This is an unofficial implementation of CVPR 2018 "Stacked Conditional Generative Adversarial Networks for Jointly Learning Shadow Detection and Shadow Removal"
- what's the difference between natural scene and documents shadow removal?
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stcgan_fusion.py
| S_img | -----> | STCGAN | -----> | N1_img | -----> | | --------- ---------- ---------- | | --------- | | Blending | -----> | N_img | | ---------- ---------- | | --------- ------------> | ACCV | -----> | N2_img | -----> | | ---------- ---------- ------------
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pix2pix.py
| S_img | -----> | ACCV | -----> | N1_img | -----> | STCGAN | -----> | N_img |
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stcgan_concat.py
| S_img | -----> | ACCV | -----> | N1_img | -----> | | --------- ---------- ---------- | | --------- | | STCGAN | -----> | N_img | | | | --------- ----------------------------------------------------> | | ----------
- find the best model
- record the iteration and save it in to file, so it can be resumed when reloaded
- plot the training loss
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train model ./run.sh --mode train --gpu_id 0 --epochs 500 --root_dir /media/yslin/SSD_DATA/research/stcgan/ --batch_size 8 --dataset_name DSRD_BW ./run.sh --mode train --gpu_id 0 --epochs 500 --root_dir /media/yslin/SSD_DATA/research/stcgan/ --batch_size 8 --dataset_name DSRD_all
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test model ./run.sh --mode test --load_model --gpu_id 0 --root_dir /media/yslin/SSD_DATA/research/stcgan/ --batch_size_test 10 --dataset_name DSRD_aligned --model_name latest ./run.sh --mode test --load_model --gpu_id 0 --root_dir /media/yslin/SSD_DATA/research/stcgan/ --batch_size_test 10 --dataset_name DSRD_aligned
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evaluate all model python src/eval_all_model.py --root_dir /media/yslin/SSD_DATA/research/stcgan/ --batch_size_test 10 --lrG 0.001 --lrD 0.001 --dataset_name DSRD_aligned python src/eval_all_model.py --root_dir /media/yslin/SSD_DATA/research/stcgan/ --batch_size_test 10 --lrG 0.001 --lrD 0.001 --dataset_name ISTD
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eval result python src/eval.py ISTD /media/yslin/SSD_DATA/research/stcgan/processed_dataset/ISTD/result/Guo/ python src/eval.py ISTD /media/yslin/SSD_DATA/research/stcgan/processed_dataset/ISTD/result/Yang/ python src/eval.py ISTD /media/yslin/SSD_DATA/research/stcgan/processed_dataset/ISTD/result/Gong/ python src/eval.py ISTD /media/yslin/SSD_DATA/research/stcgan/processed_dataset/ISTD/result/ST-CGAN/ python src/eval.py ISTD /media/yslin/SSD_DATA/research/stcgan/task/stcgan_lrG_0.001_lrD_0.001/ISTD/result/non_shadow
python src/eval.py DSRD_aligned /media/yslin/SSD_DATA/research/processed_dataset/DSRD_aligned/test/shadow python src/eval.py DSRD_aligned /media/yslin/SSD_DATA/research/processed_dataset/DSRD_aligned/result/ACCV2016 python src/eval.py DSRD_all /media/yslin/SSD_DATA/research/stcgan/task/stcgan_lrG_0.001_lrD_0.001/DSRD_all/result/non_shadow
python src/eval.py Blender39_aligned /media/yslin/SSD_DATA/research/processed_dataset/Blender39_aligned/test/shadow
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visualize grid result python src/displayPairDSRD.py