LaMa-Demo-ONNX / fetch_data /places_standard_evaluation_prepare_data.sh
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# 0. folder preparation
mkdir -p places_standard_dataset/evaluation/hires/
mkdir -p places_standard_dataset/evaluation/random_thick_512/
mkdir -p places_standard_dataset/evaluation/random_thin_512/
mkdir -p places_standard_dataset/evaluation/random_medium_512/
mkdir -p places_standard_dataset/evaluation/random_thick_256/
mkdir -p places_standard_dataset/evaluation/random_thin_256/
mkdir -p places_standard_dataset/evaluation/random_medium_256/
# 1. sample 2000 new images
OUT=$(python3 fetch_data/eval_sampler.py)
echo ${OUT}
FILELIST=$(cat places_standard_dataset/original/eval_random_files.txt)
for i in $FILELIST
do
$(cp ${i} places_standard_dataset/evaluation/hires/)
done
# 2. generate all kinds of masks
# all 512
python3 bin/gen_mask_dataset.py \
$(pwd)/configs/data_gen/random_thick_512.yaml \
places_standard_dataset/evaluation/hires \
places_standard_dataset/evaluation/random_thick_512/
python3 bin/gen_mask_dataset.py \
$(pwd)/configs/data_gen/random_thin_512.yaml \
places_standard_dataset/evaluation/hires \
places_standard_dataset/evaluation/random_thin_512/
python3 bin/gen_mask_dataset.py \
$(pwd)/configs/data_gen/random_medium_512.yaml \
places_standard_dataset/evaluation/hires \
places_standard_dataset/evaluation/random_medium_512/
python3 bin/gen_mask_dataset.py \
$(pwd)/configs/data_gen/random_thick_256.yaml \
places_standard_dataset/evaluation/hires \
places_standard_dataset/evaluation/random_thick_256/
python3 bin/gen_mask_dataset.py \
$(pwd)/configs/data_gen/random_thin_256.yaml \
places_standard_dataset/evaluation/hires \
places_standard_dataset/evaluation/random_thin_256/
python3 bin/gen_mask_dataset.py \
$(pwd)/configs/data_gen/random_medium_256.yaml \
places_standard_dataset/evaluation/hires \
places_standard_dataset/evaluation/random_medium_256/