rpautrat / lisrd Goto Github PK

Hello, When I am running your evaluation notebook in notebooks/hpatches_evaluation.ipynb, I get the error: No such file or directory: '/home/victor/LISRD/Documents/datasets/Hpatches_sequences/i_nijmegen/1.ppm.kornia_sift'. Where can I get these pictures?

Thank you very much for the code and models.I encountered a problem when using the lisrd_sift option.

File "g:/project/3d/LISRD-master/lisrd/export_features.py", line 88, in export
func.grid_sample(torch.Tensor(descs[k]), grid_points),
File "C:\Users\Administrator\anaconda3\lib\site-packages\torch\nn\functional.py", line 4304, in grid_sample
return torch.grid_sampler(input, grid, mode_enum, padding_mode_enum, align_corners)
RuntimeError: grid_sampler(): expected grid to have size 1 in last dimension, but got grid with sizes [1, 2000, 1, 2]

How can I solve this issue?
Looking forward to your replay

the four variance/invariance occasions is the same in your defination, I 'm confused , how to perform four var/invar occsions with one image input ? thank you.

Is it possible to find the similarities in Pointcloud?

I feel that ALIKE is better than SuperPoint in the way of obtaining key points, comparing SuperPoint + LISRD and ALIKE + LISRD. I feel that such a result should not be, you can try this ALIKE, the robustness of your network.

I feel confused about that inkeypoint_detectors.py line 14 and line 22 ,you just change keypoint x,y order as y,x, is there any significance in doing this?

Thx for u brilliant work and looking forward to your reply

Hello, thank you very much for your code, can you convert the post-processing part of your code into C++, I have encountered many problems and there is no way to solve them.

LISRD2ONNX
Problem 1: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
if (h % tile != 0) or (w % tile != 0): # should be divisible by tile
First check is not problem
ONNX2NCCN
Problem 2:
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Hello, sorry to disturb you.
I try to use the same two images for evaluation---superpoint-master and LISRD
Such as superpoint-master : python match_features_demo.py sp_v6 $DATA_PATH/HPatches/i_pool/1.ppm $DATA_PATH/i_pool/6.ppm
and demo_lisrd.ipynb
But the matching result of superpoint is much better than demo_lisrd，the result in lisrd There are only a few matches, and the accuracy is not high.Maybe it’s a mistake in my operation. I think the result should not be like this. LISRD maybe better in day-night changes or rotation changes.
So could you give me some suggestions? Thank you very much.

Thank you very much for the code and models. Could you explain why the size of the model lisrd_aachen.pth is smaller than that of the model lisrd_vidit.pth?

The time I measured is that LISRD is an order of magnitude less than the SuperGlue time, which makes me feel weird. I want to know the result of your comparison.

Hi, @rpautrat,
I‘m very interested to your excellent work. And I‘ve done some other tests with your work. I tried to use meta descriptors (tile set 1*1) to do image retrieval task. And the results were not good. Do you think it's reasonable to use this network for retrieval?
Really hope to get your advice. Thanks in advance and have a nice day.

Hi @rpautrat ,

thanks for your great work! I wanna ask will you release the code evaluating on RNIM dataset, big thanks!

hi~The following is my error warning.

[09/03/2021 10:30:01 INFO] Running command TRAIN
Traceback (most recent call last):
File "C:\Users\User\AppData\Local\Programs\Python\Python36\lib\runpy.py", line 193, in _run_module_as_main
"main", mod_spec)
File "C:\Users\User\AppData\Local\Programs\Python\Python36\lib\runpy.py", line 85, in _run_code
exec(code, run_globals)
File "C:\Users\User\Desktop\python\STUDY_CNN_imagematching\LISRD-master\lisrd\experiment.py", line 130, in
args.func(config, exper_dir, args)
File "C:\Users\User\Desktop\python\STUDY_CNN_imagematching\LISRD-master\lisrd\experiment.py", line 39, in _train
config['data']['name'])(config['data'], device)
File "C:\Users\User\Desktop\python\STUDY_CNN_imagematching\LISRD-master\lisrd\datasets\mixed_dataset.py", line 32, in init
device))
File "C:\Users\User\Desktop\python\STUDY_CNN_imagematching\LISRD-master\lisrd\datasets\vidit.py", line 27, in init
files = np.sort(files).reshape(300, 40)
ValueError: cannot reshape array of size 11999 into shape (300,40)

here are many datasets, which dataset is suitable for use?
And what should the folder structure be?

Hi, I am a freshman to this field, and I prepare using the pretrained model to see your work's result on my own images. Therefore, whether I needn't training my own model? Could you give an advice about how to use the command?
"python -m lisrd.export_features <name of the model (lisrd or lisrd_sift)> --checkpoint --keypoints <type of keypoints (sift or superpoint)> --num_kp <number of keypoints (default: 2000)>"
I haven't found any file or folder named checkpoint, how can I get this file?

Hello，I am learning your code. I didn't find your code about table 2(Comparison to the state of the art on HPatches). I hope to get your code and data set about this part. thank you very much!!!

Sorry to bother you,
I have evaluated the pertrained models on HPatches and RDNIM datasets already.
If I want to compare the performance of SuperPoint on these datasets, how can I do it? Could you give me some details?
Thanks!