controlnet_module.py

import torch
from diffusers import StableDiffusionControlNetPipeline, ControlNetModel  # <- KORREKT!
from controlnet_aux import OpenposeDetector
from PIL import Image
import random
import cv2
import numpy as np
import gradio as gr


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
        
    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:
                # WICHTIG: torchvision 0.20.0 hat MiDaS integriert
                import torchvision.transforms as T
                
                # MiDaS Small (weniger VRAM)
                self.midas_model = torch.hub.load(
                    "intel-isl/MiDaS", 
                    "DPT_Hybrid", 
                    trust_repo=True
                )
                
                self.midas_model.to(self.device)
                self.midas_model.eval()
                
                # Transform für MiDaS
                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"))
            
            # Canny Edge Detection
            gray = cv2.cvtColor(img_array, cv2.COLOR_RGB2GRAY)
            edges = cv2.Canny(gray, 100, 200)
            
            # Zu 3-Kanal Bild konvertieren
            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:
            # Versuche MiDaS
            midas = self.load_midas_model()
            if midas is not None:
                print("🎯 Verwende MiDaS für Depth Map...")
                
                import torchvision.transforms as T
                from PIL import Image
                
                # Bild vorbereiten
                img_transformed = self.midas_transform(image).unsqueeze(0).to(self.device)
                
                # Depth Map berechnen
                with torch.no_grad():
                    prediction = midas(img_transformed)
                    prediction = torch.nn.functional.interpolate(
                        prediction.unsqueeze(1),
                        size=image.size[::-1],  # (height, width)
                        mode="bicubic",
                        align_corners=False,
                    ).squeeze()
                
                # Normalisieren für Ausgabe
                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:
                # Fallback auf einfache Methode
                print("⚠️ MiDaS nicht verfügbar, verwende Fallback...")
                raise Exception("MiDaS nicht geladen")
                
        except Exception as e:
            print(f"⚠️ MiDaS Fehler: {e}. Verwende Fallback...")
            # Fallback auf einfache Depth Map
            try:
                img_array = np.array(image.convert("RGB"))
                gray = cv2.cvtColor(img_array, cv2.COLOR_RGB2GRAY)

                # Depth-ähnliche Map erstellen
                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:
            # Depth + Canny
            print("   Modus: Depth + Canny")
            conditioning_images = [
                self.extract_depth_map(image),
                self.extract_canny_edges(image)
            ]
        else:
            # OpenPose + Canny
            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  # Rückgabe: Liste der PIL Images
             

    def prepare_inpaint_input(self, image, keep_environment=False):
        """
        Bereitet das Input-Bild für Inpaint vor
        """
        if keep_environment:
            print("🎯 Inpaint: Depth+Canny Info (Outside-Box ändern)")
            depth_image = self.extract_depth_map(image)
            canny_image = self.extract_canny_edges(image)
            combined_map = Image.blend(depth_image.convert("RGB"), canny_image.convert("RGB"), alpha=0.5)
            return combined_map, {"type": "depth_canny", "image": combined_map}
        else:
            print("🎯 Inpaint: Originalbild (Inside-Box ändern)")
            return image, {"type": "original", "image": image}


# 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)