# -*- coding: utf-8 -*-
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#匹配相应的模板
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__author__ = '上海色宁'
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import sys
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import numpy as np
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import cv2
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from matplotlib import pyplot as plt
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MIN_MATCH_COUNT = 10 #设置最低特征点匹配数量为10
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template = cv2.imread(sys.argv[1],0) # queryImage
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target = cv2.imread(sys.argv[2],0) # trainImage
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# Initiate SIFT detector创建sift检测器
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sift = cv2.SIFT_create()
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# find the keypoints and descriptors with SIFT
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kp1, des1 = sift.detectAndCompute(template,None)
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kp2, des2 = sift.detectAndCompute(target,None)
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#创建设置FLANN匹配
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FLANN_INDEX_KDTREE = 0
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index_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5)
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search_params = dict(checks = 50)
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flann = cv2.FlannBasedMatcher(index_params, search_params)
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matches = flann.knnMatch(des1,des2,k=2)
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# store all the good matches as per Lowe's ratio test.
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good = []
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#舍弃大于0.7的匹配
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for m,n in matches:
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if m.distance < 0.7*n.distance:
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good.append(m)
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if len(good)>MIN_MATCH_COUNT:
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# 获取关键点的坐标
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src_pts = np.float32([ kp1[m.queryIdx].pt for m in good ]).reshape(-1,1,2)
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dst_pts = np.float32([ kp2[m.trainIdx].pt for m in good ]).reshape(-1,1,2)
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#计算变换矩阵和MASK
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M, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0)
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matchesMask = mask.ravel().tolist()
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h,w = template.shape
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# 使用得到的变换矩阵对原图像的四个角进行变换，获得在目标图像上对应的坐标
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pts = np.float32([ [0,0],[0,h-1],[w-1,h-1],[w-1,0] ]).reshape(-1,1,2)
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dst = cv2.perspectiveTransform(pts,M)
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cv2.polylines(target,[np.int32(dst)],True,0,2, cv2.LINE_AA)
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else:
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print( "Not enough matches are found - %d/%d" % (len(good),MIN_MATCH_COUNT))
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matchesMask = None
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draw_params = dict(matchColor=(0,255,0),
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singlePointColor=None,
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matchesMask=matchesMask,
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flags=2)
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result = cv2.drawMatches(template,kp1,target,kp2,good,None,**draw_params)
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plt.imshow(result, 'gray')
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plt.show()
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