The error is for the following line in the code:- plt.imshow(img3), plt.show()

The error is for the following line in the code:- plt.imshow(img3), plt.show()

The error is for the following line in the code:- plt.imshow(img3), plt.show()plt.show() Please find the attachments. One of the two attachments contains the source code, whereas the second contains the Traceback Error. BFMatchingthree.png BFMatching_three.png

The error is for the following line in the code:- plt.imshow(img3), plt.show() Please find the attachments. One of the two attachments contains the source code, whereas the second contains the Traceback Error. Error.

Text Version of Code.:-

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

img1 = cv2.imread('../CarData/TestImages/test-0.pgm',0)# queryImage img2 = cv2.imread('../CarData/TestImages/test-1.pgm',0) # trainImage

Initiate SIFT detector

sift = cv2.SIFT()

Initiate the ORB detector

orb = cv2.ORB_create()

find the keypoints and descriptors with SIFT

kp1, des1 = sift.detectAndCompute(img1,None)

kp2, des2 = sift.detectAndCompute(img2,None)

find the keypoints and descriptors with ORB

kp1, des1 = orb.detectAndCompute(img1,None) kp2, des2 = orb.detectAndCompute(img2,None)

BFMatcher with default params

bf = cv2.BFMatcher() matches = bf.knnMatch(des1,des2, k=2)

Apply ratio test

good = [] for m,n in matches: if m.distance < 0.75*n.distance: good.append([m])

cv2.drawMatchesKnn expects list of lists as matches.

img_temp = np.zeros((1,1)) img3 = cv2.drawMatchesKnn(img1,kp1,img2,kp2,good,img_temp,flags=2)

plt.imshow(img3),plt.show() BFMatchingthree.png BFMatching_three.png

The error is for the following line in the code:- plt.imshow(img3), plt.show() Please find the attachments. One of the two attachments contains the source code, whereas the second contains the Traceback Error.

Text Version of Code.:-

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

img1 = cv2.imread('../CarData/TestImages/test-0.pgm',0)# queryImage
img2 = cv2.imread('../CarData/TestImages/test-1.pgm',0) # trainImage
 
trainImage

# Initiate SIFT detector
 
sift = cv2.SIFT()
 
Initiate detector
#sift = cv2.SIFT()
#Initiate the ORB detector
 
detector
orb = cv2.ORB_create()
 
cv2.ORB_create()

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

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

# BFMatcher with default params
 
params
bf = cv2.BFMatcher()
matches = bf.knnMatch(des1,des2, k=2)
 
k=2)

# Apply ratio test
 
test
good = []
for m,n in matches:
    if m.distance < 0.75*n.distance:
        good.append([m])
 
good.append([m])

# cv2.drawMatchesKnn expects list of lists as matches.
 
matches.
img_temp = np.zeros((1,1))
img3 = cv2.drawMatchesKnn(img1,kp1,img2,kp2,good,img_temp,flags=2)
 
cv2.drawMatchesKnn(img1,kp1,img2,kp2,good,img_temp,flags=2)

----------------------------------------------------------------------------------------------------------------------------------------------------------------------
plt.imshow(img3),plt.show()