Refactor: Integrate backend API and normalize data

This commit integrates the backend API for fetching and updating report data. It also includes a normalization function to handle data consistency between the API and local storage.

Co-authored-by: anthonymuncher <anthonymuncher@gmail.com>

backend/test/Machine_Learning/tets_sevarity.py

@@ -0,0 +1,41 @@
+import cv2
+from ultralytics import YOLO
+
+# Load your trained YOLOv12 model
+model = YOLO("checkpoints/pothole_detector/weights/best.pt")  # Path to your trained weights
+
+# Define severity thresholds (you can adjust these based on your dataset)
+def classify_severity(box, image_height):
+    x1, y1, x2, y2 = box
+    area = (x2 - x1) * (y2 - y1)
+    if area > 50000 or y2 > image_height * 0.75:
+        return "High"
+    elif area > 20000 or y2 > image_height * 0.5:
+        return "Medium"
+    else:
+        return "Low"
+
+# Draw bounding boxes with severity
+def draw_boxes_and_severity(image, results):
+    for r in results:  # iterate over Results objects
+        for box in r.boxes.xyxy:  # xyxy format
+            x1, y1, x2, y2 = map(int, box.cpu().numpy())
+            conf = float(r.boxes.conf[0]) if hasattr(r.boxes, "conf") else 0.0
+            severity = classify_severity((x1, y1, x2, y2), image.shape[0])
+            color = (0, 255, 0) if severity == "Low" else (0, 255, 255) if severity == "Medium" else (0, 0, 255)
+            cv2.rectangle(image, (x1, y1), (x2, y2), color, 2)
+            cv2.putText(image, f"{severity} ({conf:.2f})", (x1, y1 - 10),
+                        cv2.FONT_HERSHEY_SIMPLEX, 0.6, color, 2)
+    return image
+
+# Detect potholes in an image
+def detect_potholes(image_path, output_path="output.jpg"):
+    image = cv2.imread(image_path)
+    results = model(image)  # Run inference
+    image = draw_boxes_and_severity(image, results)
+    cv2.imwrite(output_path, image)
+    print(f"Output saved to {output_path}")
+
+# Example usage
+if __name__ == "__main__":
+    detect_potholes(r"images\pothole_1.jpg")