README.md 4.4 KB
Noise detection using 26 attributes
Description
Noise detection on synthesis images with 26 attributes obtained using few filters.
Filters list:
- average
- wiener
- median
- gaussian
- wavelet
Requirements
pip install -r requirements.txt
Generate all needed data for each metrics (which requires the the whole dataset. In order to get it, you need to contact us).
python generate_all_data.py --metric all
How to use
Multiple directories and scripts are available:
- dataset/*: all scene files information (zones of each scene, SVD descriptor files information and so on...).
- train_model.py: script which is used to run specific model available.
- data/*: folder which will contain all .train & .test files in order to train model.
- saved_models/*.joblib: all scikit learn models saved.
- models_info/*: all markdown files generated to get quick information about model performance and prediction. This folder contains also model_comparisons.csv obtained after running runAll_maxwell.sh script.
- modules/*: contains all modules usefull for the whole project (such as configuration variables)
Remark: Note here that all python script have --help command.
python generate_data_model.py --help
python generate_data_model.py --output xxxx --interval 0,20 --kind svdne --scenes "A, B, D" --zones "0, 1, 2" --percent 0.7 --sep: --rowindex 1 --custom custom_min_max_filename
Parameters explained:
- output: filename of data (which will be split into two parts, .train and .test relative to your choices).
- interval: the interval of data you want to use from SVD vector.
- kind: kind of data ['svd', 'svdn', 'svdne']; not normalize, normalize vector only and normalize together.
- scenes: scenes choice for training dataset.
- zones: zones to take for training dataset.
- percent: percent of data amount of zone to take (choose randomly) of zone
- custom: specify if you want your data normalized using interval and not the whole singular values vector. If it is, the value of this parameter is the output filename which will store the min and max value found. This file will be usefull later to make prediction with model (optional parameter).
Train model
This is an example of how to train a model
python train_model.py --data 'data/xxxx' --output 'model_file_to_save' --choice 'model_choice'
Expected values for the choice parameter are ['svm_model', 'ensemble_model', 'ensemble_model_v2'].
Predict image using model
Now we have a model trained, we can use it with an image as input:
python predict_noisy_image_svd.py --image path/to/image.png --interval "x,x" --model saved_models/xxxxxx.joblib --metric 'lab' --mode 'svdn' --custom 'min_max_filename'
- metric: metric choice need to be one of the listed above.
- custom: specify filename with custom min and max from your data interval. This file was generated using custom parameter of one of the generate_data_model*.py script (optional parameter).
The model will return only 0 or 1:
- 1 means noisy image is detected.
- 0 means image seem to be not noisy.
All SVD metrics developed need:
- Name added into metric_choices_labels global array variable of modules/utils/config.py file.
- A specification of how you compute the metric into get_svd_data method of modules/utils/data_type.py file.
Predict scene using model
Now we have a model trained, we can use it with an image as input:
python prediction_scene.py --data path/to/xxxx.csv --model saved_model/xxxx.joblib --output xxxxx --scene xxxx
Remark: scene parameter expected need to be the correct name of the Scene.
Visualize data
All scripts with names display_*.py are used to display data information or results.
Just use --help option to get more information.
Simulate model on scene
All scripts named predict_seuil_expe*.py are used to simulate model prediction during rendering process. Do not forget the custom parameter filename if necessary.
Once you have simulation done. Checkout your threshold_map/%MODEL_NAME%/simulation_curves_zones_*/ folder and use it with help of display_simulation_curves.py script.
Others...
All others bash scripts are used to combine and run multiple model combinations...