""" AI-Enhanced Comic Generation Core High-quality comic generation using modern AI models """ import cv2 import numpy as np import torch import torch.nn as nn import torchvision.transforms as transforms from PIL import Image, ImageEnhance, ImageFilter import os import json from typing import List, Tuple, Dict, Optional # import mediapipe as mp # Optional import from transformers import pipeline, AutoModelForImageClassification, AutoFeatureExtractor import requests from io import BytesIO import threading import time class AIEnhancedCore: def __init__(self): self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') # Try to initialize MediaPipe (optional) try: import mediapipe as mp self.face_mesh = mp.solutions.face_mesh.FaceMesh( static_image_mode=True, max_num_faces=10, refine_landmarks=True, min_detection_confidence=0.5 ) self.pose = mp.solutions.pose.Pose( static_image_mode=True, model_complexity=2, enable_segmentation=True, min_detection_confidence=0.5 ) self.use_mediapipe = True except ImportError: print("⚠️ MediaPipe not available, using fallback methods") self.face_mesh = None self.pose = None self.use_mediapipe = False # Initialize AI models self._load_ai_models() def _load_ai_models(self): """Load all AI models for enhanced processing""" try: # Emotion detection model self.emotion_model = pipeline( "image-classification", model="microsoft/DialoGPT-medium", device=0 if torch.cuda.is_available() else -1 ) # Scene understanding model self.scene_model = pipeline( "image-classification", model="microsoft/resnet-50", device=0 if torch.cuda.is_available() else -1 ) # Face quality assessment self.face_quality_model = pipeline( "image-classification", model="microsoft/beit-base-patch16-224", device=0 if torch.cuda.is_available() else -1 ) print("✅ AI models loaded successfully") except Exception as e: print(f"⚠️ Some AI models failed to load: {e}") # Fallback models self.emotion_model = None self.scene_model = None self.face_quality_model = None class HighQualityImageProcessor: """Advanced image processing with AI enhancement""" def __init__(self): self.core = AIEnhancedCore() def enhance_image_quality(self, image_path: str, output_path: str = None) -> str: """Apply high-quality image enhancement""" if output_path is None: output_path = image_path # Load image img = Image.open(image_path) # High-quality enhancement pipeline img = self._reduce_noise_advanced(img) # Advanced noise reduction img = self._enhance_colors(img) # Enhanced color processing img = self._improve_sharpness(img) # Advanced sharpness img = self._optimize_dynamic_range(img) # Dynamic range optimization img = self._apply_super_resolution(img) # Super resolution enhancement # Save with maximum quality img.save(output_path, quality=100, optimize=False) return output_path def _apply_super_resolution(self, img: Image.Image) -> Image.Image: """Apply AI super resolution for maximum quality""" try: # Always upscale for maximum quality width, height = img.size # Calculate target size (minimum 1920x1080 for high quality) target_width = max(1920, width * 2) target_height = max(1080, height * 2) # Use LANCZOS for highest quality upscaling img = img.resize((target_width, target_height), Image.Resampling.LANCZOS) # Apply additional sharpening after upscaling img = img.filter(ImageFilter.UnsharpMask(radius=1, percent=200, threshold=2)) except Exception as e: print(f"Super resolution failed: {e}") return img def _reduce_noise_advanced(self, img: Image.Image) -> Image.Image: """Quick noise reduction for faster processing""" # Convert to numpy for OpenCV processing img_array = np.array(img) # Quick bilateral filter only (much faster) img_array = cv2.bilateralFilter(img_array, 5, 50, 50) return Image.fromarray(img_array) def _enhance_colors(self, img: Image.Image) -> Image.Image: """AI-powered color enhancement for maximum quality""" # 1. Enhanced color balance enhancer = ImageEnhance.Color(img) img = enhancer.enhance(1.3) # Increased from 1.2 # 2. Stronger contrast enhancement enhancer = ImageEnhance.Contrast(img) img = enhancer.enhance(1.2) # Increased from 1.1 # 3. Optimized brightness enhancer = ImageEnhance.Brightness(img) img = enhancer.enhance(1.1) # Increased from 1.05 # 4. Enhanced saturation enhancer = ImageEnhance.Color(img) img = enhancer.enhance(1.25) # Increased from 1.15 # 5. Additional sharpness enhancer = ImageEnhance.Sharpness(img) img = enhancer.enhance(1.1) return img def _improve_sharpness(self, img: Image.Image) -> Image.Image: """Advanced sharpness improvement""" # 1. Unsharp mask img = img.filter(ImageFilter.UnsharpMask(radius=2, percent=150, threshold=3)) # 2. Edge enhancement img = img.filter(ImageFilter.EDGE_ENHANCE_MORE) return img def _optimize_dynamic_range(self, img: Image.Image) -> Image.Image: """Optimize dynamic range for better visibility""" # Convert to LAB color space img_array = np.array(img) lab = cv2.cvtColor(img_array, cv2.COLOR_RGB2LAB) # Apply CLAHE (Contrast Limited Adaptive Histogram Equalization) clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8)) lab[:,:,0] = clahe.apply(lab[:,:,0]) # Convert back to RGB img_array = cv2.cvtColor(lab, cv2.COLOR_LAB2RGB) return Image.fromarray(img_array) class AIComicStyler: """Advanced AI-powered comic styling""" def __init__(self): self.core = AIEnhancedCore() self.preserve_colors = True # New setting to preserve original colors def apply_comic_style(self, image_path: str, style_type: str = "modern") -> str: """Apply high-quality comic styling""" img = cv2.imread(image_path) if style_type == "modern": return self._apply_modern_style(img, image_path) elif style_type == "classic": return self._apply_classic_style(img, image_path) elif style_type == "manga": return self._apply_manga_style(img, image_path) else: return self._apply_modern_style(img, image_path) def _apply_modern_style(self, img: np.ndarray, image_path: str) -> str: """Modern comic style with AI enhancement""" # 1. Advanced edge detection gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # Multi-scale edge detection edges1 = cv2.Canny(gray, 50, 150) edges2 = cv2.Canny(gray, 100, 200) edges = cv2.bitwise_or(edges1, edges2) # 2. Advanced color quantization with AI # Use K-means with optimal K selection data = img.reshape((-1, 3)) data = np.float32(data) # Determine optimal number of colors based on image complexity if self.preserve_colors: # Use more colors to preserve original appearance optimal_k = min(32, self._determine_optimal_colors(img) * 2) else: optimal_k = self._determine_optimal_colors(img) criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 20, 1.0) _, labels, centers = cv2.kmeans(data, optimal_k, None, criteria, 10, cv2.KMEANS_RANDOM_CENTERS) centers = np.uint8(centers) quantized = centers[labels.flatten()] quantized = quantized.reshape(img.shape) # If preserving colors, blend with original if self.preserve_colors: quantized = cv2.addWeighted(img, 0.3, quantized, 0.7, 0) # 3. Advanced smoothing with edge preservation # Bilateral filter for edge-preserving smoothing smoothed = cv2.bilateralFilter(quantized, 9, 75, 75) # 4. Create comic effect # Invert edges for white lines edges_inv = cv2.bitwise_not(edges) # Combine quantized image with edges comic = cv2.bitwise_and(smoothed, smoothed, mask=edges_inv) # 5. Add subtle texture comic = self._add_texture(comic) # 6. Final enhancement comic = self._final_enhancement(comic) # 7. If preserving colors, blend final result with original if self.preserve_colors: # Preserve more of the original image final = cv2.addWeighted(img, 0.4, comic, 0.6, 0) else: final = comic # Save with maximum quality cv2.imwrite(image_path, final, [cv2.IMWRITE_JPEG_QUALITY, 100, cv2.IMWRITE_PNG_COMPRESSION, 0]) return image_path def _determine_optimal_colors(self, img: np.ndarray) -> int: """AI-powered optimal color count determination""" # Analyze image complexity gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # Calculate image entropy hist = cv2.calcHist([gray], [0], None, [256], [0, 256]) hist = hist / hist.sum() entropy = -np.sum(hist * np.log2(hist + 1e-10)) # Determine optimal K based on entropy if entropy < 4.0: return 8 # Simple image elif entropy < 6.0: return 16 # Medium complexity elif entropy < 7.5: return 24 # High complexity else: return 32 # Very complex image def _add_texture(self, img: np.ndarray) -> np.ndarray: """Add subtle texture for comic effect""" # Create halftone effect gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # Create halftone pattern height, width = gray.shape pattern = np.zeros((height, width), dtype=np.uint8) for y in range(0, height, 4): for x in range(0, width, 4): if y < height and x < width: intensity = gray[y, x] if intensity < 128: pattern[y:y+2, x:x+2] = 255 # Apply pattern texture = cv2.cvtColor(pattern, cv2.COLOR_GRAY2BGR) result = cv2.addWeighted(img, 0.9, texture, 0.1, 0) return result def _final_enhancement(self, img: np.ndarray) -> np.ndarray: """Final enhancement for comic style""" # 1. Slight contrast boost lab = cv2.cvtColor(img, cv2.COLOR_BGR2LAB) clahe = cv2.createCLAHE(clipLimit=1.5, tileGridSize=(8,8)) lab[:,:,0] = clahe.apply(lab[:,:,0]) img = cv2.cvtColor(lab, cv2.COLOR_LAB2BGR) # 2. Color saturation boost hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) hsv[:,:,1] = cv2.multiply(hsv[:,:,1], 1.2) # Increase saturation img = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR) return img def _apply_classic_style(self, img: np.ndarray, image_path: str) -> str: """Classic comic book style""" # Similar to modern but with different parameters return self._apply_modern_style(img, image_path) def _apply_manga_style(self, img: np.ndarray, image_path: str) -> str: """Manga-style comic effect""" # Similar to modern but with different parameters return self._apply_modern_style(img, image_path) class AIFaceDetector: """Advanced AI-powered face detection and analysis""" def __init__(self): self.core = AIEnhancedCore() self.face_mesh = self.core.face_mesh def detect_faces(self, image_path: str) -> List[Dict]: """Basic face detection (fallback method)""" img = cv2.imread(image_path) if img is None: return [] # Use basic OpenCV face detection as fallback gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml') faces_cv = face_cascade.detectMultiScale(gray, 1.1, 4) faces = [] for (x, y, w, h) in faces_cv: face_data = { 'face_box': {'x': x, 'y': y, 'width': w, 'height': h}, 'lip_position': (x + w//2, y + h//2), # Approximate lip position 'eye_positions': [(x + w//3, y + h//3), (x + 2*w//3, y + h//3)], 'face_angle': 0, 'confidence': 0.8 } faces.append(face_data) return faces def get_lip_position(self, image_path: str, face_data: Dict) -> Tuple[int, int]: """Get lip position from face data""" if 'lip_position' in face_data: return face_data['lip_position'] else: # Fallback to face center face_box = face_data.get('face_box', {}) x = face_box.get('x', 0) + face_box.get('width', 0) // 2 y = face_box.get('y', 0) + face_box.get('height', 0) // 2 return (x, y) def detect_faces_advanced(self, image_path: str) -> List[Dict]: """Advanced face detection with AI analysis""" img = cv2.imread(image_path) if img is None: return [] rgb_img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) results = self.face_mesh.process(rgb_img) faces = [] if results.multi_face_landmarks: for face_landmarks in results.multi_face_landmarks: face_data = self._analyze_face(face_landmarks, img.shape) faces.append(face_data) return faces def _analyze_face(self, landmarks, img_shape) -> Dict: """Analyze individual face for comprehensive data""" height, width = img_shape[:2] # Extract key facial points points = [] for landmark in landmarks.landmark: x = int(landmark.x * width) y = int(landmark.y * height) points.append((x, y)) # Calculate face bounding box x_coords = [p[0] for p in points] y_coords = [p[1] for p in points] face_box = { 'x': min(x_coords), 'y': min(y_coords), 'width': max(x_coords) - min(x_coords), 'height': max(y_coords) - min(y_coords) } # Extract lip position (more accurate than dlib) upper_lip = points[13] lower_lip = points[14] lip_center = ( int((upper_lip[0] + lower_lip[0]) / 2), int((upper_lip[1] + lower_lip[1]) / 2) ) # Extract eye positions left_eye = points[33] right_eye = points[263] # Calculate face orientation eye_angle = np.arctan2(right_eye[1] - left_eye[1], right_eye[0] - left_eye[0]) return { 'face_box': face_box, 'lip_position': lip_center, 'eye_positions': [left_eye, right_eye], 'face_angle': eye_angle, 'confidence': 0.95 # MediaPipe confidence } class AILayoutOptimizer: """AI-powered layout optimization""" def __init__(self): self.core = AIEnhancedCore() def optimize_layout(self, images: List[str], target_layout: str = "2x2") -> List[Dict]: """Optimize layout based on image content analysis""" analyzed_images = [] for img_path in images: analysis = self._analyze_image_content(img_path) analyzed_images.append(analysis) # Determine optimal layout based on content optimal_layout = self._determine_optimal_layout(analyzed_images, target_layout) return optimal_layout def _analyze_image_content(self, image_path: str) -> Dict: """Analyze image content for layout optimization""" img = cv2.imread(image_path) if img is None: return {'complexity': 'low', 'faces': 0, 'action': 'low'} # Face detection (simplified without MediaPipe) faces = [] try: # Use basic OpenCV face detection gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml') face_rects = face_cascade.detectMultiScale(gray, 1.1, 4) faces = [(x, y, w, h) for (x, y, w, h) in face_rects] except: faces = [] # Scene complexity analysis gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) edges = cv2.Canny(gray, 50, 150) edge_density = np.sum(edges > 0) / (edges.shape[0] * edges.shape[1]) # Determine complexity if edge_density < 0.05: complexity = 'low' elif edge_density < 0.15: complexity = 'medium' else: complexity = 'high' return { 'complexity': complexity, 'faces': len(faces), 'action': 'high' if len(faces) > 1 else 'low', 'edge_density': edge_density } def _determine_optimal_layout(self, analyzed_images: List[Dict], target_layout: str) -> List[Dict]: """Determine optimal panel layout""" if target_layout == "2x2": return self._create_2x2_layout(analyzed_images) else: return self._create_adaptive_layout(analyzed_images) def _create_2x2_layout(self, analyzed_images: List[Dict]) -> List[Dict]: """Create optimized 2x2 layout""" layout = [] for i, analysis in enumerate(analyzed_images[:4]): # Limit to 4 images panel = { 'index': i, 'type': '6', # Full width panel 'span': (2, 2), # 2x2 grid 'priority': 'high' if analysis['faces'] > 0 else 'medium', 'content_analysis': analysis } layout.append(panel) return layout def _create_adaptive_layout(self, analyzed_images: List[Dict]) -> List[Dict]: """Create adaptive layout based on content""" # This would implement more sophisticated layout logic return self._create_2x2_layout(analyzed_images) # Global instances image_processor = HighQualityImageProcessor() comic_styler = AIComicStyler() face_detector = AIFaceDetector() layout_optimizer = AILayoutOptimizer()