Sift key point detection and feature matching (implemented in Python)

List of articles

• • 1.Harris and Shi-Tomasi feature detection
  • 2.SIFT(Scale-Invariant Feature Transform) Key point detection
  • 3.SIFT Using process
  • • （1） establish SIFT object
    • （2） To test ,（ Key points ）KP=sift.detect(img,……)
    • （3） Draw key points ,drawKeypoints(gray,KP,img)
  • 4. Code combat
  • 5. Feature matching
  • • （1） Mode one ： Key match
    • （2） Mode two ： Key match

1.Harris and Shi-Tomasi feature detection

Harris: https://blog.csdn.net/Keep_Trying_Go/article/details/125384144
Shi-Tomasi:https://blog.csdn.net/Keep_Trying_Go/article/details/125384218

2.SIFT(Scale-Invariant Feature Transform) Key point detection

reflection ： Given the Harris Corner detection , Why SIFT Key point detection ？
（1）Harris The corner has the property of rotation invariance ; But after zooming , The original corner may not necessarily be a corner ;

You can see that when the corners of the first picture are enlarged , It becomes the second picture , But when we go to the test again, we can't detect any corners , It's the edge . So I put forward SIFT.

3.SIFT Using process

（1） establish SIFT object

（2） To test ,（ Key points ）KP=sift.detect(img,……)

（3） Draw key points ,drawKeypoints(gray,KP,img)

among gray—— original image , Three channel or single channel images can be made ;KP—— Key points ;img—— Draw points onto the original image ;color—— Color information of the drawn feature points , The default drawing is random color ;
Flags——
（1）cv2.DRAW_MATCHES_FLAGS_DEFAULT： Create output image matrix , Draw matching pairs and feature points using existing output images , Draw only the middle point for each key
（2）cv2.DRAW_MATCHES_FLAGS_DRAW_OVER_OUTIMG： Do not create output image matrix , Instead, draw matching pairs on the output image
（3）cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS： Draw a key point graph with size and direction for each feature point
（4）cv2.DRAW_MATCHES_FLAGS_NOT_DRAW_SINGLE_POINTS： The feature points of a single point are not drawn

4. Code combat

import os
import cv2
import cv2
img=cv2.imread('images/HaLiSi.jpg')
img=cv2.resize(src=img,dsize=(450,450))
gray=cv2.cvtColor(src=img,code=cv2.COLOR_BGR2GRAY)
#SIFT objects creating 
sift=cv2.SIFT_create()
# To test , The second parameter is None, Indicates to test the whole picture 
kp=sift.detect(gray,None)
# Draw corners 
cv2.drawKeypoints(image=gray,keypoints=kp,outImage=img,color=(0,255,0))
cv2.imshow('img',img)
cv2.waitKey(0)
cv2.destroyAllWindows()
if __name__ == '__main__':
print('pycharm')

5. Feature matching

（1） Key points ： Location , Size and direction ;
Calculation SIFT Narrator ：
（2） A set of vector values of the pixels around the key that contribute to it are recorded , It is not subject to affine transformation , The influence of light transformation, etc .

（1） Mode one ： Key match

 Key match ：
# Mode one ： Feature matching 
kp,des=sift.compute(img,kp)
import os
import cv2
import numpy as np
img=cv2.imread('images/HaLiSi.jpg')
img=cv2.resize(src=img,dsize=(450,450))
gray=cv2.cvtColor(src=img,code=cv2.COLOR_BGR2GRAY)
#SIFT objects creating 
sift=cv2.SIFT_create()
# To test , The second parameter is None, Indicates to test the whole picture 
kp=sift.detect(gray,None)
# Feature matching 
kp,des=sift.compute(gray,kp)
print(des)
# kp,des=sift.detectAndCompute(gray)
# Draw corners 
cv2.drawKeypoints(image=gray,keypoints=kp,outImage=img,color=(0,255,0))
cv2.imshow('img',img)
cv2.waitKey(0)
cv2.destroyAllWindows()
if __name__ == '__main__':
print('Pycharm')

（2） Mode two ： Key match

At the same time, key point detection and feature matching
Kp,des=sift.detectAndCompute(img,None）
Img: Input image （ grayscale ）;
Mask： Appoint img Which area in the .