Train and Deploy RCNN and CNN Model

Backend/detection.py

@@ -1,138 +1,32 @@
-import time
-import audio
-import head_pose
-import matplotlib.pyplot as plt
+from dataclasses import dataclass
+from typing import List, Dict, Optional
 import numpy as np
-import logging
-
-# Configure logging
-logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
-
-PLOT_LENGTH = 200
-
-# Placeholders 
-GLOBAL_CHEAT = 0
-PERCENTAGE_CHEAT = 0
-CHEAT_THRESH = 0.6
-XDATA = list(range(200))
-YDATA = [0] * 200
-
-# Global flag to check if window is open
-is_running = True
-
-def avg(current, previous):
-    if previous > 1:
-        return 0.65
-    if current == 0:
-        if previous < 0.01:
-            return 0.01
-        return previous / 1.01
-    if previous == 0:
-        return current
-    return 1 * previous + 0.1 * current
-
-def process():
-    global GLOBAL_CHEAT, PERCENTAGE_CHEAT, CHEAT_THRESH
-
-    try:
-        if GLOBAL_CHEAT == 0:
-            if head_pose.X_AXIS_CHEAT == 0:
-                if head_pose.Y_AXIS_CHEAT == 0:
-                    if audio.AUDIO_CHEAT == 0:
-                        PERCENTAGE_CHEAT = avg(0, PERCENTAGE_CHEAT)
-                    else:
-                        PERCENTAGE_CHEAT = avg(0.2, PERCENTAGE_CHEAT)
-                else:
-                    if audio.AUDIO_CHEAT == 0:
-                        PERCENTAGE_CHEAT = avg(0.2, PERCENTAGE_CHEAT)
-                    else:
-                        PERCENTAGE_CHEAT = avg(0.4, PERCENTAGE_CHEAT)
-            else:
-                if head_pose.Y_AXIS_CHEAT == 0:
-                    if audio.AUDIO_CHEAT == 0:
-                        PERCENTAGE_CHEAT = avg(0.1, PERCENTAGE_CHEAT)
-                    else:
-                        PERCENTAGE_CHEAT = avg(0.4, PERCENTAGE_CHEAT)
-                else:
-                    if audio.AUDIO_CHEAT == 0:
-                        PERCENTAGE_CHEAT = avg(0.15, PERCENTAGE_CHEAT)
-                    else:
-                        PERCENTAGE_CHEAT = avg(0.25, PERCENTAGE_CHEAT)
-        else:
-            if head_pose.X_AXIS_CHEAT == 0:
-                if head_pose.Y_AXIS_CHEAT == 0:
-                    if audio.AUDIO_CHEAT == 0:
-                        PERCENTAGE_CHEAT = avg(0, PERCENTAGE_CHEAT)
-                    else:
-                        PERCENTAGE_CHEAT = avg(0.55, PERCENTAGE_CHEAT)
-                else:
-                    if audio.AUDIO_CHEAT == 0:
-                        PERCENTAGE_CHEAT = avg(0.55, PERCENTAGE_CHEAT)
-                    else:
-                        PERCENTAGE_CHEAT = avg(0.85, PERCENTAGE_CHEAT)
-            else:
-                if head_pose.Y_AXIS_CHEAT == 0:
-                    if audio.AUDIO_CHEAT == 0:
-                        PERCENTAGE_CHEAT = avg(0.6, PERCENTAGE_CHEAT)
-                    else:
-                        PERCENTAGE_CHEAT = avg(0.85, PERCENTAGE_CHEAT)
-                else:
-                    if audio.AUDIO_CHEAT == 0:
-                        PERCENTAGE_CHEAT = avg(0.5, PERCENTAGE_CHEAT)
-                    else:
-                        PERCENTAGE_CHEAT = avg(0.85, PERCENTAGE_CHEAT)
-
-        if PERCENTAGE_CHEAT > CHEAT_THRESH:
-            GLOBAL_CHEAT = 1
-            print("CHEATING")
-        else:
-            GLOBAL_CHEAT = 0
-        print("Cheat percent: ", PERCENTAGE_CHEAT, GLOBAL_CHEAT)
+from datetime import datetime
+
+@dataclass
+class Detection:
+    """Class for storing detection results"""
+    frame: Optional[np.ndarray] = None
+    faces: List[Dict] = None
+    timestamp: datetime = None
+    is_cheating: bool = False
+    confidence: float = 0.0
+    detected_processes: List[Dict] = None
 
-    except Exception as e:
-        logging.error(f"Error in process: {e}")
-        print("An error occurred during processing. Please check the logs.")
-
-def on_close(event):
-    global is_running
-    is_running = False
-    # Set flag to False when the window is closed
-
-def run_detection():
-    global XDATA, YDATA, is_running
-
-    try:
-        fig, axes = plt.subplots()
-
-        axes.set_xlim(0, 200)
-        axes.set_ylim(0, 1)
-        line, = axes.plot(XDATA, YDATA, 'r-')
-        plt.title("Suspicious Behaviour Detection")
-        plt.xlabel("Time")
-        plt.ylabel("Cheat Probability")
-
-        # Connect the close event to the callback
-        fig.canvas.mpl_connect('close_event', on_close)
-
-        while is_running:
-            YDATA.pop(0)
-            YDATA.append(PERCENTAGE_CHEAT)
-            line.set_xdata(XDATA)
-            line.set_ydata(YDATA)
-            plt.draw()
-            plt.pause(1e-17)
-            process()
-            time.sleep(1 / 5)
-
-        plt.close(fig)
-
-    except Exception as e:
-        logging.error(f"Error in run_detection: {e}")
-        print("An error occurred while running the detection. Please check the logs.")
-
-if __name__ == "__main__":
-    try:
-        run_detection()
-    except KeyboardInterrupt:
-        logging.info("Detection interrupted by user.")
-        print("Terminated detection.")
+    def __post_init__(self):
+        if self.faces is None:
+            self.faces = []
+        if self.detected_processes is None:
+            self.detected_processes = []
+
+    def add_face(self, face: Dict):
+        """Add a face detection result"""
+        self.faces.append(face)
+
+    def get_face_count(self) -> int:
+        """Return number of faces detected"""
+        return len(self.faces)
+
+    def get_process_count(self) -> int:
+        """Return number of detected unauthorized processes"""
+        return len(self.detected_processes)