fly6516
b1e90c4c9d
- 添加 U-Net 模型实现图像分割功能- 实现 SIFT 特征提取算法 - 创建实验报告模板和环境配置指南 - 添加数据集下载脚本和目录结构设置脚本 - 实现模型训练和测试流程
58 lines
1.6 KiB
Python
58 lines
1.6 KiB
Python
import cv2
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import numpy as np
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import time
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import os
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def sift_feature_extraction(image_path):
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# 读取图像
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img = cv2.imread(image_path)
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if img is None:
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print("无法加载图像，请检查路径")
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return
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# 转换为灰度图
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gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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# 创建SIFT对象
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sift = cv2.SIFT_create()
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# 记录开始时间
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start_time = time.time()
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# 检测关键点和描述符
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keypoints, descriptors = sift.detectAndCompute(gray, None)
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# 计算耗时
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end_time = time.time()
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processing_time = end_time - start_time
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# 绘制关键点
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img_with_keypoints = cv2.drawKeypoints(
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gray, keypoints, img,
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flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS
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)
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# 创建输出目录
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output_dir = "output/sift_results"
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os.makedirs(output_dir, exist_ok=True)
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# 保存结果
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output_path = os.path.join(output_dir, "sift_result.jpg")
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cv2.imwrite(output_path, img_with_keypoints)
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# 输出结果信息
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print(f"检测到 {len(keypoints)} 个关键点")
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print(f"描述符形状: {descriptors.shape if descriptors is not None else 'None'}")
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print(f"特征提取耗时: {processing_time:.4f} 秒")
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print(f"结果已保存至: {output_path}")
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# 显示结果（可选）
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cv2.imshow('SIFT Features', img_with_keypoints)
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cv2.waitKey(0)
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cv2.destroyAllWindows()
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if __name__ == "__main__":
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# 示例用法
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image_path = "../data/image.jpg" # 需要替换为实际图像路径
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print("开始SIFT特征提取...")
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sift_feature_extraction(image_path) |