Create services/shadow_detection.py

services/shadow_detection.py

@@ -1,5 +1,45 @@
-import random
+# services/shadow_detection.py
+import cv2
+import numpy as np
 
-def detect_shadow_coverage(image_path):
-    shadow_percent = random.randint(25, 40)
-    return shadow_percent > 30
+def detect_shadows(frame):
+    """
+    Detect shadows in a frame using basic image processing.
+    Args:
+        frame: Input frame (numpy array)
+    Returns:
+        dict: Shadow detection results with numbered labels
+        numpy array: Annotated frame
+    """
+    # Convert to grayscale
+    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+    # Apply thresholding to detect dark areas (potential 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 < 100:  # 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({
+            "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
+
+    return {"detections": detections, "frame": frame}