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)