我正在使用OpenCV
,SIFT
和Homography
来检测图片中的所有对象。
我的全局图片如下:
我想要检测图片上的所有灯具,即使每个灯具的方向不完全相同。
我的模特图片如下:
我用Python编写了这个脚本:
#-*- coding: utf-8! -*-
import os, shutil
import numpy as np
import cv2
#########################
# SIFT descriptors part #
#########################
img1 = cv2.imread('/Users/test/Desktop/SIFT/Ville/ville.jpg',0)
img2 = cv2.imread('/Users/test/Desktop/SIFT/Ville/lampe.jpg',0)
##########################
# Initiate SIFT detector #
##########################
sift = cv2.xfeatures2d.SIFT_create()
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)
#bf = cv2.BFMatcher()
matches = flann.knnMatch(des1,des2,k=2)
good = []
for m,n in matches :
if m.distance < 0.7*n.distance :
good.append([m])
MIN_MATCH_COUNT = 3
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, 'grayfinal.jpg'),plt.show()
cv2.imwrite('matches.jpg',img3)
我收到此错误:
Traceback (most recent call last):
File "image.py", line 34, in <module>
src_pts = np.float32([ kp1[m.queryIdx].pt for m in good ]).reshape(-1,1,2)
AttributeError: 'list' object has no attribute 'queryIdx'
你有什么想法吗?
编辑:
通过假定的解决方案,我得到了一些看起来正确的东西。但是我如何才能发现照片上的其他灯?
答案 0 :(得分:3)
您应该只将m
附加到good
,而不是good.append([m])
。
因为现在good
中的每个元素都是列表,其中包含一个元素(m
),并且您尝试访问其queryIdx
。这就是您收到此AttributeError: 'list' object has no attribute 'queryIdx'
答案 1 :(得分:0)
您可以维护两个列表,例如
good = []
good_without_list = []
for m, n in matches:
if m.distance < 0.75 * n.distance:
good.append([m])
good_without_list.append(m)
然后您可以使用
“ cv2.drawMatchesKnn”的“好”列表
knn_image = cv2.drawMatchesKnn(img1, kp1, img2, kp2, good, None, flags=2)
“ cv2.drawMatches”的“ good_without_list”列表 (就您而言)
img3 = cv2.drawMatches(img1, kp1, img2, kp2, good_without_list, None, **draw_params)