From 49d2c71fdd73708334cd1dc1e05c78968c597c10 Mon Sep 17 00:00:00 2001
From: ninghongbin <2409766686@qq.com>
Date: Thu, 4 Sep 2025 10:39:41 +0800
Subject: [PATCH] 1

---
 ocr/feature_extraction.py | 156 ++++++++++++++++++++++++++++++++++++++
 1 file changed, 156 insertions(+)
 create mode 100644 ocr/feature_extraction.py

diff --git a/ocr/feature_extraction.py b/ocr/feature_extraction.py
new file mode 100644
index 0000000..ac9e1e9
--- /dev/null
+++ b/ocr/feature_extraction.py
@@ -0,0 +1,156 @@
+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}")