如何在最佳匹配上绘制边界框？

Question

如何使用 Python 在 BF MATCHER 中的最佳匹配上绘制边界框？

Answer 1

以下是该方法的摘要，它应该是一个正确的解决方案：

1. 在查询图像 (img1) 上检测关键点和描述符
2. 检测目标图像上的关键点和描述符（img2）
3. 找出两组描述符之间的匹配或对应关系
4. 使用最好的 10 个匹配来形成一个变换矩阵
5. 根据变换矩阵变换img1周围的矩形
6. 添加偏移量以将边界框放在正确的位置
7. 显示结果图像（如下）。

这是代码：

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

img1 = cv2.imread('box.png', 0)          # query Image
img2 = cv2.imread('box_in_scene.png',0)  # target Image

# Initiate SIFT detector
orb = cv2.ORB_create()

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

# create BFMatcher object
bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)

# Match descriptors.
matches = bf.match(des1,des2)

# Sort them in the order of their distance.
matches = sorted(matches, key = lambda x:x.distance)

good_matches = matches[:10]

src_pts = np.float32([ kp1[m.queryIdx].pt for m in good_matches     ]).reshape(-1,1,2)
dst_pts = np.float32([ kp2[m.trainIdx].pt for m in good_matches ]).reshape(-1,1,2)
M, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC,5.0)
matchesMask = mask.ravel().tolist()
h,w = img1.shape[:2]
pts = np.float32([ [0,0],[0,h-1],[w-1,h-1],[w-1,0] ]).reshape(-1,1,2)

dst = cv2.perspectiveTransform(pts,M)
dst += (w, 0)  # adding offset

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_matches, None,**draw_params)

# Draw bounding box in Red
img3 = cv2.polylines(img3, [np.int32(dst)], True, (0,0,255),3, cv2.LINE_AA)

cv2.imshow("result", img3)
cv2.waitKey()
# or another option for display output
#plt.imshow(img3, 'result'), plt.show()