# -*- coding: utf-8 -*-




#匹配相应的模板
__author__ = '上海色宁'

import sys

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