Statistical analysis of sample values to approximate the final pixel value

Jérôme BUISINE ebdd1786e0 Merge branch 'release/v0.0.5' il y a 5 ans
models_info 87b8d18661 Write Keras result script il y a 5 ans
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analyse.R 6b7c5d854b First project version; Add of dataset generation; Run script; il y a 5 ans
compare_images.py 44a461faff First keras model version; Reconstruction for keras model available il y a 5 ans
generate_data.sh cccee3b412 Update of Keras scripts il y a 5 ans
make_dataset.py 44a461faff First keras model version; Reconstruction for keras model available il y a 5 ans
reconstruct.py 787d73d8f8 Scripts updates il y a 5 ans
reconstruct_keras.py 44a461faff First keras model version; Reconstruction for keras model available il y a 5 ans
reconstruct_scene_mean.py 44a461faff First keras model version; Reconstruction for keras model available il y a 5 ans
run.sh 87b8d18661 Write Keras result script il y a 5 ans
run_keras.sh 87b8d18661 Write Keras result script il y a 5 ans
train_model.py 44a461faff First keras model version; Reconstruction for keras model available il y a 5 ans
train_model_keras.py 87b8d18661 Write Keras result script il y a 5 ans
write_result_keras.py 87b8d18661 Write Keras result script il y a 5 ans

README.md

Sample Analysis

Description

The aim of this project is to predict the mean pixel value from monte carlo process rendering in synthesis images using only few samples information in input for model.

Data

Data are all scenes samples information obtained during the rendering process.

For each pixel we have a list of all grey value estimated (samples).

Models

List of models tested :

  • Ridge Regression
  • SGD
  • SVR (with rbf kernel)

How to use

First you need to contact jerome.buisine@univ-littoral.fr in order to get datatset version. The dataset is not available with this source code.

python make_dataset.py --n 10 --each_row 8 --each_column 8
python reconstruct.py --scene Scene1 --model_path saved_models/Model1.joblib --n 10 --image_name output.png