[Feature Added] > motion detector. This detects motion using Farneback Flow, which tracks motion using mathematical equations. If motion is violent and closer to 1.0 - It is AI, If motion is smooth and lower - it is Natural.

backend/app/ai/video/motion_detector.py

@@ -0,0 +1,39 @@
+import cv2
+import numpy as np
+import logging
+from typing import Optional
+
+logger = logging.getLogger(__name__)
+
+def compute_motion_anomaly(prev_frame: np.ndarray, curr_frame: np.ndarray) -> Optional[float]:
+    try:
+        if prev_frame is None or curr_frame is None:
+            return None
+
+        if prev_frame.shape != curr_frame.shape:
+            logger.warning("Motion Anomaly: Frame resolution mismatch. Cannot compute flow.")
+            return None
+        
+        prev_gray = cv2.cvtColor(prev_frame, cv2.COLOR_BGR2GRAY)
+        curr_gray = cv2.cvtColor(curr_frame, cv2.COLOR_BGR2GRAY)
+
+        scale_target = (320, 240)
+        prev_small = cv2.resize(prev_gray, scale_target, interpolation=cv2.INTER_AREA)
+        curr_small = cv2.resize(curr_gray, scale_target, interpolation=cv2.INTER_AREA)
+
+        flow = cv2.calcOpticalFlowFarneback(
+            prev_small, curr_small,
+            flow=None,
+            pyr_scale=0.5, levels=3, winsize=15, 
+            iterations=3, poly_n=5, poly_sigma=1.2, flags=0
+        )
+
+        magnitude, _ = cv2.cartToPolar(flow[..., 0], flow[..., 1])
+        motion_variance = np.std(magnitude)
+        normalized_score = min(motion_variance / 20.0, 1.0)
+
+        return float(normalized_score)
+    
+    except Exception as e:
+        logger.error(f"Optiocal Flow Crash: {str(e)}")
+        return 0.0