Create model.py

model.py

@@ -0,0 +1,54 @@
+# model.py
+import tensorflow as tf
+from ultralytics import YOLO
+import numpy as np
+from sklearn.preprocessing import StandardScaler
+
+class SuspiciousActivityModel:
+    def __init__(self, lstm_model_path, yolo_model_path):
+        # Load YOLO model
+        self.yolo_model = YOLO(yolo_model_path)
+        # Load LSTM model
+        self.lstm_model = tf.keras.models.load_model(lstm_model_path)
+        self.scaler = StandardScaler()
+
+    def extract_keypoints(self, frame):
+        """
+        Extracts normalized keypoints from a frame using YOLO pose model.
+        """
+        results = self.yolo_model(frame, verbose=False)
+        for r in results:
+            if r.keypoints is not None and len(r.keypoints) > 0:
+                keypoints = r.keypoints.xyn.tolist()[0]
+                flattened_keypoints = [kp for keypoint in keypoints for kp in keypoint[:2]]
+                return flattened_keypoints
+        return None
+
+    def process_frame(self, frame):
+        results = self.yolo_model(frame, verbose=False)
+
+        for box in results[0].boxes:
+            cls = int(box.cls[0])  # Class ID
+            confidence = float(box.conf[0])
+
+            if cls == 0 and confidence > 0.5:
+                x1, y1, x2, y2 = map(int, box.xyxy[0])
+
+                # Extract ROI for classification
+                roi = frame[y1:y2, x1:x2]
+                if roi.size > 0:
+                    keypoints = self.extract_keypoints(roi)
+                    if keypoints is not None and len(keypoints) > 0:
+                        # Standardize and reshape keypoints for LSTM input
+                        keypoints_scaled = self.scaler.fit_transform([keypoints])
+                        keypoints_reshaped = keypoints_scaled.reshape((1, 1, len(keypoints)))
+
+                        # Predict with LSTM model
+                        prediction = (self.lstm_model.predict(keypoints_reshaped) > 0.5).astype(int)[0][0]
+
+                        # Return label
+                        return 'Suspicious' if prediction == 1 else 'Normal'
+        return 'Normal'
+
+    def detect_activity(self, frame):
+        return self.process_frame(frame)