This is MODS version, which allows you using state-of-the-art deep descriptors like HardNet without linking MODS to any of deep learning library. It contains very small number of detectors and descriptors implemented inside -- for easier compilation. Instead it uses zeromq library for communication with separately run CNN daemons. Examples with python PyTorch AffNet and HardNet++ descriptors is provided, but you can use any language and any DL package you like, just modify corresponding scripts.
I expect, that you have already installed latest PyTorch (0.5)
cd build
cmake ..
make
Relevant config-files are: config_aff_ori_desc_zeromq.ini and iters_HessianZMQ.ini With Hessian-AffNet, OriNet and HardNet++
./run_zmq_servers.sh
Wait until initialization on GPU is done and you see:
Extracting on GPU
Extracting on GPU
Extracting on GPU
Now you can run matching:
./mods imgs/graf1.png imgs/graf6.png out1_deep.jpg out2_deep.jpg k1.txt k2.txt m.txt l.log 0 0 abcd.txt config_aff_ori_desc_zeromq.ini iters_HessianZMQ.ini
Expected output:
Maximum threads can be used: 4
View synthesis, detection and description...
Iteration 0
HessianAffine: 1 synthesis will be done.
('processing', 0.07718610763549805, 1.6556436644250974e-05, ' per patch')
('processing', 0.061591148376464844, 1.6110684900984786e-05, ' per patch')
('processing', 0.07169699668884277, 1.5837640090312078e-05, ' per patch')
('processing', 0.05922508239746094, 1.5873782470506817e-05, ' per patch')
('processing', 0.1312699317932129, 3.1877108254787004e-05, ' per patch')
('processing', 0.10080504417419434, 3.0019369914888128e-05, ' per patch')
Matching ...
Matching ...
3358 4118
264 tentatives found.
Duplicate filtering before RANSAC with threshold = 2 pixels.
254 unique tentatives left
LO-RANSAC(homography) verification is used...
147 RANSAC correspondences got
147 true matches are identified in 0.003 seconds
Done in 1 iterations
*********************
Writing files...
HessianAffine 2
HessianAffine 2
Writing images with matches... done
Image1: regions descriptors | Image2: regions descriptors
3731 3358 | 4527 4118
True matches | unique tentatives
147 | 254 | 57.9% 1st geom inc
Main matching | All Time:
2.02 | 2.52 seconds
Timings: (sec/%)
Synth|Detect|Orient|Desc|Match|RANSAC|MISC|Total
0.011 0.721 0.568 0.463 0.229 0.003 0.527 2.52
0.438 28.6 22.5 18.4 9.08 0.119 20.9 100
Don`t forget to kill server process after work done.
Now run with classical HessianAffine(Baumberg) + RootSIFT:
./mods imgs/graf1.png imgs/graf6.png out1_classic.jpg out2_classic.jpg k1.txt k2.txt m.txt l.log 0 0 abcd.txt config_affori_classic.ini iters_HessianSIFT.ini
Relevant config-files are: config_affori_classic.ini and iters_HessianSIFT.ini
Expected output:
Maximum threads can be used: 4
View synthesis, detection and description...
Iteration 0
HessianAffine: 1 synthesis will be done.
Matching ...
Matching ...
2331 2912
76 tentatives found.
Duplicate filtering before RANSAC with threshold = 2 pixels.
74 unique tentatives left
LO-RANSAC(homography) verification is used...
21 RANSAC correspondences got
21 true matches are identified in 0.002 seconds
Done in 1 iterations
*********************
Writing files...
HessianAffine 2
HessianAffine 2
Writing images with matches... done
Image1: regions descriptors | Image2: regions descriptors
2665 2331 | 3287 2912
True matches | unique tentatives
21 | 74 | 28.4% 1st geom inc
Main matching | All Time:
0.915 | 1.25 seconds
Timings: (sec/%)
Synth|Detect|Orient|Desc|Match|RANSAC|MISC|Total
0.0106 0.183 0.0771 0.439 0.169 0.002 0.37 1.25
0.85 14.6 6.16 35.1 13.5 0.16 29.6 100
As you can see, deep descriptors are much better, although slower If you need to match really hard pairs, use iters_MODS_ZMQ.ini config file, or write your own configuation for view synthesis.
Generate two text files, one with paths to the input images (one path per line) and one with output path for features. Then run extract_features_batch util.
find imgs/* -type f > imgs_to_extract_list.txt
mkdir output_features
python get_output_fnames.py imgs_to_extract_list.txt output_features extracted_features_fnames.txt
./run_zmq_servers.sh
./extract_features_batch imgs_to_extract_list.txt extracted_features_fnames.txt config_aff_ori_desc_zeromq.ini iters_HessianZMQ.ini
Extracted features will be in output_features directory, in OxAff-like format: x y a b c desc[128]
It is simple python(might be any other language) script with following three main parts. See desc_server.py for example.
1)zeromq socket initialization:
context = zmq.Context()
socket = context.socket(zmq.REP)
socket.bind("tcp://*:" + args.port)
port number should be the same, as listening port in corresponding section of config_aff_ori_desc_zeromq.ini file:
[zmqDescriptor]
port=tcp://localhost:5555
patchSize=32; width and height of the patch
mrSize=5.1962 ;
2)Waiting for input patches. Patches come as grayscale uint8 png image with size (ps * n_patches, ps), where ps is set in config_aff_ori_desc_zeromq.ini
while True:
# Wait for next request from client
message = socket.recv()
img = decode_msg(message).astype(np.float32)
3)Getting descriptors and sending them back, as numpy float32 (num_patches,desc_dim) array.
descr = describe_patches(model, img, args.cuda, DESCR_OUT_DIM).astype(np.float32)
buff = np.getbuffer(descr)
socket.send(buff)
Now you can save keypoints in .npz format. To do this, just pass k1.npz instead k1.txt in command line. It will create .npz file with keys "xy", "responses", "scales", "A", "descs".
https://github.com/ducha-aiki/mods-light-zmq/blob/master/imagerepresentation.cpp#L1266
Powered by great library https://github.com/rogersce/cnpy/
Please cite us if you use this code:
@article{Mishkin2015MODS,
title = "MODS: Fast and robust method for two-view matching ",
journal = "Computer Vision and Image Understanding ",
year = "2015",
issn = "1077-3142",
doi = "http://dx.doi.org/10.1016/j.cviu.2015.08.005",
url = "http://www.sciencedirect.com/science/article/pii/S1077314215001800",
author = "Dmytro Mishkin and Jiri Matas and Michal Perdoch"
}
And if you use provided deep descriptors, please cite:
@article{HardNet2017,
author = {Anastasiya Mishchuk, Dmytro Mishkin, Filip Radenovic, Jiri Matas},
title = "{Working hard to know your neighbor's margins: Local descriptor learning loss}",
booktitle = {Proceedings of NIPS},
year = 2017,
month = dec}
@article{AffNet2017,
author = {Dmytro Mishkin, Filip Radenovic, Jiri Matas},
title = "{Learning Discriminative Affine Regions via Discriminability}",
year = 2017,
month = nov}
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