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+from fastapi import APIRouter, HTTPException, Depends, Query
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+from mysql.connector.pooling import PooledMySQLConnection
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+import os
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+import json
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+import math
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+from pathlib import Path
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+from typing import List, Dict, Any, Optional
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+from PIL import Image
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+
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+from app.core.logger import get_logger
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+from app.core.config import settings
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+from app.core.database_loader import get_db_connection
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+from app.crud import crud_card
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+from app.utils.scheme import ImageType
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+
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+logger = get_logger(__name__)
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+router = APIRouter()
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+
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+# 定义新的缺陷图片存储路径
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+DEFECT_IMAGE_DIR = settings.DEFECT_IMAGE_DIR
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+
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+
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+def _get_active_json(image_data: Any) -> Optional[Dict]:
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+ """获取有效的json数据，优先 modified_json"""
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+ if not image_data:
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+ return None
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+
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+ # image_data 可能是 Pydantic 对象或 字典，做兼容处理
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+ if hasattr(image_data, "modified_json"):
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+ mj = image_data.modified_json
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+ dj = image_data.detection_json
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+ else:
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+ mj = image_data.get("modified_json")
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+ dj = image_data.get("detection_json")
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+
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+ # 注意：根据 schema.py，这里读出来已经是 dict 了，不需要 json.loads
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+ # 如果数据库里存的是 null，读出来是 None
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+ if mj:
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+ return mj
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+ return dj
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+
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+
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+def _crop_defect_image(original_image_path_str: str, min_rect: List, output_filename: str) -> str:
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+ """
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+ 切割缺陷图片为正方形
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+ min_rect 结构: [[center_x, center_y], [width, height], angle]
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+ """
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+ try:
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+ # 构建绝对路径
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+ # 假设 original_image_path_str 是 "/Data/..." 格式
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+ rel_path = original_image_path_str.lstrip('/\\')
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+ full_path = settings.BASE_PATH / rel_path
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+
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+ if not full_path.exists():
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+ logger.warning(f"原图不存在: {full_path}")
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+ return ""
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+
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+ with Image.open(full_path) as img:
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+ img_w, img_h = img.size
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+
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+ # 解析 min_rect
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+ # min_rect[0] 是中心点 [x, y]
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+ # min_rect[1] 是宽高 [w, h]
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+ center_x, center_y = min_rect[0]
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+ rect_w, rect_h = min_rect[1]
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+
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+ # 确定裁剪的正方形边长：取宽高的最大值，并适当外扩 (例如 1.5 倍) 以展示周围环境
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+ # 如果缺陷非常小，设置一个最小尺寸（例如 100px），避免切图太模糊
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+ side_length = max(rect_w, rect_h) * 1.5
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+ side_length = max(side_length, 100)
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+
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+ half_side = side_length / 2
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+
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+ # 计算裁剪框 (left, top, right, bottom)
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+ left = center_x - half_side
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+ top = center_y - half_side
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+ right = center_x + half_side
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+ bottom = center_y + half_side
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+
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+ # 边界检查，防止超出图片范围
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+ # 如果只是想保持正方形，超出部分可以填黑，或者简单的移动框的位置
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+ # 这里简单处理：如果超出边界，就移动框，实在移不动就截断
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+ if left < 0:
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+ right -= left # 往右移
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+ left = 0
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+ if top < 0:
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+ bottom -= top # 往下移
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+ top = 0
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+ if right > img_w:
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+ left -= (right - img_w) # 往左移
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+ right = img_w
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+ if bottom > img_h:
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+ top -= (bottom - img_h) # 往上移
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+ bottom = img_h
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+
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+ # 再次检查防止负数（比如图片本身比框还小）
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+ left = max(0, left)
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+ top = max(0, top)
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+ right = min(img_w, right)
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+ bottom = min(img_h, bottom)
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+
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+ crop_box = (left, top, right, bottom)
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+ cropped_img = img.crop(crop_box)
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+
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+ # 保存
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+ save_path = DEFECT_IMAGE_DIR / output_filename
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+ cropped_img.save(save_path, quality=95)
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+
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+ # 返回 URL 路径 (相对于项目根目录的 web 路径)
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+ return f"/DefectImage/{output_filename}"
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+
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+ except Exception as e:
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+ logger.error(f"切割图片失败: {e}")
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+ return ""
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+
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+
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+@router.get("/generate", status_code=200, summary="生成评级报告数据")
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+def generate_rating_report(
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+ card_id: int,
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+ db_conn: PooledMySQLConnection = Depends(get_db_connection)
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+):
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+ top_n_defects = 3
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+ """
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+ 根据 Card ID 生成评级报告 JSON
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+ """
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+ # 1. 获取卡片详情 (复用 Crud 逻辑，确保能拿到所有图片)
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+ card_data = crud_card.get_card_with_details(db_conn, card_id)
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+ if not card_data:
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+ raise HTTPException(status_code=404, detail="未找到该卡片信息")
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+
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+ # 初始化返回结构
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+ response_data = {
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+ "backImageUrl": "",
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+ "frontImageUrl": "",
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+ "cardNo": "",
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+ "centerBack": "",
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+ "centerFront": "",
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+ "measureLength": 0.0,
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+ "measureWidth": 0.0,
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+ "cornerBackNum": 0,
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+ "sideBackNum": 0,
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+ "surfaceBackNum": 0,
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+ "cornerFrontNum": 0,
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+ "sideFrontNum": 0,
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+ "surfaceFrontNum": 0,
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+ "popNum": 0, # 暂时无数据来源，置0
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+ "scoreThreshold": float(card_data.get("detection_score") or 0),
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+ "evaluateNo": str(card_data.get("id")),
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+ "recognizedInfoDTO": {
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+ "cardSet": "",
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+ "player": "",
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+ "series": "",
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+ "year": ""
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+ },
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+ "defectDetailList": []
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+ }
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+
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+ # 临时列表用于收集所有缺陷，最后排序取 Top N
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+ all_defects_collected = []
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+
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+ # 遍历图片寻找 Front Ring 和 Back Ring
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+ images = card_data.get("images", [])
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+
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+ # 辅助字典：defect_type 到 统计字段 的映射
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+ defect_map_keys = {
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+ "front_ring": {
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+ "corner": "cornerFrontNum",
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+ "edge": "sideFrontNum",
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+ "face": "surfaceFrontNum"
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+ },
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+ "back_ring": {
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+ "corner": "cornerBackNum",
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+ "edge": "sideBackNum",
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+ "face": "surfaceBackNum"
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+ }
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+ }
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+
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+ for img in images:
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+ img_type = img.image_type
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+
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+ # 只处理环光图
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+ if img_type not in ["front_ring", "back_ring"]:
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+ continue
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+
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+ # 设置主图 URL
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+ if img_type == "front_ring":
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+ response_data["frontImageUrl"] = img.image_path
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+ elif img_type == "back_ring":
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+ response_data["backImageUrl"] = img.image_path
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+
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+ # 获取有效 JSON
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+ json_data = _get_active_json(img)
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+ if not json_data or "result" not in json_data:
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+ continue
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+
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+ result_node = json_data["result"]
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+
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+ # 1. 处理居中 (Center)
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+ center_inf = result_node.get("center_result", {}).get("box_result", {}).get("center_inference", {})
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+ if center_inf:
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+ # 格式: L/R=47/53, T/B=51/49 (取整)
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+ # center_inference 包含 center_left, center_right, center_top, center_bottom
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+ c_str = (
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+ f"L/R={int(round(center_inf.get('center_left', 0)))}/{int(round(center_inf.get('center_right', 0)))}, "
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+ f"T/B={int(round(center_inf.get('center_top', 0)))}/{int(round(center_inf.get('center_bottom', 0)))}"
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+ )
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+ if img_type == "front_ring":
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+ response_data["centerFront"] = c_str
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+ # 2. 处理尺寸 (仅从正面取，或者只要有就取) - mm 转 cm，除以 10，保留2位
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+ rw_mm = center_inf.get("real_width_mm", 0)
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+ rh_mm = center_inf.get("real_height_mm", 0)
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+ response_data["measureWidth"] = round(rw_mm / 10.0, 2)
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+ response_data["measureLength"] = round(rh_mm / 10.0, 2)
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+ else:
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+ response_data["centerBack"] = c_str
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+
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+ # 2. 处理缺陷 (Defects)
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+ defects = result_node.get("defect_result", {}).get("defects", [])
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+
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+ for defect in defects:
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+ # 过滤 edit_type == 'del'
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+ if defect.get("edit_type") == "del":
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+ continue
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+
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+ d_type = defect.get("defect_type", "") # corner, edge, face
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+ d_label = defect.get("label", "") # scratch, wear, etc.
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+
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+ # 统计数量
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+ count_key = defect_map_keys.get(img_type, {}).get(d_type)
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+ if count_key:
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+ response_data[count_key] += 1
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+
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+ # 收集详细信息用于 Top N 列表
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+ # 需要保存：缺陷对象本身，图片路径，正反面标识
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+ side_str = "FRONT" if img_type == "front_ring" else "BACK"
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+
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+ all_defects_collected.append({
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+ "defect_data": defect,
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+ "image_path": img.image_path,
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+ "side": side_str,
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+ "area": defect.get("actual_area", 0)
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+ })
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+
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+ # 3. 处理 defectDetailList (Top N 切图)
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+ # 按实际面积从大到小排序
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+ all_defects_collected.sort(key=lambda x: x["area"], reverse=True)
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+
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+ top_defects = all_defects_collected[:top_n_defects]
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+
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+ final_defect_list = []
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+ for idx, item in enumerate(top_defects, start=1):
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+ defect = item["defect_data"]
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+ side = item["side"]
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+ original_img_path = item["image_path"]
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+
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+ # 构造 ID
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+ d_id = idx # 1, 2, 3
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+
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+ # 构造文件名: {card_id}_{seq_id}.jpg
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+ filename = f"{card_id}_{d_id}.jpg"
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+
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+ # 执行切图
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+ min_rect = defect.get("min_rect")
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+ defect_img_url = ""
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+ location_str = ""
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+
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+ if min_rect and len(min_rect) == 3:
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+ # 切图并保存
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+ defect_img_url = _crop_defect_image(original_img_path, min_rect, filename)
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+
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+ # 计算 Location (中心坐标)
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+ # min_rect[0] 是 [x, y]
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+ cx, cy = min_rect[0]
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+ location_str = f"{int(cx)},{int(cy)}"
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+
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+ # 构造 Type 字符串: defect_type + label (大写)
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+ # 例如: defect_type="edge", label="wear" -> "EDGE WEAR"
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+ d_type_raw = defect.get("defect_type", "")
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+ d_label_raw = defect.get("label", "")
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+ type_str = f"{d_type_raw.upper()} {d_label_raw.upper()}".strip()
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+
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+ final_defect_list.append({
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+ "id": d_id,
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+ "side": side,
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+ "location": location_str,
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+ "type": type_str,
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+ "defectImgUrl": defect_img_url
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+ })
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+
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+ response_data["defectDetailList"] = final_defect_list
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+
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+ return response_data
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AnlaAnla