Models comparisons

Description

Project developed in order to compare models performances using simulation of these models.

Installation

git clone --recursive https://github.com/prise-3d/models-comparisons.git

pip install -r requirements.txt

Train model ?

Precompute the whole expected features

First you need to generate data using thresholds file (file obtained from SIN3D app):

python processing/generate_all_data_file.py --feature lab --dataset /path/to/folder --output output --thresholds file.csv

--output: save automatically output into data/generated

Well compare models

In order to well compare models, you need to set the training and testing zones used for your dataset:

python processing/generate_selected_zones_file.py --dataset /path/to/folder --n_zones 12 --output file --thresholds file.csv

--output: save automatically output into data/learned_zones

Each image is cut out into 16 zones, then you need to use the n_zones parameter to set you number of zones selected for training part.

The generated output file contains information for each scene about indices used for training and testing sets.

Generate your dataset

Then, you can generate your dataset:

python processing/generate_all_data_file.py --data data/generated/output --thresholds file.csv --selected_zones data/learned_zones/file --interval 0,40 --kind svdn --feature lab --output data/datasets/name

--data: specify the output data folder path generated when precomputing features.
--selected_zones: the previous output file generated in order to set.
--interval: set the interval to use from feature generated.
--kind: normalization level (svn, svdn, svdne).
--output: save automatically output into data/datasets.

Train your model

You can now use your dataset to train your model:

python train_model.py --data data/datasets/dataset/dataset --output modelv1 --choice svm_model

--data: specify the dataset name (without .train and .test generated extension) obtained from previous script.
--output: save automatically output into data/saved_models.

Simulations

Obtained model simulation on scene

python simulation/estimate_thresholds_file.py --model data/saved_models/modelv1.joblib --method lab --interval 0,40 --kind svdn --folder /path/to/scene --save filename.csv --label "Simulate modelv1"

--folder: scene folder to simulate on.
--save: filename to use as output simulation results (append simulation results)

Display and compare scene simulations

python display/display_estimated_file.py --simulation filename.csv --learned_zones --data/learned_zones/file --scene /path/to/scene --thresholds file.csv

Contributors

jbuisine

License

MIT