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+import json
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+from typing import List, Dict, Any, Union
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+
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+
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+class CardScorer:
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+ """
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+ 它从一个JSON配置文件加载评分规则，并根据输入的卡片数据计算分数。
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+ """
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+
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+ def __init__(self, config_path: str):
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+ try:
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+ with open(config_path, 'r', encoding='utf-8') as f:
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+ self.config = json.load(f)
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+ self.base_score = self.config.get("base_score", 10.0)
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+ except FileNotFoundError:
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+ raise ValueError(f"配置文件未找到: {config_path}")
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+ except json.JSONDecodeError:
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+ raise ValueError(f"配置文件格式错误: {config_path}")
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+
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+ def _get_score_from_tiers(self, value: float, rules: List[List[Any]]) -> float:
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+ """
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+ 一个通用的辅助函数，根据分层规则查找值对应的分数。
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+ """
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+ for tier in rules:
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+ threshold, score = tier
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+ if threshold == "inf" or value < threshold:
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+ return float(score)
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+ return 0.0 # 如果没有匹配的规则，不扣分
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+
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+ def calculate_defect_score(self,
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+ card_defect_type: str,
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+ card_aspect: str,
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+ defect_data: Dict,
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+ is_write_score: bool = False) -> Union[float, dict]:
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+ """
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+ 一个通用的缺陷计分函数，用于计算角、边、表面的加权分数。
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+ card_defect_type: 'corner', 'edge', 'face'
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+ card_aspect: 为 front或 back
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+ is_write_score: 是否将分数写入json并返回
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+ """
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+ if card_defect_type != "corner" and card_defect_type != "edge" and card_defect_type != "face":
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+ raise TypeError("calculate_centering_score:card_type 只能为 'corner', 'edge', 'face'")
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+ if card_aspect != "front" and card_aspect != "back":
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+ raise TypeError("calculate_defect_score:card_type 只能为 front 或 back")
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+
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+ aspect_config = self.config[card_defect_type]
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+ total_deduction = 0.0
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+ weighted_scores = {}
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+
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+ # 1. 计算每种缺陷类型的总扣分
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+ for defect in defect_data['defects']:
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+ if defect['defect_type'] != card_defect_type:
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+ continue
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+
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+ if card_defect_type == 'corner' or card_defect_type == 'edge':
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+ if defect['label'] in ['wear', 'wear_and_impact', 'wear_and_stain']:
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+ defect_type = "wear_area"
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+ elif defect['label'] in ['impact', 'damaged']:
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+ defect_type = "loss_area"
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+ else:
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+ raise TypeError(f"数据缺陷类型不存在: {defect['label']}")
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+ else:
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+ if defect['label'] in ['scratch']:
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+ defect_type = "scratch_length"
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+ elif defect['label'] in ['pit']:
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+ defect_type = "pit_area"
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+ elif defect['label'] in ['stain']:
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+ defect_type = "stain_area"
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+ else:
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+ raise TypeError(f"数据缺陷类型不存在: {defect['label']}")
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+
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+ rules = aspect_config['rules'][defect_type]
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+ # 对于划痕取长度, 其他取面积
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+ if defect_type == "scratch_length":
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+ area_mm = max(defect['width'], defect['height'])
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+ else:
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+ area_mm = defect['actual_area']
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+ # 累加所有同类型缺陷的扣分
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+ if defect_type not in weighted_scores.keys():
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+ weighted_scores[defect_type] = 0
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+
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+ # 计算单个缺陷扣分
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+ the_score = self._get_score_from_tiers(area_mm, rules)
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+ print(f"[{card_defect_type}, {defect_type}]: {area_mm}, {the_score}")
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+ weighted_scores[defect_type] += the_score
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+
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+ # 将分数写入json
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+ if is_write_score:
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+ defect['score'] = the_score
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+
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+ # 2. 根据权重/系数计算最终扣分
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+ weights = aspect_config.get(f"{card_aspect}_weights") or aspect_config.get("coefficients")
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+ if not weights:
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+ raise ValueError(f"在配置中未找到 '{card_defect_type}' 的权重/系数")
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+
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+ print(weighted_scores)
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+ for defect_type, score in weighted_scores.items():
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+ total_deduction += score * weights.get(defect_type, 1.0)
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+
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+ final_weights = aspect_config["final_weights"][card_aspect]
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+ final_score = total_deduction * final_weights
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+ print(f"final weights: {final_weights}, final score: {final_score}")
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+
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+ if is_write_score:
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+ defect_data[f'{card_aspect}_{card_defect_type}_score'] = final_score
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+ return defect_data
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+ else:
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+ return final_score
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+
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+ def calculate_centering_score(self,
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+ card_aspect: str,
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+ center_data: dict,
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+ is_write_score: bool = False) -> Union[float, dict]:
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+ """
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+ 计算居中度分数。
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+ card_type 为 front或 back
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+ is_write_score: 是否将分数写入json并返回
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+ """
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+ if card_aspect != "front" and card_aspect != "back":
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+ raise TypeError("calculate_centering_score:card_type 只能为 front 或 back")
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+
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+ centering_config = self.config['centering'][card_aspect]
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+ rules = centering_config['rules']
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+ coefficients = centering_config['coefficients']
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+
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+ center_left = center_data['center_result']['center_inference']['center_left']
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+ center_right = center_data['center_result']['center_inference']['center_right']
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+ center_top = center_data['center_result']['center_inference']['center_top']
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+ center_bottom = center_data['center_result']['center_inference']['center_bottom']
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+
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+ # 将比例转换为用于查找规则的单个最大值
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+ h_lookup_val = max(center_left, center_right)
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+ v_lookup_val = max(center_top, center_bottom)
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+
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+ h_deduction = self._get_score_from_tiers(h_lookup_val, rules) * coefficients['horizontal']
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+ v_deduction = self._get_score_from_tiers(v_lookup_val, rules) * coefficients['vertical']
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+
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+ print(h_deduction, v_deduction)
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+ final_weight = self.config['centering']["final_weights"][card_aspect]
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+ final_score = (h_deduction + v_deduction) * final_weight
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+
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+ print(f"final weight: {final_weight}, final score: {final_score}")
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+
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+ if is_write_score:
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+ center_data[f'{card_aspect}_score'] = final_score
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+ return center_data
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+ else:
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+ return final_score
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+
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+
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+if __name__ == '__main__':
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+ # 1. 初始化评分器，加载规则
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+ scorer = CardScorer(r"C:\Code\ML\Project\CheckCardBoxAndDefectServer\app\core\scoring_config.json")
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+ # rulers = scorer.config['corners']['rules']['wear_area']
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+ #
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+ # score = scorer._get_score_from_tiers(0.06, rulers)
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+ # print(score)
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+ # print()
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+
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+ # 居中分数
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+ center_data_path = r"C:\Code\ML\Project\CheckCardBoxAndDefectServer\_temp_work\pokemon_card_center-center_result.json"
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+ with open(center_data_path, 'r', encoding='utf-8') as f:
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+ center_data = json.load(f)
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+ center_data = scorer.calculate_centering_score("front", center_data, True)
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+ print(center_data)
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+
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+
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+ # 边角分数
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+ edge_corner_data_path = r"C:\Code\ML\Project\CheckCardBoxAndDefectServer\_temp_work\pokemon_front_corner_no_reflect_defect-corner_result.json"
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+ with open(edge_corner_data_path, 'r', encoding='utf-8') as f:
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+ edge_corner_data = json.load(f)
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+
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+ corner_data = scorer.calculate_defect_score("corner", 'front', edge_corner_data, True)
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+ print(corner_data)
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+
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+ score = scorer.calculate_defect_score("edge", 'front', edge_corner_data, True)
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+ print(score)
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+
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+ # 面分数
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+ face_data_path = r"C:\Code\ML\Project\CheckCardBoxAndDefectServer\_temp_work\pokemon_front_face_no_reflect_defect-face_result.json"
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+ with open(face_data_path, 'r', encoding='utf-8') as f:
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+ face_data = json.load(f)
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+
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+ score = scorer.calculate_defect_score("face", 'front', face_data, True)
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+ print(score)
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