# -*- coding: utf-8 -*-
"""
Created on Mon Sep 10 09:59:22 2018

@author: zy
"""

'''
使用BRIEF特征描述符
'''
import  cv2

def  brief_test():
    #加载图片  灰色
    img1 = cv2.imread('./image/match1.jpg')
    img1 = cv2.resize(img1,dsize=(600,400))
    gray1 = cv2.cvtColor(img1,cv2.COLOR_BGR2GRAY)
    img2 = cv2.imread('./image/match2.jpg')
    img2 = cv2.resize(img2,dsize=(600,400))
    gray2 = cv2.cvtColor(img2,cv2.COLOR_BGR2GRAY)
    image1 = gray1.copy()
    image2 = gray2.copy()

    image1 = cv2.medianBlur(image1,5) 
    image2 = cv2.medianBlur(image2,5) 
    
    
    '''
    1.使用SURF算法检测关键点
    '''
    #创建一个SURF对象 阈值越高，能识别的特征就越少，因此可以采用试探法来得到最优检测。
    surf = cv2.xfeatures2d.SURF_create(3000)
    keypoints1 =  surf.detect(image1)
    keypoints2 =  surf.detect(image2)   
    print(keypoints1[0].pt)        
    #在图像上绘制关键点
    image1 = cv2.drawKeypoints(image=image1,keypoints = keypoints1,outImage=image1,color=(255,0,255),flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
    image2 = cv2.drawKeypoints(image=image2,keypoints = keypoints2,outImage=image2,color=(255,0,255),flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
    #显示图像
    cv2.imshow('surf_keypoints1',image1)
    cv2.imshow('surf_keypoints2',image2)
    cv2.waitKey(20)
        
    
    '''
    2.计算特征描述符
    '''    
    brief =  cv2.xfeatures2d.BriefDescriptorExtractor_create(16)
    keypoints1, descriptors1 = brief.compute(image1, keypoints1)
    keypoints2, descriptors2 = brief.compute(image2, keypoints2)
    print(descriptors1[:5])
    print(descriptors2[:5])
    
    print('descriptors1:',len(descriptors1),descriptors1.shape,'descriptors2',len(descriptors2))    

    '''
    3.匹配  汉明距离匹配特征点
    '''
    matcher = cv2.BFMatcher_create(cv2.HAMMING_NORM_TYPE)    
    matchePoints = matcher.match(descriptors1,descriptors2)
    print(type(matchePoints),len(matchePoints),matchePoints[0])
    
    #提取强匹配特征点
    minMatch = 1
    maxMatch = 0
    for i in range(len(matchePoints)):
        if  minMatch > matchePoints[i].distance:
            minMatch = matchePoints[i].distance
        if  maxMatch < matchePoints[i].distance:
            maxMatch = matchePoints[i].distance
    print('最佳匹配值是:',minMatch)        
    print('最差匹配值是:',maxMatch)      
    
    #获取排雷在前边的几个最优匹配结果
    goodMatchePoints = []
    for i in range(len(matchePoints)):
        if matchePoints[i].distance < minMatch + (maxMatch-minMatch)/3:
            goodMatchePoints.append(matchePoints[i])
    
    #绘制最优匹配点
    outImg = None
    outImg = cv2.drawMatches(img1,keypoints1,img2,keypoints2,goodMatchePoints,outImg,matchColor=(0,255,0),flags=cv2.DRAW_MATCHES_FLAGS_DEFAULT)
    cv2.imshow('matche',outImg)
    cv2.waitKey(0)
    cv2.destroyAllWindows()
    

if __name__ == '__main__':
    brief_test()