如何在最佳匹配上绘制边界框? [英] How to draw bounding box on best matches?
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问题描述
如何使用Python在BF MATCHER的最佳匹配项上绘制边界框?
How can I draw a bounding box on best matches in BF MATCHER using Python?
推荐答案
以下是应作为适当解决方案的方法的摘要:
Here is a summary of the approach it should be a proper solution:
- 检测查询图像(img1)上的关键点和描述符
- 检测目标图像(img2)上的关键点和描述符
- 找到两组描述符之间的匹配或对应关系
- 使用最佳的10个匹配项来形成转换矩阵
- 基于变换矩阵变换img1周围的矩形
- 添加偏移量以将边界框放置在正确的位置
- 显示结果图像(如下所示).
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()
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