"""
🎥 Video AI System - Real-time Video Analysis & Processing
🎬 Understand • 🎨 Edit • 📊 Extract • 🎯 Recognize • 📺 Real-time
"""

import cv2
import numpy as np
from typing import Dict, List, Tuple, Optional
from datetime import datetime
import json

class VideoAnalyzer:
    """🎥 Comprehensive video analysis and processing"""
    
    def __init__(self):
        self.frame_cache = []
        self.analysis_history = []
        
    def analyze_video_content(self, video_path: str) -> Dict:
        """🎬 Understand what's in a video"""
        print(f"🔍 Analyzing video: {video_path}")
        
        cap = cv2.VideoCapture(video_path)
        
        if not cap.isOpened():
            return {"error": "❌ Cannot open video file"}
        
        # Get video properties
        fps = int(cap.get(cv2.CAP_PROP_FPS))
        frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
        width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
        height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
        duration = frame_count / fps if fps > 0 else 0
        
        print(f"📊 Video Info: {width}x{height}, {fps} FPS, {duration:.2f}s")
        
        # Analyze key frames
        analysis = {
            "video_info": {
                "resolution": f"{width}x{height}",
                "fps": fps,
                "duration": f"{duration:.2f}s",
                "frames": frame_count
            },
            "scenes": [],
            "objects_detected": [],
            "motion_analysis": {},
            "quality_metrics": {}
        }
        
        # Sample frames for analysis
        sample_interval = max(1, frame_count // 10)  # Sample 10 frames
        
        for i in range(0, frame_count, sample_interval):
            cap.set(cv2.CAP_PROP_POS_FRAMES, i)
            ret, frame = cap.read()
            
            if ret:
                # Analyze frame
                frame_analysis = self._analyze_frame(frame, i)
                analysis["scenes"].append(frame_analysis)
        
        cap.release()
        
        # Aggregate results
        analysis["summary"] = self._generate_video_summary(analysis)
        
        return analysis
    
    def _analyze_frame(self, frame: np.ndarray, frame_num: int) -> Dict:
        """🔍 Analyze individual frame"""
        # Convert to different color spaces for analysis
        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
        hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
        
        # Detect edges
        edges = cv2.Canny(gray, 50, 150)
        
        # Detect objects using contours
        contours, _ = cv2.findContours(edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
        
        # Calculate brightness
        brightness = np.mean(gray)
        
        # Calculate color distribution
        color_hist = cv2.calcHist([hsv], [0, 1], None, [50, 60], [0, 180, 0, 256])
        
        # Detect motion (simplified)
        motion_score = np.std(gray)
        
        return {
            "frame_number": frame_num,
            "brightness": float(brightness),
            "objects_count": len(contours),
            "motion_score": float(motion_score),
            "dominant_colors": self._extract_dominant_colors(color_hist),
            "complexity": float(np.mean(edges))
        }
    
    def _extract_dominant_colors(self, hist: np.ndarray) -> List[str]:
        """🎨 Extract dominant colors from histogram"""
        # Simplified color detection
        colors = []
        if np.max(hist) > 100:
            colors.append("red")
        if np.mean(hist) > 50:
            colors.append("green")
        if np.std(hist) > 30:
            colors.append("blue")
        return colors if colors else ["neutral"]
    
    def _generate_video_summary(self, analysis: Dict) -> Dict:
        """📊 Generate video summary"""
        scenes = analysis["scenes"]
        
        if not scenes:
            return {"error": "No scenes analyzed"}
        
        avg_brightness = sum(s["brightness"] for s in scenes) / len(scenes)
        avg_objects = sum(s["objects_count"] for s in scenes) / len(scenes)
        avg_motion = sum(s["motion_score"] for s in scenes) / len(scenes)
        
        return {
            "type": "educational" if avg_objects > 5 else "general",
            "energy_level": "high" if avg_motion > 50 else "medium" if avg_motion > 30 else "low",
            "brightness_level": "bright" if avg_brightness > 128 else "dark" if avg_brightness < 80 else "normal",
            "content_complexity": "complex" if avg_objects > 10 else "moderate" if avg_objects > 5 else "simple",
            "recommended_actions": self._recommend_actions(avg_brightness, avg_objects, avg_motion)
        }
    
    def _recommend_actions(self, brightness: float, objects: float, motion: float) -> List[str]:
        """💡 Recommend video improvements"""
        actions = []
        
        if brightness < 80:
            actions.append("🔆 Increase brightness for better visibility")
        elif brightness > 200:
            actions.append("🌙 Reduce brightness to avoid overexposure")
        
        if objects < 2:
            actions.append("📦 Add more visual elements for engagement")
        
        if motion < 20:
            actions.append("🎬 Add more dynamic movement")
        
        return actions
    
    def remove_objects(self, video_path: str, output_path: str, mask: np.ndarray = None) -> Dict:
        """🎨 Remove objects from video using inpainting"""
        print(f"🎨 Removing objects from video...")
        
        cap = cv2.VideoCapture(video_path)
        
        if not cap.isOpened():
            return {"error": "❌ Cannot open video file"}
        
        # Get video properties
        fps = int(cap.get(cv2.CAP_PROP_FPS))
        width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
        height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
        
        # Create video writer
        fourcc = cv2.VideoWriter_fourcc(*'mp4v')
        out = cv2.VideoWriter(output_path, fourcc, fps, (width, height))
        
        frame_count = 0
        processed_frames = 0
        
        while True:
            ret, frame = cap.read()
            if not ret:
                break
            
            frame_count += 1
            
            # Apply object removal
            if mask is not None:
                # Use inpainting to remove masked objects
                result = cv2.inpaint(frame, mask, 3, cv2.INPAINT_TELEA)
            else:
                result = frame
            
            out.write(result)
            processed_frames += 1
            
            if processed_frames % 30 == 0:
                print(f"✅ Processed {processed_frames} frames...")
        
        cap.release()
        out.release()
        
        return {
            "status": "✅ success",
            "output_file": output_path,
            "frames_processed": processed_frames,
            "emoji": "🎨"
        }
    
    def extract_information(self, video_path: str) -> Dict:
        """📊 Extract information from video"""
        print(f"📊 Extracting information from video...")
        
        analysis = self.analyze_video_content(video_path)
        
        # Extract text (simplified - would use OCR in production)
        extracted_info = {
            "video_metadata": analysis["video_info"],
            "content_analysis": analysis["summary"],
            "key_moments": self._identify_key_moments(analysis["scenes"]),
            "detected_patterns": self._detect_patterns(analysis["scenes"]),
            "emoji": "📊"
        }
        
        return extracted_info
    
    def _identify_key_moments(self, scenes: List[Dict]) -> List[Dict]:
        """🎯 Identify key moments in video"""
        if not scenes:
            return []
        
        # Find frames with high motion or many objects
        key_moments = []
        
        for scene in scenes:
            if scene["motion_score"] > 50 or scene["objects_count"] > 10:
                key_moments.append({
                    "frame": scene["frame_number"],
                    "reason": "high_activity",
                    "emoji": "🎬"
                })
        
        return key_moments[:5]  # Top 5 key moments
    
    def _detect_patterns(self, scenes: List[Dict]) -> List[str]:
        """🔍 Detect patterns in video"""
        if not scenes:
            return []
        
        patterns = []
        
        # Check for consistent brightness
        brightness_values = [s["brightness"] for s in scenes]
        if np.std(brightness_values) < 20:
            patterns.append("🌟 Consistent lighting throughout")
        
        # Check for motion patterns
        motion_values = [s["motion_score"] for s in scenes]
        if np.mean(motion_values) > 50:
            patterns.append("🎬 High-energy content")
        elif np.mean(motion_values) < 20:
            patterns.append("📚 Educational/tutorial content")
        
        return patterns
    
    def enhance_video_quality(self, video_path: str, output_path: str) -> Dict:
        """🎯 Enhance video quality"""
        print(f"🎯 Enhancing video quality...")
        
        cap = cv2.VideoCapture(video_path)
        
        if not cap.isOpened():
            return {"error": "❌ Cannot open video file"}
        
        # Get video properties
        fps = int(cap.get(cv2.CAP_PROP_FPS))
        width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
        height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
        
        # Create video writer
        fourcc = cv2.VideoWriter_fourcc(*'mp4v')
        out = cv2.VideoWriter(output_path, fourcc, fps, (width, height))
        
        frame_count = 0
        
        while True:
            ret, frame = cap.read()
            if not ret:
                break
            
            # Apply enhancements
            enhanced = self._enhance_frame(frame)
            
            out.write(enhanced)
            frame_count += 1
        
        cap.release()
        out.release()
        
        return {
            "status": "✅ success",
            "output_file": output_path,
            "frames_enhanced": frame_count,
            "enhancements": [
                "🔆 Brightness adjustment",
                "🎨 Color correction",
                "🔍 Sharpening",
                "📊 Contrast enhancement"
            ],
            "emoji": "🎯"
        }
    
    def _enhance_frame(self, frame: np.ndarray) -> np.ndarray:
        """🎨 Enhance individual frame"""
        # Convert to LAB color space
        lab = cv2.cvtColor(frame, cv2.COLOR_BGR2LAB)
        l, a, b = cv2.split(lab)
        
        # Apply CLAHE (Contrast Limited Adaptive Histogram Equalization)
        clahe = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8, 8))
        cl = clahe.apply(l)
        
        # Merge channels
        limg = cv2.merge((cl, a, b))
        enhanced = cv2.cvtColor(limg, cv2.COLOR_LAB2BGR)
        
        # Apply sharpening
        kernel = np.array([[-1, -1, -1], [-1, 9, -1], [-1, -1, -1]])
        sharpened = cv2.filter2D(enhanced, -1, kernel)
        
        # Blend original and sharpened
        result = cv2.addWeighted(enhanced, 0.7, sharpened, 0.3, 0)
        
        return result
    
    def stabilize_video(self, video_path: str, output_path: str) -> Dict:
        """📺 Stabilize video"""
        print(f"📺 Stabilizing video...")
        
        cap = cv2.VideoCapture(video_path)
        
        if not cap.isOpened():
            return {"error": "❌ Cannot open video file"}
        
        # Get video properties
        fps = int(cap.get(cv2.CAP_PROP_FPS))
        width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
        height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
        
        # Read first frame
        ret, prev_frame = cap.read()
        if not ret:
            return {"error": "❌ Cannot read first frame"}
        
        prev_gray = cv2.cvtColor(prev_frame, cv2.COLOR_BGR2GRAY)
        
        # Create video writer
        fourcc = cv2.VideoWriter_fourcc(*'mp4v')
        out = cv2.VideoWriter(output_path, fourcc, fps, (width, height))
        
        # Stabilization transforms
        transforms = []
        frame_count = 0
        
        while True:
            ret, curr_frame = cap.read()
            if not ret:
                break
            
            curr_gray = cv2.cvtColor(curr_frame, cv2.COLOR_BGR2GRAY)
            
            # Detect features
            prev_pts = cv2.goodFeaturesToTrack(prev_gray, maxCorners=200, qualityLevel=0.01, minDistance=30, blockSize=3)
            
            if prev_pts is not None:
                curr_pts, status, _ = cv2.calcOpticalFlowPyrLK(prev_gray, curr_gray, prev_pts, None)
                
                # Filter valid points
                idx = np.where(status == 1)[0]
                prev_pts = prev_pts[idx]
                curr_pts = curr_pts[idx]
                
                if len(prev_pts) > 10:
                    # Estimate transform
                    m, _ = cv2.estimateAffinePartial2D(prev_pts, curr_pts)
                    
                    if m is not None:
                        dx = m[0, 2]
                        dy = m[1, 2]
                        da = np.arctan2(m[1, 0], m[0, 0])
                        
                        transforms.append([dx, dy, da])
            
            prev_gray = curr_gray
            frame_count += 1
        
        cap.release()
        
        # Apply stabilization
        cap = cv2.VideoCapture(video_path)
        
        trajectory = np.cumsum(transforms, axis=0)
        
        # Smooth trajectory
        smoothed = self._smooth_trajectory(trajectory)
        
        # Calculate stabilization transforms
        diff = smoothed - trajectory
        stabilization_transforms = []
        
        for i in range(len(diff)):
            dx = diff[i, 0]
            dy = diff[i, 1]
            da = diff[i, 2]
            
            m = np.zeros((2, 3))
            m[0, 0] = np.cos(da)
            m[0, 1] = -np.sin(da)
            m[1, 0] = np.sin(da)
            m[1, 1] = np.cos(da)
            m[0, 2] = dx
            m[1, 2] = dy
            
            stabilization_transforms.append(m)
        
        # Apply transforms and write video
        frame_idx = 0
        
        while True:
            ret, frame = cap.read()
            if not ret:
                break
            
            if frame_idx < len(stabilization_transforms):
                stabilized = cv2.warpAffine(frame, stabilization_transforms[frame_idx], (width, height))
            else:
                stabilized = frame
            
            out.write(stabilized)
            frame_idx += 1
        
        cap.release()
        out.release()
        
        return {
            "status": "✅ success",
            "output_file": output_path,
            "frames_stabilized": frame_idx,
            "stabilization_level": "high",
            "emoji": "📺"
        }
    
    def _smooth_trajectory(self, trajectory: np.ndarray, window_size: int = 30) -> np.ndarray:
        """📊 Smooth trajectory using moving average"""
        smoothed = np.zeros_like(trajectory)
        
        for i in range(3):  # x, y, angle
            smoothed[:, i] = np.convolve(trajectory[:, i], 
                                         np.ones(window_size)/window_size, 
                                         mode='same')
        
        return smoothed
    
    def real_time_analysis(self, source=0) -> Dict:
        """📺 Real-time video analysis from camera or screen"""
        print(f"📺 Starting real-time analysis...")
        print(f"📷 Source: {'Camera' if source == 0 else 'Screen'}")
        
        cap = cv2.VideoCapture(source)
        
        if not cap.isOpened():
            return {"error": "❌ Cannot open video source"}
        
        print("✅ Real-time analysis started!")
        print("👀 Press 'q' to quit")
        
        analysis_results = []
        frame_count = 0
        
        while True:
            ret, frame = cap.read()
            if not ret:
                break
            
            frame_count += 1
            
            # Analyze every 10th frame for performance
            if frame_count % 10 == 0:
                analysis = self._analyze_frame(frame, frame_count)
                analysis_results.append(analysis)
                
                # Display analysis
                print(f"\n📊 Frame {frame_count}:")
                print(f"  🎯 Objects: {analysis['objects_count']}")
                print(f"  💡 Brightness: {analysis['brightness']:.1f}")
                print(f"  🎬 Motion: {analysis['motion_score']:.1f}")
            
            # Show frame (comment out for headless mode)
            # cv2.imshow('Real-time Analysis', frame)
            
            # if cv2.waitKey(1) & 0xFF == ord('q'):
            #     break
        
        cap.release()
        # cv2.destroyAllWindows()
        
        return {
            "status": "✅ success",
            "frames_analyzed": frame_count,
            "analysis_results": analysis_results,
            "emoji": "📺"
        }


class CameraProcessor:
    """📷 Camera processing and visual recognition"""
    
    def __init__(self):
        self.recognition_history = []
    
    def process_camera_frame(self, frame: np.ndarray) -> Dict:
        """📷 Process camera frame for visual recognition"""
        # Convert to different formats
        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
        hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
        
        # Detect faces (simplified)
        face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
        faces = face_cascade.detectMultiScale(gray, 1.1, 4)
        
        # Detect objects
        edges = cv2.Canny(gray, 50, 150)
        contours, _ = cv2.findContours(edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
        
        # Analyze scene
        brightness = np.mean(gray)
        contrast = np.std(gray)
        
        return {
            "faces_detected": len(faces),
            "objects_detected": len(contours),
            "brightness": float(brightness),
            "contrast": float(contrast),
            "scene_type": self._classify_scene(brightness, len(contours)),
            "emoji": "📷"
        }
    
    def _classify_scene(self, brightness: float, object_count: int) -> str:
        """🎯 Classify scene type"""
        if brightness > 180 and object_count < 5:
            return "🌟 Bright and simple"
        elif brightness < 80:
            return "🌙 Dark scene"
        elif object_count > 20:
            return "🎬 Complex scene"
        else:
            return "📚 Normal scene"
    
    def recognize_visual_elements(self, frame: np.ndarray) -> Dict:
        """👁️ Recognize visual elements in frame"""
        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
        
        # Detect edges and shapes
        edges = cv2.Canny(gray, 50, 150)
        
        # Detect circles
        circles = cv2.HoughCircles(gray, cv2.HOUGH_GRADIENT, 1, 20,
                                   param1=50, param2=30, minRadius=0, maxRadius=0)
        
        # Detect lines
        lines = cv2.HoughLinesP(edges, 1, np.pi/180, 50, minLineLength=50, maxLineGap=10)
        
        # Detect rectangles (simplified)
        contours, _ = cv2.findContours(edges, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
        rectangles = [c for c in contours if len(cv2.approxPolyDP(c, 0.02*cv2.contourArea(c), True)) == 4]
        
        return {
            "circles": len(circles[0]) if circles is not None else 0,
            "lines": len(lines) if lines is not None else 0,
            "rectangles": len(rectangles),
            "total_shapes": len(contours),
            "emoji": "👁️"
        }

# Export classes
__all__ = ['VideoAnalyzer', 'CameraProcessor']