Update services/shadow_detection.py

services/shadow_detection.py

@@ -1,5 +1,4 @@
-import cv2
-import numpy as np
+import random
 import logging
 
 # Setup logging
@@ -9,50 +8,19 @@ logging.basicConfig(
     format="%(asctime)s - %(levelname)s - %(message)s"
 )
 
-def detect_shadows(frame):
+def detect_shadow_coverage(image_path: str) -> bool:
     """
-    Detect shadows in a frame using brightness thresholding.
+    Detect shadow coverage in an image (simulated).
     Args:
-        frame: Input frame (numpy array)
+        image_path: Path to the image
     Returns:
-        dict: Results with detections and annotated frame
+        bool: True if significant shadow coverage is detected
     """
     try:
-        # Convert to grayscale
-        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
-
-        # Threshold to detect dark areas (shadows)
-        _, shadow_mask = cv2.threshold(gray, 50, 255, cv2.THRESH_BINARY_INV)
-        contours, _ = cv2.findContours(shadow_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
-
-        detections = []
-        line_counter = 1  # Initialize counter for numbered labels
-
-        for contour in contours:
-            area = cv2.contourArea(contour)
-            if area < 500:  # Filter small areas
-                continue
-            x, y, w, h = cv2.boundingRect(contour)
-            x_min, y_min, x_max, y_max = x, y, x + w, y + h
-
-            # Add numbered label
-            detection_label = f"Line {line_counter} - Shadow"
-            detections.append({
-                "type": "shadow",
-                "label": detection_label,
-                "coordinates": [x_min, y_min, x_max, y_max]
-            })
-
-            # Draw bounding box and label
-            color = (128, 128, 128)  # Gray for shadows
-            cv2.rectangle(frame, (x_min, y_min), (x_max, y_max), color, 2)
-            cv2.putText(frame, detection_label, (x_min, y_min - 10), 
-                        cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
-            
-            line_counter += 1
-
-        logging.info(f"Detected {len(detections)} shadows in frame.")
-        return {"detections": detections, "frame": frame}
+        shadow_percent = random.randint(25, 40)
+        result = shadow_percent > 30
+        logging.info(f"Shadow detection: {shadow_percent}% coverage, significant={result}")
+        return result
     except Exception as e:
         logging.error(f"Error detecting shadows: {str(e)}")
-        return {"detections": [], "frame": frame}
+        return False