Install the database as mentioned below. Change the search method in line 53 of main.py run "bash runbench.sh" Results are stored in the results/ directory"
The journal article describing the FFS framework can be accessed here. You can cite this article as follows:
D. Mouris, C. Gouert, N. Gupta and N. G. Tsoutsos,
"Peak Your Frequency: Advanced Search of 3D CAD Files in the Fourier Domain,"
in IEEE Access, vol. 8, pp. 141481-141496, 2020, doi: 10.1109/ACCESS.2020.3013284.
$ git clone https://github.com/TrustworthyComputing/Fourier-Fingerprint-Search.git
$ cd Fourier-Fingerprint-Search
$ pip3 install -r requirements.txt
The file main.py invokes the FFS framework.
Proving the -h or --help command line argument prints a help message with the various parameters that can be passed to the framework.
$ python main.py -h
FFS supports two modes, learn and search. In the former, FFS populates the database while in the latter searches for potential matches to the file(s) given as command line arguments.
For instance, the following command stores all the files from the Bolts directory to the database using 2 slices.
$ python main.py --mode learn --stl benchmarks/FabWave/Bolts --N 2
Here, our query is the Bolts/07b46ed1-3801-45ad-9f42-5adfffb4e1c7-ascii.stl 3D model using both the fine-grained and the neighborhoods techniques.
FFS returns the query as the first match and 4 similar ones.
$ python main.py --mode search --stl benchmarks/FabWave/Bolts/07b46ed1-3801-45ad-9f42-5adfffb4e1c7-ascii.stl --N 2 --neighborhoods --print_fine_grained
Files matched with benchmarks/FabWave/Bolts/07b46ed1-3801-45ad-9f42-5adfffb4e1c7-ascii.stl using the number of signatures :
benchmarks/FabWave/Bolts/07b46ed1-3801-45ad-9f42-5adfffb4e1c7-ascii.stl : 0.985
benchmarks/FabWave/Bolts/634a2f17-872e-45d0-9650-faa70156afad-ascii.stl : 0.119
benchmarks/FabWave/Bolts/b8d3657b-a054-4c83-816c-9bce74c59724-ascii.stl : 0.061
benchmarks/FabWave/Bolts/4ced7e80-446c-42f1-bc05-5f1eb472198b-ascii.stl : 0.035
benchmarks/FabWave/Bolts/a773fc47-9f49-40d9-93ce-aa1ceeb10b00-ascii.stl : 0.019
Files matched with benchmarks/FabWave/Bolts/07b46ed1-3801-45ad-9f42-5adfffb4e1c7-ascii.stl using the number of neighborhoods :
benchmarks/FabWave/Bolts/07b46ed1-3801-45ad-9f42-5adfffb4e1c7-ascii.stl : 1.0
benchmarks/FabWave/Bolts/634a2f17-872e-45d0-9650-faa70156afad-ascii.stl : 0.431
benchmarks/FabWave/Bolts/b8d3657b-a054-4c83-816c-9bce74c59724-ascii.stl : 0.293
benchmarks/FabWave/Bolts/4ced7e80-446c-42f1-bc05-5f1eb472198b-ascii.stl : 0.19
benchmarks/FabWave/Bolts/a773fc47-9f49-40d9-93ce-aa1ceeb10b00-ascii.stl : 0.172
This material is based upon work supported by the National Science Foundation under Grant No. 1931916. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
This material was developed by the Trustworthy Computing Group at the University of Delaware.