SAR/test_data/xie/feature_shift.py

import cv2
from skimage import io
import matplotlib.pyplot as plt
import sys
import os

# image = io.imread('D:\\hiddz\\SAR\\test_data\\TEST\\A11\\HB14931.JPG')
#
# # 创建SIFT特征检测器
# sift = cv2.SIFT_create()
#
# # 检测关键点和提取描述符
# keypoints, descriptors = sift.detectAndCompute(image, None)
#
# # 显示关键点
# output_image = cv2.drawKeypoints(image, keypoints, None, flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
# # 显示可视化结果
# # 保存带有特征点的图像
#
# plt.imshow(output_image)
# plt.title('SHIFT Image')
# plt.show()

def process_image(image_path):
    image = io.imread(image_path)

    # 创建SIFT特征检测器
    sift = cv2.SIFT_create()

    # 检测关键点和提取描述符
    keypoints, descriptors = sift.detectAndCompute(image, None)

    # 显示关键点
    output_image = cv2.drawKeypoints(image, keypoints, None, flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
    # 显示可视化结果
    # 保存带有特征点的图像

    plt.imshow(output_image)
    plt.title('SHIFT Image')
    folder_name = os.path.basename(os.path.dirname(image_path))

    # 创建保存图像的文件夹
    save_folder = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'shift', folder_name)
    os.makedirs(save_folder, exist_ok=True)

    # 保存图像
    save_path = os.path.join(save_folder, 'shift_' + os.path.basename(image_path))
    plt.imsave(save_path, output_image, cmap='gray')
    print(save_path)

    # plt.show()


if __name__ == "__main__":
    if len(sys.argv) != 2:
        sys.exit(1)

    image_path = sys.argv[1]
    process_image(image_path)