11.0
+keras==2.10.0
 keras-adabound==0.6.0
 keras-efficientnet-v2==1.2.2
 keras-gradient-noise==0.11
@@ -15,12 +14,15 @@ model-profiler==1.1.8
 numpy==1.25.1
 opencv-python==4.8.0.74
 pandas==2.0.3
-Pillow==10.1.0
+Pillow==10.2.0
+psutil==5.9.5
 py-cpuinfo==9.0.0
+pydicom==2.4.3
+requests==2.31.0
 scikit-learn==1.3.0
 scipy==1.11.1
 seaborn==0.12.2
+tensorflow==2.10.1
 tensorflow-addons==0.22.0
+tensorflow-model-optimization==0.7.5
 tqdm==4.66.1
-imblearn~=0.0
-future~=0.18.3