controlnet_module.py

import torch
from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
from controlnet_aux import OpenposeDetector
from PIL import Image, ImageFilter
import random
import cv2
import numpy as np
import gradio as gr
from segment_anything import sam_model_registry, SamPredictor


class ControlNetProgressCallback:
    def __init__(self, progress, total_steps):
        self.progress = progress
        self.total_steps = total_steps
        self.current_step = 0

    def __call__(self, pipe, step_index, timestep, callback_kwargs):
        self.current_step = step_index + 1
        progress_percentage = self.current_step / self.total_steps

        if self.progress is not None:
            self.progress(progress_percentage, desc=f"ControlNet: Schritt {self.current_step}/{self.total_steps}")

        print(f"ControlNet Fortschritt: {self.current_step}/{self.total_steps} ({progress_percentage:.1%})")
        return callback_kwargs


class ControlNetProcessor:
    def __init__(self, device="cuda", torch_dtype=torch.float32):
        self.device = device
        self.torch_dtype = torch_dtype
        self.pose_detector = None
        self.midas_model = None
        self.midas_transform = None
        self.sam_predictor = None
        self.sam_initialized = False
        
    def _lazy_load_sam(self):
        """Lazy Loading von SAM 2 Tiny - Optimiert für Hugging Face Spaces"""
        if self.sam_initialized:
            return True
            
        try:
            print("🔄 Lade SAM 2 Tiny von Hugging Face Hub...")
            
            # KORRIGIERT: Nur der Hugging Face Model-ID Pfad
            model_id = "facebook/sam2-hiera-tiny"
            
            # SAM 2 Modell direkt von Hugging Face laden
            sam = sam_model_registry["sam2_hiera_tiny"](checkpoint=model_id)
            sam.to(self.device)
            self.sam_predictor = SamPredictor(sam)
            
            self.sam_initialized = True
            print(f"✅ SAM 2 ({model_id}) erfolgreich geladen")
            return True
            
        except Exception as e:
            print(f"❌ SAM 2 konnte nicht geladen werden: {str(e)[:100]}")
            print("ℹ️ Verwende rechteckige Masken als Fallback")
            self.sam_predictor = None
            self.sam_initialized = True  # Verhindert weitere Ladeversuche
            return False
    
    def _validate_bbox(self, image, bbox_coords):
        """Validiert und korrigiert BBox-Koordinaten"""
        width, height = image.size
        x1, y1, x2, y2 = bbox_coords
        
        # Stelle sicher, dass x1 <= x2 und y1 <= y2
        x1, x2 = min(x1, x2), max(x1, x2)
        y1, y2 = min(y1, y2), max(y1, y2)
        
        # Begrenze auf Bildgrenzen
        x1 = max(0, min(x1, width - 1))
        y1 = max(0, min(y1, height - 1))
        x2 = max(0, min(x2, width - 1))
        y2 = max(0, min(y2, height - 1))
        
        # Stelle sicher, dass BBox gültig ist
        if x2 - x1 < 10 or y2 - y1 < 10:
            # Fallback auf sinnvolle Größe
            size = min(width, height) * 0.3
            x1 = max(0, width/2 - size/2)
            y1 = max(0, height/2 - size/2)
            x2 = min(width, width/2 + size/2)
            y2 = min(height, height/2 + size/2)
        
        return int(x1), int(y1), int(x2), int(y2)
    
    def _smooth_mask(self, mask_array, blur_radius=3):
        """Glättet die Maske für bessere Übergänge (5-Pixel Randbereich)"""
        try:
            # Gaussian Blur für weiche Kanten - nur der Randbereich wird beeinflusst
            if blur_radius > 0:
                mask_array = cv2.GaussianBlur(mask_array, 
                                            (blur_radius*2+1, blur_radius*2+1), 
                                            0)
            
            return mask_array
        except:
            return mask_array
    
    def create_sam_mask(self, image, bbox_coords, mode):
        """
        Erstellt präzise Maske mit SAM 2 (transparent für Benutzer)
        Gibt PIL Image in L-Modus zurück (0=schwarz=erhalten, 255=weiß=verändern)
        """
        try:
            # Lade SAM bei Bedarf (automatisch für Hugging Face Spaces)
            if not self.sam_initialized:
                self._lazy_load_sam()
            
            # Fallback wenn SAM nicht verfügbar
            if self.sam_predictor is None:
                return self._create_rectangular_mask(image, bbox_coords, mode)
            
            # Validiere BBox
            x1, y1, x2, y2 = self._validate_bbox(image, bbox_coords)
            
            # Konvertiere zu numpy array (RGB)
            image_np = np.array(image.convert("RGB"))
            
            # SAM vorbereiten
            try:
                self.sam_predictor.set_image(image_np)
            except Exception as e:
                print(f"⚠️ SAM set_image Fehler: {e}")
                return self._create_rectangular_mask(image, bbox_coords, mode)
            
            # BBox für SAM formatieren
            input_box = np.array([x1, y1, x2, y2])
            
            print(f"🎯 SAM 2: Segmentiere Bereich {x1},{y1}-{x2},{y2}")
            
            # SAM Prediction
            masks, scores, _ = self.sam_predictor.predict(
                point_coords=None,
                point_labels=None,
                box=input_box[None, :],
                multimask_output=False,
                return_logits=False
            )
            
            # Beste Maske extrahieren und glätten (5-Pixel Übergang)
            mask_array = masks[0].astype(np.uint8) * 255
            mask_array = self._smooth_mask(mask_array, blur_radius=2)  # ~5 Pixel Rand
            
            # Zu PIL Image konvertieren
            mask = Image.fromarray(mask_array).convert("L")
            
            # Modus-spezifische Anpassung
            if mode == "environment_change":
                # MODUS 1: Umgebung ändern
                # Objekt schwarz (0) = ERHALTEN, Umgebung weiß (255) = VERÄNDERN
                mask = Image.eval(mask, lambda x: 255 - x)
                print("   SAM-Modus: Umgebung ändern (Objekt erhalten)")
            else:
                # MODUS 2 & 3: Focus oder Gesicht ändern
                # Objekt weiß (255) = VERÄNDERN, Umgebung schwarz (0) = ERHALTEN
                print("   SAM-Modus: Focus/Gesicht ändern (Objekt verändern)")
            
            print(f"✅ SAM 2: Präzise Maske erstellt ({mask.size})")
            return mask
            
        except Exception as e:
            print(f"⚠️ SAM 2 Fehler: {str(e)[:100]}")
            print("ℹ️ Fallback auf rechteckige Maske")
            return self._create_rectangular_mask(image, bbox_coords, mode)
    
    def _create_rectangular_mask(self, image, bbox_coords, mode):
        """Fallback: Erstellt rechteckige Maske"""
        from PIL import ImageDraw
        
        mask = Image.new("L", image.size, 0)
        
        if bbox_coords and all(coord is not None for coord in bbox_coords):
            x1, y1, x2, y2 = self._validate_bbox(image, bbox_coords)
            draw = ImageDraw.Draw(mask)
            
            if mode == "environment_change":
                # MODUS 1: Alles außer Box verändern
                draw.rectangle([0, 0, image.size[0], image.size[1]], fill=255)
                draw.rectangle([x1, y1, x2, y2], fill=0)
            else:
                # MODUS 2 & 3: Nur Box verändern
                draw.rectangle([x1, y1, x2, y2], fill=255)
        
        print("ℹ️ Rechteckige Maske (SAM Fallback)")
        return mask
    
    def load_pose_detector(self):
        """Lädt nur den Pose-Detector"""
        if self.pose_detector is None:
            print("Loading Pose Detector...")
            try:
                self.pose_detector = OpenposeDetector.from_pretrained("lllyasviel/ControlNet")
                print("✅ Pose-Detector geladen")
            except Exception as e:
                print(f"⚠️ Pose-Detector konnte nicht geladen werden: {e}")
        return self.pose_detector
    
    def load_midas_model(self):
        """Lädt MiDaS Model für Depth Maps"""
        if self.midas_model is None:
            print("🔄 Lade MiDaS Modell für Depth Maps...")
            try:
                import torchvision.transforms as T
                
                self.midas_model = torch.hub.load(
                    "intel-isl/MiDaS", 
                    "DPT_Hybrid", 
                    trust_repo=True
                )
                
                self.midas_model.to(self.device)
                self.midas_model.eval()
                
                self.midas_transform = T.Compose([
                    T.Resize(384),
                    T.ToTensor(),
                    T.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
                ])
                
                print("✅ MiDaS Modell erfolgreich geladen")
            except Exception as e:
                print(f"❌ MiDaS konnte nicht geladen werden: {e}")
                print("ℹ️ Verwende Fallback-Methode")
                self.midas_model = None
        
        return self.midas_model

    def extract_pose_simple(self, image):
        """Einfache Pose-Extraktion ohne komplexe Abhängigkeiten"""
        try:
            img_array = np.array(image.convert("RGB"))
            edges = cv2.Canny(img_array, 100, 200)
            pose_image = Image.fromarray(edges).convert("RGB")
            print("⚠️ Verwende Kanten-basierte Pose-Approximation")
            return pose_image
        except Exception as e:
            print(f"Fehler bei einfacher Pose-Extraktion: {e}")
            return image.convert("RGB").resize((512, 512))

    def extract_pose(self, image):
        """Extrahiert Pose-Map aus Bild mit Fallback"""
        try:
            detector = self.load_pose_detector()
            if detector is None:
                return self.extract_pose_simple(image)

            pose_image = detector(image, hand_and_face=True)
            return pose_image
        except Exception as e:
            print(f"Fehler bei Pose-Extraktion: {e}")
            return self.extract_pose_simple(image)

    def extract_canny_edges(self, image):
        """Extrahiert Canny Edges für Umgebungserhaltung"""
        try:
            img_array = np.array(image.convert("RGB"))
            
            gray = cv2.cvtColor(img_array, cv2.COLOR_RGB2GRAY)
            edges = cv2.Canny(gray, 100, 200)
            
            edges_rgb = cv2.cvtColor(edges, cv2.COLOR_GRAY2RGB)
            edges_image = Image.fromarray(edges_rgb)
            
            print("✅ Canny Edge Map erstellt")
            return edges_image
        except Exception as e:
            print(f"Fehler bei Canny Edge Extraction: {e}")
            return image.convert("RGB").resize((512, 512))

    def extract_depth_map(self, image):
        """
        Extrahiert Depth Map mit MiDaS (Fallback auf Filter)
        """
        try:
            midas = self.load_midas_model()
            if midas is not None:
                print("🎯 Verwende MiDaS für Depth Map...")
                
                import torchvision.transforms as T
                
                img_transformed = self.midas_transform(image).unsqueeze(0).to(self.device)
                
                with torch.no_grad():
                    prediction = midas(img_transformed)
                    prediction = torch.nn.functional.interpolate(
                        prediction.unsqueeze(1),
                        size=image.size[::-1],
                        mode="bicubic",
                        align_corners=False,
                    ).squeeze()
                
                depth_np = prediction.cpu().numpy()
                depth_min, depth_max = depth_np.min(), depth_np.max()
                
                if depth_max > depth_min:
                    depth_np = (depth_np - depth_min) / (depth_max - depth_min)
                
                depth_np = (depth_np * 255).astype(np.uint8)
                depth_image = Image.fromarray(depth_np).convert("RGB")
                
                print("✅ MiDaS Depth Map erfolgreich erstellt")
                return depth_image
            
            else:
                raise Exception("MiDaS nicht geladen")
                
        except Exception as e:
            print(f"⚠️ MiDaS Fehler: {e}. Verwende Fallback...")
            try:
                img_array = np.array(image.convert("RGB"))
                gray = cv2.cvtColor(img_array, cv2.COLOR_RGB2GRAY)

                depth_map = cv2.GaussianBlur(gray, (5, 5), 0)
                depth_rgb = cv2.cvtColor(depth_map, cv2.COLOR_GRAY2RGB)
                depth_image = Image.fromarray(depth_rgb)
                
                print("✅ Fallback Depth Map erstellt")
                return depth_image
            except Exception as fallback_error:
                print(f"❌ Auch Fallback fehlgeschlagen: {fallback_error}")
                return image.convert("RGB").resize((512, 512))

    def prepare_controlnet_maps(self, image, keep_environment=False):
        """
        ERSTELLT NUR CONDITIONING-MAPS, generiert KEIN Bild.
        """
        print("🎯 ControlNet: Erstelle Conditioning-Maps...")
        
        if keep_environment:
            print("   Modus: Depth + Canny")
            conditioning_images = [
                self.extract_depth_map(image),
                self.extract_canny_edges(image)
            ]
        else:
            print("   Modus: OpenPose + Canny")
            conditioning_images = [
                self.extract_pose(image),
                self.extract_canny_edges(image)
            ]
        
        print(f"✅ {len(conditioning_images)} Conditioning-Maps erstellt.")
        return conditioning_images


# Globale Instanz
device = "cuda" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16 if device == "cuda" else torch.float32
controlnet_processor = ControlNetProcessor(device=device, torch_dtype=torch_dtype)