from app.utils.scheme import ImageType, CardImageResponse from app.core.logger import get_logger from typing import List, Dict, Any logger = get_logger(__name__) def calculate_scores_from_images(images: List[CardImageResponse]) -> Dict[str, Any]: """ 根据一个包含4张图片的列表，计算原始分数和修改后分数。 返回一个包含分数详情的字典。 """ scores = { "detection_score": None, "modified_score": None, "detection_score_detail": { "detection_center_score": None, "detection_corner_score": None, "detection_edge_score": None, "detection_face_score": None }, "modified_score_detail": { "modified_center_score": None, "modified_corner_score": None, "modified_edge_score": None, "modified_face_score": None }, "is_edited": False, } if len(images) != 4: return scores try: # ---------- detection_score ---------- detection_score = 10.0 detection_center_score = 10.0 detection_corner_score = 10.0 detection_edge_score = 10.0 detection_face_score = 10.0 for img in images: try: add_val = img.detection_json.get("result", {}).get("_used_compute_deduct_score", 0) detection_score += float(add_val or 0) if img.image_type == ImageType.front_edge: center_reduct_val = img.detection_json.get("result", {}).get("center_result", {}).get( "deduct_score", 0) corner_reduct_val = img.detection_json.get("result", {}).get("defect_result", {}).get( "front_corner_deduct_score", 0) edge_reduct_val = img.detection_json.get("result", {}).get("defect_result", {}).get( "front_edge_deduct_score", 0) detection_center_score += float(center_reduct_val or 0) detection_corner_score += float(corner_reduct_val or 0) detection_edge_score += float(edge_reduct_val or 0) elif img.image_type == ImageType.back_edge: center_reduct_val = img.detection_json.get("result", {}).get("center_result", {}).get( "deduct_score", 0) corner_reduct_val = img.detection_json.get("result", {}).get("defect_result", {}).get( "back_corner_deduct_score", 0) edge_reduct_val = img.detection_json.get("result", {}).get("defect_result", {}).get( "back_edge_deduct_score", 0) detection_center_score += float(center_reduct_val or 0) detection_corner_score += float(corner_reduct_val or 0) detection_edge_score += float(edge_reduct_val or 0) elif img.image_type == ImageType.front_face: face_reduct_val = img.detection_json.get("result", {}).get("defect_result", {}).get( "front_face_deduct_score", 0) detection_face_score += float(face_reduct_val or 0) elif img.image_type == ImageType.back_face: face_reduct_val = img.detection_json.get("result", {}).get("defect_result", {}).get( "back_face_deduct_score", 0) detection_face_score += float(face_reduct_val or 0) except Exception as e: logger.warning(f"解析 detection_json 分数失败 (image_id={img.id}): {e}") scores["detection_score"] = detection_score scores["detection_score_detail"] = { "detection_center_score": detection_center_score, "detection_corner_score": detection_corner_score, "detection_edge_score": detection_edge_score, "detection_face_score": detection_face_score, } # ---------- modified_score and is_edited status ---------- modified_score = 10.0 modified_center_score = 10.0 modified_corner_score = 10.0 modified_edge_score = 10.0 modified_face_score = 10.0 # 检查是否存在任何非空的 modified_json is_edited = any(img.modified_json is not None for img in images) scores["is_edited"] = is_edited if is_edited: for img in images: src = img.modified_json if img.modified_json is not None else img.detection_json try: add_val = src.get("result", {}).get("_used_compute_deduct_score", 0) modified_score += float(add_val or 0) if img.image_type == ImageType.front_edge: center_reduct_val = src.get("result", {}).get("center_result", {}).get("deduct_score", 0) corner_reduct_val = src.get("result", {}).get("defect_result", {}).get( "front_corner_deduct_score", 0) edge_reduct_val = src.get("result", {}).get("defect_result", {}).get("front_edge_deduct_score", 0) modified_center_score += float(center_reduct_val or 0) modified_corner_score += float(corner_reduct_val or 0) modified_edge_score += float(edge_reduct_val or 0) elif img.image_type == ImageType.back_edge: center_reduct_val = src.get("result", {}).get("center_result", {}).get("deduct_score", 0) corner_reduct_val = src.get("result", {}).get("defect_result", {}).get( "back_corner_deduct_score", 0) edge_reduct_val = src.get("result", {}).get("defect_result", {}).get("back_edge_deduct_score", 0) modified_center_score += float(center_reduct_val or 0) modified_corner_score += float(corner_reduct_val or 0) modified_edge_score += float(edge_reduct_val or 0) elif img.image_type == ImageType.front_face: face_reduct_val = src.get("result", {}).get("defect_result", {}).get("front_face_deduct_score", 0) modified_face_score += float(face_reduct_val or 0) elif img.image_type == ImageType.back_face: face_reduct_val = src.get("result", {}).get("defect_result", {}).get("back_face_deduct_score", 0) modified_face_score += float(face_reduct_val or 0) except Exception as e: logger.warning(f"解析 modified_json 分数失败 (image_id={img.id}): {e}") scores["modified_score"] = modified_score scores["modified_score_detail"] = { "modified_center_score": modified_center_score, "modified_corner_score": modified_corner_score, "modified_edge_score": modified_edge_score, "modified_face_score": modified_face_score, } except Exception as e: logger.error(f"计算分数过程异常: {e}") return scores