# -*-coding:utf-8-*-
#author: lyp time: 2018/8/3
import cv2
import numpy as np

img = cv2.imread(‘lyp.jpg‘)
grayImg = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

surf = cv2.xfeatures2d.SURF_create(300, upright=True)
kp, des = surf.detectAndCompute(grayImg, None)

img2 = cv2.drawKeypoints(img, kp, None, (0, 255, 0), cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
cv2.imshow(
 
‘SURF Image‘, img2)

cv2.waitKey()
cv2.destroyAllWindows()

0.函數講解

retval=cv2.xfeatures2d.SURF_create([, hessianThreshold[, nOctaves[, nOctaveLayers[, extended[, upright]]]]])

hessianThreshold: H矩陣閥值，默認值是100， 大於該閥值的關鍵點將會被顯示出來，推薦範圍為300-500
extended: 是否擴展SURF描述維度，默認值是False，擴展時將計算128維度的擴展描述，否則默認計算64維度的描述（速度快）
upright:是否計算SURF描述的方向，默認值是False, 不計算則運行較快

keypoints=cv2.Feature2D.detect(image[, mask]) 關鍵點
keypoints, descriptors=cv2.Feature2D.compute(image, keypoints[, descriptors]) 特征描述向量

1.參考來自
小林的CV視覺工坊

OpenCV-Python: SURT Demo one