CardScoreDataServer/app/utils/card_score_calculate.py

from app.utils.scheme import CardDetailResponse, ImageType
from app.core.logger import get_logger
from typing import List

logger = get_logger(__name__)


def card_score_calculate(card_data: dict, images: List) -> CardDetailResponse:
    card_data["detection_score"] = None
    card_data["modified_score"] = None
    card_data["detection_score_detail"] = {}
    card_data["modified_score_detail"] = {}
    if len(images) == 4:
        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:
                    # 总分的计算
                    add_val = img.detection_json.get("result", {}).get("_used_compute_deduct_score", 0)
                    detection_score += float(add_val or 0)

                    # 累加不同类型的扣分项
                    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}")
            card_data["detection_score"] = detection_score
            card_data["detection_score_detail"]["detection_center_score"] = detection_center_score
            card_data["detection_score_detail"]["detection_corner_score"] = detection_corner_score
            card_data["detection_score_detail"]["detection_edge_score"] = detection_edge_score
            card_data["detection_score_detail"]["detection_face_score"] = detection_face_score

            # ---------- modified_score ----------
            modified_score = 10.0
            modified_center_score = 10.0
            modified_corner_score = 10.0
            modified_edge_score = 10.0
            modified_face_score = 10.0

            all_modified_none = all(img.modified_json is None for img in images)
            if not all_modified_none:

                for img in images:
                    src = img.modified_json if img.modified_json is not None else img.detection_json
                    try:
                        # 总分的计算
                        add_val = src.get("result", {}).get("_used_compute_deduct_score", 0)
                        modified_score += float(add_val or 0)

                        # 累加不同修改后数据类型的扣分项
                        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}")
                card_data["modified_score"] = modified_score

                card_data["modified_score_detail"]["modified_center_score"] = modified_center_score
                card_data["modified_score_detail"]["modified_corner_score"] = modified_corner_score
                card_data["modified_score_detail"]["modified_edge_score"] = modified_edge_score
                card_data["modified_score_detail"]["modified_face_score"] = modified_face_score
            else:
                card_data["modified_score"] = None
                card_data["modified_score_detail"]["modified_center_score"] = None
                card_data["modified_score_detail"]["modified_corner_score"] = None
                card_data["modified_score_detail"]["modified_edge_score"] = None
                card_data["modified_score_detail"]["modified_face_score"] = None

        except Exception as e:
            logger.error(f"计算分数过程异常: {e}")

    # 组合成最终响应
    card_response = CardDetailResponse.model_validate(card_data)
    card_response.images = images

    return card_response