#!/usr/bin/env python # -*- coding: utf-8 -*- """ API客户端调用示例 演示如何使用三个核心接口 """ import requests import json import os from typing import List, Dict, Optional # API基础地址 API_BASE = "http://localhost:8000" class FaceAPIClient: """人脸特征API客户端""" def __init__(self, base_url: str = "http://localhost:8000"): self.base_url = base_url.rstrip('/') def extract_feature(self, image_path: str) -> Optional[List[float]]: """ 接口1: 提取人脸特征 Args: image_path: 图像文件路径 Returns: 特征向量列表,失败返回None """ url = f"{self.base_url}/api/extract_feature" try: with open(image_path, "rb") as f: files = {"image": f} response = requests.post(url, files=files) response.raise_for_status() result = response.json() if result["success"]: print(f"✓ 特征提取成功") print(f" - 特征维度: {result['feature_dim']}") print(f" - 质量检查: {'通过' if result['quality_passed'] else '未通过'}") print(f" - 处理时间: {result['processing_time']:.3f}秒") return result["feature"] else: print(f"✗ 特征提取失败: {result['message']}") return None except FileNotFoundError: print(f"✗ 文件不存在: {image_path}") return None except Exception as e: print(f"✗ 请求失败: {e}") return None def compare_features(self, feature1: List[float], feature2: List[float]) -> Optional[Dict]: """ 接口2: 对比两个特征向量 Args: feature1: 特征向量1 feature2: 特征向量2 Returns: 对比结果字典,失败返回None """ url = f"{self.base_url}/api/compare_features" try: data = { "feature1": feature1, "feature2": feature2 } response = requests.post(url, json=data) response.raise_for_status() result = response.json() if result["success"]: print(f"✓ 特征对比成功") print(f" - 相似度: {result['similarity']:.4f}") print(f" - 判断结果: {'同一人' if result['is_same_person'] else '不是同一人'}") print(f" - 阈值: {result['threshold']}") return result else: print(f"✗ 特征对比失败: {result['message']}") return None except Exception as e: print(f"✗ 请求失败: {e}") return None def compare_image_feature(self, image_path: str, feature: List[float]) -> Optional[Dict]: """ 接口3: 对比图像和特征向量 Args: image_path: 图像文件路径 feature: 目标特征向量 Returns: 对比结果字典,失败返回None """ url = f"{self.base_url}/api/compare_image_feature" try: with open(image_path, "rb") as f: files = {"image": f} data = {"feature": json.dumps(feature)} response = requests.post(url, files=files, data=data) response.raise_for_status() result = response.json() if result["success"]: print(f"✓ 图像特征对比成功") print(f" - 相似度: {result['similarity']:.4f}") print(f" - 判断结果: {'同一人' if result['is_same_person'] else '不是同一人'}") print(f" - 处理时间: {result['processing_time']:.3f}秒") return result else: print(f"✗ 图像特征对比失败: {result['message']}") return None except FileNotFoundError: print(f"✗ 文件不存在: {image_path}") return None except Exception as e: print(f"✗ 请求失败: {e}") return None def health_check(self) -> bool: """健康检查""" url = f"{self.base_url}/health" try: response = requests.get(url) result = response.json() if result["status"] == "healthy": print(f"✓ API服务运行正常") return True else: print(f"✗ API服务异常: {result}") return False except Exception as e: print(f"✗ 无法连接到API服务: {e}") return False # ==================== 示例场景 ==================== def example1_extract_and_compare(): """示例1: 提取特征并对比""" print("\n" + "="*60) print("示例1: 提取两张图像的特征并对比") print("="*60) client = FaceAPIClient() # 检查服务状态 if not client.health_check(): return # 图像路径（请替换为实际路径） image1 = "person1.jpg" image2 = "person1_verify.jpg" print(f"\n步骤1: 提取第一张图像特征") feature1 = client.extract_feature(image1) if not feature1: return print(f"\n步骤2: 提取第二张图像特征") feature2 = client.extract_feature(image2) if not feature2: return print(f"\n步骤3: 对比两个特征") result = client.compare_features(feature1, feature2) def example2_image_feature_compare(): """示例2: 使用组合接口对比图像和特征""" print("\n" + "="*60) print("示例2: 使用组合接口对比图像和特征") print("="*60) client = FaceAPIClient() # 先提取一个已知用户的特征 known_user_image = "person1.jpg" test_image = "person1_verify.jpg" print(f"\n步骤1: 提取已知用户的特征") known_feature = client.extract_feature(known_user_image) if not known_feature: return print(f"\n步骤2: 使用组合接口验证新图像") result = client.compare_image_feature(test_image, known_feature) def example3_face_recognition_system(): """示例3: 简单的人脸识别系统""" print("\n" + "="*60) print("示例3: 简单的人脸识别系统") print("="*60) client = FaceAPIClient() # 模拟用户数据库 user_database = {} # 注册用户 users_to_register = [ ("张三", "person1.jpg"), # ("李四", "person2.jpg"), # ("王五", "person3.jpg"), ] print("\n[注册阶段]") for user_name, image_path in users_to_register: if not os.path.exists(image_path): print(f"⚠ 跳过 {user_name}: 文件不存在 {image_path}") continue print(f"\n注册用户: {user_name}") feature = client.extract_feature(image_path) if feature: user_database[user_name] = feature print(f"✓ {user_name} 注册成功") else: print(f"✗ {user_name} 注册失败") print(f"\n当前数据库中有 {len(user_database)} 个用户") # 识别测试 test_images = [ "test_person1.jpg", ] print("\n[识别阶段]") for test_image in test_images: if not os.path.exists(test_image): print(f"⚠ 跳过: 文件不存在 {test_image}") continue print(f"\n识别图像: {test_image}") # 提取待识别图像的特征 test_feature = client.extract_feature(test_image) if not test_feature: continue # 与数据库中所有用户对比 best_match = None best_similarity = 0.0 for user_name, user_feature in user_database.items(): result = client.compare_features(test_feature, user_feature) if result and result['similarity'] > best_similarity: best_similarity = result['similarity'] best_match = user_name # 输出结果 if best_similarity >= 0.7: print(f"✓ 识别结果: {best_match} (相似度: {best_similarity:.4f})") else: print(f"✗ 未识别到已注册用户 (最高相似度: {best_similarity:.4f})") def main(): """主函数""" print("人脸特征API客户端示例") print("请确保API服务正在运行: python app.py") # 运行示例 # example1_extract_and_compare() # example2_image_feature_compare() example3_face_recognition_system() if __name__ == "__main__": main()