test: 移除快速测试 API 和人脸检测测试脚本及所有相关测试图片。
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import requests
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import json
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import cv2
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import numpy as np
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import os
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# 配置服务地址
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JAVA_BACKEND_URL = "http://localhost:18080"
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PYTHON_ALGO_URL = "http://localhost:18000"
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def create_test_image(filename="test_face.jpg"):
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"""创建一张简单的测试图片（如果不存在）"""
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if not os.path.exists(filename):
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print(f"Creating test image: {filename}...")
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# 创建一个 640x480 的黑色图像
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img = np.zeros((480, 640, 3), np.uint8)
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# 画一个简单的圆代表"脸" (虽然检测不到，但可以测试文件上传流程)
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cv2.circle(img, (320, 240), 100, (255, 255, 255), -1)
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cv2.imwrite(filename, img)
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return filename
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def test_python_health():
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"""测试 Python 算法服务健康检查"""
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url = f"{PYTHON_ALGO_URL}/health"
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print(f"\n[Testing] Python Algorithm Service Health ({url})...")
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try:
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response = requests.get(url, timeout=5)
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if response.status_code == 200:
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print(f"✅ Success: {response.json()}")
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else:
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print(f"❌ Failed: Status {response.status_code}, Response: {response.text}")
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except requests.exceptions.ConnectionError:
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print("❌ Failed: Connection refused. Is the service running on port 18000?")
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except Exception as e:
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print(f"❌ Error: {e}")
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def test_extract_feature(image_path):
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"""测试人脸特征提取接口"""
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url = f"{PYTHON_ALGO_URL}/api/extract_feature"
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print(f"\n[Testing] Face Feature Extraction ({url})...")
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if not os.path.exists(image_path):
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print("❌ Error: Test image not found.")
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return
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try:
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with open(image_path, 'rb') as f:
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files = {'image': f}
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response = requests.post(url, files=files, timeout=10)
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if response.status_code == 200:
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result = response.json()
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if result.get('success'):
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dim = result.get('feature_dim')
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print(f"✅ Success: Feature extracted. Dimension: {dim}")
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else:
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# 预期内失败，因为我们的假脸可能过不了检测，但这证明接口通了
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print(f"⚠️ Service Reachable (Logic Result): {result.get('message')}")
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else:
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print(f"❌ Failed: Status {response.status_code}, Response: {response.text}")
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except requests.exceptions.ConnectionError:
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print("❌ Failed: Connection refused.")
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except Exception as e:
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print(f"❌ Error: {e}")
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def test_java_group_list():
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"""测试 Java 后端分组列表接口"""
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url = f"{JAVA_BACKEND_URL}/api/groups/list"
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print(f"\n[Testing] Java Backend Group List ({url})...")
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try:
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response = requests.get(url, timeout=5)
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if response.status_code == 200:
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data = response.json()
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if data.get('code') == 200:
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print(f"✅ Success: Retrieved {len(data.get('data', []))} groups.")
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print(f" Response: {json.dumps(data, ensure_ascii=False)}")
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else:
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print(f"❌ Functional Error: {data}")
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else:
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print(f"❌ Failed: Status {response.status_code}, Response: {response.text}")
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except requests.exceptions.ConnectionError:
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print("❌ Failed: Connection refused. Is the service running on port 18080?")
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except Exception as e:
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print(f"❌ Error: {e}")
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if __name__ == "__main__":
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print("=== Face Recognition System Quick Test ===")
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# 1. 准备测试图片
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test_img = create_test_image()
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# 2. 测试算法服务
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test_python_health()
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test_extract_feature(test_img)
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# 3. 测试Java后端
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test_java_group_list()
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print("\n=== Test Finished ===")
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# 清理生成的测试图 (可选，这里保留以便用户查看)
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# if os.path.exists(test_img):
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# os.remove(test_img)
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import requests
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import cv2
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import numpy as np
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import os
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import json
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import argparse
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import sys
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# 配置服务地址
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PYTHON_ALGO_URL = "http://192.168.0.37:18000"
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def get_default_image_path():
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"""获取一个默认存在的测试图片路径"""
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# 尝试找一个存在的真实图片
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potential_paths = [
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r"C:\Users\24830\Desktop\人脸.jpg",
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]
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for path in potential_paths:
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if os.path.exists(path):
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return path
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return None
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def detect_and_draw(image_path, expand_scale=0.0):
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url = f"{PYTHON_ALGO_URL}/api/detect_face"
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print(f"\n[Processing] Image: {image_path}")
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print(f"[API URL] {url}")
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print(f"[Expand Scale] {expand_scale}")
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if not os.path.exists(image_path):
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print(f"❌ Error: Image file not found: {image_path}")
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return
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try:
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# 1. 准备发送请求
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# 读取图片用于显示/画框
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img_array = np.fromfile(image_path, dtype=np.uint8)
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original_img = cv2.imdecode(img_array, cv2.IMREAD_COLOR)
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if original_img is None:
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print(f"❌ Error: Failed to read image using opencv: {image_path}")
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return
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# 2. 调用API
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data = {'expand_scale': expand_scale}
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with open(image_path, 'rb') as f:
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files = {'image': f}
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# 注意: 使用 data=data 发送表单数据，而不是 params=params (查询参数)
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response = requests.post(url, files=files, data=data, timeout=10)
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if response.status_code != 200:
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print(f"❌ Failed: Status {response.status_code}, Response: {response.text}")
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return
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result = response.json()
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print("\n=== API Response ===")
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print(json.dumps(result, indent=2))
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# 3. 处理结果并画图
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if result.get('success'):
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faces = result.get('faces', [])
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count = len(faces)
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print(f"\n✅ Success: Detected {count} faces.")
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# 创建副本用于画图
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draw_img = original_img.copy()
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for i, face in enumerate(faces):
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x1 = int(face['x1'])
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y1 = int(face['y1'])
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x2 = int(face['x2'])
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y2 = int(face['y2'])
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score = face['score']
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# 画矩形框
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# 颜色 (B, G, R) - 绿色
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color = (0, 255, 0)
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thickness = 2
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cv2.rectangle(draw_img, (x1, y1), (x2, y2), color, thickness)
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# 写文字
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label = f"Face {i+1}: {score:.2f}"
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cv2.putText(draw_img, label, (x1, y1 - 10),
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cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
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print(f" - Face {i+1}: Box({x1}, {y1}, {x2}, {y2}), Score: {score:.4f}")
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# 保存裁剪的人脸图观察效果
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face_crop = original_img[y1:y2, x1:x2]
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if face_crop.size > 0:
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crop_filename = f"face_crop_{i+1}_scale_{expand_scale}.jpg"
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cv2.imencode('.jpg', face_crop)[1].tofile(crop_filename)
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print(f" Saved crop: {crop_filename}")
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# 4. 保存结果图
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output_filename = f"result_detected_scale_{expand_scale}.jpg"
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cv2.imencode('.jpg', draw_img)[1].tofile(output_filename)
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print(f"\n✅ Result image saved to: {os.path.abspath(output_filename)}")
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else:
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print(f"⚠️ API logic returned failure: {result.get('message')}")
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except Exception as e:
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print(f"❌ Error: {e}")
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import traceback
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traceback.print_exc()
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description='Face Detection API Test Script')
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parser.add_argument('image_path', nargs='?', help='Path to the image file')
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parser.add_argument('--scale', type=float, default=0.6, help='Expand scale (default: 0.0)')
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args = parser.parse_args()
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target_path = args.image_path
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if not target_path:
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default_path = get_default_image_path()
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if default_path:
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print(f"No image path provided, using default found: {default_path}")
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target_path = default_path
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else:
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print("Usage: python test_detect_face.py <path_to_image> [--scale 0.3]")
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print("Error: No image path provided and no default test image found.")
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sys.exit(1)
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detect_and_draw(target_path, args.scale)
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import requests
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import json
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import os
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# 配置
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BASE_URL = "http://localhost:18080/api"
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GROUP_NAME = "TestGroup_Verify"
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USER_NAME = "DuplicateTestUser"
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TEST_IMG = "verify_face.jpg"
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def create_test_image():
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if not os.path.exists(TEST_IMG):
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# 创建一个简单的dummy文件
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with open(TEST_IMG, 'wb') as f:
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f.write(b'\xFF\xD8\xFF\xE0' + b'\x00' * 1000) # Fake JPG header
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return TEST_IMG
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def run_verification():
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print("=== 开始验证 ===")
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# 1. 创建测试分组
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print("\n1. 创建分组...")
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resp = requests.post(f"{BASE_URL}/groups/add", json={"name": GROUP_NAME})
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if resp.status_code == 200 and resp.json()['code'] == 200:
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group_id = resp.json()['data']['id']
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print(f"✅ 分组创建成功 ID: {group_id}")
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else:
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print(f"❌ 分组创建失败: {resp.text}")
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return
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# 准备图片
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create_test_image()
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files = {'photo': open(TEST_IMG, 'rb')}
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# 2. 添加用户 (第一次)
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print("\n2. 添加用户 (预期成功)...")
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data = {"name": USER_NAME, "groupId": group_id}
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# Re-open file for each request
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resp = requests.post(f"{BASE_URL}/users/add", data=data, files={'photo': open(TEST_IMG, 'rb')})
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if resp.status_code == 200 and resp.json()['code'] == 200:
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print(f"✅ 用户创建成功")
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else:
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# 如果是因为测试图片无法提取特征而失败，也是预期的(说明代码跑到了特征提取那一步)
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# 但我们主要验证重名逻辑，所以这里假设如果失败是因为重名则是有问题，如果是因为特征提取则是环境问题
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print(f"⚠️ 用户创建返回: {resp.json().get('msg')}")
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# 3. 添加同名用户 (预期失败)
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print("\n3. 再次添加同名用户 (预期被拦截)...")
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resp = requests.post(f"{BASE_URL}/users/add", data=data, files={'photo': open(TEST_IMG, 'rb')})
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res_json = resp.json()
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if res_json.get('code') != 200 and "已存在" in str(res_json.get('msg')):
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print(f"✅ 成功拦截重名用户: {res_json.get('msg')}")
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else:
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print(f"❌ 拦截失败或错误信息不匹配: {resp.text}")
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# 4. 测试搜索接口
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print("\n4. 测试人脸搜索接口...")
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try:
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resp = requests.post(f"{BASE_URL}/users/search", data={"groupId": group_id}, files={'photo': open(TEST_IMG, 'rb')})
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if resp.status_code == 200:
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print(f"✅ 接口调用成功: {resp.json()}")
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elif resp.status_code == 404:
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print(f"✅ 接口调用成功 (未找到匹配 - 符合预期): {resp.json()}")
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else:
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print(f"❌ 接口调用异常: {resp.status_code} - {resp.text}")
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except Exception as e:
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print(f"❌ 请求异常: {e}")
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print("\n=== 验证结束 ===")
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if __name__ == "__main__":
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try:
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run_verification()
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except Exception as e:
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print(f"Fatal Error: {e}")
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