realtime_engine_v2.py

"""
realtime_engine_v2.py — Advanced Real-Time Engine with Movement-Level Control

Features:
  - Movement classification (Left/Straight/Right per lane)
  - Phase-based signal control (4 conflict-free phases)
  - User priority injection (boost specific routes)
  - Max-pressure phase decision algorithm
  - Real-time movement metrics
"""

import cv2
import numpy as np
import threading
import time
from collections import deque
import warnings

warnings.filterwarnings('ignore')

from movement_detector import HybridMovementDetector
from phase_controller import PhaseManager

# ─────────────────────────────────────────────────────────────────
# Video Stream Generator (from v1 - unchanged)
# ─────────────────────────────────────────────────────────────────

class VideoStreamGenerator:
    """Generator that loops a video infinitely"""
    
    def __init__(self, video_path: str, resize_width: int = 640, resize_height: int = 360):
        self.video_path = video_path
        self.resize_width = resize_width
        self.resize_height = resize_height
        self.frame_count = 0
        self.cap = None
        self._init_capture()
    
    def _init_capture(self):
        self.cap = cv2.VideoCapture(self.video_path)
        if not self.cap.isOpened():
            raise RuntimeError(f"Cannot open video: {self.video_path}")
    
    def __iter__(self):
        return self
    
    def __next__(self):
        while True:
            ret, frame = self.cap.read()
            
            if not ret:
                self.cap.set(cv2.CAP_PROP_POS_FRAMES, 0)
                continue
            
            frame = cv2.resize(frame, (self.resize_width, self.resize_height))
            self.frame_count += 1
            return frame, self.frame_count


# ─────────────────────────────────────────────────────────────────
# Fast YOLO Detector (from v1 - unchanged)
# ─────────────────────────────────────────────────────────────────

class FastYOLODetector:
    """
    YOLOv8 detector tuned for dense Indian traffic (high two-wheeler density).

    Key fixes vs original:
      - Motorcycle aspect ratio check now uses OR logic (wide OR large area OR tall-but-confident)
      - Bicycle accepts aspect ratios from 0.35 (covers head-on narrow bikes)
      - Height caps raised to 180px/160px (closer cameras show taller bounding boxes)
      - Added soft NMS via IoU deduplication to remove ghost detections
      - Class-specific confidence done AFTER YOLO inference at 0.05 (unchanged, correct)
    """

    def __init__(self, model_path: str = "yolov8n.pt", skip_frames: int = 2, conf: float = 0.5):
        self.model_path = model_path
        self.skip_frames = skip_frames
        self.conf = conf
        self.frame_counter = 0
        self.last_detections = None

        # YOLO COCO classes
        self.vehicle_classes      = [2, 5, 7]   # Car, Bus, Truck
        self.two_wheeler_classes  = [1, 3]       # Bicycle, Motorcycle
        self.person_class         = 0            # MUST EXCLUDE

        # Per-class confidence thresholds (applied after YOLO runs at 0.05)
        self.conf_car         = 0.25
        self.conf_two_wheeler = 0.12   # Very lenient — dense traffic, small boxes
        self.conf_bus_truck   = 0.30

        # Minimum bounding box areas (pixels²) — small values intentional
        self.min_area_motorcycle = 5
        self.min_area_bicycle    = 4
        self.min_area_car        = 12
        self.min_area_bus_truck  = 25

        # FIX #1: Raised height caps — original 120/100 was too strict for close cameras
        self.max_height_motorcycle = 180
        self.max_height_bicycle    = 160

        # IoU threshold for deduplication
        self.nms_iou_threshold = 0.45

        try:
            from ultralytics import YOLO
            self.model = YOLO(model_path, verbose=False)
        except Exception as e:
            raise RuntimeError(f"Cannot load YOLOv8: {e}")

    # ------------------------------------------------------------------
    def _box_stats(self, box):
        """Return (area, aspect_ratio w/h, height) from a YOLO box."""
        w, h = float(box.xywh[0][2]), float(box.xywh[0][3])
        area = w * h
        ar   = w / h if h > 0 else 1.0
        return area, ar, h

    # ------------------------------------------------------------------
    def _is_valid_motorcycle(self, ar: float, area: float, height: float, conf: float) -> bool:
        """
        FIX #1 — original used AND logic that was too strict.

        A motorcycle is valid if ANY of these are true:
          a) Wide box (ar >= 0.6) — side view, typical
          b) Large enough area (area >= 80) — even a tall narrow box is probably real
          c) High confidence (conf >= 0.35) regardless of shape — model is sure
          d) NOT a person: people are very tall and narrow (ar < 0.4) AND small area

        Person rejection: only reject if BOTH ar < 0.4 AND area < 60
        (people at a distance are small AND narrow; motorcycles are rarely both)
        """
        # Hard reject: looks like a distant person silhouette
        if ar < 0.4 and area < 60:
            return False

        # Hard reject: implausibly tall (taller than 1.8× the max camera height cap)
        if height > self.max_height_motorcycle * 1.8:
            return False

        # Minimum area
        if area < self.min_area_motorcycle:
            return False

        # Accept if wide OR large OR confident
        if ar >= 0.6 or area >= 80 or conf >= 0.35:
            return True

        # Edge case: small, narrow, low-conf — skip
        return False

    # ------------------------------------------------------------------
    def _is_valid_bicycle(self, ar: float, area: float, height: float, conf: float) -> bool:
        """
        FIX #2 — original lower bound of 0.5 cut head-on bicycles (ar ~0.35).

        Accept if:
          - ar >= 0.35 (head-on narrow is OK)
          - area >= min_area_bicycle
          - NOT person silhouette: ar < 0.35 AND area < 50 AND conf < 0.3
        """
        if ar < 0.35 and area < 50 and conf < 0.3:
            return False  # Looks like a very narrow distant person

        if height > self.max_height_bicycle * 1.8:
            return False

        if area < self.min_area_bicycle:
            return False

        return 0.35 <= ar <= 2.5

    # ------------------------------------------------------------------
    def _should_keep(self, cls_id: int, conf: float, area: float,
                     ar: float, height: float) -> bool:
        """Master gate: exclude people, apply per-class rules."""
        if cls_id == self.person_class:
            return False
        if cls_id not in self.vehicle_classes and cls_id not in self.two_wheeler_classes:
            return False

        if cls_id == 2:          # Car
            return conf >= self.conf_car and area >= self.min_area_car

        if cls_id in [5, 7]:     # Bus, Truck
            return conf >= self.conf_bus_truck and area >= self.min_area_bus_truck

        if cls_id == 3:          # Motorcycle
            return conf >= self.conf_two_wheeler and self._is_valid_motorcycle(ar, area, height, conf)

        if cls_id == 1:          # Bicycle
            return conf >= self.conf_two_wheeler and self._is_valid_bicycle(ar, area, height, conf)

        return False

    # ------------------------------------------------------------------
    def _nms_deduplicate(self, detections: list) -> list:
        """
        FIX #3 — Remove overlapping detections of the same class.
        Simple IoU-based NMS (already sorted by confidence descending).
        """
        if not detections:
            return detections

        kept = []
        used = [False] * len(detections)

        for i, d in enumerate(detections):
            if used[i]:
                continue
            kept.append(d)
            x1i, y1i = d['x'] - d['w'] / 2, d['y'] - d['h'] / 2
            x2i, y2i = d['x'] + d['w'] / 2, d['y'] + d['h'] / 2

            for j in range(i + 1, len(detections)):
                if used[j]:
                    continue
                e = detections[j]
                # Only suppress same class
                if e['class'] != d['class']:
                    continue
                x1j, y1j = e['x'] - e['w'] / 2, e['y'] - e['h'] / 2
                x2j, y2j = e['x'] + e['w'] / 2, e['y'] + e['h'] / 2

                ix1, iy1 = max(x1i, x1j), max(y1i, y1j)
                ix2, iy2 = min(x2i, x2j), min(y2i, y2j)
                inter = max(0, ix2 - ix1) * max(0, iy2 - iy1)
                union = (x2i - x1i) * (y2i - y1i) + (x2j - x1j) * (y2j - y1j) - inter
                if union > 0 and inter / union > self.nms_iou_threshold:
                    used[j] = True

        return kept

    # ------------------------------------------------------------------
    def detect(self, frame: np.ndarray, lane_region: tuple = None) -> list:
        """
        Detect vehicles. Returns list of dicts with keys:
          x, y, w, h, conf, class, size, is_two_wheeler, aspect_ratio, height
        """
        self.frame_counter += 1
        if self.frame_counter % self.skip_frames != 0:
            return self.last_detections if self.last_detections is not None else []

        try:
            results = self.model(frame, verbose=False, conf=0.05)

            detections = []
            for result in results:
                for box in result.boxes:
                    cls_id = int(box.cls)
                    conf   = float(box.conf)
                    area, ar, height = self._box_stats(box)

                    if not self._should_keep(cls_id, conf, area, ar, height):
                        continue

                    x, y = int(box.xywh[0][0]), int(box.xywh[0][1])
                    w, h = float(box.xywh[0][2]), float(box.xywh[0][3])

                    if lane_region:
                        x1r, y1r, x2r, y2r = lane_region
                        if not (x1r <= x <= x2r and y1r <= y <= y2r):
                            continue

                    detections.append({
                        'x': x, 'y': y, 'w': w, 'h': h,
                        'conf': min(conf, 1.0),
                        'class': cls_id,
                        'size': area,
                        'is_two_wheeler': cls_id in self.two_wheeler_classes,
                        'aspect_ratio': ar,
                        'height': height,
                    })

            # Sort by confidence descending, then deduplicate
            detections.sort(key=lambda d: d['conf'], reverse=True)
            detections = self._nms_deduplicate(detections)

            # Cap at 120 (raised from 100 to handle dense scenes)
            self.last_detections = detections[:120]
            return self.last_detections

        except Exception:
            return self.last_detections if self.last_detections is not None else []


# ─────────────────────────────────────────────────────────────────
# Movement Queue Tracker (UPGRADED)
# ─────────────────────────────────────────────────────────────────

class MovementQueueTracker:
    """Track queues per movement (left/straight/right)"""
    
    def __init__(self, window_size: int = 10):
        self.window_size = window_size
        self.history = {
            'N': {'left': deque(maxlen=window_size), 'straight': deque(maxlen=window_size), 'right': deque(maxlen=window_size)},
            'S': {'left': deque(maxlen=window_size), 'straight': deque(maxlen=window_size), 'right': deque(maxlen=window_size)},
            'E': {'left': deque(maxlen=window_size), 'straight': deque(maxlen=window_size), 'right': deque(maxlen=window_size)},
            'W': {'left': deque(maxlen=window_size), 'straight': deque(maxlen=window_size), 'right': deque(maxlen=window_size)},
        }
    
    def update(self, raw_counts: dict) -> dict:
        """
        Update with new counts
        
        Args:
            raw_counts: {
                'N': {'left': int, 'straight': int, 'right': int},
                ...
            }
        
        Returns:
            Smoothed counts (rolling average)
        """
        
        for lane in ['N', 'S', 'E', 'W']:
            for movement in ['left', 'straight', 'right']:
                count = raw_counts.get(lane, {}).get(movement, 0)
                self.history[lane][movement].append(count)
        
        # Return rolling average
        smoothed = {}
        for lane in ['N', 'S', 'E', 'W']:
            smoothed[lane] = {}
            for movement in ['left', 'straight', 'right']:
                if len(self.history[lane][movement]) > 0:
                    avg = np.mean(list(self.history[lane][movement]))
                    smoothed[lane][movement] = int(avg)
                else:
                    smoothed[lane][movement] = 0
        
        return smoothed


# ─────────────────────────────────────────────────────────────────
# Parallel Stream Reader (from v1 - unchanged)
# ─────────────────────────────────────────────────────────────────

class ParallelStreamReader:
    """Read frames from 4 video streams in parallel"""
    
    def __init__(self, video_paths: dict):
        self.streams = {
            lane: VideoStreamGenerator(path)
            for lane, path in video_paths.items()
        }
        self.frames = {'N': None, 'S': None, 'E': None, 'W': None}
        self.frame_numbers = {'N': 0, 'S': 0, 'E': 0, 'W': 0}
        self.running = False
        self.threads = {}
    
    def start(self):
        self.running = True
        for lane in self.streams:
            thread = threading.Thread(target=self._read_stream, args=(lane,), daemon=True)
            thread.start()
            self.threads[lane] = thread
    
    def _read_stream(self, lane: str):
        for frame, frame_num in self.streams[lane]:
            if not self.running:
                break
            self.frames[lane] = frame
            self.frame_numbers[lane] = frame_num
    
    def get_frames(self) -> dict:
        return self.frames.copy()
    
    def stop(self):
        self.running = False
        for thread in self.threads.values():
            thread.join(timeout=1)


# ─────────────────────────────────────────────────────────────────
# User Priority Manager (NEW)
# ─────────────────────────────────────────────────────────────────

class UserPriorityManager:
    """
    Boost queues for user-selected route
    Makes system optimize for user's path
    """
    
    def __init__(self, priority_boost: float = 3.0):
        self.priority_boost = priority_boost
        self.user_from = None
        self.user_direction = None
    
    def set_user_route(self, from_lane: str, direction: str):
        """
        Set user's desired route
        
        Args:
            from_lane: 'N', 'S', 'E', or 'W'
            direction: 'left', 'straight', or 'right'
        """
        self.user_from = from_lane
        self.user_direction = direction
    
    def apply_priority(self, counts: dict) -> dict:
        """
        Boost queue count for user's route
        
        Args:
            counts: Movement queue counts
        
        Returns:
            Modified counts with user boost applied
        """
        
        if self.user_from is None or self.user_direction is None:
            return counts  # No user preference
        
        # Deep copy
        boosted = {
            lane: {
                movement: count
                for movement, count in counts[lane].items()
            }
            for lane in counts
        }
        
        # Apply boost
        boosted[self.user_from][self.user_direction] += self.priority_boost
        
        return boosted


# ─────────────────────────────────────────────────────────────────
# Real-Time Decision Engine V2
# ─────────────────────────────────────────────────────────────────

class RealtimeDecisionEngineV2:
    """
    Advanced engine with movement-level control
    """
    
    def __init__(self, video_paths: dict, skip_frames: int = 2):
        """
        Args:
            video_paths: {'N': path, 'S': path, 'E': path, 'W': path}
            skip_frames: Frame skip rate
        """
        
        self.stream_reader = ParallelStreamReader(video_paths)
        self.detector = FastYOLODetector(skip_frames=skip_frames)
        self.movement_detector = HybridMovementDetector(use_optical_flow=False)
        self.queue_tracker = MovementQueueTracker(window_size=10)
        self.phase_manager = PhaseManager()
        self.user_priority = UserPriorityManager()
        
        # Lane regions
        self.lane_regions = {
            'N': (0, 0, 640, 180),
            'S': (0, 180, 640, 360),
            'E': None,
            'W': None,
        }
        
        # Metrics
        self.metrics = {
            'frame_count': 0,
            'detection_time': 0,
            'movement_time': 0,
            'decision_time': 0,
            'current_phase': 'NS_STRAIGHT',
            'movement_counts': {lane: {'left': 0, 'straight': 0, 'right': 0} for lane in ['N', 'S', 'E', 'W']},
            'signal_state': {},
        }
    
    def process_frame(self) -> dict:
        """
        Process single frame with movement-level control
        """
        
        start_time = time.time()
        
        # 1. Get frames
        raw_frames = self.stream_reader.get_frames()
        
        # 2. Detect vehicles
        detection_start = time.time()
        detections = {}
        for lane, frame in raw_frames.items():
            if frame is not None:
                region = self.lane_regions.get(lane)
                detections[lane] = self.detector.detect(frame, region)
            else:
                detections[lane] = []
        
        self.metrics['detection_time'] = time.time() - detection_start
        
        # 3. Classify movements
        movement_start = time.time()
        raw_movements = {}
        for lane, frame in raw_frames.items():
            if frame is not None:
                raw_movements[lane] = self.movement_detector.get_movements(
                    frame, detections[lane], lane
                )
            else:
                raw_movements[lane] = {'left': 0, 'straight': 0, 'right': 0}
        
        self.metrics['movement_time'] = time.time() - movement_start
        
        # 4. Smooth movements
        smoothed_movements = self.queue_tracker.update(raw_movements)
        
        # 5. Apply user priority (if set)
        prioritized_movements = self.user_priority.apply_priority(smoothed_movements)
        self.metrics['movement_counts'] = smoothed_movements.copy()
        
        # 6. Make phase decision
        decision_start = time.time()
        phase = self.phase_manager.decide_phase(prioritized_movements)
        signal_state = self.phase_manager.get_signal_state()
        
        self.metrics['decision_time'] = time.time() - decision_start
        self.metrics['current_phase'] = phase.value
        self.metrics['signal_state'] = signal_state
        
        self.metrics['frame_count'] += 1
        
        return {
            'phase': phase.value,
            'movements': smoothed_movements,
            'signal_state': signal_state,
            'raw_movements': raw_movements,
            'metrics': self.metrics.copy(),
            'total_time_ms': (time.time() - start_time) * 1000,
        }
    
    def set_user_route(self, from_lane: str, direction: str):
        """Set user's desired route for priority"""
        self.user_priority.set_user_route(from_lane, direction)
    
    def start(self):
        """Start engine"""
        self.stream_reader.start()
    
    def stop(self):
        """Stop engine"""
        self.stream_reader.stop()
    
    def get_metrics(self) -> dict:
        """Get current metrics"""
        return self.metrics.copy()


# ─────────────────────────────────────────────────────────────────
# Test/Standalone Usage
# ─────────────────────────────────────────────────────────────────

if __name__ == "__main__":
    
    print("🚦 Real-Time Decision Engine V2 (Movement-Level)")
    print("=" * 60)
    
    video_paths = {
        'N': 'test_video_N.mp4',
        'S': 'test_video_S.mp4',
        'E': 'test_video_E.mp4',
        'W': 'test_video_W.mp4',
    }
    
    # Initialize engine
    engine = RealtimeDecisionEngineV2(video_paths, skip_frames=2)
    engine.start()
    
    # Set user preference (optional)
    # engine.set_user_route(from_lane='N', direction='straight')
    
    print("✓ Engine started. Processing frames...")
    print("=" * 60)
    
    # Run for 10 seconds
    start = time.time()
    frame_count = 0
    
    while time.time() - start < 10:
        result = engine.process_frame()
        
        frame_count += 1
        
        if frame_count % 10 == 0:
            print(f"\nFrame {result['metrics']['frame_count']}")
            movements = result['movements']
            print(f"  Movements:")
            for lane in ['N', 'S', 'E', 'W']:
                print(f"    {lane}: L={movements[lane]['left']} S={movements[lane]['straight']} R={movements[lane]['right']}")
            print(f"  Phase: {result['phase']}")
            print(f"  Time: {result['total_time_ms']:.1f}ms")
        
        time.sleep(0.05)
    
    engine.stop()
    print("\n✓ Engine stopped")