Statistical analysis of sample values to approximate the final pixel value
Jérôme BUISINE
ebdd1786e0
Merge branch 'release/v0.0.5'
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5 lat temu | |
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| models_info | 5 lat temu | |
| modules | 5 lat temu | |
| notebooks | 5 lat temu | |
| .gitignore | 5 lat temu | |
| README.md | 5 lat temu | |
| analyse.R | 5 lat temu | |
| compare_images.py | 5 lat temu | |
| generate_data.sh | 5 lat temu | |
| make_dataset.py | 5 lat temu | |
| reconstruct.py | 5 lat temu | |
| reconstruct_keras.py | 5 lat temu | |
| reconstruct_scene_mean.py | 5 lat temu | |
| run.sh | 5 lat temu | |
| run_keras.sh | 5 lat temu | |
| train_model.py | 5 lat temu | |
| train_model_keras.py | 5 lat temu | |
| write_result_keras.py | 5 lat temu |
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