Array out of Bounds issue about pysift HOT 13 OPEN

The error is occurring in the following file and line of code:
File PySIFT/orientation.py", line 65, in assign_orientation

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Thank you for liking the repo. Unfortunately, I don't maintain this repo anymore so I cannot guarantee I'll have time to look into the issue. In case I do, what is the shape of the image you are passing?

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I appreciate that but I don't think I'd accept donations for this project.

I don't know of any of the forks that have actually made further commits but I just tried on a 195x195 image and it seemed to work. I recently made a commit slightly changing the main.py script. Take a look at the updated readme and try running again. Let me know if you still get the error and we can work from there.

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I can't see the attached images as this was sent to me as a GitHub issue, not an email so I cannot test your specific images. Since running on a 195x195 image works on my end, I'm not sure what else I can do right now to troubleshoot. I would suggest for now, to use a lower number of octaves or a lower s (number of images per octave) when running on smaller images. I suggest this because it is my guess that on smaller images, the higher octaves are downsampled enough that the kernel is larger than the downsampled image.

As for the output, your understanding is correct. In addition to displaying the keypoints, the keypoint pyramid and the feature pyramid (the feature vector for each detected keypoint) are saved to the results/<output prefix>_kp_pyr.pkl and results/<output prefix>_feat_pyr.pkl files.

As for using this code in your masters dissertation, I would highly advise you to familiarize yourself with the code and make changes to it instead of using it out-of-the-box. This is because while superficially the algorithm seems to confirm to the Lowe paper, there could easily be discrepancies that I have not realized. In addition, there is a known bug in scaling the keypoints from the later octaves (see the "Limitations" section of the README).

There is also an old implementation of image matching/alignment in the match.py script which could potentially be used as a starting point for any alignment research.

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Maybe upload them to google drive or something similar and post a shareable link? I'm not huge on posting my email address in public places.

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With that 'failed' image I set the number of blur levels to 3 for each octave and I set a total of 3 octaves. And it worked. And it does make sense because if you have too many blur levels the final level might be downsampled to a point that you won't be able to find any features because the image is too small. Something which is not yet clear to me is why do you keep getting less features in the final result? For instance taking your example picture in the fourth octave there are way less features as compared to the first octave. My guess it is down to the increase in blurness. In the fourth octave there is more blurness as it uses the previous results of the previous octaves.

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orientation.py

def assign_orientation(kps, octave, num_bins=36):
    .........#1
    w = int(2 * np.ceil(sigma))
    .........#2
    m, theta = get_grad(L, x, y)
    theta = 359 if(theta >=360) else theta

descriptors.py

def get_histogram_for_subregion(m, theta, num_bin, reference_angle, bin_width, subregion_w):
    .........#3
    for i, (mag, angle) in enumerate(zip(m, theta)):
        angle = (angle-reference_angle) % 360
        angle = 359 if (angle >= 360) else angle
        binno = quantize_orientation(angle, num_bin)

keypoints.py

def localize_keypoint(D, x, y, s):
    .........#4
    #offset = -LA.inv(HD).dot(J)
    x_hat = np.linalg.lstsq(HD, J)[0]
    offset = x_hat
    return offset, J, HD[:2,:2], x, y, s

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Code changes to avoid errors

I got the same error as you, I changed the code like this, the code runs normally

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