Update services/under_construction/culvert_check.py

services/under_construction/culvert_check.py

@@ -1,23 +1,37 @@
-import numpy as np
 import cv2
+import numpy as np
+from typing import List, Tuple, Dict, Any
 
-def process_culverts(frame: np.ndarray) -> tuple[list[dict[str, any]], np.ndarray]:
+def process_culverts(frame: np.ndarray) -> Tuple[List[Dict[str, Any]], np.ndarray]:
     """
-    Placeholder function to detect culverts in the frame.
+    Detect culverts in the frame.
     Args:
-        frame: Input frame as a NumPy array (BGR format).
+        frame: Input frame as a numpy array.
     Returns:
-        Tuple of (detections, processed_frame).
-        Detections are a list of dictionaries with bounding boxes and labels.
+        Tuple of (list of detections, annotated frame).
     """
-    # Placeholder: Simulate culvert detection with a single bounding box
-    height, width = frame.shape[:2]
-    detections = [
-        {
-            "box": [int(width * 0.3), int(height * 0.3), int(width * 0.5), int(height * 0.5)],  # [x_min, y_min, x_max, y_max]
-            "type": "culvert",
-            "confidence": 0.9
-        }
-    ]
+    # Convert to grayscale
+    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+    
+    # Apply edge detection
+    edges = cv2.Canny(gray, 50, 150)
+    
+    # Find contours
+    contours, _ = cv2.findContours(edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+    
+    detections = []
+    for i, contour in enumerate(contours):
+        area = cv2.contourArea(contour)
+        if area < 300:  # Ignore small contours
+            continue
+        
+        x, y, w, h = cv2.boundingRect(contour)
+        x_min, y_min, x_max, y_max = x, y, x + w, y + h
+        
+        detections.append({
+            "box": [x_min, y_min, x_max, y_max],
+            "label": f"Culvert {i+1}",
+            "type": "culvert"
+        })
     
     return detections, frame