# Noise detection using SVM ## Requirements ``` pip install -r requirements.txt ``` Generate all needed data for each metrics ```bash 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 : ```bash 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 ```bash 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 : ```bash 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 ```bash 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 : ```bash 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 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. ```bash 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. ```bash 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](https://danielkummer.github.io/git-flow-cheatsheet/) implementation. You are free to contribute to it.git