CheckCardBoxAndDefectServer/app/services/score_service.py

import cv2

from app.core.config import settings
from app.core.logger import get_logger
from app.services.defect_service import DefectInferenceService
from app.services.card_rectify_and_center import CardRectifyAndCenter
from app.utils.score_inference.CardScorer import CardScorer
import numpy as np
import json

logger = get_logger(__name__)


class ScoreService:
    def __init__(self):
        self.scoring_config_path = settings.SCORE_CONFIG_PATH
        self.card_scorer = CardScorer(config_path=self.scoring_config_path)

        self.defect_service = DefectInferenceService()
        self.rectify_center_service = CardRectifyAndCenter()

    def score_inference(self, score_type: str, is_reflect_card: bool,
                        img_bgr: np.ndarray) -> dict:

        # 解包返回值，获取转正后的外框数据
        img_bgr, transformed_outer_json = self.rectify_center_service.rectify_and_center(img_bgr)
        if img_bgr is None:
            raise ValueError("图像转正处理失败")

        imageHeight, imageWidth = img_bgr.shape[:2]

        logger.info("开始进行卡片分数推理, 使用变换后的外框")
        # 定义通用参数，传入 pre_calculated_outer_result
        defect_kwargs = {
            "img_bgr": img_bgr.copy(),
            "pre_calculated_outer_result": transformed_outer_json
        }


        if score_type == 'front_ring' or score_type == 'front_coaxial':
            center_data = self.defect_service.defect_inference("pokemon_front_card_center", **defect_kwargs)
        else:
            center_data = self.defect_service.defect_inference("pokemon_back_card_center", **defect_kwargs)

        if is_reflect_card:
            if score_type == 'front_ring':
                defect_data = self.defect_service.defect_inference('pokemon_front_face_reflect_ring_light_defect',
                                                                   **defect_kwargs)
            elif score_type == 'front_coaxial':
                defect_data = self.defect_service.defect_inference('pokemon_front_face_reflect_coaxial_light_defect',
                                                                   **defect_kwargs)
            elif score_type == 'back_ring':
                defect_data = self.defect_service.defect_inference('pokemon_back_face_ring_light_defect',
                                                                   **defect_kwargs)
            elif score_type == 'back_coaxial':
                defect_data = self.defect_service.defect_inference('pokemon_back_face_coaxial_light_defect',
                                                                   **defect_kwargs)
            else:
                return {}
        else:
            if score_type == 'front_ring':
                defect_data = self.defect_service.defect_inference('pokemon_front_face_no_reflect_ring_light_defect',
                                                                   **defect_kwargs)
            elif score_type == 'front_coaxial':
                defect_data = self.defect_service.defect_inference('pokemon_front_face_no_reflect_coaxial_light_defect',
                                                                   **defect_kwargs)
            elif score_type == 'back_ring':
                defect_data = self.defect_service.defect_inference('pokemon_back_face_ring_light_defect',
                                                                   **defect_kwargs)
            elif score_type == 'back_coaxial':
                defect_data = self.defect_service.defect_inference('pokemon_back_face_coaxial_light_defect',
                                                                   **defect_kwargs)
            else:
                return {}

        logger.info("模型推理结束, 开始计算分数")
        if score_type == 'front_ring' or score_type == 'front_coaxial':
            card_aspect = "front"
        else:
            card_aspect = "back"
        if score_type == 'front_ring' or score_type == 'back_ring':
            card_light_type = "ring"
        else:
            card_light_type = "coaxial"

        if card_light_type == 'ring':
            center_score_data = self.card_scorer.calculate_centering_score(card_aspect, center_data, True)

            # 计算角, 边, 面的分数, 会把分数写入json, 然后传入刚写好的, 继续写边的分数
            defect_data = self.card_scorer.calculate_defect_score('corner', card_aspect, card_light_type,
                                                                  defect_data, True)
            defect_data = self.card_scorer.calculate_defect_score('edge', card_aspect, card_light_type,
                                                                  defect_data, True)
            defect_score_data = self.card_scorer.calculate_defect_score('face', card_aspect, card_light_type,
                                                                        defect_data, True)
        elif card_light_type == 'coaxial':
            # 居中
            center_score_data = {}
            # 同轴光照片只计算面缺陷
            defect_score_data = self.card_scorer.calculate_defect_score('face', card_aspect, card_light_type,
                                                                        defect_data, True)
        else:
            return {}

        result_json = self.card_scorer.formate_one_card_result(center_score_data, defect_score_data,
                                                               card_light_type=card_light_type,
                                                               card_aspect=card_aspect,
                                                               imageHeight=imageHeight,
                                                               imageWidth=imageWidth)

        temp_score_json_path = settings.TEMP_WORK_DIR / f'{score_type}_score.json'
        with open(temp_score_json_path, 'w', encoding='utf-8') as f:
            json.dump(result_json, f, ensure_ascii=False, indent=2)
        logger.info("分数推理完成 ")
        return result_json

    @staticmethod
    def _has_valid_center_box(center_json: dict) -> bool:
        """判断 center_result 是否包含可用于居中重算的内外框 shapes。

        StitchFusion(同轴光)输出的 center_result 里 inner_box/outer_box 的 shapes 为空,
        无法做居中重算; 此时应降级为只算面缺陷, 避免索引空列表报错。
        """
        if not center_json:
            return False
        box_result = center_json.get('box_result') or {}
        inner_shapes = (box_result.get('inner_box') or {}).get('shapes') or []
        outer_shapes = (box_result.get('outer_box') or {}).get('shapes') or []
        return len(inner_shapes) > 0 and len(outer_shapes) > 0

    def recalculate_defect_score(self, score_type: str, json_data: dict):

        center_json_data = json_data["result"]['center_result']
        defect_json_data = json_data["result"]['defect_result']
        imageHeight = json_data["result"].get('imageHeight', 0)
        imageWidth = json_data["result"].get('imageWidth', 0)

        # 正反面类型分类
        if score_type == 'front_ring' or score_type == 'front_coaxial':
            card_aspect = "front"
        else:
            card_aspect = "back"

        if score_type == 'front_ring' or score_type == 'back_ring':
            card_light_type = "ring"
        else:
            card_light_type = "coaxial"

        # 环光重算依赖 center_result 的内外框; 若缺失(如同轴光/StitchFusion 数据),
        # 自动降级为同轴光面缺陷重算, 避免 list index out of range。
        if card_light_type == 'ring' and not self._has_valid_center_box(center_json_data):
            logger.warning("score_type 为环光但 center_result 无有效内外框, 自动降级为同轴光(coaxial)面缺陷重算")
            card_light_type = "coaxial"

        logger.info("开始进行缺陷信息重计算")
        defect_data = self.defect_service.re_inference_from_json(card_light_type=card_light_type,
                                                                 center_json=center_json_data,
                                                                 defect_json=defect_json_data)

        logger.info("开始重新计算分数")
        if card_light_type == 'ring':
            logger.info("开始进行居中信息重计算")
            center_data = self.defect_service.re_inference_from_json(card_light_type="center",
                                                                     center_json=center_json_data,
                                                                     defect_json=defect_json_data)

            center_score_data = self.card_scorer.calculate_centering_score(card_aspect, center_data, True)

            # 计算角, 边, 面的分数, 会把分数写入json, 然后传入刚写好的, 继续写边的分数
            logger.info("开始重新计算:边角面")
            defect_data = self.card_scorer.calculate_defect_score('corner', card_aspect, card_light_type,
                                                                  defect_data, True)
            defect_data = self.card_scorer.calculate_defect_score('edge', card_aspect, card_light_type,
                                                                  defect_data, True)
            defect_score_data = self.card_scorer.calculate_defect_score('face', card_aspect, card_light_type,
                                                                        defect_data, True)
        elif card_light_type == 'coaxial':
            # 居中
            center_score_data = {}
            # 同轴光照片只计算面缺陷
            logger.info("开始重新计算:面")
            defect_score_data = self.card_scorer.calculate_defect_score('face', card_aspect, card_light_type,
                                                                        defect_data, True)
        else:
            return {}

        result_json = self.card_scorer.formate_one_card_result(center_score_data, defect_score_data,
                                                               card_light_type=card_light_type,
                                                               card_aspect=card_aspect,
                                                               imageHeight=imageHeight,
                                                               imageWidth=imageWidth)

        temp_score_json_path = settings.TEMP_WORK_DIR / f're_{score_type}_score.json'
        with open(temp_score_json_path, 'w', encoding='utf-8') as f:
            json.dump(result_json, f, ensure_ascii=False, indent=2)
        logger.info("分数推理完成 ")
        return result_json