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python opencv的sift特征检测(Scale-Invariant Feature Transform)

article 2025/1/7 4:22:20

sift

官方文档地址：https://docs.opencv.org/4.10.0/da/df5/tutorial_py_sift_intro.html

创建SIFT实例cv2.SIFT.create()
特征检测sift.detect
描述子sift.compute/sift.detectAndCompute
画特征cv2.drawKeypoints

原图

在这里插入图片描述

特征点

在这里插入图片描述

代码

import cv2

first = './12.png'
'''
sift 特征检测
官方文档地址：https://docs.opencv.org/4.10.0/da/df5/tutorial_py_sift_intro.html
'''
if __name__ == "__main__":
    # 读取图片，转成灰度图
    img = cv2.imread(first)
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

    # 创建SIFT实例
    sift = cv2.SIFT.create()
    # 特征检测
    # sift.detect() function finds the keypoint in the images. You can pass a mask if you want to search only a part of image.
    # Each keypoint is a special structure which has many attributes like its (x,y) coordinates,
    # size of the meaningful neighbourhood, angle which specifies its orientation,
    # response that specifies strength of keypoints etc.
    kp = sift.detect(gray, None)
    print(f"关键点：{kp}")
    print(f"f关键点：{type(kp)}")
    print(f"f关键点：{len(kp)}")
    print(f"f关键点：{kp[0]}")

    # 画出关键点
    # DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS可以展示特征点的大小和方向
    img = cv2.drawKeypoints(gray, kp, img, flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
    cv2.imshow("sift", img)
    cv2.waitKey(0)
    cv2.destroyAllWindows()

    # kp, des = sift.compute(gray, kp) 用计算出来的特征点计算描述子
    # 或者直接计算特征点和描述子
    # Here kp will be a list of keypoints and des is a numpy array of shape: len(kp) X 128
    kp, des = sift.detectAndCompute(gray, None)
    # print(kp)
    # print(type(kp))
    # print(kp[0])
    print(des.shape)
    print(des[0])

关键点：(< cv2.KeyPoint 0x11f179110>, < cv2.KeyPoint 0x11f179140>, < cv2.KeyPoint 0x11f179470>, < cv2.KeyPoint 0x11f179560>, < cv2.KeyPoint 0x11f179530>, < cv2.KeyPoint 0x11f179500>, < cv2.KeyPoint 0x11f1794d0>, < cv2.KeyPoint 0x11f1794a0>, < cv2.KeyPoint 0x11f17a9d0>, < cv2.KeyPoint 0x11f228c60>, < cv2.KeyPoint 0x11f228c30>, < cv2.KeyPoint 0x11f2288a0>, < cv2.KeyPoint 0x11f228930>, < cv2.KeyPoint 0x11f2289c0>, < cv2.KeyPoint 0x11f2289f0>, < cv2.KeyPoint 0x11f228ab0>, < cv2.KeyPoint 0x11f22bed0>, < cv2.KeyPoint 0x11f244570>, < cv2.KeyPoint 0x11f2443c0>, < cv2.KeyPoint 0x11f2443f0>, < cv2.KeyPoint 0x11f244510>, < cv2.KeyPoint 0x11f2445a0>, < cv2.KeyPoint 0x11f244630>, < cv2.KeyPoint 0x11f244660>, < cv2.KeyPoint 0x11f244690>, < cv2.KeyPoint 0x11f2446c0>, < cv2.KeyPoint 0x11f2446f0>, < cv2.KeyPoint 0x11f244720>)
f关键点：<class 'tuple'>
f关键点：28
f关键点：< cv2.KeyPoint 0x11f179110>
(28, 128)
[  0.   0.   0.   0.   0.   0.   0.   2.   1.   0.   0.   0.   0.   3.
  25.  18.   0.   0.   0.   0.   0.  18.  23.   2.   0.   0.   0.   0.
   0.   2.   1.   1.  23.   0.   0.   0.   0.   0.   0.  10. 139.   2.
   0.   0.   6.  29.  81. 136.  13.   0.   0.   0. 106. 162.  82.  26.
  23.   0.   0.   0.  22.  16.   7.  36.  41.   4.   0.   0.   0.   0.
   0.   1. 162.  34.   0.   0.  26.  11.   5.  22.  71.   6.   0.   0.
 162.  83.   5.   7. 122.   7.   0.   0.  70.  11.   1.  17.  22.   3.
   0.   0.   0.   0.   0.   0. 162.  37.   0.   0.  15.   2.   0.   0.
  83.  10.   0.   0. 162.  36.   0.   0.  94.  11.   0.   0.  78.   9.
   0.   0.]