import cv2
import numpy as np
import time
import os

def sift_feature_extraction(image_path):
    # 读取图像
    img = cv2.imread(image_path)
    if img is None:
        print("无法加载图像，请检查路径")
        return
    
    # 转换为灰度图
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    
    # 创建SIFT对象
    sift = cv2.SIFT_create()
    
    # 记录开始时间
    start_time = time.time()
    
    # 检测关键点和描述符
    keypoints, descriptors = sift.detectAndCompute(gray, None)
    
    # 计算耗时
    end_time = time.time()
    processing_time = end_time - start_time
    
    # 绘制关键点
    img_with_keypoints = cv2.drawKeypoints(
        gray, keypoints, img, 
        flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS
    )
    
    # 创建输出目录
    output_dir = "output/sift_results"
    os.makedirs(output_dir, exist_ok=True)
    
    # 保存结果
    output_path = os.path.join(output_dir, "sift_result.jpg")
    cv2.imwrite(output_path, img_with_keypoints)
    
    # 输出结果信息
    print(f"检测到 {len(keypoints)} 个关键点")
    print(f"描述符形状: {descriptors.shape if descriptors is not None else 'None'}")
    print(f"特征提取耗时: {processing_time:.4f} 秒")
    print(f"结果已保存至: {output_path}")
    
    # 显示结果（可选）
    cv2.imshow('SIFT Features', img_with_keypoints)
    cv2.waitKey(0)
    cv2.destroyAllWindows()

if __name__ == "__main__":
    # 示例用法
    image_path = "../data/image.jpg"  # 需要替换为实际图像路径
    print("开始SIFT特征提取...")
    sift_feature_extraction(image_path)