# 5.10 特征匹配和使用单应性匹配来搜索物体

## 目标

• 我们将混合特征匹配和来自`calib3d`的单应性匹配来从一个复杂的图像中寻找已知的物体。

## 基础

`cv2.findHomography()`返回一个确定了inlier和outlier的掩码。

## 代码

``````import numpy as np
import cv2
from matplotlib import pyplot as plt
MIN_MATCH_COUNT = 10
# 初始化SIFT检测器
sift = cv2.xfeatures2d.SIFT_create()
# 用SIFT检测器搜索关键点和描述子
kp1, des1 = sift.detectAndCompute(img1,None)
kp2, des2 = sift.detectAndCompute(img2,None)
FLANN_INDEX_KDTREE = 1
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)
# 对所有好的匹配进行Lowe's比率测试
good = []
for m,n in matches:
if m.distance < 0.7*n.distance:
good.append(m)``````

``````if len(good)>MIN_MATCH_COUNT:
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)

M, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC,5.0)

h,w,d = img1.shape
pts = np.float32([ [0,0],[0,h-1],[w-1,h-1],[w-1,0] ]).reshape(-1,1,2)
dst = cv2.perspectiveTransform(pts,M)

img2 = cv2.polylines(img2,[np.int32(dst)],True,255,3, cv2.LINE_AA)
else:
print( "Not enough matches are found - {}/{}".format(len(good), MIN_MATCH_COUNT) )

``````draw_params = dict(matchColor = (0,255,0), # 用绿色画出匹配点
singlePointColor = None,
flags = 2)
img3 = cv2.drawMatches(img1,kp1,img2,kp2,good,None,**draw_params)
plt.imshow(img3, 'gray'),plt.show()``````