61 lines
2.0 KiB
Python
61 lines
2.0 KiB
Python
# 导入必要的库
|
|
import cv2 # OpenCV库用于图像处理
|
|
import numpy as np # NumPy库用于数值计算
|
|
import time # 时间模块用于计时
|
|
import os # 操作系统模块用于文件和目录操作
|
|
|
|
def sift_feature_extraction(image_path):
|
|
# 读取输入图像
|
|
img = cv2.imread(image_path)
|
|
# 检查图像是否加载成功
|
|
if img is None:
|
|
print("无法加载图像，请检查路径")
|
|
return
|
|
|
|
# 将图像转换为灰度图
|
|
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
|
|
|
|
# 创建SIFT特征检测器对象
|
|
sift = cv2.SIFT_create()
|
|
|
|
# 记录特征提取开始时间
|
|
start_time = time.time()
|
|
|
|
# 检测关键点并计算描述符
|
|
keypoints, descriptors = sift.detectAndCompute(gray, None)
|
|
|
|
# 计算特征提取耗时
|
|
end_time = time.time()
|
|
processing_time = end_time - start_time
|
|
|
|
# 绘制检测到的关键点
|
|
img_with_keypoints = cv2.drawKeypoints(
|
|
gray, keypoints, img,
|
|
flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS
|
|
)
|
|
|
|
# 创建输出目录（如果不存在）
|
|
output_dir = "output/sift_results"
|
|
os.makedirs(output_dir, exist_ok=True)
|
|
|
|
# 构建输出文件路径
|
|
output_path = os.path.join(output_dir, "sift_result.jpg")
|
|
# 保存结果图像
|
|
cv2.imwrite(output_path, img_with_keypoints)
|
|
|
|
# 输出特征提取结果信息
|
|
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}")
|
|
|
|
# 显示结果窗口（可选）
|
|
cv2.imshow('SIFT Features', img_with_keypoints)
|
|
cv2.waitKey(0) # 等待按键
|
|
cv2.destroyAllWindows() # 关闭所有窗口
|
|
|
|
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) |