generate_noise_all_curves.sh 2.1 KB

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  1. for file in "images"/*; do
  2. IFS='/' # space is set as delimiter
  3. read -ra ADDR <<< "$file" # str is read into an array as tokens separated by IFS
  4. IFS=' '
  5. image=${ADDR[1]%".png"}
  6. echo $image
  7. if [ "$image" != "calibration" ] || [ "$image" != *"min_max_values"* ]; then
  8. #for metric in {"lab","mscn_revisited","low_bits_2","low_bits_3","low_bits_4","low_bits_5","low_bits_6","low_bits_4_shifted_2"}; do
  9. for metric in {"lab","low_bits_4","low_bits_5","low_bits_4_shifted_2"}; do
  10. for noise in {"cauchy","gaussian","laplace","log_normal","mut_white","white","salt_pepper"}; do
  11. for mode in {"svdn","svdne"}; do
  12. for error in {"MAE","MSE"}; do
  13. #echo "${image}_${metric}_${noise}_${mode}_${error}_norm0" >> output_test.txt
  14. #echo "${image}_${metric}_${noise}_${mode}_${error}_norm1" >> output_test.txt
  15. #echo "${image}_${metric}_${noise}_${mode}_${error}_norm0_color" >> output_test.txt
  16. #echo "${image}_${metric}_${noise}_${mode}_${error}_norm1_color" >> output_test.txt
  17. python noise_svd_tend_visualization.py --prefix generated/${image}/${noise} --metric ${metric} --n 1000 --mode ${mode} --interval "0, 800" --step 40 --norm 0 --ylim "0, 0.05" --error ${error}
  18. python noise_svd_tend_visualization.py --prefix generated/${image}/${noise} --metric ${metric} --n 1000 --mode ${mode} --interval "0, 800" --step 40 --norm 1 --ylim "0, 0.1" --error ${error}
  19. python noise_svd_tend_visualization.py --prefix generated/${image}/${noise} --metric ${metric} --n 1000 --mode ${mode} --interval "0, 800" --step 40 --norm 0 --color 1 --ylim "0, 0.05" --error ${error}
  20. python noise_svd_tend_visualization.py --prefix generated/${image}/${noise} --metric ${metric} --n 1000 --mode ${mode} --interval "0, 800" --step 40 --norm 1 --color 1 --ylim "0, 0.1" --error ${error}
  21. done
  22. done
  23. done
  24. done
  25. fi
  26. done