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Update of documentation

Jérôme BUISINE 2 years ago
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9f3d62ed93
1 changed files with 8 additions and 82 deletions
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      README.md

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README.md

@@ -1,4 +1,10 @@
-# Noise detection using SVM
+# Noise detection using 26 attributes
+
+## Description
+
+Noise detection on synthesis images with 26 attributes obtained using few filters. 
+
+TODO : list filters used
 
 ## Requirements
 
@@ -12,23 +18,6 @@ Generate all needed data for each metrics (which requires the the whole dataset.
 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
-```
-
-- **step**: keep only image if image id % 50 == 0 (assumption is that keeping spaced data will let model better fit).
-
 ## How to use
 
 ### Multiple directories and scripts are available:
@@ -41,13 +30,6 @@ python generate_all_data.py --metric mscn --step 50
 - **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.
 
@@ -64,8 +46,6 @@ Parameters explained:
 - **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
 - **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
@@ -118,64 +98,10 @@ All scripts named **predict_seuil_expe\*.py** are used to simulate model predict
 
 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 --custom 'custom_min_max_filename'
-```
-
-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).
-- **custom**: custom filename where min and max values are stored (optional parameter).
-
-### 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...
 
 ## License
 
-[The MIT license](https://github.com/prise-3d/Thesis-NoiseDetection-metrics/blob/master/LICENSE)
+[The MIT license](https://github.com/prise-3d/Thesis-NoiseDetection-26-attributes/blob/master/LICENSE)