Thesis-NoiseDetection-attributes

Noise detection using SVM

Requirements

pip install -r requirements.txt

Generate all needed data for each metrics

python generate_all_data.py --metric all

For noise detection, many metrics are available :

• lab
• mscn
• mscn_revisited
• low_bits_2
• low_bits_4
• low_bits_5
• low_bits_6
• low_bits_4_shifted_2

You can also specify metric you want to compute and image step to avoid some images :

python generate_all_data.py --metric mscn --step 50

How to use

Multiple folders and scripts are availables :

• fichiersSVD_light/* : all scene files information (zones of each scene, SVD descriptor files information and so on...).
• models/*.py : all models developed to predict noise in image.
• 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)

Scripts for generating data files

Two scripts can be used for generating data in order to fit model :

• generate_data_model.py : zones are specified and stayed fixed for each scene
• generate_data_model_random.py : zones are chosen randomly (just a number of zone is specified)
• generate_data_model_random_maxwell.py : zones are chosen randomly (just a number of zone is specified). Only maxwell scene are used.

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

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
• sep : output csv file seperator used
• rowindex : if 1 then row will be like that 1:xxxxx, 2:xxxxxx, ..., n:xxxxxx

Train model

This is an example of how to train a model

python models/xxxxx.py --data 'data/xxxxx.train' --output 'model_file_to_save'

Predict image using model

Now we have a model trained, we can use it with an image as input :

python metrics_predictions/predict_noisy_image_svd_lab.py --image path/to/image.png --interval "x,x" --model saved_models/xxxxxx.joblib --mode 'svdn'

The model will return only 0 or 1 :

• 1 means noisy image is detected.
• 0 means image seem to be not noisy.

You can also use other specific metric

python metrics_predictions/predict_noisy_image_svd_mscn.py --image path/to/image.png --interval "x,x" --model saved_models/xxxxxx.joblib --mode 'svdn'

All SVD metrics you developed need :

• Name added into metric_choices global array variable of generate_all_data.py file.
• A specification of how you compute the metric into generate_data_svd method of generate_all_data.py file.
• A prediction script into metrics_predictions folder. Name need to follow this rule : predict_noisy_image_svd_xxxx.py

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.

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 scripts

Test model on all scene data

In order to see if a model well generalized, a bash script is available :

bash testModelByScene.sh '100' '110' 'saved_models/xxxx.joblib' 'svdne' 'lab'

Parameters list :

• 1 : Begin of interval of data from SVD to use
• 2 : End of interval of data from SVD to use
• 3 : Model we want to test
• 4 : Kind of data input used by trained model
• 5 : Metric used by model

Get treshold map

Main objective of this project is to predict as well as a human the noise perception on a photo realistic image. Human threshold is available from training data. So a script was developed to give the predicted treshold from model and compare predicted treshold from the expected one.

python predict_seuil_expe.py --interval "x,x" --model 'saved_models/xxxx.joblib' --mode ["svd", "svdn", "svdne"] --metric ['lab', 'mscn', ...] --limit_detection xx

Parameters list :

• model : mode file saved to use
• interval : the interval of data you want to use from SVD vector.
• mode : kind of data ['svd', 'svdn', 'svdne']; not normalize, normalize vector only and normalize together.
• limit_detection : number of not noisy images found to stop and return threshold (integer).

Display model performance information

Another script was developed to display into Mardown format the performance of a model.

The content will be divised into two parts :

• Predicted performance on all scenes
• Treshold maps obtained from model on each scenes

The previous script need to already have ran to obtain and display treshold maps on this markdown file.

python save_model_result_in_md.py --interval "xx,xx" --model saved_models/xxxx.joblib --mode ["svd", "svdn", "svdne"] --metric ['lab', 'mscn']

Parameters list :

• model : mode file saved to use
• interval : the interval of data you want to use from SVD vector.
• mode : kind of data ['svd', 'svdn', 'svdne']; not normalize, normalize vector only and normalize together.

Markdown file with all information is saved using model name into models_info folder.

Others...

All others bash scripts are used to combine and run multiple model combinations...

How to contribute

This git project uses git-flow implementation. You are free to contribute to it.git