477 lines
19 KiB
Python
477 lines
19 KiB
Python
# -*- coding: utf-8 -*-
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"""
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AI 图案生成接口
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功能：生成预览图、面料图、裁切、细化
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"""
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from fastapi import APIRouter, HTTPException, Depends
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from pydantic import BaseModel
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from typing import Optional
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import json, base64, logging
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from app.core.config import settings
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from app.core.security import get_current_user
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from app.db import get_db
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from sqlalchemy.orm import Session
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from app.models.user import User
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from app.api.v1.ai_llm import (
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call_image_model,
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call_image_model_batch,
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get_runtime_ai_config,
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has_ai_config,
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)
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log = logging.getLogger(__name__)
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if not log.handlers:
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log.setLevel(logging.INFO)
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log.propagate = False
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_h = logging.StreamHandler()
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_h.setFormatter(
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logging.Formatter("[%(asctime)s][%(name)s][%(levelname)s] %(message)s")
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)
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log.addHandler(_h)
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router = APIRouter()
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# ==================== 数据模型 ====================
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class GeneratePatternRequest(BaseModel):
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image_base64: str
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canvas_base64: Optional[str] = None
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prompt: Optional[str] = None
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image_edit_model: Optional[str] = None
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class GenerateFabricRequest(BaseModel):
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image_base64: str
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fabric_info: dict
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image_edit_model: Optional[str] = None
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class GenerateDesignImagesRequest(BaseModel):
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prompt: str
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reference_image_base64: Optional[str] = None
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count: int = 1
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size: Optional[str] = "2K"
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image_edit_model: Optional[str] = None
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class CropPieceRequest(BaseModel):
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preview_base64: str
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canvas_width: int
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canvas_height: int
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piece_left: float
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piece_top: float
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piece_width: float
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piece_height: float
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piece_name: str
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padding: float = 0.15
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class RefinePieceRequest(BaseModel):
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cropped_base64: str
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piece_name: str
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pattern_type: str = "fill_pattern"
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aspect_ratio: Optional[str] = None
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piece_width: Optional[int] = None
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piece_height: Optional[int] = None
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prompt: Optional[str] = None
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image_edit_model: Optional[str] = None
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class ExtractPieceRequest(BaseModel):
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preview_base64: str
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piece_name: str
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piece_description: str = ""
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prompt: Optional[str] = None
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# ==================== 图案生成接口 ====================
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@router.post("/ai/generate-preview")
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async def generate_preview(
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data: GeneratePatternRequest,
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current_username: str = Depends(get_current_user),
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db: Session = Depends(get_db),
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):
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"""阶段1：成衣照片 + 裁片截图 → AI 生成带轮廓的花样预览图"""
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log.info(f"{'=' * 60}")
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log.info(
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f"[Preview] 收到预览请求, 成衣图: {len(data.image_base64) // 1024}KB, 裁片图: {len(data.canvas_base64) // 1024 if data.canvas_base64 else 0}KB"
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)
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if data.prompt:
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log.info(f"[Preview] 自定义提示词: {data.prompt[:150]}")
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user = db.query(User).filter(User.username == current_username).first()
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runtime_ai_config = get_runtime_ai_config(user)
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effective_image_model = (
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data.image_edit_model
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or (user.ai_image_model if user else None)
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or settings.AI_IMAGE_EDIT_MODEL
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)
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if not has_ai_config(runtime_ai_config) or not effective_image_model:
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raise HTTPException(400, "请先在 API 配置页填写转发 Key，或联系管理员配置默认 AI_API_KEY")
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try:
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images = [data.image_base64]
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if data.canvas_base64:
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images.append(data.canvas_base64)
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default_prompt = (
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(
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"Image 1 是一件成衣照片，Image 2 是裁片轮廓图。"
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"请将 Image 1 成衣上的面料花样提取出来，填充到 Image 2 的每个裁片轮廓内。"
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"要求：保持花样颜色、比例、方向一致；保留裁片轮廓线；不要改变裁片排列位置。"
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)
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if data.canvas_base64
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else ("请从这件成衣中提取面料花样，生成一张干净的花样平铺图。")
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)
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result_url, desc = call_image_model(
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images_b64=images,
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prompt=data.prompt or default_prompt,
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model_override=effective_image_model,
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runtime_config=runtime_ai_config,
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)
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return {"code": 200, "data": {"image_url": result_url, "description": desc}}
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except Exception as e:
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log.error(f"预览生成失败: {e}", exc_info=True)
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err_msg = str(e)
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if "data_inspection_failed" in err_msg or "inappropriate" in err_msg:
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raise HTTPException(
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400,
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"图片内容未通过平台安全审核，请更换图片后重试（避免使用含版权角色/敏感内容的图片）",
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)
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raise HTTPException(500, f"预览生成失败: {err_msg}")
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@router.post("/ai/generate-fabric")
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async def generate_fabric(
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data: GenerateFabricRequest,
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current_username: str = Depends(get_current_user),
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db: Session = Depends(get_db),
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):
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"""all_over 模式：根据成衣照片 + fabric_info 生成面料平铺图"""
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log.info(f"{'=' * 60}")
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log.info(f"[Fabric] 生成面料图, 成衣: {len(data.image_base64) // 1024}KB")
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log.info(
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f"[Fabric] fabric_info: {json.dumps(data.fabric_info, ensure_ascii=False)}"
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)
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user = db.query(User).filter(User.username == current_username).first()
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runtime_ai_config = get_runtime_ai_config(user)
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effective_image_model = (
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data.image_edit_model
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or (user.ai_image_model if user else None)
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or settings.AI_IMAGE_EDIT_MODEL
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)
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if not has_ai_config(runtime_ai_config) or not effective_image_model:
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raise HTTPException(400, "请先在 API 配置页填写转发 Key，或联系管理员配置默认 AI_API_KEY")
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try:
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fi = data.fabric_info
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prompt = (
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f"Please generate a high-resolution flat fabric pattern image based on this garment photo.\n"
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f"\nRequirements:\n"
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f"1. Output a RECTANGULAR flat fabric image (not garment shape, just the fabric laid flat)\n"
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f"2. Fabric type: {fi.get('fabric_type', 'digital print')}\n"
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f"3. Pattern elements: {fi.get('pattern_elements', 'floral')}\n"
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f"4. Pattern direction: {fi.get('pattern_direction', 'uniform')}\n"
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f"5. Color transition: {fi.get('color_transition', 'none')}\n"
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f"6. Pattern density: {fi.get('density', 'medium')}\n"
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f"7. Remove all garment wrinkles/shadows - show flat fabric only\n"
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f"8. The pattern must be clear, colors accurate, suitable for garment piece overlay\n"
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f"9. Aspect ratio: 3:4\n"
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f"10. The image should look like a piece of fabric photographed from directly above on a flat surface"
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)
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log.info(f"[Fabric] 提示词: {prompt[:200]}")
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result_url, desc = call_image_model(
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images_b64=[data.image_base64],
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prompt=prompt,
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model_override=effective_image_model,
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runtime_config=runtime_ai_config,
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)
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log.info(f"[Fabric] 面料图生成完成")
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return {"code": 200, "data": {"fabric_url": result_url, "description": desc}}
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except Exception as e:
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log.error(f"面料图生成失败: {e}", exc_info=True)
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raise HTTPException(500, f"面料图生成失败: {str(e)}")
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@router.post("/ai/generate-design-images")
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async def generate_design_images(
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data: GenerateDesignImagesRequest,
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current_username: str = Depends(get_current_user),
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db: Session = Depends(get_db),
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):
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"""通用豆包出图：支持纯文生图和基于参考图的连续组图。"""
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log.info(f"{'=' * 60}")
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log.info(
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f"[DesignImage] 通用出图, 参考图={'是' if data.reference_image_base64 else '否'}, count={data.count}, size={data.size or '2K'}"
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)
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user = db.query(User).filter(User.username == current_username).first()
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runtime_ai_config = get_runtime_ai_config(user)
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effective_image_model = (
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data.image_edit_model
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or (user.ai_image_model if user else None)
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or settings.AI_IMAGE_EDIT_MODEL
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)
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if not has_ai_config(runtime_ai_config) or not effective_image_model:
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raise HTTPException(400, "请先在 API 配置页填写转发 Key，或联系管理员配置默认 AI_API_KEY")
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prompt = (data.prompt or "").strip()
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if not prompt:
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raise HTTPException(400, "prompt 不能为空")
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try:
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count = max(1, min(int(data.count or 1), 4))
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images_b64 = [data.reference_image_base64] if data.reference_image_base64 else []
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result = call_image_model_batch(
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images_b64=images_b64,
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prompt=prompt,
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model_override=effective_image_model,
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size=data.size or "2K",
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max_images=count,
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stream=(count > 1),
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runtime_config=runtime_ai_config,
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)
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images = result.get("images", [])
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return {
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"code": 200,
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"data": {
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"images": images,
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"image_urls": [item["url"] for item in images],
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"count": len(images),
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"used_reference_image": bool(data.reference_image_base64),
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"usage": result.get("usage"),
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},
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}
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except Exception as e:
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log.error(f"通用出图失败: {e}", exc_info=True)
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err_msg = str(e)
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if "data_inspection_failed" in err_msg or "inappropriate" in err_msg:
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raise HTTPException(400, "图片内容未通过平台安全审核，请更换提示词或参考图")
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raise HTTPException(500, f"通用出图失败: {err_msg}")
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@router.post("/ai/crop-piece")
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async def crop_piece(
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data: CropPieceRequest, current_username: str = Depends(get_current_user)
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):
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"""从预览图中按坐标裁切指定裁片区域（Pillow，像素级精准）"""
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log.info(f"{'=' * 60}")
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log.info(f"[CropPiece] 裁切: {data.piece_name}")
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log.info(
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f"[CropPiece] 画布: {data.canvas_width}x{data.canvas_height}, 裁片区域: ({data.piece_left},{data.piece_top}) {data.piece_width}x{data.piece_height}"
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)
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try:
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result_b64 = _crop_piece_from_preview(data)
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return {
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"code": 200,
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"data": {"cropped_base64": result_b64, "piece_name": data.piece_name},
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}
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except Exception as e:
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log.error(f"裁切失败: {e}", exc_info=True)
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raise HTTPException(500, f"裁切失败: {str(e)}")
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@router.post("/ai/refine-piece")
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async def refine_piece(
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data: RefinePieceRequest,
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current_username: str = Depends(get_current_user),
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db: Session = Depends(get_db),
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):
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"""AI 细化裁切图：根据图案类型生成不同提示词"""
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log.info(f"{'=' * 60}")
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log.info(
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f"[RefinePiece] 细化: {data.piece_name}, 类型: {data.pattern_type}, 比例: {data.aspect_ratio}"
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)
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log.info(
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f"[RefinePiece] 裁片尺寸: {data.piece_width}x{data.piece_height}, 图片: {len(data.cropped_base64) // 1024}KB"
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)
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user = db.query(User).filter(User.username == current_username).first()
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runtime_ai_config = get_runtime_ai_config(user)
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effective_image_model = (
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data.image_edit_model
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or (user.ai_image_model if user else None)
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or settings.AI_IMAGE_EDIT_MODEL
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)
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if not has_ai_config(runtime_ai_config) or not effective_image_model:
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raise HTTPException(400, "请先在 API 配置页填写转发 Key，或联系管理员配置默认 AI_API_KEY")
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try:
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size_hint = ""
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if data.aspect_ratio:
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size_hint = f"输出图片宽高比必须为 {data.aspect_ratio}。"
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if data.piece_width and data.piece_height:
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size_hint += f"输出尺寸至少 {int(data.piece_width * 1.15)}x{int(data.piece_height * 1.15)} 像素。"
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if data.pattern_type == "fill_pattern":
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prompt = data.prompt or (
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f"这是从服装裁片中裁切出来的花型图案区域。"
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f"请将它处理为一张干净的矩形花型图："
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f"去掉所有轮廓线、标签文字和边缘空白；"
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f"花型图案要完整铺满整个矩形，无空白边距；"
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f"向四周自然延展重复纹样，保持花型连续无接缝；"
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f"保持花样的颜色和细节清晰。{size_hint}"
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)
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elif data.pattern_type == "theme_pattern":
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prompt = data.prompt or (
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f"这是从服装裁片中裁切出来的主题图案区域（如卡通人物、Logo、印花）。"
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f"请提取其中的主题图案，输出为纯白色背景(#FFFFFF)上的主题图案："
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f"主题图案要完整清晰，保持原始颜色和细节；"
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f"背景必须是纯白色(#FFFFFF)，不要任何其他底色或纹理；"
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f"去掉轮廓线和标签文字；主题图案在画面中居中放置。{size_hint}"
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)
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elif data.pattern_type == "mixed_fill":
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prompt = data.prompt or (
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f"这是从服装裁片中裁切出来的区域，包含主题图案和花型底纹。"
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f"请只提取底部的花型/纹理图案，去掉上面的主题图案（人物/Logo）："
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f"用底纹自然填充主题图案原来的位置；"
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f"花型要完整铺满整个矩形，保持纹样连续；"
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f"去掉轮廓线和标签文字。{size_hint}"
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)
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elif data.pattern_type == "mixed_theme":
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prompt = data.prompt or (
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f"这是从服装裁片中裁切出来的区域，包含主题图案和花型底纹。"
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f"请只提取主题图案（人物/Logo/大面积印花），输出为纯白色背景(#FFFFFF)："
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f"主题图案要完整清晰，保持原始颜色和细节；"
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f"背景必须是纯白色(#FFFFFF)，去掉所有底纹；"
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f"去掉轮廓线和标签文字；主题图案在画面中居中放置。{size_hint}"
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)
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else:
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prompt = (
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data.prompt
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or f"细化这张图片，去掉轮廓线和标签文字，保持清晰。{size_hint}"
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)
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log.info(f"[RefinePiece] 提示词: {prompt[:200]}")
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result_url, desc = call_image_model(
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images_b64=[data.cropped_base64],
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prompt=prompt,
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model_override=effective_image_model,
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runtime_config=runtime_ai_config,
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)
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return {
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"code": 200,
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"data": {
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"refined_url": result_url,
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"piece_name": data.piece_name,
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"pattern_type": data.pattern_type,
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},
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}
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except Exception as e:
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log.error(f"细化失败: {e}", exc_info=True)
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err_msg = str(e)
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if "data_inspection_failed" in err_msg:
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raise HTTPException(400, "图片内容未通过安全审核")
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raise HTTPException(500, f"细化失败: {err_msg}")
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@router.post("/ai/extract-piece-pattern")
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async def extract_piece_pattern(
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data: ExtractPieceRequest,
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current_username: str = Depends(get_current_user),
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db: Session = Depends(get_db),
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):
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"""阶段2：从预览图中提取指定裁片区域 → 输出矩形花样图"""
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log.info(f"{'=' * 60}")
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log.info(
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f"[ExtractPiece] 裁片: {data.piece_name}, 描述: '{data.piece_description}', 预览图: {len(data.preview_base64) // 1024}KB"
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)
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user = db.query(User).filter(User.username == current_username).first()
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runtime_ai_config = get_runtime_ai_config(user)
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effective_image_model = (
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getattr(data, "image_edit_model", None)
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or (user.ai_image_model if user else None)
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or settings.AI_IMAGE_EDIT_MODEL
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)
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if not has_ai_config(runtime_ai_config) or not effective_image_model:
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raise HTTPException(400, "请先在 API 配置页填写转发 Key，或联系管理员配置默认 AI_API_KEY")
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try:
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prompt = data.prompt or (
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f"这是一张服装裁片花样预览图，包含多个裁片。"
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f"请提取其中【{data.piece_name}】的花样区域"
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f"{'（' + data.piece_description + '）' if data.piece_description else ''}，"
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f"将该区域的花样输出为一张完整的矩形图片。"
|
||
f"要求：只保留花样内容，去掉轮廓线，填满整个矩形，保持清晰。"
|
||
)
|
||
result_url, desc = call_image_model(
|
||
images_b64=[data.preview_base64],
|
||
prompt=prompt,
|
||
model_override=effective_image_model,
|
||
runtime_config=runtime_ai_config,
|
||
)
|
||
return {
|
||
"code": 200,
|
||
"data": {
|
||
"pattern_url": result_url,
|
||
"piece_name": data.piece_name,
|
||
"description": desc,
|
||
},
|
||
}
|
||
except Exception as e:
|
||
log.error(f"裁片提取失败: {e}", exc_info=True)
|
||
raise HTTPException(500, f"裁片提取失败: {str(e)}")
|
||
|
||
|
||
# ==================== 工具函数 ====================
|
||
|
||
|
||
def _crop_piece_from_preview(data: CropPieceRequest) -> str:
|
||
"""用 Pillow 从预览图中按 PS 坐标裁切指定区域（带出血线）"""
|
||
from PIL import Image
|
||
import io
|
||
|
||
img_bytes = base64.b64decode(data.preview_base64)
|
||
preview_img = Image.open(io.BytesIO(img_bytes))
|
||
pw, ph = preview_img.size
|
||
|
||
scale_x = pw / data.canvas_width
|
||
scale_y = ph / data.canvas_height
|
||
|
||
bleed_x = data.piece_width * data.padding
|
||
bleed_y = data.piece_height * data.padding
|
||
|
||
left = max(0, (data.piece_left - bleed_x) * scale_x)
|
||
top = max(0, (data.piece_top - bleed_y) * scale_y)
|
||
right = min(pw, (data.piece_left + data.piece_width + bleed_x) * scale_x)
|
||
bottom = min(ph, (data.piece_top + data.piece_height + bleed_y) * scale_y)
|
||
|
||
log.info(f"[CropPiece] PS 画布: {data.canvas_width}x{data.canvas_height}")
|
||
log.info(f"[CropPiece] 预览图: {pw}x{ph}")
|
||
log.info(f"[CropPiece] 缩放比: x={scale_x:.4f}, y={scale_y:.4f}")
|
||
log.info(
|
||
f"[CropPiece] PS 裁片区域: ({data.piece_left},{data.piece_top}) {data.piece_width}x{data.piece_height}"
|
||
)
|
||
log.info(
|
||
f"[CropPiece] 出血线: {data.padding * 100:.0f}% → x±{bleed_x:.0f}px, y±{bleed_y:.0f}px"
|
||
)
|
||
log.info(
|
||
f"[CropPiece] 预览图裁切: ({left:.0f},{top:.0f})-({right:.0f},{bottom:.0f}) = {right - left:.0f}x{bottom - top:.0f}px"
|
||
)
|
||
|
||
cropped = preview_img.crop((int(left), int(top), int(right), int(bottom)))
|
||
|
||
import os, time
|
||
|
||
debug_dir = os.path.join(
|
||
os.path.dirname(__file__), "..", "..", "..", "debug_images"
|
||
)
|
||
os.makedirs(debug_dir, exist_ok=True)
|
||
ts = int(time.time())
|
||
debug_path = os.path.join(debug_dir, f"{ts}_crop_{data.piece_name}.png")
|
||
cropped.save(debug_path)
|
||
log.info(f"[CropPiece] 裁切结果已保存: {debug_path}")
|
||
|
||
buf = io.BytesIO()
|
||
cropped.save(buf, format="PNG")
|
||
return base64.b64encode(buf.getvalue()).decode()
|