Python // 如何在OpenCV中使用SIFT提取的特征获取目标对象周围的矩形

Question

这是我的代码：

import numpy as np
import cv2
from matplotlib import pyplot as plt
from scipy import misc
import matplotlib.pyplot as plt

MIN_MATCH_COUNT = 10

img1 = cv2.imread('Screenshot_2.png',0)          
img2 = cv2.imread('Screenshot_12.png',0)

# 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)


good = []
for m,n in matches:
    if m.distance < 0.7*n.distance:
        good.append(m)
print good
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

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()

我想用 OpenCV 用这种方法跟踪我检测到的对象的矩形，但我不知道如何开始得到我想要的。

我找不到用 Python 解决我的问题的地方

你有什么建议可以给我执行我的项目吗？:(

Answer 1

只需获取 src_pts where mask==1 并在源图像上找到矩形的 min_X、min_Y、max_X、max_Y。

下面是我试过的代码..

pts = src_pts[mask==1]
min_x, min_y = np.int32(pts.min(axis=0))
max_x, max_y = np.int32(pts.max(axis=0))

在这里，左上点为 (min_x, min_y)，右下点为 (max_x, max_y)。下面的代码用于在 originalImage 上显示边界框。

cv2.rectangle(originalImage,(min_x, min_y), (max_x,max_y), 255,2)
plt.imshow(originalImage, cmap='gray')