摘要：為了解決這個問題，算法使用和可以通過參數來設定所以好的匹配提供的正確的估計被稱為，剩下的被稱為返回一個掩模，這個掩模確定了和點

Feature Matching + Homography to find Objects

聯合使用特征提取和 calib3d 模塊中的 findHomography 在復雜圖像中查找已知對象.

之前在一張雜亂的圖像中找到了一個對象（的某些部分）的位置.這些信息足以幫助我們在目標圖像中準確的找到（查詢圖像）對象.為了達到這個目的可以使用 calib3d 模塊中的cv2.findHomography()函數.如果將這兩幅圖像中的特征點集傳給這個函數，他就會找到這個對象的透視圖變換.然后就可以使用函數 cv2.perspectiveTransform() 找到這個對象了.至少要 4 個正確的點才能找到這種變換.在匹配過程可能會有一些錯誤，而這些錯誤會影響最終結果。為了解決這個問題，算法使用 RANSAC 和 LEAST_MEDIAN(可以通過參數來設定).所以好的匹配提供的正確的估計被稱為 inliers，剩下的被稱為outliers.cv2.findHomography() 返回一個掩模，這個掩模確定了 inlier 和outlier 點.

import numpy as np
import cv2
import matplotlib.pyplot as plt

MIN_MATCH_COUNT = 10

img1 = cv2.imread("img.jpg",0)          # queryImage
img2 = cv2.imread("img1.jpg",0) # trainImage

# Initiate SIFT detector
sift = cv2.xfeatures2d.SIFT_create()


# find the keypoints and descriptors with SIFT
kp1, des1 = sift.detectAndCompute(img1,None)
kp2, des2 = sift.detectAndCompute(img2,None)

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 = []
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)
    matchesMask = mask.ravel().tolist()

    h,w = 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 - %d/%d" % (len(good),MIN_MATCH_COUNT))
    matchesMask = None

# Finally we draw our inliers (if successfully found the object) or matching keypoints (if failed).

draw_params = dict(matchColor = (0,255,0), # draw matches in green color
                   singlePointColor = None,
                   matchesMask = matchesMask, # draw only inliers
                   flags = 2)

img3 = cv2.drawMatches(img1,kp1,img2,kp2,good,None,**draw_params)

plt.imshow(img3, "gray"),plt.show()