Create helpers.py

helpers.py

@@ -0,0 +1,65 @@
+import logging
+import numpy as np
+from PIL import Image
+import cv2  # Ensure OpenCV is installed
+
+# Set up logging configuration
+logging.basicConfig(level=logging.INFO, filename='image_processing.log', filemode='a')
+
+def load_image(image_path):
+    """Load an image from the specified path."""
+    try:
+        image = Image.open(image_path)
+        logging.info(f"Image loaded successfully from {image_path}.")
+        return image
+    except Exception as e:
+        logging.error(f"Failed to load image from {image_path}: {e}")
+        return None
+
+def preprocess_image(image, target_size=(640, 640)):
+    """Preprocess the image for YOLO model."""
+    try:
+        # Resize and convert to RGB
+        image = image.resize(target_size)
+        image = image.convert("RGB")
+        logging.info(f"Image preprocessed to size {target_size}.")
+        return np.array(image) / 255.0  # Normalize to [0, 1]
+    except Exception as e:
+        logging.error(f"Error in preprocessing image: {e}")
+        return None
+
+def draw_bounding_boxes(image, detections):
+    """Draw bounding boxes on the image based on YOLO detections."""
+    try:
+        image = np.array(image)
+        for detection in detections:
+            x1, y1, x2, y2 = map(int, detection['coordinates'])  # Ensure coordinates are integers
+            label = detection.get('name', 'Unknown')
+            color = (255, 0, 0)  # Red color for bounding boxes
+
+            # Draw rectangle
+            cv2.rectangle(image, (x1, y1), (x2, y2), color, 2)
+
+            # Draw label
+            cv2.putText(image, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
+
+        logging.info("Bounding boxes drawn on the image.")
+        return Image.fromarray(image)
+    except Exception as e:
+        logging.error(f"Error in drawing bounding boxes: {e}")
+        return image
+
+def format_detection_output(detections):
+    """Format the YOLO detection output for reporting."""
+    formatted_detections = []
+    for detection in detections:
+        formatted_detection = {
+            "name": detection.get("name", "Unknown"),
+            "class": detection.get("class", "Unknown"),
+            "confidence": round(detection.get("confidence", 0.0), 2),
+            "coordinates": detection.get("coordinates", [])
+        }
+        formatted_detections.append(formatted_detection)
+
+    logging.info("Detection output formatted successfully.")
+    return formatted_detections