cv2 drawMatches draws on blank screen?
imageCorrespondence = cv2.drawMatches(imageLeft, kpLeft, imageRight, kpRight, [goodMatches[0]], None, flags=2)
cv2.imwrite('imageCorrespondence.png', imageCorrespondence)
gives expected output in jupyter notebook, but when I save the file using python script, it is drawing matches and flags=4
is drawing keypoints just fine, except everything is happening on a black image (of right size: left + right combined).
Possible backend selection issues like we have with matplotlib
?
The example code works just fine:
import numpy as np
import cv2
def getCorrespondence(imageLeft, imageRight):
orb = cv2.ORB_create()
kpLeft, desLeft = orb.detectAndCompute(imageLeft, None)
kpRight, desRight = orb.detectAndCompute(imageRight, None)
bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
matches = bf.match(desLeft, desRight)
goodMatches =
for m in matches:
if m.distance < 100:
goodMatches.append(m)
print('Matches', len(goodMatches))
imageCorrespondence = cv2.drawMatches(imageLeft, kpLeft, imageRight, kpRight, [goodMatches[0]], None, flags=2)
return imageCorrespondence
if __name__ == '__main__':
imageLeft = cv2.imread('image_001.png')
imageRight = cv2.imread('image_002.png')
imageCorrespondence = getCorrespondence(imageLeft, imageRight)
cv2.imwrite('imageCorrespondence.png', imageCorrespondence)
print('Image Saved')
But as soon as I start using the function with other images loaded from elsewhere, it is breaking something. I made sure if those images have content and cv2.imwrite('imageLeft', imageLeft)
works just fine and the image gets saved fine.
python opencv
add a comment |
imageCorrespondence = cv2.drawMatches(imageLeft, kpLeft, imageRight, kpRight, [goodMatches[0]], None, flags=2)
cv2.imwrite('imageCorrespondence.png', imageCorrespondence)
gives expected output in jupyter notebook, but when I save the file using python script, it is drawing matches and flags=4
is drawing keypoints just fine, except everything is happening on a black image (of right size: left + right combined).
Possible backend selection issues like we have with matplotlib
?
The example code works just fine:
import numpy as np
import cv2
def getCorrespondence(imageLeft, imageRight):
orb = cv2.ORB_create()
kpLeft, desLeft = orb.detectAndCompute(imageLeft, None)
kpRight, desRight = orb.detectAndCompute(imageRight, None)
bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
matches = bf.match(desLeft, desRight)
goodMatches =
for m in matches:
if m.distance < 100:
goodMatches.append(m)
print('Matches', len(goodMatches))
imageCorrespondence = cv2.drawMatches(imageLeft, kpLeft, imageRight, kpRight, [goodMatches[0]], None, flags=2)
return imageCorrespondence
if __name__ == '__main__':
imageLeft = cv2.imread('image_001.png')
imageRight = cv2.imread('image_002.png')
imageCorrespondence = getCorrespondence(imageLeft, imageRight)
cv2.imwrite('imageCorrespondence.png', imageCorrespondence)
print('Image Saved')
But as soon as I start using the function with other images loaded from elsewhere, it is breaking something. I made sure if those images have content and cv2.imwrite('imageLeft', imageLeft)
works just fine and the image gets saved fine.
python opencv
Provide a Minimal, Complete, and Verifiable example. Shouldn't have to remind a 5 year user with reputation in thousands about that....
– Dan Mašek
Nov 20 '18 at 0:16
Sorry, I thought I missed something in those two lines I posted initially (esp because I set the output image param to None) which I assumed would be obvious to opencv users, hence didn't add the full code. Added it now :)
– Saravanabalagi Ramachandran
Nov 20 '18 at 11:38
add a comment |
imageCorrespondence = cv2.drawMatches(imageLeft, kpLeft, imageRight, kpRight, [goodMatches[0]], None, flags=2)
cv2.imwrite('imageCorrespondence.png', imageCorrespondence)
gives expected output in jupyter notebook, but when I save the file using python script, it is drawing matches and flags=4
is drawing keypoints just fine, except everything is happening on a black image (of right size: left + right combined).
Possible backend selection issues like we have with matplotlib
?
The example code works just fine:
import numpy as np
import cv2
def getCorrespondence(imageLeft, imageRight):
orb = cv2.ORB_create()
kpLeft, desLeft = orb.detectAndCompute(imageLeft, None)
kpRight, desRight = orb.detectAndCompute(imageRight, None)
bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
matches = bf.match(desLeft, desRight)
goodMatches =
for m in matches:
if m.distance < 100:
goodMatches.append(m)
print('Matches', len(goodMatches))
imageCorrespondence = cv2.drawMatches(imageLeft, kpLeft, imageRight, kpRight, [goodMatches[0]], None, flags=2)
return imageCorrespondence
if __name__ == '__main__':
imageLeft = cv2.imread('image_001.png')
imageRight = cv2.imread('image_002.png')
imageCorrespondence = getCorrespondence(imageLeft, imageRight)
cv2.imwrite('imageCorrespondence.png', imageCorrespondence)
print('Image Saved')
But as soon as I start using the function with other images loaded from elsewhere, it is breaking something. I made sure if those images have content and cv2.imwrite('imageLeft', imageLeft)
works just fine and the image gets saved fine.
python opencv
imageCorrespondence = cv2.drawMatches(imageLeft, kpLeft, imageRight, kpRight, [goodMatches[0]], None, flags=2)
cv2.imwrite('imageCorrespondence.png', imageCorrespondence)
gives expected output in jupyter notebook, but when I save the file using python script, it is drawing matches and flags=4
is drawing keypoints just fine, except everything is happening on a black image (of right size: left + right combined).
Possible backend selection issues like we have with matplotlib
?
The example code works just fine:
import numpy as np
import cv2
def getCorrespondence(imageLeft, imageRight):
orb = cv2.ORB_create()
kpLeft, desLeft = orb.detectAndCompute(imageLeft, None)
kpRight, desRight = orb.detectAndCompute(imageRight, None)
bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
matches = bf.match(desLeft, desRight)
goodMatches =
for m in matches:
if m.distance < 100:
goodMatches.append(m)
print('Matches', len(goodMatches))
imageCorrespondence = cv2.drawMatches(imageLeft, kpLeft, imageRight, kpRight, [goodMatches[0]], None, flags=2)
return imageCorrespondence
if __name__ == '__main__':
imageLeft = cv2.imread('image_001.png')
imageRight = cv2.imread('image_002.png')
imageCorrespondence = getCorrespondence(imageLeft, imageRight)
cv2.imwrite('imageCorrespondence.png', imageCorrespondence)
print('Image Saved')
But as soon as I start using the function with other images loaded from elsewhere, it is breaking something. I made sure if those images have content and cv2.imwrite('imageLeft', imageLeft)
works just fine and the image gets saved fine.
python opencv
python opencv
edited Nov 20 '18 at 11:26
Saravanabalagi Ramachandran
asked Nov 19 '18 at 22:28
Saravanabalagi RamachandranSaravanabalagi Ramachandran
2,86622153
2,86622153
Provide a Minimal, Complete, and Verifiable example. Shouldn't have to remind a 5 year user with reputation in thousands about that....
– Dan Mašek
Nov 20 '18 at 0:16
Sorry, I thought I missed something in those two lines I posted initially (esp because I set the output image param to None) which I assumed would be obvious to opencv users, hence didn't add the full code. Added it now :)
– Saravanabalagi Ramachandran
Nov 20 '18 at 11:38
add a comment |
Provide a Minimal, Complete, and Verifiable example. Shouldn't have to remind a 5 year user with reputation in thousands about that....
– Dan Mašek
Nov 20 '18 at 0:16
Sorry, I thought I missed something in those two lines I posted initially (esp because I set the output image param to None) which I assumed would be obvious to opencv users, hence didn't add the full code. Added it now :)
– Saravanabalagi Ramachandran
Nov 20 '18 at 11:38
Provide a Minimal, Complete, and Verifiable example. Shouldn't have to remind a 5 year user with reputation in thousands about that....
– Dan Mašek
Nov 20 '18 at 0:16
Provide a Minimal, Complete, and Verifiable example. Shouldn't have to remind a 5 year user with reputation in thousands about that....
– Dan Mašek
Nov 20 '18 at 0:16
Sorry, I thought I missed something in those two lines I posted initially (esp because I set the output image param to None) which I assumed would be obvious to opencv users, hence didn't add the full code. Added it now :)
– Saravanabalagi Ramachandran
Nov 20 '18 at 11:38
Sorry, I thought I missed something in those two lines I posted initially (esp because I set the output image param to None) which I assumed would be obvious to opencv users, hence didn't add the full code. Added it now :)
– Saravanabalagi Ramachandran
Nov 20 '18 at 11:38
add a comment |
1 Answer
1
active
oldest
votes
I initially thought the sixth param None
was causing this, but that is not causing any trouble.
cv2.drawMatches()
takes in imageLeft and imageRight as numpy arrays as mentioned in the docs:
outImg = cv.drawMatches( img1, keypoints1, img2, keypoints2, matches1to2, outImg[, matchColor[, singlePointColor[, matchesMask[,flags]]]] )
Parameters
img1
First source image.
keypoints1
Keypoints from the first source image.
img2
Second source image.
keypoints2
Keypoints from the second source image. ...
However something that breaks this is the alpha layer, if you happen to load the alpha layer in the numpy array, it would draw on a black image. When I removed the alpha layer in the numpy array manually and had only three channels, it started working fine. This could be because of the way matplotlib
handles alpha layer differs from how cv2.imwrite
handles the same, that it seemed to work in Jupyter notebook but not using the Python script.
I initially thought I needed to switch from BGRA to ABGR, but that's not the case BGRA is just fine, I was getting a black screen if the input images had a fourth alpha layer. Opencv usually strips the alpha layer when reading an image...!
1
Good catch. Looking at the source code, here is the culprit and here's another. The random colour generation leaves the alpha channel at default value of 0, so whatever it draws ends up transparent. I'll file a bug report, this is a trivial fix.
– Dan Mašek
Nov 20 '18 at 13:07
github.com/opencv/opencv/issues/13219
– Dan Mašek
Nov 20 '18 at 13:15
And one more: github.com/opencv/opencv/issues/13227 | Fix for the previous issue is already merged, should be fine in 3.4.5 and the next release of 4.x. I guess this is really what you ran into.
– Dan Mašek
Nov 20 '18 at 20:38
add a comment |
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votes
I initially thought the sixth param None
was causing this, but that is not causing any trouble.
cv2.drawMatches()
takes in imageLeft and imageRight as numpy arrays as mentioned in the docs:
outImg = cv.drawMatches( img1, keypoints1, img2, keypoints2, matches1to2, outImg[, matchColor[, singlePointColor[, matchesMask[,flags]]]] )
Parameters
img1
First source image.
keypoints1
Keypoints from the first source image.
img2
Second source image.
keypoints2
Keypoints from the second source image. ...
However something that breaks this is the alpha layer, if you happen to load the alpha layer in the numpy array, it would draw on a black image. When I removed the alpha layer in the numpy array manually and had only three channels, it started working fine. This could be because of the way matplotlib
handles alpha layer differs from how cv2.imwrite
handles the same, that it seemed to work in Jupyter notebook but not using the Python script.
I initially thought I needed to switch from BGRA to ABGR, but that's not the case BGRA is just fine, I was getting a black screen if the input images had a fourth alpha layer. Opencv usually strips the alpha layer when reading an image...!
1
Good catch. Looking at the source code, here is the culprit and here's another. The random colour generation leaves the alpha channel at default value of 0, so whatever it draws ends up transparent. I'll file a bug report, this is a trivial fix.
– Dan Mašek
Nov 20 '18 at 13:07
github.com/opencv/opencv/issues/13219
– Dan Mašek
Nov 20 '18 at 13:15
And one more: github.com/opencv/opencv/issues/13227 | Fix for the previous issue is already merged, should be fine in 3.4.5 and the next release of 4.x. I guess this is really what you ran into.
– Dan Mašek
Nov 20 '18 at 20:38
add a comment |
I initially thought the sixth param None
was causing this, but that is not causing any trouble.
cv2.drawMatches()
takes in imageLeft and imageRight as numpy arrays as mentioned in the docs:
outImg = cv.drawMatches( img1, keypoints1, img2, keypoints2, matches1to2, outImg[, matchColor[, singlePointColor[, matchesMask[,flags]]]] )
Parameters
img1
First source image.
keypoints1
Keypoints from the first source image.
img2
Second source image.
keypoints2
Keypoints from the second source image. ...
However something that breaks this is the alpha layer, if you happen to load the alpha layer in the numpy array, it would draw on a black image. When I removed the alpha layer in the numpy array manually and had only three channels, it started working fine. This could be because of the way matplotlib
handles alpha layer differs from how cv2.imwrite
handles the same, that it seemed to work in Jupyter notebook but not using the Python script.
I initially thought I needed to switch from BGRA to ABGR, but that's not the case BGRA is just fine, I was getting a black screen if the input images had a fourth alpha layer. Opencv usually strips the alpha layer when reading an image...!
1
Good catch. Looking at the source code, here is the culprit and here's another. The random colour generation leaves the alpha channel at default value of 0, so whatever it draws ends up transparent. I'll file a bug report, this is a trivial fix.
– Dan Mašek
Nov 20 '18 at 13:07
github.com/opencv/opencv/issues/13219
– Dan Mašek
Nov 20 '18 at 13:15
And one more: github.com/opencv/opencv/issues/13227 | Fix for the previous issue is already merged, should be fine in 3.4.5 and the next release of 4.x. I guess this is really what you ran into.
– Dan Mašek
Nov 20 '18 at 20:38
add a comment |
I initially thought the sixth param None
was causing this, but that is not causing any trouble.
cv2.drawMatches()
takes in imageLeft and imageRight as numpy arrays as mentioned in the docs:
outImg = cv.drawMatches( img1, keypoints1, img2, keypoints2, matches1to2, outImg[, matchColor[, singlePointColor[, matchesMask[,flags]]]] )
Parameters
img1
First source image.
keypoints1
Keypoints from the first source image.
img2
Second source image.
keypoints2
Keypoints from the second source image. ...
However something that breaks this is the alpha layer, if you happen to load the alpha layer in the numpy array, it would draw on a black image. When I removed the alpha layer in the numpy array manually and had only three channels, it started working fine. This could be because of the way matplotlib
handles alpha layer differs from how cv2.imwrite
handles the same, that it seemed to work in Jupyter notebook but not using the Python script.
I initially thought I needed to switch from BGRA to ABGR, but that's not the case BGRA is just fine, I was getting a black screen if the input images had a fourth alpha layer. Opencv usually strips the alpha layer when reading an image...!
I initially thought the sixth param None
was causing this, but that is not causing any trouble.
cv2.drawMatches()
takes in imageLeft and imageRight as numpy arrays as mentioned in the docs:
outImg = cv.drawMatches( img1, keypoints1, img2, keypoints2, matches1to2, outImg[, matchColor[, singlePointColor[, matchesMask[,flags]]]] )
Parameters
img1
First source image.
keypoints1
Keypoints from the first source image.
img2
Second source image.
keypoints2
Keypoints from the second source image. ...
However something that breaks this is the alpha layer, if you happen to load the alpha layer in the numpy array, it would draw on a black image. When I removed the alpha layer in the numpy array manually and had only three channels, it started working fine. This could be because of the way matplotlib
handles alpha layer differs from how cv2.imwrite
handles the same, that it seemed to work in Jupyter notebook but not using the Python script.
I initially thought I needed to switch from BGRA to ABGR, but that's not the case BGRA is just fine, I was getting a black screen if the input images had a fourth alpha layer. Opencv usually strips the alpha layer when reading an image...!
edited Nov 20 '18 at 12:09
answered Nov 20 '18 at 11:33
Saravanabalagi RamachandranSaravanabalagi Ramachandran
2,86622153
2,86622153
1
Good catch. Looking at the source code, here is the culprit and here's another. The random colour generation leaves the alpha channel at default value of 0, so whatever it draws ends up transparent. I'll file a bug report, this is a trivial fix.
– Dan Mašek
Nov 20 '18 at 13:07
github.com/opencv/opencv/issues/13219
– Dan Mašek
Nov 20 '18 at 13:15
And one more: github.com/opencv/opencv/issues/13227 | Fix for the previous issue is already merged, should be fine in 3.4.5 and the next release of 4.x. I guess this is really what you ran into.
– Dan Mašek
Nov 20 '18 at 20:38
add a comment |
1
Good catch. Looking at the source code, here is the culprit and here's another. The random colour generation leaves the alpha channel at default value of 0, so whatever it draws ends up transparent. I'll file a bug report, this is a trivial fix.
– Dan Mašek
Nov 20 '18 at 13:07
github.com/opencv/opencv/issues/13219
– Dan Mašek
Nov 20 '18 at 13:15
And one more: github.com/opencv/opencv/issues/13227 | Fix for the previous issue is already merged, should be fine in 3.4.5 and the next release of 4.x. I guess this is really what you ran into.
– Dan Mašek
Nov 20 '18 at 20:38
1
1
Good catch. Looking at the source code, here is the culprit and here's another. The random colour generation leaves the alpha channel at default value of 0, so whatever it draws ends up transparent. I'll file a bug report, this is a trivial fix.
– Dan Mašek
Nov 20 '18 at 13:07
Good catch. Looking at the source code, here is the culprit and here's another. The random colour generation leaves the alpha channel at default value of 0, so whatever it draws ends up transparent. I'll file a bug report, this is a trivial fix.
– Dan Mašek
Nov 20 '18 at 13:07
github.com/opencv/opencv/issues/13219
– Dan Mašek
Nov 20 '18 at 13:15
github.com/opencv/opencv/issues/13219
– Dan Mašek
Nov 20 '18 at 13:15
And one more: github.com/opencv/opencv/issues/13227 | Fix for the previous issue is already merged, should be fine in 3.4.5 and the next release of 4.x. I guess this is really what you ran into.
– Dan Mašek
Nov 20 '18 at 20:38
And one more: github.com/opencv/opencv/issues/13227 | Fix for the previous issue is already merged, should be fine in 3.4.5 and the next release of 4.x. I guess this is really what you ran into.
– Dan Mašek
Nov 20 '18 at 20:38
add a comment |
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Provide a Minimal, Complete, and Verifiable example. Shouldn't have to remind a 5 year user with reputation in thousands about that....
– Dan Mašek
Nov 20 '18 at 0:16
Sorry, I thought I missed something in those two lines I posted initially (esp because I set the output image param to None) which I assumed would be obvious to opencv users, hence didn't add the full code. Added it now :)
– Saravanabalagi Ramachandran
Nov 20 '18 at 11:38