Add files via upload

Engine/Model/convert_full.py

@@ -0,0 +1,55 @@
+from PIL import Image
+import numpy, pefile, os
+
+####################################################################################################
+
+def preprocess_image(file_data, size, channels=1):
+    width, height = size
+    wah = int(numpy.ceil(numpy.sqrt(len(file_data) / channels)))
+    arr = numpy.frombuffer(file_data, dtype=numpy.uint8)
+    img = numpy.zeros(wah*wah*channels, dtype=numpy.uint8)
+    img[:len(file_data)] = arr
+    image = Image.fromarray(img.reshape((wah, wah)), 'L')
+    return image.resize((width, height), Image.Resampling.NEAREST)
+
+####################################################################################################
+
+def get_type(file_path):
+    suffix = {".com", ".dll", ".drv", ".exe", ".ocx", ".scr", ".sys", ".mui", ".cpl"}
+    try:
+        file_content = None
+        if not os.path.splitext(file_path)[-1].lower() in suffix:
+            return file_content
+        with open(file_path, 'rb') as f:
+            file_content = f.read()
+    except:
+        pass
+    return file_content
+
+####################################################################################################
+
+def file_to_images(input_dir, output_dir):
+    for root, dirs, files in os.walk(input_dir):
+        for file in files:
+            try:
+                file_path = os.path.join(root, file)
+                file_content = get_type(file_path)
+                if file_content:
+                    print(file_path)
+                    relative_path = os.path.relpath(root, input_dir)
+                    output_path_dir = os.path.join(output_dir, relative_path)
+
+                    if not os.path.exists(output_path_dir):
+                        os.makedirs(output_path_dir)
+                    image = preprocess_image(file_content, (224, 224))
+                    safe_name = os.path.basename(file_path).replace('/', '_').replace('\\', '_')
+                    image.save(os.path.join(output_path_dir, f"{safe_name}.png"))
+            except Exception as e:
+                print(e)
+
+####################################################################################################
+
+input_directory = input('Enter the folder path: ')
+output_directory = "./Image_Output"
+file_to_images(input_directory, output_directory)
+input('Conversion Complete')

Engine/Model/train.py

@@ -11,7 +11,7 @@ tf.keras.mixed_precision.set_global_policy('mixed_float16')
 
 ####################################################################################################
 
-def load_dataset(data_dir, image_size=(224, 224), val_split=0.01, batch_size=256, color_mode="grayscale"):
+def load_dataset(data_dir, image_size=(224, 224), val_split=0.00001, batch_size=256, color_mode="grayscale"):
     channels = 1 if color_mode == "grayscale" else 3
     file_paths, labels, class_indices = get_file_list(data_dir)
     
@@ -142,7 +142,7 @@ class CustomModelCheckpoint(callbacks.Callback):
 
 ####################################################################################################
 
-train_ds, val_ds = load_dataset(r'.\Image_File_Pefile')
+train_ds, val_ds = load_dataset(r'.\Image_Full_Pefile')
 for imgs, _ in train_ds.take(1):
     train_ds.image_shape = imgs.shape[1:]
     break