Create image_preprocess.py

image_preprocess.py

@@ -0,0 +1,39 @@
+import cv2
+import numpy as np
+import logging
+import psutil
+
+def log_memory(stage):
+    memory = psutil.virtual_memory()
+    logging.info(f"{stage} - Memory usage: {memory.percent}%")
+
+def preprocess(image_path):
+    log_memory("Before loading image")
+    try:
+        # Define the size to which the image will be resized
+        image_size = 224  # Reduce to 128x128 for memory efficiency
+
+        # Load the image using OpenCV
+        image = cv2.imread(image_path)
+
+        # Handle grayscale images
+        if len(image.shape) == 2:  # If grayscale
+            image = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB)
+
+        # Convert the image from BGR to RGB format
+        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+
+        # Resize the image
+        image = cv2.resize(image, (image_size, image_size))
+
+        # Expand dimensions to match model input
+        X = np.expand_dims(image, axis=0)
+
+        # Normalize the pixel values to 0-1
+        X = X / 255.0
+
+        log_memory("After preprocessing")
+        return X
+    except Exception as e:
+        logging.error(f"Error in preprocessing: {e}")
+        raise