python SIFT算法

这里的cv2是：opencv-contrib-python 3.4.2.16，3.4.3以上的版本有算法保护，无法直接调用SIFT算法

import numpy as np import cv2 from matplotlib import pyplot as plt imgname = '../hmi.sharp_720s.1.20100504_160000_TAI.magnetogram.jpg' sift = cv2.xfeatures2d.SIFT_create() img = cv2.imread(imgname) gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) kp,des = sift.detectAndCompute(img,None) # 关键点，SIFT特征向量 img1=cv2.drawKeypoints(img,kp,img,color=(255,255,0)) for i in kp: print(list(i.pt)) # 打印点坐标 cv2.imshow('point',img1) cv2.waitKey(0)

以下是根据SIFT关键点的灰度值进行分类：

import numpy as np import cv2 from matplotlib import pyplot as plt from PIL import Image imgname = r'D:\train_jpg_input\hmi.sharp_720s.3295.20131023_062400_TAI.magnetogram.jpg' sift = cv2.xfeatures2d.SIFT_create() img = cv2.imread(imgname) gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) kp, des = sift.detectAndCompute(img, None) # 关键点，SIFT特征向量 im = Image.open(imgname) # Can be many different formats. pix = im.load() # 读取灰度值 # print(im.size) # 获取图像宽度和高度 exists_ls = [] new_kp_white = [] new_kp_grey = [] new_kp_black = [] for i in kp: if list(i.pt) not in exists_ls: # 取点去重 exists_ls.append(list(i.pt)) gray_light = pix[list(i.pt)[0], list(i.pt)[1]] if gray_light >= 150: # 根据灰度值分成两类 new_kp_white.append(i) elif gray_light <= 100: new_kp_black.append(i) else: continue img1 = cv2.drawKeypoints(img, new_kp_white, img, color=(0, 255, 0)) # 白色点画成绿色 img2 = cv2.drawKeypoints(img, new_kp_black, img, color=(255, 0, 0)) # 黑色点画成蓝色 cv2.imshow('point', img1) cv2.imshow('point', img2) cv2.waitKey(0)