import cv2
import numpy as np
import insightface
from insightface.app import FaceAnalysis
from io import BytesIO
from PIL import Image


class BinaryFaceFeatureHandler:
    """
    专门处理图片二进制数据的特征提取器，支持分批次接收二进制数据并累积计算平均特征
    """

    def __init__(self):
        self.app = self._init_insightface()
        self.feature_list = []  # 存储所有图片二进制数据提取的特征

    def _init_insightface(self):
        """初始化InsightFace引擎"""
        try:
            print("正在初始化InsightFace引擎...")
            app = FaceAnalysis(name='buffalo_l', root='~/.insightface')
            app.prepare(ctx_id=0, det_size=(640, 640))
            print("InsightFace引擎初始化完成")
            return app
        except Exception as e:
            print(f"InsightFace初始化失败: {e}")
            return None

    def add_binary_data(self, binary_data):
        """
        接收单张图片的二进制数据，提取特征并保存

        参数:
            binary_data: 图片的二进制数据（bytes类型）

        返回:
            成功提取特征时返回 (True, 特征值numpy数组)
            失败时返回 (False, None)
        """
        if not self.app:
            print("引擎未初始化，无法处理")
            return False, None

        try:
            # 直接处理二进制数据：转换为图像格式
            img = Image.open(BytesIO(binary_data))
            frame = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)

            # 提取特征
            faces = self.app.get(frame)
            if faces:
                # 获取当前提取的特征值
                current_feature = faces[0].embedding
                # 添加到特征列表
                self.feature_list.append(current_feature)
                print(f"已累计 {len(self.feature_list)} 个特征")
                # 返回成功标志和当前特征值
                return True,current_feature
            else:
                print("二进制数据中未检测到人脸")
                return False, None
        except Exception as e:
            print(f"处理二进制数据出错: {e}")
            return False, None

    def get_average_feature(self, features):
        """
        计算多个特征向量的平均值

        参数:
            features: 特征值列表，每个元素可以是字符串格式或numpy数组
                      例如: [feature1, feature2, ...]
        返回:
            单一平均特征向量的numpy数组，若无可计算数据则返回None
        """
        try:
            # 验证输入是否为列表且不为空
            if not isinstance(features, list) or len(features) == 0:
                print("输入必须是包含至少一个特征值的列表")
                return None

            # 处理每个特征值
            processed_features = []
            for i, embedding in enumerate(features):
                try:
                    if isinstance(embedding, str):
                        # 处理包含括号和逗号的字符串格式
                        embedding_str = embedding.replace('[', '').replace(']', '').replace(',', ' ').strip()
                        embedding_list = [float(num) for num in embedding_str.split() if num.strip()]
                        embedding_np = np.array(embedding_list, dtype=np.float32)
                    else:
                        embedding_np = np.array(embedding, dtype=np.float32)

                    # 验证特征值格式
                    if len(embedding_np.shape) == 1:
                        processed_features.append(embedding_np)
                        print(f"已添加第 {i + 1} 个特征值用于计算平均值")
                    else:
                        print(f"跳过第 {i + 1} 个特征值，不是一维数组")

                except Exception as e:
                    print(f"处理第 {i + 1} 个特征值时出错: {e}")

            # 确保有有效的特征值
            if not processed_features:
                print("没有有效的特征值用于计算平均值")
                return None

            # 检查所有特征向量维度是否相同
            dims = {feat.shape[0] for feat in processed_features}
            if len(dims) > 1:
                print(f"特征值维度不一致，无法计算平均值。检测到的维度: {dims}")
                return None

            # 计算平均值
            avg_feature = np.mean(processed_features, axis=0)
            print(f"成功计算 {len(processed_features)} 个特征值的平均特征向量，维度: {avg_feature.shape[0]}")

            return avg_feature

        except Exception as e:
            print(f"计算平均特征值时出错: {e}")
            return None

    # def clear(self):
    #     """清空已存储的特征数据"""
    #     self.feature_list = []
    #     print("已清空所有特征数据")


# # 使用示例
# if __name__ == "__main__":
#     handler = BinaryFaceFeatureHandler()
#
#     # 模拟接收图片二进制数据
#     try:
#         # 第一次接收
#         with open(r"D:\Git\bin\video\ocr\known_faces\B\102-f.jpg_1140x855.jpg", "rb") as f:
#             bin_data1 = f.read()
#         success, feature1 = handler.add_binary_data(bin_data1)
#         if success:
#             print(f"第一次提取的特征值前5个: {feature1[:5]}")
#
#         # 第二次接收
#         with open(r"D:\Git\bin\video\ocr\known_faces\B\104-1.jpg", "rb") as f:
#             bin_data2 = f.read()
#         success, feature2 = handler.add_binary_data(bin_data2)
#         if success:
#             print(f"第二次提取的特征值前5个: {feature2[:5]}")
#
#         # 计算平均值
#         avg_feature = handler.get_average_feature()
#
#     except Exception as e:
#         print(f"处理过程出错: {e}")