【产品照片输入】（手机随手拍的产品图）
   ↓
【AI去背景】- 使用rembg或SAM模型
   ↓
【场景生成】
   ├─ 白底图（电商主图标准）
   ├─ 场景图（客厅/卧室/办公室等）
   ├─ 氛围图（暖光/冷光/自然光）
   └─ 对比图（使用前后对比）
   ↓
【批量处理】- ComfyUI工作流一键生成
   ↓
【质量检查】- AI评分+人工抽检
   ↓
【输出交付】- 按平台规格导出

from rembg import remove
from PIL import Image
import io

class ProductImageProcessor:
    def __init__(self):
        self.sd_api = StableDiffusionAPI()
    
    def remove_background(self, image_path):
        """AI去除产品背景"""
        with open(image_path, 'rb') as f:
            input_image = f.read()
        
        # 使用rembg去背景
        output_image = remove(input_image)
        
        # 转换为PIL Image
        img = Image.open(io.BytesIO(output_image))
        return img
    
    def generate_white_bg(self, product_img, size=(800, 800)):
        """生成标准电商白底图"""
        # 创建白色背景
        bg = Image.new('RGBA', size, (255, 255, 255, 255))
        
        # 计算产品居中位置
        product_img.thumbnail((int(size[0]*0.8), int(size[1]*0.8)))
        offset = ((size[0] - product_img.width) // 2,
                  (size[1] - product_img.height) // 2)
        
        # 合成
        bg.paste(product_img, offset, product_img)
        return bg.convert('RGB')
    
    def generate_scene_image(self, product_img, scene_prompt):
        """生成场景图"""
        # 使用ControlNet保持产品形态
        result = self.sd_api.img2img(
            init_image=product_img,
            prompt=scene_prompt,
            controlnet_model="canny",
            controlnet_weight=0.8,
            denoising_strength=0.6,
            width=1024,
            height=1024
        )
        return result
    
    def batch_generate(self, product_images, scenes):
        """批量生成所有场景图"""
        results = []
        for img_path in product_images:
            product = self.remove_background(img_path)
            
            # 生成白底图
            white_bg = self.generate_white_bg(product)
            results.append({"type": "white_bg", "image": white_bg})
            
            # 生成各场景图
            for scene in scenes:
                scene_img = self.generate_scene_image(product, scene)
                results.append({"type": "scene", "scene": scene, "image": scene_img})
        
        return results

# 家居产品场景Prompt模板
SCENE_PROMPTS = {
    "客厅": {
        "prompt": "modern living room, {product} on coffee table, warm lighting, "
                  "minimalist interior design, soft sofa in background, "
                  "natural sunlight from window, 8k, professional photography",
        "negative": "blurry, low quality, distorted, watermark"
    },
    "卧室": {
        "prompt": "cozy bedroom, {product} on nightstand, soft warm light, "
                  "clean white bedding, wooden furniture, morning atmosphere, "
                  "professional product photography, 8k",
        "negative": "blurry, low quality, distorted, watermark"
    },
    "办公室": {
        "prompt": "modern office desk, {product} on workspace, clean minimal, "
                  "MacBook nearby, green plant, natural daylight, "
                  "professional commercial photography, 8k",
        "negative": "blurry, low quality, distorted, watermark"
    },
    "户外": {
        "prompt": "outdoor garden setting, {product} on wooden table, "
                  "green plants background, golden hour lighting, "
                  "lifestyle photography, bokeh, 8k",
        "negative": "blurry, low quality, distorted, watermark"
    }
}

def get_scene_prompt(scene_name, product_description):
    """获取场景化Prompt"""
    template = SCENE_PROMPTS[scene_name]
    prompt = template["prompt"].replace("{product}", product_description)
    return {
        "prompt": prompt,
        "negative_prompt": template["negative"]
    }

import json

def create_comfyui_workflow(product_list, output_dir):
    """生成ComfyUI批量处理工作流配置"""
    workflow = {
        "nodes": [
            {
                "id": 1,
                "type": "LoadImage",
                "inputs": {"image_list": product_list}
            },
            {
                "id": 2,
                "type": "RemoveBackground",
                "inputs": {"image": ["1", 0], "model": "u2net"}
            },
            {
                "id": 3,
                "type": "ControlNetPreprocessor",
                "inputs": {"image": ["2", 0], "preprocessor": "canny"}
            },
            {
                "id": 4,
                "type": "KSampler",
                "inputs": {
                    "model": "sd_xl_base_1.0",
                    "positive": "professional product photography, white background, studio lighting",
                    "negative": "blurry, distorted, low quality",
                    "steps": 30,
                    "cfg": 7.5,
                    "sampler": "euler_ancestral",
                    "scheduler": "normal",
                    "controlnet": ["3", 0]
                }
            },
            {
                "id": 5,
                "type": "SaveImage",
                "inputs": {
                    "images": ["4", 0],
                    "filename_prefix": "product_",
                    "output_path": output_dir
                }
            }
        ]
    }
    return workflow

class AIAvatarStudio:
    def __init__(self):
        self.styles = {
            "赛博朋克": "cyberpunk style, neon lights, futuristic, digital art",
            "水彩风": "watercolor painting, soft colors, artistic, delicate",
            "动漫风": "anime style, Japanese animation, vibrant colors, detailed",
            "油画风": "oil painting, classical art, rich textures, masterpiece",
            "像素风": "pixel art, retro game style, 8-bit, colorful",
            "国潮风": "Chinese traditional art, modern fusion, red and gold",
            "极简风": "minimalist, clean lines, geometric, modern art",
            "梦幻风": "dreamy, ethereal, soft glow, fantasy, magical",
            "复古风": "vintage, retro 80s, film grain, warm tones",
            "未来科技": "sci-fi, holographic, metallic, advanced technology"
        }
    
    def train_face_lora(self, user_photos, user_id):
        """训练用户专属LoRA模型（保持面部特征）"""
        # 使用InstantID或IP-Adapter保持面部一致性
        config = {
            "base_model": "sd_xl_base_1.0",
            "training_images": user_photos,
            "instance_prompt": f"photo of sks_{user_id} person",
            "class_prompt": "photo of a person",
            "learning_rate": 1e-4,
            "max_train_steps": 800,
            "resolution": 1024
        }
        
        # 训练LoRA（约5分钟）
        lora_path = train_lora(config)
        return lora_path
    
    def generate_styled_avatars(self, user_photos, user_id):
        """生成全套风格化头像"""
        results = {}
        
        # 使用IP-Adapter保持面部特征（无需训练，更快）
        face_embedding = self.extract_face_embedding(user_photos[0])
        
        for style_name, style_prompt in self.styles.items():
            prompt = f"portrait of a person, {style_prompt}, high quality, detailed face"
            
            image = self.sd_api.generate(
                prompt=prompt,
                negative_prompt="blurry, distorted face, extra limbs",
                ip_adapter_image=user_photos[0],
                ip_adapter_scale=0.6,
                width=1024,
                height=1024,
                steps=30
            )
            
            results[style_name] = image
        
        return results
    
    def process_order(self, order):
        """处理一个订单"""
        user_photos = order["photos"]
        user_id = order["user_id"]
        
        # 生成10种风格头像
        avatars = self.generate_styled_avatars(user_photos, user_id)
        
        # 拼接预览图
        preview = self.create_preview_grid(avatars)
        
        # 打包高清图
        package = self.package_hd_images(avatars)
        
        return {
            "preview": preview,
            "download_link": package,
            "styles": list(avatars.keys())
        }

class OrderAutomation:
    def __init__(self):
        self.studio = AIAvatarStudio()
        self.pending_orders = []
    
    async def auto_process_pipeline(self):
        """自动化处理流水线"""
        while True:
            # 1. 从闲鱼/小红书获取新订单
            new_orders = await self.fetch_new_orders()
            
            for order in new_orders:
                try:
                    # 2. 自动回复确认
                    await self.send_confirmation(order)
                    
                    # 3. 生成头像
                    result = self.studio.process_order(order)
                    
                    # 4. 发送预览图
                    await self.send_preview(order, result["preview"])
                    
                    # 5. 确认满意后发送高清图
                    await self.send_final_delivery(order, result["download_link"])
                    
                    # 6. 请求好评
                    await self.request_review(order)
                    
                    print(f"订单完成: {order['user_id']}")
                    
                except Exception as e:
                    print(f"订单处理失败: {e}")
                    await self.notify_admin(order, str(e))
            
            await asyncio.sleep(60)  # 每分钟检查一次