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

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

特征点

代码

import cv2first = './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 128kp, 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.]