adas_ultra_advanced.py

#!/usr/bin/env python3
"""
ULTRA-ADVANCED ADAS PERCEPTION SYSTEM
Complete Implementation with All Advanced Features

Features Included:
- 3D Point Cloud Visualization
- 360° Panoramic View
- Time-Lapse & Slow Motion Recording
- Multi-View Synchronized Display
- Real-Time Performance Graphs
- Heatmap Timeline
- Object Trajectory Maps
- Scene Classification (Time/Road/Traffic/Weather)
- Pedestrian Pose Estimation
- Vehicle Type Classification
- License Plate Detection
- Optical Flow Visualization
- Motion Prediction
- Ensemble Detection
- Driving Behavior Analysis
- And much more...

Author: DeepMost AI Perception Team
Version: 3.0.0 ULTIMATE
"""

import wx
import wx.lib.newevent
import cv2
import numpy as np
import threading
import queue
import time
import json
import os
import logging
from dataclasses import dataclass, field
from typing import Optional, List, Dict, Tuple, Any
from collections import deque, OrderedDict
from enum import Enum, auto
from pathlib import Path
from datetime import datetime
import warnings

# Advanced imports
try:
    import matplotlib
    matplotlib.use('WXAgg')
    import matplotlib.pyplot as plt
    from matplotlib.backends.backend_wxagg import FigureCanvasWxAgg
    from matplotlib.figure import Figure
    MATPLOTLIB_AVAILABLE = True
except ImportError:
    MATPLOTLIB_AVAILABLE = False
    print("Warning: matplotlib not available - graphs disabled")

try:
    import mediapipe as mp
    MEDIAPIPE_AVAILABLE = True
except ImportError:
    MEDIAPIPE_AVAILABLE = False
    print("Warning: mediapipe not available - pose estimation disabled")

warnings.filterwarnings('ignore')

# Configure logging
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
    handlers=[
        logging.FileHandler('adas_ultra.log'),
        logging.StreamHandler()
    ]
)
logger = logging.getLogger('ADAS_Ultra')

# Custom events
FrameUpdateEvent, EVT_FRAME_UPDATE = wx.lib.newevent.NewEvent()
MetricsUpdateEvent, EVT_METRICS_UPDATE = wx.lib.newevent.NewEvent()
GraphUpdateEvent, EVT_GRAPH_UPDATE = wx.lib.newevent.NewEvent()

# ============================================================================
# ENUMS & DATA CLASSES
# ============================================================================

class TimeOfDay(Enum):
    NIGHT = auto()
    DAWN = auto()
    DAY = auto()
    DUSK = auto()

class RoadType(Enum):
    HIGHWAY = auto()
    URBAN = auto()
    RURAL = auto()
    PARKING = auto()
    UNKNOWN = auto()

class TrafficDensity(Enum):
    LOW = auto()
    MEDIUM = auto()
    HIGH = auto()

class RoadCondition(Enum):
    DRY = auto()
    WET = auto()
    ICY = auto()
    DAMAGED = auto()

class VehicleType(Enum):
    SEDAN = auto()
    SUV = auto()
    TRUCK = auto()
    BUS = auto()
    MOTORCYCLE = auto()
    BICYCLE = auto()
    UNKNOWN = auto()

class DrivingStyle(Enum):
    CAUTIOUS = auto()
    NORMAL = auto()
    AGGRESSIVE = auto()

@dataclass
class Detection3D:
    """Detection with 3D information"""
    bbox_2d: Tuple[int, int, int, int]
    confidence: float
    class_id: int
    class_name: str
    distance: float = 0.0
    depth: float = 0.0
    position_3d: Tuple[float, float, float] = (0, 0, 0)
    velocity_3d: Tuple[float, float, float] = (0, 0, 0)
    point_cloud: Optional[np.ndarray] = None
    vehicle_type: VehicleType = VehicleType.UNKNOWN
    has_license_plate: bool = False

@dataclass
class PoseKeypoints:
    """Pedestrian pose keypoints"""
    keypoints: List[Tuple[float, float, float]]  # x, y, confidence
    bbox: Tuple[int, int, int, int]
    confidence: float
    action: str = "standing"  # walking, running, standing

@dataclass
class SceneContext:
    """Complete scene understanding"""
    time_of_day: TimeOfDay = TimeOfDay.DAY
    road_type: RoadType = RoadType.UNKNOWN
    traffic_density: TrafficDensity = TrafficDensity.LOW
    road_condition: RoadCondition = RoadCondition.DRY
    visibility_score: float = 1.0
    complexity_score: float = 0.0
    weather: str = "clear"

@dataclass
class OpticalFlowData:
    """Optical flow information"""
    flow: np.ndarray
    magnitude: np.ndarray
    angle: np.ndarray
    dominant_motion: Tuple[float, float]

@dataclass
class TrajectoryPrediction:
    """Predicted future trajectory"""
    predicted_positions: List[Tuple[float, float]]
    confidence: float
    collision_probability: float

# ============================================================================
# 3D POINT CLOUD RECONSTRUCTION
# ============================================================================

class PointCloudReconstructor:
    """Reconstruct 3D point cloud from depth and camera"""

    def __init__(self, fx=800, fy=800, cx=640, cy=360):
        self.fx = fx  # Focal length x
        self.fy = fy  # Focal length y
        self.cx = cx  # Principal point x
        self.cy = cy  # Principal point y

    def reconstruct(self, rgb_frame: np.ndarray, depth_map: np.ndarray) -> np.ndarray:
        """Create 3D point cloud from RGB and depth"""
        height, width = depth_map.shape

        # Create meshgrid of pixel coordinates
        u, v = np.meshgrid(np.arange(width), np.arange(height))

        # Convert to 3D coordinates
        z = depth_map.astype(np.float32)
        x = (u - self.cx) * z / self.fx
        y = (v - self.cy) * z / self.fy

        # Stack coordinates
        points_3d = np.stack([x, y, z], axis=-1)

        # Get colors from RGB frame
        colors = cv2.cvtColor(rgb_frame, cv2.COLOR_BGR2RGB)

        # Flatten
        points = points_3d.reshape(-1, 3)
        colors = colors.reshape(-1, 3)

        # Filter invalid points
        valid = z.flatten() > 0
        points = points[valid]
        colors = colors[valid]

        # Combine points and colors
        point_cloud = np.hstack([points, colors])

        return point_cloud

    def visualize_point_cloud(self, point_cloud: np.ndarray, size=(800, 600)) -> np.ndarray:
        """Render point cloud to 2D image (simple projection)"""
        if point_cloud is None or len(point_cloud) == 0:
            return np.zeros((*size, 3), dtype=np.uint8)

        # Extract coordinates and colors
        points = point_cloud[:, :3]
        colors = point_cloud[:, 3:6].astype(np.uint8)

        # Simple orthographic projection (top-down view)
        img = np.zeros((*size, 3), dtype=np.uint8)

        # Scale and project
        x_proj = ((points[:, 0] + 10) * 20).astype(int)
        y_proj = ((20 - points[:, 2]) * 20).astype(int)

        # Draw points
        for i in range(len(points)):
            if 0 <= x_proj[i] < size[1] and 0 <= y_proj[i] < size[0]:
                cv2.circle(img, (x_proj[i], y_proj[i]), 1, colors[i].tolist(), -1)

        return img

# ============================================================================
# 360° PANORAMIC VIEW STITCHER
# ============================================================================

class PanoramaStitcher:
    """Stitch multiple camera views into panoramic view"""

    def __init__(self):
        self.stitcher = cv2.Stitcher_create(cv2.Stitcher_PANORAMA)
        self.cached_panorama = None

    def stitch(self, images: List[np.ndarray]) -> Optional[np.ndarray]:
        """Stitch multiple images into panorama"""
        if len(images) < 2:
            return images[0] if images else None

        try:
            status, panorama = self.stitcher.stitch(images)

            if status == cv2.Stitcher_OK:
                self.cached_panorama = panorama
                return panorama
            else:
                logger.warning(f"Panorama stitching failed: {status}")
                return self.cached_panorama
        except Exception as e:
            logger.error(f"Panorama stitching error: {e}")
            return self.cached_panorama

# ============================================================================
# TIME-LAPSE & SLOW MOTION RECORDER
# ============================================================================

class AdvancedRecorder:
    """Record with time-lapse and slow motion"""

    def __init__(self):
        self.writers = {}
        self.frame_buffers = {}
        self.recording_modes = {}

    def start_recording(self, output_path: str, frame_size: Tuple[int, int],
                       mode: str = "normal", fps: float = 30.0):
        """Start recording with specific mode"""
        fourcc = cv2.VideoWriter_fourcc(*'mp4v')

        if mode == "timelapse":
            fps = fps / 10  # 10x speed up
        elif mode == "slowmotion":
            fps = fps * 2  # 2x slow down

        writer = cv2.VideoWriter(output_path, fourcc, fps, frame_size)
        self.writers[mode] = writer
        self.frame_buffers[mode] = deque(maxlen=1000)
        self.recording_modes[mode] = {
            'frame_skip': 10 if mode == "timelapse" else 1,
            'frame_count': 0,
            'output_path': output_path
        }

        logger.info(f"Started {mode} recording: {output_path}")

    def write_frame(self, frame: np.ndarray, mode: str = "normal"):
        """Write frame with mode-specific handling"""
        if mode not in self.writers:
            return

        info = self.recording_modes[mode]
        info['frame_count'] += 1

        # Frame skipping for time-lapse
        if mode == "timelapse":
            if info['frame_count'] % info['frame_skip'] == 0:
                self.writers[mode].write(frame)
        # Duplicate frames for slow motion
        elif mode == "slowmotion":
            self.writers[mode].write(frame)
            self.writers[mode].write(frame)  # Write twice
        else:
            self.writers[mode].write(frame)

    def stop_recording(self, mode: str = "normal"):
        """Stop recording for specific mode"""
        if mode in self.writers:
            self.writers[mode].release()
            del self.writers[mode]
            logger.info(f"Stopped {mode} recording")

# ============================================================================
# SCENE CLASSIFIER
# ============================================================================

class SceneClassifier:
    """Classify scene characteristics"""

    def classify(self, frame: np.ndarray, detections: List) -> SceneContext:
        """Comprehensive scene classification"""
        context = SceneContext()

        # Time of day classification
        context.time_of_day = self._classify_time_of_day(frame)

        # Road type detection
        context.road_type = self._classify_road_type(frame, detections)

        # Traffic density
        context.traffic_density = self._classify_traffic_density(detections)

        # Road condition
        context.road_condition = self._classify_road_condition(frame)

        # Visibility score
        context.visibility_score = self._calculate_visibility(frame)

        # Scene complexity
        context.complexity_score = self._calculate_complexity(frame, detections)

        return context

    def _classify_time_of_day(self, frame: np.ndarray) -> TimeOfDay:
        """Classify time of day from brightness and color"""
        # Convert to HSV
        hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
        brightness = hsv[:, :, 2].mean()

        # Convert to LAB for better brightness detection
        lab = cv2.cvtColor(frame, cv2.COLOR_BGR2LAB)
        l_mean = lab[:, :, 0].mean()

        # Color temperature (warm = dawn/dusk, cool = day/night)
        b_mean = frame[:, :, 0].mean()
        r_mean = frame[:, :, 2].mean()
        color_temp = r_mean - b_mean

        if brightness < 60:
            return TimeOfDay.NIGHT
        elif brightness < 120 and color_temp > 10:
            return TimeOfDay.DAWN if color_temp > 20 else TimeOfDay.DUSK
        elif brightness > 120 and color_temp > 5:
            return TimeOfDay.DUSK
        else:
            return TimeOfDay.DAY

    def _classify_road_type(self, frame: np.ndarray, detections: List) -> RoadType:
        """Classify road type"""
        height, width = frame.shape[:2]

        # Count vehicles
        vehicle_count = len([d for d in detections if hasattr(d, 'class_name') and
                           d.class_name in ['car', 'truck', 'bus']])

        # Detect lane markings (simplified)
        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
        edges = cv2.Canny(gray, 50, 150)
        bottom_half = edges[height//2:, :]
        line_density = np.sum(bottom_half > 0) / (width * height / 2)

        # Classification logic
        if line_density > 0.05 and vehicle_count > 5:
            return RoadType.HIGHWAY
        elif vehicle_count > 2:
            return RoadType.URBAN
        elif line_density < 0.02:
            return RoadType.PARKING
        elif vehicle_count < 2:
            return RoadType.RURAL
        else:
            return RoadType.UNKNOWN

    def _classify_traffic_density(self, detections: List) -> TrafficDensity:
        """Classify traffic density"""
        vehicle_count = len([d for d in detections if hasattr(d, 'class_name') and
                           d.class_name in ['car', 'truck', 'bus', 'motorcycle']])

        if vehicle_count < 3:
            return TrafficDensity.LOW
        elif vehicle_count < 8:
            return TrafficDensity.MEDIUM
        else:
            return TrafficDensity.HIGH

    def _classify_road_condition(self, frame: np.ndarray) -> RoadCondition:
        """Detect road surface condition"""
        # Convert to HSV
        hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)

        # Focus on bottom part (road)
        height = frame.shape[0]
        road_region = hsv[int(height*0.7):, :]

        # Calculate saturation and brightness
        saturation = road_region[:, :, 1].mean()
        brightness = road_region[:, :, 2].mean()

        # Detect reflections (wet road)
        gray_road = cv2.cvtColor(frame[int(height*0.7):, :], cv2.COLOR_BGR2GRAY)
        _, bright_spots = cv2.threshold(gray_road, 200, 255, cv2.THRESH_BINARY)
        reflection_ratio = np.sum(bright_spots > 0) / bright_spots.size

        if reflection_ratio > 0.1 and brightness > 100:
            return RoadCondition.WET
        elif saturation < 20 and brightness < 80:
            return RoadCondition.ICY
        else:
            return RoadCondition.DRY

    def _calculate_visibility(self, frame: np.ndarray) -> float:
        """Calculate visibility score (0-1)"""
        # Convert to LAB
        lab = cv2.cvtColor(frame, cv2.COLOR_BGR2LAB)

        # Calculate contrast
        l_channel = lab[:, :, 0]
        contrast = l_channel.std()

        # Calculate sharpness (edge density)
        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
        edges = cv2.Canny(gray, 50, 150)
        edge_density = np.sum(edges > 0) / edges.size

        # Combine metrics
        visibility = min(1.0, (contrast / 50.0) * 0.7 + (edge_density * 100) * 0.3)

        return visibility

    def _calculate_complexity(self, frame: np.ndarray, detections: List) -> float:
        """Calculate scene complexity (0-1)"""
        # Number of objects
        object_count = len(detections)

        # Edge density
        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
        edges = cv2.Canny(gray, 50, 150)
        edge_density = np.sum(edges > 0) / edges.size

        # Color variety
        hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
        hue_std = hsv[:, :, 0].std()

        # Combine
        complexity = min(1.0, (object_count / 20.0) * 0.5 +
                        (edge_density * 50) * 0.3 +
                        (hue_std / 50.0) * 0.2)

        return complexity

# ============================================================================
# PEDESTRIAN POSE ESTIMATOR
# ============================================================================

class PedestrianPoseEstimator:
    """Estimate pedestrian poses using MediaPipe"""

    def __init__(self):
        self.enabled = MEDIAPIPE_AVAILABLE
        if self.enabled:
            self.mp_pose = mp.solutions.pose
            self.pose = self.mp_pose.Pose(
                static_image_mode=False,
                model_complexity=1,
                min_detection_confidence=0.5
            )

    def estimate(self, frame: np.ndarray, person_bbox: Tuple) -> Optional[PoseKeypoints]:
        """Estimate pose for person in bounding box"""
        if not self.enabled:
            return None

        try:
            x1, y1, x2, y2 = person_bbox
            person_img = frame[y1:y2, x1:x2]

            if person_img.size == 0:
                return None

            # Process with MediaPipe
            rgb = cv2.cvtColor(person_img, cv2.COLOR_BGR2RGB)
            results = self.pose.process(rgb)

            if not results.pose_landmarks:
                return None

            # Extract keypoints
            keypoints = []
            for landmark in results.pose_landmarks.landmark:
                # Convert to original image coordinates
                x = int(landmark.x * (x2 - x1) + x1)
                y = int(landmark.y * (y2 - y1) + y1)
                keypoints.append((x, y, landmark.visibility))

            # Determine action
            action = self._classify_action(keypoints)

            return PoseKeypoints(
                keypoints=keypoints,
                bbox=person_bbox,
                confidence=0.8,
                action=action
            )
        except Exception as e:
            logger.error(f"Pose estimation error: {e}")
            return None

    def _classify_action(self, keypoints: List) -> str:
        """Classify pedestrian action from keypoints"""
        if len(keypoints) < 33:
            return "unknown"

        # Simple action classification based on keypoint positions
        # (This is simplified - real implementation would be more complex)

        # Get key joints
        left_hip = keypoints[23]
        right_hip = keypoints[24]
        left_knee = keypoints[25]
        right_knee = keypoints[26]

        # Calculate angles (simplified)
        hip_y = (left_hip[1] + right_hip[1]) / 2
        knee_y = (left_knee[1] + right_knee[1]) / 2

        if abs(left_knee[1] - right_knee[1]) > 50:
            return "walking"
        else:
            return "standing"

# Continue in next message...