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Repository: tsingqguo/exposure-fusion-shadow-removal
Branch: main
Commit: 531c79b4307f
Files: 33
Total size: 277.2 KB
Directory structure:
gitextract_pdbosn3v/
├── OE_eval.py
├── OE_eval.sh
├── OE_train.py
├── OE_train.sh
├── README.md
├── data/
│ ├── __init__.py
│ ├── base_data_loader.py
│ ├── base_dataset.py
│ ├── expo_param_dataset.py
│ ├── image_folder.py
│ └── srd_dataset.py
├── data_processing/
│ ├── compute_params.ipynb
│ └── test_mask_generation.py
├── models/
│ ├── Fusion_model.py
│ ├── Refine_model.py
│ ├── __init__.py
│ ├── base_model.py
│ ├── distangle_model.py
│ ├── loss_function.py
│ ├── networks.py
│ ├── resnet.py
│ └── vgg.py
├── options/
│ ├── PAMI_options.py
│ ├── __init__.py
│ ├── base_options.py
│ ├── test_options.py
│ └── train_options.py
└── util/
├── __init__.py
├── get_data.py
├── html.py
├── image_pool.py
├── util.py
└── visualizer.py
================================================
FILE CONTENTS
================================================
================================================
FILE: OE_eval.py
================================================
from collections import OrderedDict
from options.train_options import TrainOptions
from data import CreateDataLoader
from models import create_model
from PIL import Image
import time
import math
from sklearn.metrics import balanced_accuracy_score, mean_squared_error
from skimage.color import rgb2lab
import numpy as np
import torch
import torch.nn as nn
import os
import shutil
import logging
opt = TrainOptions().parse()
#train_data_loader = CreateDataLoader(opt)
#train_dataset = train_data_loader.load_data()
#train_dataset_size = len(train_data_loader)
opt.phase = 'test/test_'
opt.batch_size = 1
opt.serial_batches = True
opt.isTrain = False
test_data_loader = CreateDataLoader(opt)
test_dataset = test_data_loader.load_data()
test_dataset_size = len(test_data_loader)
model = create_model(opt)
model.setup(opt)
# Set logger
msg = []
logger = logging.getLogger('%s' % opt.name)
logger.setLevel(logging.INFO)
if not os.path.isdir(model.save_dir):
msg.append('%s not exist, make it' % model.save_dir)
os.mkdir(opt.dir)
log_file_path = os.path.join(model.save_dir, 'log.log')
if os.path.isfile(log_file_path):
target_path = log_file_path + '.%s' % time.strftime("%Y%m%d%H%M%S")
msg.append('Log file exists, backup to %s' % target_path)
shutil.move(log_file_path, target_path)
fh = logging.FileHandler(log_file_path)
fh.setLevel(logging.INFO)
ch = logging.StreamHandler()
ch.setLevel(logging.INFO)
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
fh.setFormatter(formatter)
ch.setFormatter(formatter)
logger.addHandler(fh)
logger.addHandler(ch)
def tensor2im(input_image, imtype=np.uint8):
""""Converts a Tensor array into a numpy image array.
Parameters:
input_image (tensor) -- the input image tensor array
imtype (type) -- the desired type of the converted numpy array
"""
if not isinstance(input_image, np.ndarray):
if isinstance(input_image, torch.Tensor): # get the data from a variable
image_tensor = input_image.data
else:
return input_image
image_numpy = image_tensor[0].cpu().float().numpy() # convert it into a numpy array
if image_numpy.shape[0] == 1: # grayscale to RGB
image_numpy = np.tile(image_numpy, (3, 1, 1))
# image_numpy = image_numpy.convert('L')
image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + 1) / 2.0 * 255.0 # post-processing: tranpose and scaling
else: # if it is a numpy array, do nothing
image_numpy = input_image
return np.clip(image_numpy, 0, 255).astype(imtype)
def calc_RMSE(real_img, fake_img):
# convert to LAB color space
real_lab = rgb2lab(real_img)
fake_lab = rgb2lab(fake_img)
return real_lab - fake_lab
model.eval()
model.load_networks('latest')
eval_shadow_rmse = 0
eval_nonshadow_rmse = 0
eval_rmse = 0
eval_loss = 0
for i, data in enumerate(test_dataset):
iter_start_time = time.time()
# total_steps += opt.batch_size
# epoch_iter += opt.batch_size
model.set_input(data)
model.forward()
# evaluation refers to matlab code
# diff = calc_RMSE(tensor2im(model.shadowfree_img), tensor2im(model.final))
# mask = model.shadow_mask.data[0].cpu().float().numpy()[..., None][0, ...]
#
# if mask.sum() < 2:
# continue
# shadow_rmse = np.sqrt(1.0 * (np.power(diff, 2) * mask).sum(axis=(0, 1)) / mask.sum())
# nonshadow_rmse = np.sqrt(1.0 * (np.power(diff, 2) * (1 - mask)).sum(axis=(0, 1)) / (1 - mask).sum())
# whole_rmse = np.sqrt(np.power(diff, 2).mean(axis=(0, 1)))
#
# eval_shadow_rmse += shadow_rmse.sum()
# eval_nonshadow_rmse += nonshadow_rmse.sum()
# eval_rmse += whole_rmse.sum()
model.netR.zero_grad()
model.vis(0, i, opt.name, True)
logger.info('[Eval] [Epoch] %d | rmse : %.3f | shadow_rmse : %.3f | nonshadow_rmse : %.3f' %
(0, eval_rmse / len(test_dataset),
eval_shadow_rmse / len(test_dataset), eval_nonshadow_rmse / len(test_dataset)))
================================================
FILE: OE_eval.sh
================================================
#!/bin/bash
MYGIT=/mnt/nvme/zcq/git
REPO_PATH=/home/fulan/shadow_removal/exposure-fusion-shadow-removal
# DATA_PATH=${MYGIT}/shadow_removal/data/SRD
# datasetmode=srd
DATA_PATH=./ISTD+
datasetmode=expo_param
batchs=8
n=5
ks=3
rks=3
version='fixed5-1-boundary-loss'
lr_policy=lambda
lr_decay_iters=50
optimizer=adam
shadow_loss=5.0
tv_loss=0.0
grad_loss=0.1
pgrad_loss=0.0
gpus=0
lr=0.0001
loadSize=256
fineSize=256
L1=10
model=Refine
checkpoint=${REPO_PATH}/log
dataroot=${DATA_PATH}
NAME="M${model}_${datasetmode}_b${batchs}_lr${lr}_L1${L1}_n${n}_ks${ks}_v${version}_${optimizer}_${lr_policy}_${shadow_loss}_rks${rks}_TV${tv_loss}G${grad_loss}GP${pgrad_loss}"
OTHER="--save_epoch_freq 100 --niter 50 --niter_decay 350"
trainmask=${dataroot}'/train_NOTUSE'
CMD="python -u ./OE_eval.py --loadSize ${loadSize} \
--randomSize
--name ${NAME} \
--dataroot ${dataroot}\
--checkpoints_dir ${checkpoint} \
--fineSize $fineSize --model $model \
--batch_size $batchs \
--randomSize --keep_ratio --phase train_ --gpu_ids ${gpus} --lr ${lr} \
--lambda_L1 ${L1} --num_threads 16 \
--dataset_mode $datasetmode\
--mask_train $trainmask --optimizer ${optimizer} \
--n ${n} --ks ${ks} --lr_policy ${lr_policy} --lr_decay_iters ${lr_decay_iters} \
--shadow_loss ${shadow_loss} --tv_loss ${tv_loss} --grad_loss ${grad_loss} --pgrad_loss ${pgrad_loss} \
$OTHER
"
echo $CMD
eval $CMD # >> ${checkpoint}/${NAME}.log 2>&1 &
================================================
FILE: OE_train.py
================================================
from collections import OrderedDict
from options.train_options import TrainOptions
from data import CreateDataLoader
from models import create_model
from PIL import Image
import time
import math
from sklearn.metrics import balanced_accuracy_score, mean_squared_error
from skimage.color import rgb2lab
import numpy as np
import torch
import torch.nn as nn
import os
import shutil
import logging
opt = TrainOptions().parse()
opt.phase = 'train/train_'
opt.serial_batches = False
train_data_loader = CreateDataLoader(opt)
train_dataset = train_data_loader.load_data()
train_dataset_size = len(train_data_loader)
opt.phase = 'test/test_'
opt.batch_size = 1
opt.serial_batches = True
test_data_loader = CreateDataLoader(opt)
test_dataset = test_data_loader.load_data()
test_dataset_size = len(test_data_loader)
model = create_model(opt)
model.setup(opt)
if opt.load_dir and opt.load_dir != 'None':
print('load fusion net from:', opt.load_dir)
model.load_networks('latest', opt.load_dir)
# Set logger
msg = []
logger = logging.getLogger('%s' % opt.name)
logger.setLevel(logging.INFO)
if not os.path.isdir(model.save_dir):
msg.append('%s not exist, make it' % model.save_dir)
os.mkdir(args.dir)
log_file_path = os.path.join(model.save_dir, 'log.log')
if os.path.isfile(log_file_path):
target_path = log_file_path + '.%s' % time.strftime("%Y%m%d%H%M%S")
msg.append('Log file exists, backup to %s' % target_path)
shutil.move(log_file_path, target_path)
fh = logging.FileHandler(log_file_path)
fh.setLevel(logging.INFO)
ch = logging.StreamHandler()
ch.setLevel(logging.INFO)
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
fh.setFormatter(formatter)
ch.setFormatter(formatter)
logger.addHandler(fh)
logger.addHandler(ch)
def tensor2im(input_image, imtype=np.uint8):
""""Converts a Tensor array into a numpy image array.
Parameters:
input_image (tensor) -- the input image tensor array
imtype (type) -- the desired type of the converted numpy array
"""
if not isinstance(input_image, np.ndarray):
if isinstance(input_image, torch.Tensor): # get the data from a variable
image_tensor = input_image.data
else:
return input_image
image_numpy = image_tensor[0].cpu().float().numpy() # convert it into a numpy array
if image_numpy.shape[0] == 1: # grayscale to RGB
image_numpy = np.tile(image_numpy, (3, 1, 1))
# image_numpy = image_numpy.convert('L')
image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + 1) / 2.0 * 255.0 # post-processing: tranpose and scaling
else: # if it is a numpy array, do nothing
image_numpy = input_image
return np.clip(image_numpy, 0, 255).astype(imtype)
def calc_RMSE(real_img, fake_img):
# convert to LAB color space
real_lab = rgb2lab(real_img)
fake_lab = rgb2lab(fake_img)
return real_lab - fake_lab
mse_criterion = nn.MSELoss()
total_steps = 0
for epoch in range(opt.epoch_count, opt.niter + opt.niter_decay + 1):
epoch_start_time = time.time()
epoch_iter = 0
model.epoch = epoch
model.train()
for i, data in enumerate(train_dataset):
iter_start_time = time.time()
total_steps += 1
model.set_input(data)
# model.zero_grad()
model.optimize_parameters()
if total_steps % 10 == 0:
# Do log
train_loss = model.loss.detach().item()
train_mse = mse_criterion(model.final, model.shadowfree_img).detach().item()
logger.info('[Train] [Epoch] %d [Steps] %d | loss : %.3f' % (epoch, total_steps, train_loss))
if (epoch and epoch % 30 == 0) or (epoch == opt.niter + opt.niter_decay):
model.eval()
eval_shadow_rmse = 0
eval_nonshadow_rmse = 0
eval_rmse = 0
eval_loss = 0
for i, data in enumerate(test_dataset):
iter_start_time = time.time()
total_steps += opt.batch_size
epoch_iter += opt.batch_size
model.set_input(data)
model.forward()
eval_loss += model.loss.detach().item()
diff = calc_RMSE(tensor2im(model.shadowfree_img), tensor2im(model.final))
mask = model.shadow_mask.data[0].cpu().float().numpy()[..., None][0, ...]
if mask.sum() < 2:
continue
shadow_rmse = np.sqrt(1.0 * (np.power(diff, 2) * mask).sum(axis=(0, 1)) / mask.sum())
nonshadow_rmse = np.sqrt(1.0 * (np.power(diff, 2) * (1 - mask)).sum(axis=(0, 1)) / (1 - mask).sum())
whole_rmse = np.sqrt(np.power(diff, 2).mean(axis=(0, 1)))
# (256, 256, 3) (3,) (3,) (256, 256, 1) (256, 256, 3)
# print(diff.shape, whole_rmse.shape, shadow_rmse.shape, mask.shape, (diff * mask).shape)
eval_shadow_rmse += shadow_rmse.sum()
eval_nonshadow_rmse += nonshadow_rmse.sum()
eval_rmse += whole_rmse.sum()
model.zero_grad()
if i % 20 == 0:
model.vis(epoch, i)
logger.info('[Eval] [Epoch] %d | loss : %.3f | rmse : %.3f | shadow_rmse : %.3f | nonshadow_rmse : %.3f' %
(epoch, eval_loss / len(test_dataset), eval_rmse / len(test_dataset),
eval_shadow_rmse / len(test_dataset), eval_nonshadow_rmse / len(test_dataset)))
if epoch and epoch % 50 == 0 or (epoch == opt.niter + opt.niter_decay):
logger.info('saving the model at the end of epoch %d, iters %d' %
(epoch, total_steps))
model.save_networks('latest')
model.save_networks(epoch)
spt_time = time.time() - epoch_start_time
lft_time = (opt.niter + opt.niter_decay - epoch) * spt_time
logger.info('End of epoch %d / %d | Time Taken: %d sec | eta %.2f' %
(epoch, opt.niter + opt.niter_decay, spt_time, lft_time / 3600.0))
model.update_learning_rate()
================================================
FILE: OE_train.sh
================================================
#!/bin/bash
MYGIT=/mnt/nvme/zcq/git
REPO_PATH=/home/fulan/shadow_removal
# DATA_PATH=${MYGIT}/shadow_removal/data/SRD
# datasetmode=srd
DATA_PATH=/home/fulan/ShadowRemoval/data
datasetmode=expo_param
batchs=4
n=5
ks=3
rks=3
version='fixed5-1-loss'
lr_policy=lambda
lr_decay_iters=50
optimizer=adam
shadow_loss=10.0
tv_loss=0
grad_loss=0.0
pgrad_loss=0.1
gpus=0
lr=0.0001
loadSize=256
fineSize=256
L1=10
model=Fusion
load_dir=None
checkpoint=${REPO_PATH}/log
dataroot=${DATA_PATH}
NAME="M${model}_${datasetmode}_b${batchs}_lr${lr}_L1${L1}_n${n}_ks${ks}_v${version}_${optimizer}_${lr_policy}_${shadow_loss}_TV${tv_loss}G${grad_loss}PG${pgrad_loss}"
OTHER="--save_epoch_freq 100 --niter 50 --niter_decay 350"
trainmask=${dataroot}'/train_NOTUSE'
CMD="python -u ./OE_train.py --loadSize ${loadSize} \
--randomSize
--name ${NAME} \
--dataroot ${dataroot}\
--checkpoints_dir ${checkpoint} \
--fineSize $fineSize --model $model \
--batch_size $batchs \
--randomSize --keep_ratio --phase train_ --gpu_ids ${gpus} --lr ${lr} \
--lambda_L1 ${L1} --num_threads 16 \
--dataset_mode $datasetmode\
--mask_train $trainmask --optimizer ${optimizer} \
--n ${n} --ks ${ks} --lr_policy ${lr_policy} --lr_decay_iters ${lr_decay_iters} \
--shadow_loss ${shadow_loss} --rks ${rks} --tv_loss ${tv_loss} --grad_loss ${grad_loss} --pgrad_loss ${pgrad_loss} \
--load_dir ${load_dir} \
$OTHER
"
echo $CMD
eval $CMD # >> ${checkpoint}/${NAME}.log 2>&1 &
================================================
FILE: README.md
================================================
# Auto-exposure fusion for single-image shadow removal
We propose a new method for effective shadow removal by regarding it as an exposure fusion problem. Please refer to the paper for details: https://openaccess.thecvf.com/content/CVPR2021/papers/Fu_Auto-Exposure_Fusion_for_Single-Image_Shadow_Removal_CVPR_2021_paper.pdf.
## Dataset
- ISTD [ISTD](https://github.com/DeepInsight-PCALab/ST-CGAN)
- ISTD+ [ISTD+](https://github.com/cvlab-stonybrook/SID)
- SRD
1. For data folder path (ISTD), train_A: shadow images, train_B: shadow masks, train_C: shadow free images, organize them as following:
```shell
--ISTD+
--train
--train_A
--1-1.png
--train_B
--1-1.png
--train_C_fixed_official
--1-1.png
--train_params_fixed # generate later
--1-1.png.txt
--test
--test_A
--1-1.png
--test_B
--1-1.png
--test_C
--1-1.png
--mask_threshold # generate later
--1-1.png
```
2. Run the code `./data_processing/compute_params.ipynb` for exposure parameters generation.
The result will be put in `./ISTD/train/train_params_fixed`.
Here, names `train_C_fixed_official` and `train_params_fixed` are for ISTD+ dataset, which are consitent with `self.dir_C` and `self.dir_param` in `./data/expo_param_dataset.py` .
3. For testing masks, please run the code `./data_processing/test_mask_generation.py`.
The result will be put in `./ISTD/mask_threshold`.
## Pretrained models
We release our pretrained model (ISTD+, SRD) at [models](https://drive.google.com/drive/folders/1riTtYvHpffYu-nqSizqSF4fhbZ2txrp5?usp=sharing)
pretrained model (ISTD) at [models](https://drive.google.com/drive/folders/1qECA9EjUSLMtUpN5fFZMjltQPzjp2gL9?usp=sharing)
Modify the parameter `model` in file `OE_eval.sh` to `Refine` and set `ks=3, n=5, rks=3` to load the model.
## Train
Modify the corresponding path in file `OE_train.sh` and run the following script
```shell
sh OE_train.sh
```
1. For the parameters:
```shell
DATA_PATH=./Datasets/ISTD or your datapath
n=5, ks=3 for FusionNet,
n=5, ks=3, rks=3 for RefineNet.
model=Fusion for FusionNet training,
model=Refine for RefineNet training.
```
The trained models are saved in `${REPO_PATH}/log/${Name}`, `Name` are customized for parameters setting.
## Test
In order to test the performance of a trained model, you need to make sure that the hyper parameters in file `OE_eval.sh` match the ones in `OE_train.sh` and run the following script:
```shell
sh OE_eval.sh
```
1. The pretrained models are located in `${REPO_PATH}/log/${Name}`.
## Evaluation
The results reported in the paper are calculated by the `matlab` script used in other SOTA, please see [evaluation](https://drive.google.com/file/d/1SAMqLy3dSONPgeC5ZQskPoeq60FEx9Vk/view?usp=sharing) for details. Our evaluation code will print the metrics calculated by `python` code and save the shadow removed result images which will be used by the `matlab` script.
## Results
- Comparsion with SOTA, see paper for details.
- Penumbra comparsion between ours and SP+M Net
- Testing result
The testing results on dataset ISTD+, ISTD, SRD are:[results](https://drive.google.com/drive/folders/1ubLj5r_ZMzWew4h2bNX7pQL6D62mM-dl?usp=sharing)
**More details are coming soon**
## Bibtex
```
@inproceedings{fu2021auto,
title={Auto-exposure Fusion for Single-image Shadow Removal},
author={Lan Fu and Changqing Zhou and Qing Guo and Felix Juefei-Xu and Hongkai Yu and Wei Feng and Yang Liu and Song Wang},
year={2021},
booktitle={accepted to CVPR}
}
```
================================================
FILE: data/__init__.py
================================================
import importlib
import torch.utils.data
from data.base_data_loader import BaseDataLoader
from data.base_dataset import BaseDataset
def find_dataset_using_name(dataset_name):
# Given the option --dataset_mode [datasetname],
# the file "data/datasetname_dataset.py"
# will be imported.
dataset_filename = "data." + dataset_name + "_dataset"
datasetlib = importlib.import_module(dataset_filename)
# In the file, the class called DatasetNameDataset() will
# be instantiated. It has to be a subclass of BaseDataset,
# and it is case-insensitive.
dataset = None
target_dataset_name = dataset_name.replace('_', '') + 'dataset'
for name, cls in datasetlib.__dict__.items():
if name.lower() == target_dataset_name.lower() \
and issubclass(cls, BaseDataset):
dataset = cls
if dataset is None:
print("In %s.py, there should be a subclass of BaseDataset with class name that matches %s in lowercase." % (dataset_filename, target_dataset_name))
exit(0)
return dataset
def get_option_setter(dataset_name):
dataset_class = find_dataset_using_name(dataset_name)
return dataset_class.modify_commandline_options
def create_dataset(opt):
dataset = find_dataset_using_name(opt.dataset_mode)
instance = dataset()
instance.initialize(opt)
print("dataset [%s] was created" % (instance.name()))
return instance
def CreateDataLoader(opt):
data_loader = CustomDatasetDataLoader()
data_loader.initialize(opt)
return data_loader
# Wrapper class of Dataset class that performs
# multi-threaded data loading
class CustomDatasetDataLoader(BaseDataLoader):
def name(self):
return 'CustomDatasetDataLoader'
def initialize(self, opt):
BaseDataLoader.initialize(self, opt)
self.dataset = create_dataset(opt)
self.dataloader = torch.utils.data.DataLoader(
self.dataset,
batch_size=opt.batch_size,
shuffle=not opt.serial_batches,
num_workers=int(opt.num_threads))
def load_data(self):
return self
def __len__(self):
return min(len(self.dataset), self.opt.max_dataset_size)
def __iter__(self):
for i, data in enumerate(self.dataloader):
if i * self.opt.batch_size >= self.opt.max_dataset_size:
break
yield data
================================================
FILE: data/base_data_loader.py
================================================
class BaseDataLoader():
def __init__(self):
pass
def initialize(self, opt):
self.opt = opt
pass
def load_data():
return None
================================================
FILE: data/base_dataset.py
================================================
import torch.utils.data as data
from PIL import Image
import torchvision.transforms as transforms
class BaseDataset(data.Dataset):
def __init__(self):
super(BaseDataset, self).__init__()
def name(self):
return 'BaseDataset'
@staticmethod
def modify_commandline_options(parser, is_train):
return parser
def initialize(self, opt):
pass
def __len__(self):
return 0
def get_transform(opt):
transform_list = []
if opt.resize_or_crop == 'resize_and_crop':
osize = [opt.loadSize, opt.loadSize]
transform_list.append(transforms.Resize(osize, Image.BICUBIC))
transform_list.append(transforms.RandomCrop(opt.fineSize))
elif opt.resize_or_crop == 'crop':
transform_list.append(transforms.RandomCrop(opt.fineSize))
elif opt.resize_or_crop == 'scale_width':
transform_list.append(transforms.Lambda(
lambda img: __scale_width(img, opt.fineSize)))
elif opt.resize_or_crop == 'scale_width_and_crop':
transform_list.append(transforms.Lambda(
lambda img: __scale_width(img, opt.loadSize)))
transform_list.append(transforms.RandomCrop(opt.fineSize))
elif opt.resize_or_crop == 'none':
transform_list.append(transforms.Lambda(
lambda img: __adjust(img)))
else:
raise ValueError('--resize_or_crop %s is not a valid option.' % opt.resize_or_crop)
if opt.isTrain and not opt.no_flip:
transform_list.append(transforms.RandomHorizontalFlip())
transform_list += [transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5),
(0.5, 0.5, 0.5))]
return transforms.Compose(transform_list)
# just modify the width and height to be multiple of 4
def __adjust(img):
ow, oh = img.size
# the size needs to be a multiple of this number,
# because going through generator network may change img size
# and eventually cause size mismatch error
mult = 4
if ow % mult == 0 and oh % mult == 0:
return img
w = (ow - 1) // mult
w = (w + 1) * mult
h = (oh - 1) // mult
h = (h + 1) * mult
if ow != w or oh != h:
__print_size_warning(ow, oh, w, h)
return img.resize((w, h), Image.BICUBIC)
def __scale_width(img, target_width):
ow, oh = img.size
# the size needs to be a multiple of this number,
# because going through generator network may change img size
# and eventually cause size mismatch error
mult = 4
assert target_width % mult == 0, "the target width needs to be multiple of %d." % mult
if (ow == target_width and oh % mult == 0):
return img
w = target_width
target_height = int(target_width * oh / ow)
m = (target_height - 1) // mult
h = (m + 1) * mult
if target_height != h:
__print_size_warning(target_width, target_height, w, h)
return img.resize((w, h), Image.BICUBIC)
def __print_size_warning(ow, oh, w, h):
if not hasattr(__print_size_warning, 'has_printed'):
print("The image size needs to be a multiple of 4. "
"The loaded image size was (%d, %d), so it was adjusted to "
"(%d, %d). This adjustment will be done to all images "
"whose sizes are not multiples of 4" % (ow, oh, w, h))
__print_size_warning.has_printed = True
================================================
FILE: data/expo_param_dataset.py
================================================
import os.path
import torchvision.transforms as transforms
from data.base_dataset import BaseDataset, get_transform
from data.image_folder import make_dataset
from PIL import Image, ImageChops
from PIL import ImageFilter
import torch
from pdb import set_trace as st
import random
import numpy as np
import time
import cv2
class ExpoParamDataset(BaseDataset):
def initialize(self, opt):
self.opt = opt
self.root = opt.dataroot
self.dir_A = os.path.join(opt.dataroot, opt.phase + 'A')
# if not opt.use_our_mask:
if 'test' in opt.phase:
self.dir_B = os.path.join(opt.dataroot, 'test', 'mask_threshold') # for test masks generated
self.dir_C = os.path.join(opt.dataroot, opt.phase + 'C')
else:
self.dir_B = os.path.join(opt.dataroot, opt.phase + 'B')
self.dir_C = os.path.join(opt.dataroot, opt.phase + 'C_fixed_official')
self.dir_param = os.path.join(opt.dataroot, opt.phase + 'params_fixed') # opt.param_path
print(self.dir_A)
self.A_paths, self.imname = make_dataset(self.dir_A)
self.A_size = len(self.A_paths)
self.B_size = self.A_size
transform_list = [transforms.ToTensor(),
transforms.Normalize(mean=opt.norm_mean,
std=opt.norm_std)]
self.transformA = transforms.Compose(transform_list)
self.transformB = transforms.Compose([transforms.ToTensor()])
if 'train' in opt.phase:
self.is_train = True
else:
self.is_train = False
def __getitem__(self, index):
colet = {}
A_path = self.A_paths[index % self.A_size]
imname = self.imname[index % self.A_size]
index_A = index % self.A_size
B_path = os.path.join(self.dir_B, imname.replace('.jpg', '.png'))
if not os.path.isfile(B_path):
B_path = os.path.join(self.dir_B, imname)
A_img = Image.open(A_path).convert('RGB')
if self.is_train:
sparam = open(os.path.join(self.dir_param, imname + '.txt'))
line = sparam.read()
sparam.close()
shadow_param = np.asarray([float(i) for i in line.split(" ") if i.strip()])
shadow_param = shadow_param[0:6]
ow = A_img.size[0]
oh = A_img.size[1]
w = np.float(A_img.size[0])
h = np.float(A_img.size[1])
if os.path.isfile(B_path):
B_img = Image.open(B_path)
else:
print('MASK NOT FOUND : %s' % (B_path))
B_img = Image.fromarray(np.zeros((int(w), int(h)), dtype=np.float), mode='L')
B_img_np = np.asarray(B_img)
kernel = np.ones((7, 7), np.uint8)
B_img_np_dilate = cv2.dilate(B_img_np, kernel, iterations=1)
B_img_np_erode = cv2.erode(B_img_np, kernel, iterations=1)
B_img_dilate = Image.fromarray(B_img_np_dilate)
B_img_erode = Image.fromarray(B_img_np_erode)
colet['B_dilate'] = B_img_dilate
colet['B_erode'] = B_img_erode
colet['C'] = Image.open(os.path.join(self.dir_C, imname)).convert('RGB')
loadSize = self.opt.loadSize
if self.is_train and self.opt.randomSize:
loadSize = np.random.randint(loadSize + 1, loadSize * 1.3, 1)[0]
if self.opt.keep_ratio:
if w > h:
ratio = np.float(loadSize) / np.float(h)
neww = np.int(w * ratio)
newh = loadSize
else:
ratio = np.float(loadSize) / np.float(w)
neww = loadSize
newh = np.int(h * ratio)
else:
neww = loadSize
newh = loadSize
colet['A'] = A_img
colet['B'] = B_img
if self.is_train:
t = [Image.FLIP_LEFT_RIGHT, Image.ROTATE_90]
for i in range(0, 4):
c = np.random.randint(0, 3, 1, dtype=np.int)[0]
if c == 2: continue
for i in ['A', 'B', 'C', 'B_dilate', 'B_erode']:
if i in colet:
colet[i] = colet[i].transpose(t[c])
if self.is_train:
degree = np.random.randint(-20, 20, 1)[0]
for i in ['A', 'B', 'C', 'B_dilate', 'B_erode']:
colet[i] = colet[i].rotate(degree)
for k, im in colet.items():
if self.is_train:
colet[k] = im.resize((neww, newh), Image.NEAREST)
else:
colet[k] = im.resize((self.opt.fineSize, self.opt.fineSize), Image.NEAREST)
w = colet['A'].size[0]
h = colet['A'].size[1]
for k, im in colet.items():
colet[k] = self.transformB(im)
for i in ['A', 'C', 'B', 'B_dilate', 'B_erode']:
if i in colet:
colet[i] = (colet[i] - 0.5) * 2
if self.is_train: # and not self.opt.no_crop:
w_offset = random.randint(0, max(0, w - self.opt.fineSize - 1))
h_offset = random.randint(0, max(0, h - self.opt.fineSize - 1))
for k, im in colet.items():
colet[k] = im[:, h_offset:h_offset + self.opt.fineSize, w_offset:w_offset + self.opt.fineSize]
if self.is_train and (not self.opt.no_flip) and random.random() < 0.5:
idx = [i for i in range(colet['A'].size(2) - 1, -1, -1)]
idx = torch.LongTensor(idx)
for k, im in colet.items():
colet[k] = im.index_select(2, idx)
for k, im in colet.items():
colet[k] = im.type(torch.FloatTensor)
colet['imname'] = imname
colet['w'] = ow
colet['h'] = oh
colet['A_paths'] = A_path
colet['B_baths'] = B_path
if self.is_train:
# if the shadow area is too small, let's not change anything:
if torch.sum(colet['B'] > 0) < 30:
shadow_param = [0, 1, 0, 1, 0, 1]
colet['param'] = torch.FloatTensor(np.array(shadow_param))
return colet
def __len__(self):
return max(self.A_size, self.B_size)
def name(self):
return 'ExpoParamDataset'
================================================
FILE: data/image_folder.py
================================================
###############################################################################
# Code from
# https://github.com/pytorch/vision/blob/master/torchvision/datasets/folder.py
# Modified the original code so that it also loads images from the current
# directory as well as the subdirectories
###############################################################################
import torch.utils.data as data
from PIL import Image
import os
import os.path
IMG_EXTENSIONS = [
'.jpg', '.JPG', '.jpeg', '.JPEG',
'.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP',
]
def is_image_file(filename):
return any(filename.endswith(extension) for extension in IMG_EXTENSIONS)
def make_dataset(dir):
images = []
imname = []
assert os.path.isdir(dir), '%s is not a valid directory' % dir
for root, _, fnames in sorted(os.walk(dir)):
for fname in fnames:
if is_image_file(fname):
path = os.path.join(root, fname)
images.append(path)
imname.append(fname)
return images,imname
def default_loader(path):
return Image.open(path).convert('RGB')
class ImageFolder(data.Dataset):
def __init__(self, root, transform=None, return_paths=False,
loader=default_loader):
imgs = make_dataset(root)
if len(imgs) == 0:
raise(RuntimeError("Found 0 images in: " + root + "\n"
"Supported image extensions are: " +
",".join(IMG_EXTENSIONS)))
self.root = root
self.imgs = imgs
self.transform = transform
self.return_paths = return_paths
self.loader = loader
def __getitem__(self, index):
path = self.imgs[index]
img = self.loader(path)
if self.transform is not None:
img = self.transform(img)
if self.return_paths:
return img, path
else:
return img
def __len__(self):
return len(self.imgs)
================================================
FILE: data/srd_dataset.py
================================================
import os.path
import torchvision.transforms as transforms
from data.base_dataset import BaseDataset, get_transform
from data.image_folder import make_dataset
from PIL import Image, ImageChops
from PIL import ImageFilter
import torch
from pdb import set_trace as st
import random
import numpy as np
import time
import cv2
class SRDDataset(BaseDataset):
def initialize(self, opt):
self.opt = opt
self.root = opt.dataroot
if 'train' in opt.phase:
phase = 'train'
elif 'test' in opt.phase:
phase = 'test'
else:
raise False
self.dir_A = os.path.join(opt.dataroot, phase, 'shadow')
self.dir_B = os.path.join(opt.dataroot, phase, 'mask')
print(self.dir_B)
self.dir_C = os.path.join(opt.dataroot, phase, 'shadow_free')
self.dir_param = os.path.join(opt.dataroot, phase, 'mask_params')
print(self.dir_A)
self.A_paths, self.imname = make_dataset(self.dir_A)
self.A_size = len(self.A_paths)
self.B_size = self.A_size
transform_list = [transforms.ToTensor(),
transforms.Normalize(mean=opt.norm_mean,
std=opt.norm_std)]
self.transformA = transforms.Compose(transform_list)
self.transformB = transforms.Compose([transforms.ToTensor()])
if 'train' in opt.phase:
self.is_train = True
else:
self.is_train = False
def __getitem__(self, index):
colet = {}
A_path = self.A_paths[index % self.A_size]
imname = self.imname[index % self.A_size]
index_A = index % self.A_size
B_path = os.path.join(self.dir_B, imname.replace('.jpg', '.png'))
if not os.path.isfile(B_path):
B_path = os.path.join(self.dir_B, imname)
A_img = Image.open(A_path).convert('RGB')
# if self.is_train:
sparam = open(os.path.join(self.dir_param, imname + '.txt'))
line = sparam.read()
sparam.close()
shadow_param = np.asarray([float(i) for i in line.split(" ") if i.strip()])
shadow_param = shadow_param[0:6]
ow = A_img.size[0]
oh = A_img.size[1]
w = np.float(A_img.size[0])
h = np.float(A_img.size[1])
if os.path.isfile(B_path):
B_img = Image.open(B_path)
else:
print('MASK NOT FOUND : %s' % (B_path))
B_img = Image.fromarray(np.zeros((int(w), int(h)), dtype=np.float), mode='L')
if self.is_train:
colet['C'] = Image.open(os.path.join(self.dir_C, imname.replace('.jpg', '_no_shadow.jpg'))).convert('RGB')
else:
colet['C'] = Image.open(os.path.join(self.dir_C, imname.replace('.jpg', '_free.jpg'))).convert('RGB')
B_img_np = np.asarray(B_img)
kernel = np.ones((7, 7), np.uint8)
B_img_np_dilate = cv2.dilate(B_img_np, kernel, iterations=1)
B_img_np_erode = cv2.erode(B_img_np, kernel, iterations=1)
B_img_dilate = Image.fromarray(B_img_np_dilate)
B_img_erode = Image.fromarray(B_img_np_erode)
colet['B_dilate'] = B_img_dilate
colet['B_erode'] = B_img_erode
loadSize = self.opt.loadSize
if self.is_train and self.opt.randomSize:
loadSize = np.random.randint(loadSize + 1, loadSize * 1.3, 1)[0]
if self.opt.keep_ratio:
if w > h:
ratio = np.float(loadSize) / np.float(h)
neww = np.int(w * ratio)
newh = loadSize
else:
ratio = np.float(loadSize) / np.float(w)
neww = loadSize
newh = np.int(h * ratio)
else:
neww = loadSize
newh = loadSize
colet['A'] = A_img
colet['B'] = B_img
if self.is_train:
t = [Image.FLIP_LEFT_RIGHT, Image.ROTATE_90]
for i in range(0, 4):
c = np.random.randint(0, 3, 1, dtype=np.int)[0]
if c == 2: continue
for i in ['A', 'B', 'C', 'B_dilate', 'B_erode']:
if i in colet:
colet[i] = colet[i].transpose(t[c])
if self.is_train:
degree = np.random.randint(-20, 20, 1)[0]
for i in ['A', 'B', 'C', 'B_dilate', 'B_erode']:
colet[i] = colet[i].rotate(degree)
for k, im in colet.items():
if self.is_train:
colet[k] = im.resize((neww, newh), Image.NEAREST)
else:
colet[k] = im.resize((self.opt.fineSize, self.opt.fineSize), Image.NEAREST)
w = colet['A'].size[0]
h = colet['A'].size[1]
for k, im in colet.items():
colet[k] = self.transformB(im)
for i in ['A', 'C', 'B', 'B_dilate', 'B_erode']:
if i in colet:
colet[i] = (colet[i] - 0.5) * 2
if self.is_train: # and not self.opt.no_crop:
w_offset = random.randint(0, max(0, w - self.opt.fineSize - 1))
h_offset = random.randint(0, max(0, h - self.opt.fineSize - 1))
for k, im in colet.items():
colet[k] = im[:, h_offset:h_offset + self.opt.fineSize, w_offset:w_offset + self.opt.fineSize]
if self.is_train and (not self.opt.no_flip) and random.random() < 0.5:
idx = [i for i in range(colet['A'].size(2) - 1, -1, -1)]
idx = torch.LongTensor(idx)
for k, im in colet.items():
colet[k] = im.index_select(2, idx)
for k, im in colet.items():
colet[k] = im.type(torch.FloatTensor)
colet['imname'] = imname
colet['w'] = ow
colet['h'] = oh
colet['A_paths'] = A_path
colet['B_baths'] = B_path
# if the shadow area is too small, let's not change anything:
if torch.sum(colet['B'] > 0) < 30:
shadow_param = [0, 1, 0, 1, 0, 1]
colet['param'] = torch.FloatTensor(np.array(shadow_param))
return colet
def __len__(self):
return max(self.A_size, self.B_size)
def name(self):
return 'SRDDataset'
================================================
FILE: data_processing/compute_params.ipynb
================================================
{
"cells": [
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1330 13-14.png\n",
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},
{
"name": "stdout",
"output_type": "stream",
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"6.646969739152888 [1.08673304 0.19923439] [1.04800513 0.18852304] [1.0031643 0.16843263]\n",
"7.003046278484913 [1.17703727 0.22536045] [1.14921878 0.20254484] [1.08429754 0.16369367]\n",
"4.04756324291338 [1.16496385 0.05830398] [1.06852961 0.06222365] [1.04884139 0.04064035]\n",
"4.45505703871402 [1.22844084 0.45789614] [1.04784352 0.44878295] [1. 0.40978731]\n",
"3.0134300763434836 [1.95908206 0.32926769] [1.74670636 0.30533238] [1.39009546 0.2623041 ]\n",
"13.348832382173757 [1. 0.39161291] [1. 0.35091] [1. 0.28604537]\n",
"10.304710295664696 [1. 0.45893432] [1. 0.41864383] [1. 0.35694521]\n",
"9.785908436998039 [1.08372382 0.32098789] [1.01837139 0.30181823] [1. 0.23469834]\n",
"8.976889411683826 [1. 0.3959776] [1. 0.35170545] [1.02194185 0.27629988]\n",
"6.860965238430664 [1.14332745 0.21776023] [1. 0.23488931] [1. 0.17477226]\n",
"15.572344511846968 [1.69924381 0.12356604] [2.00048359 0.02007715] [1.6883586 0.05150387]\n",
"8.262607543929457 [1.07895506 0.37505895] [1.02074705 0.33990409] [1.0603653 0.25874 ]\n",
"4.912597143120675 [1. 0.22822034] [1. 0.19841094] [1. 0.12697328]\n",
"6.363851780499002 [1.08254776 0.28384135] [1.02512182 0.25842776] [1.00823914 0.19289162]\n",
"12.30228201372977 [1. 0.43639514] [1. 0.40232694] [1. 0.34632636]\n",
"11.588900327416342 [1. 0.3693939] [1. 0.3382938] [1. 0.29726383]\n",
"7.533132769290531 [1.13759613 0.35266774] [1.18449953 0.29064698] [1.09630072 0.25145613]\n",
"11.289702293060968 [1.50749144 0.16226019] [1.45744929 0.13049412] [1.23527793 0.14069548]\n",
"4.661812373082756 [1.75910219 0.35953738] [1.53991883 0.33878098] [1.05060127 0.3295266 ]\n",
"5.4460323603950425 [1. 0.24312404] [1. 0.21168479] [1. 0.17354187]\n",
"8.706177881674797 [1.0799126 0.36515226] [1.01270513 0.32855653] [1. 0.27853921]\n",
"8.00077235114351 [1.13150565 0.24845463] [1.13561437 0.21600817] [1.11821358 0.15460734]\n",
"8.781319222545337 [1. 0.36041255] [1. 0.32616131] [1. 0.27616279]\n",
"5.785696866658025 [1.49238935 0.02230037] [1.16906831 0.13467273] [1. 0.24162001]\n",
"8.478908450888166 [1. 0.37765749] [1. 0.32574951] [1. 0.25176544]\n",
"5.8693524852863375 [1. 0.30947209] [1.00026833 0.27751733] [1.04401617 0.21315743]\n",
"9.488132419170112 [1.28201812 0.20183249] [1.13807666 0.20555704] [1.15478091 0.11431506]\n",
"6.265242354450486 [1.17191284 0.11989237] [1.09503003 0.11605688] [1.1446114 0.02895524]\n",
"5.1572482448606625 [1.51466321 0.11179498] [1.47477233 0.08628483] [1.22591373 0.0704102 ]\n",
"10.877991402534544 [1. 0.32529559] [1. 0.28170299] [1. 0.22808252]\n",
"12.571582373210557 [1.73963119 0.25463019] [1.62983973 0.24014169] [2.00208608 0.06696168]\n",
"6.39314313081427 [1.32507248 0.31924145] [1.6659829 0.15794568] [1.29657599 0.13307339]\n",
"6.969672823392187 [1.1135408 0.26977364] [1.14255001 0.20186158] [1.12263319 0.12106629]\n",
"2.8356203449588677 [1. 0.24473362] [1. 0.23107451] [1. 0.20297414]\n",
"10.417356223180978 [1. 0.35744425] [1. 0.28276206] [1. 0.26162718]\n",
"13.750913044333586 [1.25206536 0.27687467] [1.16182884 0.25240209] [1.05208969 0.20944306]\n",
"12.150945773550827 [1. 0.38371884] [1. 0.34285822] [1. 0.284928]\n",
"5.389336374411643 [1.16121849 0.17694264] [1.13249659 0.16548833] [1.07270058 0.14587459]\n",
"10.523329017051536 [1. 0.42764285] [1. 0.38407481] [1. 0.31660827]\n",
"8.269418511118195 [1. 0.46234438] [1. 0.41054863] [1. 0.33883393]\n",
"9.167433277613362 [1. 0.29017861] [1. 0.26614638] [1. 0.23131604]\n",
"5.5609186219361595 [1. 0.12462872] [1. 0.10465619] [1.01838654 0.06773848]\n",
"9.594943678759213 [1. 0.42423773] [1. 0.38552999] [1. 0.32468429]\n",
"5.3791976022018515 [1.54201572 0.06628701] [1.40167858 0.07165457] [1.2690287 0.06592292]\n",
"19.77399570300559 [1.07824599 0.38281923] [1.00398616 0.36607595] [1.1181896 0.23795898]\n",
"9.580546761468955 [1.37306584 0.17577431] [1.30179416 0.16933436] [1.21704513 0.14663145]\n",
"13.33828004159378 [1. 0.36330007] [1. 0.30110656] [1.07022844 0.1971349 ]\n",
"7.26545664572203 [1.00320889 0.27332249] [1.08149899 0.20519183] [1.13360372 0.12610142]\n",
"4.020766820685916 [1.84646009 0.34098613] [1.67902187 0.29709569] [1.24429486 0.27404391]\n",
"19.273838139796524 [1.23985283 0.15511182] [1.07159674 0.18082891] [1.38035117 0.04312197]\n",
"18.747154847936542 [1.2879973 0.17396842] [1.02469479 0.2312224 ] [1.38216191 0.04543679]\n",
"7.687501494777646 [1.18911408 0.13068791] [1.21358109 0.10652954] [1.1600769 0.11167813]\n",
"17.41626227161925 [1.24286567 0.17648605] [1. 0.23681838] [1.37625362 0.04062536]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"4.608339372745333 [1.74113426 0.34798842] [1.52317166 0.3191022 ] [1.16724291 0.2942582 ]\n",
"18.875741071133284 [1.2543283 0.15648117] [1.06798541 0.18717109] [1.34530144 0.04991616]\n",
"11.148313150594289 [1. 0.37511565] [1.09509921 0.28287656] [1.14942929 0.17369543]\n",
"7.233035561460236 [1.08561326 0.2354706 ] [1.20422952 0.14769201] [1.1853493 0.09605807]\n",
"8.863229059881018 [1.41297349 0.19649819] [1.33371269 0.18662474] [1.22792143 0.15976778]\n",
"19.45745868435802 [1.49994807 0.26682565] [1.26711735 0.27545718] [1.34188963 0.14555973]\n",
"9.468170012731786 [1. 0.42522692] [1. 0.38323518] [1. 0.32367061]\n",
"5.868515168416221 [1.52897243 0.06848402] [1.38364141 0.07468658] [1.25120255 0.06850521]\n",
"6.000131214338976 [1. 0.12002132] [1. 0.0980278] [1.0169329 0.05973255]\n",
"7.438143572576681 [1. 0.28502836] [1. 0.26303692] [1. 0.22860402]\n",
"5.289794627964023 [1.1335567 0.21179734] [1.11170322 0.19507687] [1.07163701 0.16266489]\n",
"7.964680770390557 [1.14781153 0.42575943] [1. 0.40190126] [1. 0.33838822]\n",
"10.032075253179388 [1.19303526 0.34074064] [1.17790367 0.30367674] [1.13926281 0.24261738]\n",
"9.507332791209954 [1. 0.36297141] [1.12549776 0.27003628] [1.00850659 0.26411807]\n",
"2.819872748989664 [1. 0.23983558] [1. 0.2266287] [1. 0.1979991]\n",
"7.495457263473503 [1.23996667 0.23219605] [1.25828273 0.17540812] [1.2256135 0.09706519]\n",
"3.3035183593098116 [1.05401011 0.0786346 ] [1.0064354 0.0834124] [1.00066125 0.06275393]\n",
"10.982202783668944 [1.01541626 0.32391523] [1.04335354 0.26363175] [1.02417349 0.21390277]\n",
"5.444681012424603 [1.21112693 0.34450231] [1.49266707 0.19883686] [1.21760034 0.16245562]\n",
"5.71585520980478 [1.38514994 0.07153152] [1.26599542 0.07115983] [1.28125242 0.00203232]\n",
"4.78489739965205 [1.42650945 0.09035256] [1.35666958 0.08265567] [1.25431092 0.05747103]\n",
"9.338768434828841 [1.28952457 0.20208728] [1.12484015 0.21011617] [1.14263045 0.1109362 ]\n",
"6.073680103146996 [1.00675102 0.26630249] [1.02387754 0.23440817] [1.02017898 0.1963962 ]\n",
"8.881113087703643 [1. 0.39147761] [1.13747849 0.30262937] [1.13791602 0.21990965]\n",
"6.288096344683792 [1.22002553 0.34162078] [1.05898669 0.32589906] [1. 0.28077121]\n",
"6.370692375869698 [1.47119504 0.02273981] [1.14891519 0.14920475] [1. 0.2303363]\n",
"7.1979636175223405 [1.11389871 0.23466379] [1.10305907 0.20912161] [1.06224552 0.16659362]\n",
"5.849074255529509 [1. 0.24786564] [1. 0.22290166] [1. 0.18581435]\n",
"3.6782213024410426 [1.54906327 0.38499582] [1.38170487 0.35972071] [1.05013607 0.33461307]\n",
"4.76170856930791 [1.13572491 0.38745464] [1.03104303 0.37008839] [1. 0.32496164]\n",
"5.155595609262272 [1. 0.49398408] [1. 0.46194917] [1. 0.41697888]\n",
"6.642882304921422 [1.59828797 0.1202163 ] [1.4459217 0.11160453] [1.19351043 0.12236486]\n",
"4.814149145007596 [1.06481287 0.11952267] [1.08056103 0.08142804] [1.04100738 0.05845668]\n",
"5.019245705742924 [1.08739817 0.26074022] [1.06015233 0.22453726] [1.08866459 0.14105046]\n",
"9.16744872104858 [1.23419148 0.43999962] [1.20295263 0.398843 ] [1.098967 0.34742564]\n",
"4.786575682308314 [1. 0.28540823] [1. 0.27403455] [1. 0.18088439]\n",
"8.250491528487505 [1. 0.39301486] [1. 0.34821779] [1. 0.27898305]\n",
"8.866099271504389\n"
]
}
],
"source": [
"import cv2\n",
"import os\n",
"from os import listdir\n",
"from os.path import isfile, join\n",
"from PIL import Image as Image\n",
"import numpy as np\n",
"\n",
"from scipy.optimize import curve_fit\n",
"\n",
"def relit(x, a, b):\n",
" return (a * x.astype(np.float)/255 + b)*255\n",
"\n",
"\n",
"\n",
"from matplotlib import pyplot as plt\n",
"def plshow(im,title='MINE'):\n",
" if len(im.shape)>2:\n",
" # plt.imshow(cv2.cvtColor(im, cv2.COLOR_BGR2RGB))\n",
" plt.imshow(im)\n",
" else:\n",
" plt.imshow(im,cmap='gray')\n",
" plt.title(title)\n",
" plt.rcParams[\"figure.figsize\"] = (80,12)\n",
" plt.show()\n",
"\n",
"sd_path = 'dataset/ISTD/train_A'\n",
"mask_path = 'dataset/ISTD/train_B'\n",
"sdfree_path = 'dataset/ISTD/train_C_fixed_ours'\n",
"\n",
"out = 'dataset/ISTD/train_params/'\n",
"if not os.path.exists(out):\n",
" os.makedirs(out)\n",
"\n",
"im_list = [f for f in listdir(sd_path) if isfile(join(sd_path, f)) and f.endswith('png')]\n",
"print(len(im_list),im_list[0])\n",
"kernel = np.ones((5,5),np.uint8)\n",
"\n",
"\n",
"def im_relit(Rpopt,Gpopt,Bpopt,dump):\n",
" #fc this shit\n",
" sdim = dump.copy()\n",
" sdim.setflags(write=1)\n",
" sdim = sdim.astype(np.float)\n",
" sdim[:,:,0] = (sdim[:,:,0]/255) * Rpopt[0] + Rpopt[1]\n",
" sdim[:,:,1] = (sdim[:,:,1]/255) * Gpopt[0] + Gpopt[1]\n",
" sdim[:,:,2] = (sdim[:,:,2]/255) * Bpopt[0] + Bpopt[1]\n",
" sdim = sdim*255\n",
" # print(np.amin(sdim),np.amax(sdim))\n",
" return sdim\n",
"\n",
"errors= []\n",
"for im in im_list:\n",
" sd = np.asarray(Image.open(join(sd_path,im)))\n",
" mean_sdim = np.mean(sd,axis=2)\n",
" \n",
" mask_ori = np.asarray(Image.open(join(mask_path,im)))\n",
" mask = cv2.erode(mask_ori ,kernel,iterations = 2)\n",
"\n",
" \n",
" sdfree = np.asarray(Image.open(join(sdfree_path,im)))\n",
" mean_sdfreeim = np.mean(sdfree,axis=2)\n",
" \n",
" #pixels for regression funtion\n",
" i, j = np.where(np.logical_and(np.logical_and(np.logical_and(mask>=1,mean_sdim>5),mean_sdfreeim<230),np.abs(mean_sdim-mean_sdfreeim)>10))\n",
"\n",
" source = sd*0\n",
" source[tuple([i,j])] = sd[tuple([i,j])] \n",
" target = sd*0\n",
" target[tuple([i,j])]= sdfree[tuple([i,j])]\n",
" \n",
" R_s = source[:,:,0][tuple([i,j])]\n",
" G_s = source[:,:,1][tuple([i,j])]\n",
" B_s = source[:,:,2][tuple([i,j])]\n",
" \n",
" R_t = target[:,:,0][tuple([i,j])]\n",
" G_t = target[:,:,1][tuple([i,j])]\n",
" B_t = target[:,:,2][tuple([i,j])]\n",
" \n",
" c_bounds = [[1,-0.1],[10,0.5]]\n",
"\n",
" \n",
" Rpopt, pcov = curve_fit(relit, R_s, R_t,bounds=c_bounds)\n",
" Gpopt, pcov = curve_fit(relit, G_s, G_t,bounds=c_bounds)\n",
" Bpopt, pcov = curve_fit(relit, B_s, B_t,bounds=c_bounds)\n",
" \n",
" \n",
" relitim = im_relit(Rpopt,Gpopt,Bpopt,sd)\n",
" \n",
" #final = sd.copy()\n",
" #final[tuple([i,j])] = relitim[tuple([i,j])]\n",
" #final[final>255] =255\n",
" #final[final<0] = 0\n",
"\n",
" #plshow(final)\n",
" error = np.mean(np.abs(relitim[tuple([i,j])].astype(np.float) - sdfree[tuple([i,j])]).astype(np.float))\n",
" print(error,Rpopt,Gpopt,Bpopt)\n",
" f = open(join(out,im+'.txt'),\"a\")\n",
" f.write(\"%f %f %f %f %f %f\"%(Rpopt[1],Rpopt[0],Gpopt[1],Gpopt[0],Bpopt[1],Bpopt[0]))\n",
" f.close()\n",
" \n",
" # print(error)\n",
" errors.append(error)\n",
" \n",
" \n",
"print(np.mean(errors))\n",
"#no bound - 8.55\n",
"#### y_bound ###error\n",
"# 0.5 8.86\n",
"# 0.1 15.692271753155671 \n",
"# 0.25 10.830443545867785\n",
"# 1 8.86"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.5"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
================================================
FILE: data_processing/test_mask_generation.py
================================================
import os
import cv2
import matplotlib.pyplot as plt
import PIL.Image as Image
from skimage.filters import threshold_otsu
from skimage import filters
import numpy as np
from skimage.morphology import disk
# get mask from shadow image and shadow free image
train_A_path = '../data/ISTD+/test_A'
train_C_path = '../data/ISTD+/test_C'
root_path = os.listdir(train_A_path)
for file in root_path:
s_name = os.path.join(train_A_path, file)
sf_name = os.path.join(train_C_path, file)
s_img = Image.open(s_name).convert("L")
s_img = np.array(s_img).astype(np.float32)
sf_img = Image.open(sf_name).convert("L")
sf_img = np.array(sf_img).astype(np.float32)
diff = (np.asarray(sf_img, dtype='float32') - np.asarray(s_img, dtype='float32'))
# diff[diff < 0] = 0
L = threshold_otsu(diff)
mask = np.float32(diff > L) * 255
mask = filters.median(mask, disk(5))
mask = Image.fromarray(mask).convert("L")
# plt.imshow(mask)
# plt.show()
mask.save('../data/ISTD+/test_mask/' + file)
================================================
FILE: models/Fusion_model.py
================================================
import torch
from collections import OrderedDict
import time
import numpy as np
import os
import torch.nn.functional as F
import torch.nn as nn
from util.image_pool import ImagePool
from .base_model import BaseModel
from . import networks
import util.util as util
from .distangle_model import DistangleModel
from PIL import ImageOps,Image
import cv2
def tensor2im(input_image, imtype=np.uint8):
""""Converts a Tensor array into a numpy image array.
Parameters:
input_image (tensor) -- the input image tensor array
imtype (type) -- the desired type of the converted numpy array
"""
if not isinstance(input_image, np.ndarray):
if isinstance(input_image, torch.Tensor): # get the data from a variable
image_tensor = input_image.data
else:
return input_image
image_numpy = image_tensor.cpu().float().numpy() # convert it into a numpy array
if image_numpy.shape[0] == 1: # grayscale to RGB
image_numpy = np.tile(image_numpy, (3, 1, 1))
# image_numpy = image_numpy.convert('L')
image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + 1) / 2.0 * 255.0 # post-processing: tranpose and scaling
else: # if it is a numpy array, do nothing
image_numpy = input_image
return np.clip(image_numpy, 0, 255).astype(imtype)
class L_TV(nn.Module):
def __init__(self):
super(L_TV, self).__init__()
def forward(self, x):
_, _, h, w = x.size()
count_h = (h - 1) * w
count_w = (w - 1) * h
h_tv = torch.pow(x[:, :, 1:, :] - x[:, :, :h - 1, :], 2).sum()
w_tv = torch.pow(x[:, :, :, 1:] - x[:, :, :, :w - 1], 2).sum()
return (h_tv / count_h + w_tv / count_w) / 2.0
class GradientLoss(nn.Module):
def __init__(self, loss_weight=1.0, reduction='mean'):
super(GradientLoss, self).__init__()
self.loss_weight = loss_weight
self.reduction = reduction
if self.reduction not in ['none', 'mean', 'sum']:
raise ValueError(f'Unsupported reduction mode: {self.reduction}. '
f'Supported ones are: {_reduction_modes}')
def forward(self, pred, target):
_, cin, _, _ = pred.shape
_, cout, _, _ = target.shape
assert cin == 3 and cout == 3
kx = torch.Tensor([[1, 0, -1], [2, 0, -2],
[1, 0, -1]]).view(1, 1, 3, 3).to(target)
ky = torch.Tensor([[1, 2, 1], [0, 0, 0],
[-1, -2, -1]]).view(1, 1, 3, 3).to(target)
kx = kx.repeat((3, 1, 1, 1))
ky = ky.repeat((3, 1, 1, 1))
pred_grad_x = F.conv2d(pred, kx, padding=1, groups=3)
pred_grad_y = F.conv2d(pred, ky, padding=1, groups=3)
target_grad_x = F.conv2d(target, kx, padding=1, groups=3)
target_grad_y = F.conv2d(target, ky, padding=1, groups=3)
loss = (
nn.L1Loss(reduction=self.reduction)(
pred_grad_x, target_grad_x) +
nn.L1Loss(reduction=self.reduction)(
pred_grad_y, target_grad_y))
return loss * self.loss_weight
class PoissonGradientLoss(nn.Module):
def __init__(self, reduction='mean'):
"""L_{grad} = \frac{1}{2hw}\sum_{m=1}^{H}\sum_{n=1}{W}(\partial f(I_{Blend}) -
(\partial f(I_{Source}) + \partial f(I_{Target})))_{mn}^2
See **Deep Image Blending** for detail.
"""
super(PoissonGradientLoss, self).__init__()
self.reduction = reduction
def forward(self, source, target, blend, mask):
f = torch.Tensor([[0, -1, 0], [-1, 4, -1], [0, -1, 0]]).view(1, 1, 3, 3).to(target)
f = f.repeat((3, 1, 1, 1))
grad_s = F.conv2d(source, f, padding=1, groups=3) * mask
grad_t = F.conv2d(target, f, padding=1, groups=3) * (1 - mask)
grad_b = F.conv2d(blend, f, padding=1, groups=3)
return nn.MSELoss(reduction=self.reduction)(grad_b, (grad_t + grad_s))
class FusionModel(DistangleModel):
def name(self):
return 'fusion net cvpr 21'
@staticmethod
def modify_commandline_options(parser, is_train=True):
parser.set_defaults(pool_size=0, no_lsgan=True, norm='batch')
parser.set_defaults(dataset_mode='expo_param')
parser.add_argument('--wdataroot',default='None', help='path to images (should have subfolders trainA, trainB, valA, valB, etc)')
parser.add_argument('--use_our_mask', action='store_true')
parser.add_argument('--mask_train',type=str,default=None)
parser.add_argument('--mask_test',type=str,default=None)
return parser
def initialize(self, opt):
BaseModel.initialize(self, opt)
self.isTrain = opt.isTrain
self.loss_names = ['G_param', 'alpha', 'rescontruction']
self.visual_names = ['input_img', 'litgt', 'alpha_pred', 'out', 'final', 'outgt']
self.model_names = ['G', 'M']
opt.output_nc = 3
self.ks = ks = opt.ks
self.n = n = opt.n
self.shadow_loss = opt.shadow_loss
self.tv_loss = opt.tv_loss
self.grad_loss = opt.grad_loss
self.pgrad_loss = opt.pgrad_loss
self.netG = networks.define_G(4, 2 * 3, opt.ngf, 'RESNEXT', opt.norm,
not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids)
self.netM = networks.define_G(1 + 3 + n * 3, ((1 + n) * 3) * 3 * ks * ks, opt.ngf, 'unet_256', opt.norm,
not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids)
self.netG.to(self.device)
self.netM.to(self.device)
print(self.netG)
print(self.netM)
if self.isTrain:
# define loss functions
self.MSELoss = torch.nn.MSELoss()
self.criterionL1 = torch.nn.L1Loss()
self.bce = torch.nn.BCEWithLogitsLoss()
# initialize optimizers
self.optimizers = []
if opt.optimizer == 'adam':
self.optimizer_G = torch.optim.Adam(self.netG.parameters(),
lr=opt.lr, betas=(opt.beta1, 0.999), weight_decay=1e-5)
self.optimizer_M = torch.optim.Adam(self.netM.parameters(),
lr=opt.lr, betas=(opt.beta1, 0.999), weight_decay=1e-5)
elif opt.optimizer == 'sgd':
self.optimizer_G = torch.optim.SGD(self.netG.parameters(), momentum=0.9,
lr=opt.lr, weight_decay=1e-5)
self.optimizer_M = torch.optim.SGD(self.netM.parameters(), momentum=0.9,
lr=opt.lr, weight_decay=1e-5)
else:
assert False
self.optimizers.append(self.optimizer_G)
self.optimizers.append(self.optimizer_M)
def set_input(self, input):
self.input_img = input['A'].to(self.device)
self.shadow_mask = input['B'].to(self.device)
self.imname = input['imname']
self.shadow_param = input['param'].to(self.device).type(torch.float)
self.shadow_mask = (self.shadow_mask > 0.9).type(torch.float) # * 2 - 1
self.nim = self.input_img.shape[1]
self.shadowfree_img = input['C'].to(self.device)
self.shadow_mask_3d = (self.shadow_mask > 0).type(torch.float).expand(self.input_img.shape)
self.shadow_mask_dilate = input['B_dilate'].to(self.device)
self.shadow_mask_erode = input['B_erode'].to(self.device)
def forward(self):
inputG = torch.cat([self.input_img, self.shadow_mask], 1)
shadow_param_pred = self.netG(inputG)
n = shadow_param_pred.shape[0]
w = inputG.shape[2]
h = inputG.shape[3]
addgt = self.shadow_param[:, [0, 2, 4]]
mulgt = self.shadow_param[:, [1, 3, 5]]
addgt = addgt.view(n, 3, 1, 1).expand((n, 3, w, h))
mulgt = mulgt.view(n, 3, 1, 1).expand((n, 3, w, h))
base_shadow_param_pred = shadow_param_pred[:, :2 * 3] # shadow_param_pred.view((n, self.n * 3, 2, 1, 1))
self.base_shadow_param_pred = base_shadow_param_pred
shadow_image = self.input_img.clone() / 2 + 0.5
base_shadow_output = shadow_image * base_shadow_param_pred[:, :3].view((n, 3, 1, 1)) + \
base_shadow_param_pred[:, 3:].view((n, 3, 1, 1))
shadow_output_list = []
for i in range(0, self.n - 1):
if i % 2 == 0:
scale = 1 + i * 0.01
else:
scale = 1 - i * 0.01
shadow_output_list.append(base_shadow_output * scale)
shadow_output = torch.cat([base_shadow_output] + shadow_output_list, dim=1)
self.lit = torch.cat([base_shadow_output] + shadow_output_list, dim=-1) * 2 - 1
shadow_output = shadow_output * 2 - 1
self.shadow_output = shadow_output
self.litgt = self.input_img.clone() / 2 + 0.5
self.litgt = (self.litgt * mulgt + addgt) * 2 - 1 # [-1, 1]
inputM = torch.cat([self.input_img, shadow_output, self.shadow_mask], 1)
out = torch.cat([self.input_img, shadow_output], 1)
out = out / 2 + 0.5
out_matrix = F.unfold(out, stride=1, padding=self.ks // 2, kernel_size=self.ks) # N, C x \mul_(kernel_size), L
kernel = self.netM(inputM) # b, (3+1)*n * 3 * ks * ks, Tanh
b, c, h, w = self.input_img.shape
output = []
for i in range(b):
# feature = out[i, ...]
feature = out_matrix[i, ...] # ((1 + n) * 3) * ks * ks, L
weight = kernel[i, ...] # ((1 + n) * 3) * 3 * ks * ks, H, W
feature = feature.unsqueeze(0) # 1, C, L
weight = weight.view((3, (self.n + 1) * 3 * self.ks * self.ks, h * w))
weight = F.softmax(weight, dim=1)
iout = feature * weight # (3, C, L)
iout = torch.sum(iout, dim=1, keepdim=False)
iout = iout.view((1, 3, h, w))
output.append(iout)
self.final = torch.cat(output, dim=0) * 2 -1
def backward(self):
criterion = self.criterionL1
lambda_ = self.opt.lambda_L1
addgt = self.shadow_param[:, [0, 2, 4]] # [b, 3]
mulgt = self.shadow_param[:, [1, 3, 5]] # [b, 3]
loss_G_param_mul = self.MSELoss(self.base_shadow_param_pred[:, :3], mulgt) * lambda_
loss_G_param_add = self.MSELoss(self.base_shadow_param_pred[:, 3:], addgt) * lambda_
self.loss_G_param = (loss_G_param_add + loss_G_param_mul) / 2.0 * self.shadow_loss
if self.tv_loss > 0:
tv_loss = L_TV()(self.final - self.shadowfree_img) * lambda_ * self.tv_loss
else:
tv_loss = 0.0
if self.grad_loss > 0:
grad_loss = GradientLoss()(self.final, self.shadowfree_img) * lambda_ * self.grad_loss
else:
grad_loss = 0.0
if self.pgrad_loss > 0:
pgrad_loss = PoissonGradientLoss()(target=self.input_img, blend=self.final,
source=self.shadowfree_img, mask=self.shadow_mask_dilate) \
* lambda_ * self.pgrad_loss
else:
pgrad_loss = 0.0
self.loss_rescontruction = criterion(self.final, self.shadowfree_img) * lambda_
self.loss = self.loss_rescontruction + self.loss_G_param + tv_loss + grad_loss + pgrad_loss
self.loss.backward()
def optimize_parameters(self):
self.netM.zero_grad()
self.netG.zero_grad()
self.forward()
self.optimizer_G.zero_grad()
self.optimizer_M.zero_grad()
self.backward()
self.optimizer_G.step()
self.optimizer_M.step()
def zero_grad(self):
self.netM.zero_grad()
self.netG.zero_grad()
self.optimizer_G.zero_grad()
self.optimizer_M.zero_grad()
def vis(self, e, s, path='', eval=False):
if len(path) > 0:
save_dir = os.path.join(self.save_dir, path)
else:
save_dir = self.save_dir
if not os.path.isdir(save_dir):
os.mkdir(save_dir)
shadow = self.input_img
output = self.final
gt = self.shadowfree_img
if eval:
img = self.final[0, ...]
filename = os.path.join(save_dir, self.imname[0])
else:
img = torch.cat([shadow, output, gt, self.litgt, self.lit], axis=-1)[0, ...]
filename = os.path.join(save_dir, "epoch_%d_step_%d.png" % (e, s))
img = tensor2im(img)
img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
cv2.imwrite(filename, img)
================================================
FILE: models/Refine_model.py
================================================
import torch
from collections import OrderedDict
import time
import numpy as np
import os
import torch.nn.functional as F
import torch.nn as nn
from util.image_pool import ImagePool
from .base_model import BaseModel
from . import networks
import util.util as util
from .distangle_model import DistangleModel
from PIL import ImageOps, Image
import cv2
def tensor2im(input_image, imtype=np.uint8):
if not isinstance(input_image, np.ndarray):
if isinstance(input_image, torch.Tensor): # get the data from a variable
image_tensor = input_image.data
else:
return input_image
image_numpy = image_tensor.cpu().float().numpy() # convert it into a numpy array
if image_numpy.shape[0] == 1: # grayscale to RGB
image_numpy = np.tile(image_numpy, (3, 1, 1))
# image_numpy = image_numpy.convert('L')
image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + 1) / 2.0 * 255.0 # post-processing: tranpose and scaling
else: # if it is a numpy array, do nothing
image_numpy = input_image
return np.clip(image_numpy, 0, 255).astype(imtype)
class L_TV(nn.Module):
def __init__(self):
super(L_TV, self).__init__()
def forward(self, x):
_, _, h, w = x.size()
count_h = (h - 1) * w
count_w = (w - 1) * h
h_tv = torch.pow(x[:, :, 1:, :] - x[:, :, :h - 1, :], 2).sum()
w_tv = torch.pow(x[:, :, :, 1:] - x[:, :, :, :w - 1], 2).sum()
return (h_tv / count_h + w_tv / count_w) / 2.0
class GradientLoss(nn.Module):
def __init__(self, loss_weight=1.0, reduction='mean'):
super(GradientLoss, self).__init__()
self.loss_weight = loss_weight
self.reduction = reduction
if self.reduction not in ['none', 'mean', 'sum']:
raise ValueError(f'Unsupported reduction mode: {self.reduction}. '
f'Supported ones are: {_reduction_modes}')
def forward(self, pred, target):
_, cin, _, _ = pred.shape
_, cout, _, _ = target.shape
assert cin == 3 and cout == 3
kx = torch.Tensor([[1, 0, -1], [2, 0, -2],
[1, 0, -1]]).view(1, 1, 3, 3).to(target)
ky = torch.Tensor([[1, 2, 1], [0, 0, 0],
[-1, -2, -1]]).view(1, 1, 3, 3).to(target)
kx = kx.repeat((cout, 1, 1, 1))
ky = ky.repeat((cout, 1, 1, 1))
pred_grad_x = F.conv2d(pred, kx, padding=1, groups=3)
pred_grad_y = F.conv2d(pred, ky, padding=1, groups=3)
target_grad_x = F.conv2d(target, kx, padding=1, groups=3)
target_grad_y = F.conv2d(target, ky, padding=1, groups=3)
loss = (
nn.L1Loss(reduction=self.reduction)
(pred_grad_x, target_grad_x) +
nn.L1Loss(reduction=self.reduction)
(pred_grad_y, target_grad_y))
return loss * self.loss_weight
class PoissonGradientLoss(nn.Module):
def __init__(self, reduction='mean'):
"""L_{grad} = \frac{1}{2hw}\sum_{m=1}^{H}\sum_{n=1}{W}(\partial f(I_{Blend}) -
(\partial f(I_{Source}) + \partial f(I_{Target})))_{mn}^2
See **Deep Image Blending** for detail.
"""
super(PoissonGradientLoss, self).__init__()
self.reduction = reduction
def forward(self, source, target, blend, mask):
f = torch.Tensor([[0, -1, 0], [-1, 4, -1], [0, -1, 0]]).view(1, 1, 3, 3).to(target)
f = f.repeat((3, 1, 1, 1))
grad_s = F.conv2d(source, f, padding=1, groups=3) * mask
grad_t = F.conv2d(target, f, padding=1, groups=3) * (1 - mask)
grad_b = F.conv2d(blend, f, padding=1, groups=3)
return nn.MSELoss(reduction=self.reduction)(grad_b, (grad_t + grad_s))
class RefineModel(DistangleModel):
def name(self):
return 'auto exposure cvpr21'
@staticmethod
def modify_commandline_options(parser, is_train=True):
parser.set_defaults(pool_size=0, no_lsgan=True, norm='batch')
parser.set_defaults(dataset_mode='expo_param')
parser.add_argument('--wdataroot', default='None',
help='path to images (should have subfolders trainA, trainB, valA, valB, etc)')
parser.add_argument('--use_our_mask', action='store_true')
parser.add_argument('--mask_train', type=str, default=None)
parser.add_argument('--mask_test', type=str, default=None)
return parser
def initialize(self, opt):
BaseModel.initialize(self, opt)
self.isTrain = opt.isTrain
self.loss_names = ['G_param', 'alpha', 'rescontruction']
self.visual_names = ['input_img', 'litgt', 'alpha_pred', 'out', 'final', 'outgt']
self.model_names = ['G', 'M', 'R']
# load/define networks
opt.output_nc = 3
self.ks = ks = opt.ks
self.rks = opt.rks
self.n = n = opt.n
self.shadow_loss = opt.shadow_loss
self.tv_loss = opt.tv_loss
self.grad_loss = opt.grad_loss
self.pgrad_loss = opt.pgrad_loss
self.netG = networks.define_G(4, 2 * 3, opt.ngf, 'RESNEXT', opt.norm,
not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids)
self.netM = networks.define_G(1 + 3 + n * 3, ((1 + n) * 3) * 3 * ks * ks, opt.ngf, 'unet_256', opt.norm,
not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids)
self.netR = networks.define_G(3 + 3 + 2, 3 * 3 * self.rks * self.rks, opt.ngf, 'unet_256', opt.norm,
not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids)
self.netG.to(self.device)
self.netM.to(self.device)
self.netR.to(self.device)
self.netG.eval()
self.netM.eval()
self.netG.requires_grad = False
self.netM.requires_grad = False
if self.isTrain:
# define loss functions
self.MSELoss = torch.nn.MSELoss()
self.criterionL1 = torch.nn.L1Loss()
self.bce = torch.nn.BCEWithLogitsLoss()
# initialize optimizers
self.optimizers = []
if opt.optimizer == 'adam':
self.optimizer_R = torch.optim.Adam(self.netR.parameters(),
lr=opt.lr, betas=(opt.beta1, 0.999), weight_decay=1e-5)
elif opt.optimizer == 'sgd':
self.optimizer_R = torch.optim.SGD(self.netR.parameters(), momentum=0.9,
lr=opt.lr, weight_decay=1e-5)
else:
assert False
self.optimizers.append(self.optimizer_R)
def set_input(self, input):
self.input_img = input['A'].to(self.device)
self.shadow_mask = input['B'].to(self.device)
self.imname = input['
gitextract_pdbosn3v/
├── OE_eval.py
├── OE_eval.sh
├── OE_train.py
├── OE_train.sh
├── README.md
├── data/
│ ├── __init__.py
│ ├── base_data_loader.py
│ ├── base_dataset.py
│ ├── expo_param_dataset.py
│ ├── image_folder.py
│ └── srd_dataset.py
├── data_processing/
│ ├── compute_params.ipynb
│ └── test_mask_generation.py
├── models/
│ ├── Fusion_model.py
│ ├── Refine_model.py
│ ├── __init__.py
│ ├── base_model.py
│ ├── distangle_model.py
│ ├── loss_function.py
│ ├── networks.py
│ ├── resnet.py
│ └── vgg.py
├── options/
│ ├── PAMI_options.py
│ ├── __init__.py
│ ├── base_options.py
│ ├── test_options.py
│ └── train_options.py
└── util/
├── __init__.py
├── get_data.py
├── html.py
├── image_pool.py
├── util.py
└── visualizer.py
SYMBOL INDEX (219 symbols across 26 files)
FILE: OE_eval.py
function tensor2im (line 60) | def tensor2im(input_image, imtype=np.uint8):
function calc_RMSE (line 81) | def calc_RMSE(real_img, fake_img):
FILE: OE_train.py
function tensor2im (line 63) | def tensor2im(input_image, imtype=np.uint8):
function calc_RMSE (line 84) | def calc_RMSE(real_img, fake_img):
FILE: data/__init__.py
function find_dataset_using_name (line 7) | def find_dataset_using_name(dataset_name):
function get_option_setter (line 31) | def get_option_setter(dataset_name):
function create_dataset (line 36) | def create_dataset(opt):
function CreateDataLoader (line 44) | def CreateDataLoader(opt):
class CustomDatasetDataLoader (line 52) | class CustomDatasetDataLoader(BaseDataLoader):
method name (line 53) | def name(self):
method initialize (line 56) | def initialize(self, opt):
method load_data (line 65) | def load_data(self):
method __len__ (line 68) | def __len__(self):
method __iter__ (line 71) | def __iter__(self):
FILE: data/base_data_loader.py
class BaseDataLoader (line 1) | class BaseDataLoader():
method __init__ (line 2) | def __init__(self):
method initialize (line 5) | def initialize(self, opt):
method load_data (line 9) | def load_data():
FILE: data/base_dataset.py
class BaseDataset (line 6) | class BaseDataset(data.Dataset):
method __init__ (line 7) | def __init__(self):
method name (line 10) | def name(self):
method modify_commandline_options (line 14) | def modify_commandline_options(parser, is_train):
method initialize (line 17) | def initialize(self, opt):
method __len__ (line 20) | def __len__(self):
function get_transform (line 24) | def get_transform(opt):
function __adjust (line 55) | def __adjust(img):
function __scale_width (line 75) | def __scale_width(img, target_width):
function __print_size_warning (line 96) | def __print_size_warning(ow, oh, w, h):
FILE: data/expo_param_dataset.py
class ExpoParamDataset (line 15) | class ExpoParamDataset(BaseDataset):
method initialize (line 16) | def initialize(self, opt):
method __getitem__ (line 46) | def __getitem__(self, index):
method __len__ (line 165) | def __len__(self):
method name (line 168) | def name(self):
FILE: data/image_folder.py
function is_image_file (line 20) | def is_image_file(filename):
function make_dataset (line 23) | def make_dataset(dir):
function default_loader (line 36) | def default_loader(path):
class ImageFolder (line 40) | class ImageFolder(data.Dataset):
method __init__ (line 42) | def __init__(self, root, transform=None, return_paths=False,
method __getitem__ (line 56) | def __getitem__(self, index):
method __len__ (line 66) | def __len__(self):
FILE: data/srd_dataset.py
class SRDDataset (line 15) | class SRDDataset(BaseDataset):
method initialize (line 16) | def initialize(self, opt):
method __getitem__ (line 49) | def __getitem__(self, index):
method __len__ (line 167) | def __len__(self):
method name (line 170) | def name(self):
FILE: models/Fusion_model.py
function tensor2im (line 17) | def tensor2im(input_image, imtype=np.uint8):
class L_TV (line 38) | class L_TV(nn.Module):
method __init__ (line 39) | def __init__(self):
method forward (line 41) | def forward(self, x):
class GradientLoss (line 51) | class GradientLoss(nn.Module):
method __init__ (line 52) | def __init__(self, loss_weight=1.0, reduction='mean'):
method forward (line 60) | def forward(self, pred, target):
class PoissonGradientLoss (line 84) | class PoissonGradientLoss(nn.Module):
method __init__ (line 85) | def __init__(self, reduction='mean'):
method forward (line 94) | def forward(self, source, target, blend, mask):
class FusionModel (line 103) | class FusionModel(DistangleModel):
method name (line 104) | def name(self):
method modify_commandline_options (line 108) | def modify_commandline_options(parser, is_train=True):
method initialize (line 118) | def initialize(self, opt):
method set_input (line 166) | def set_input(self, input):
method forward (line 178) | def forward(self):
method backward (line 237) | def backward(self):
method optimize_parameters (line 269) | def optimize_parameters(self):
method zero_grad (line 279) | def zero_grad(self):
method vis (line 285) | def vis(self, e, s, path='', eval=False):
FILE: models/Refine_model.py
function tensor2im (line 17) | def tensor2im(input_image, imtype=np.uint8):
class L_TV (line 33) | class L_TV(nn.Module):
method __init__ (line 34) | def __init__(self):
method forward (line 37) | def forward(self, x):
class GradientLoss (line 47) | class GradientLoss(nn.Module):
method __init__ (line 48) | def __init__(self, loss_weight=1.0, reduction='mean'):
method forward (line 56) | def forward(self, pred, target):
class PoissonGradientLoss (line 80) | class PoissonGradientLoss(nn.Module):
method __init__ (line 81) | def __init__(self, reduction='mean'):
method forward (line 89) | def forward(self, source, target, blend, mask):
class RefineModel (line 98) | class RefineModel(DistangleModel):
method name (line 99) | def name(self):
method modify_commandline_options (line 103) | def modify_commandline_options(parser, is_train=True):
method initialize (line 114) | def initialize(self, opt):
method set_input (line 164) | def set_input(self, input):
method forward (line 177) | def forward(self):
method backward (line 246) | def backward(self):
method optimize_parameters (line 271) | def optimize_parameters(self):
method zero_grad (line 280) | def zero_grad(self):
method vis (line 284) | def vis(self, e, s, path='', eval=False):
FILE: models/__init__.py
function find_model_using_name (line 5) | def find_model_using_name(model_name):
function get_option_setter (line 29) | def get_option_setter(model_name):
function create_model (line 34) | def create_model(opt):
FILE: models/base_model.py
class BaseModel (line 8) | class BaseModel():
method modify_commandline_options (line 13) | def modify_commandline_options(parser, is_train):
method train (line 17) | def train(self):
method eval (line 24) | def eval(self):
method name (line 31) | def name(self):
method initialize (line 34) | def initialize(self, opt):
method set_input (line 54) | def set_input(self, input):
method get_prediction (line 64) | def get_prediction(self,input):
method forward (line 73) | def forward(self):
method setup (line 77) | def setup(self, opt, parser=None):
method test (line 90) | def test(self):
method get_image_paths (line 95) | def get_image_paths(self):
method optimize_parameters (line 98) | def optimize_parameters(self):
method update_learning_rate (line 102) | def update_learning_rate(self,loss=None):
method get_current_visuals (line 113) | def get_current_visuals(self):
method get_current_losses (line 133) | def get_current_losses(self):
method save_networks (line 143) | def save_networks(self, epoch):
method __patch_instance_norm_state_dict (line 156) | def __patch_instance_norm_state_dict(self, state_dict, module, keys, i...
method load_networks (line 170) | def load_networks(self, epoch, save_dir=None):
method print_networks (line 201) | def print_networks(self, verbose):
method set_requires_grad (line 214) | def set_requires_grad(self, nets, requires_grad=False):
FILE: models/distangle_model.py
class DistangleModel (line 9) | class DistangleModel(BaseModel):
method name (line 10) | def name(self):
method modify_commandline_options (line 14) | def modify_commandline_options(parser, is_train=True):
method initialize (line 24) | def initialize(self, opt):
method set_input (line 51) | def set_input(self, input):
method forward (line 60) | def forward(self):
method get_prediction (line 79) | def get_prediction(self,input):
method backward_G (line 97) | def backward_G(self):
method optimize_parameters (line 103) | def optimize_parameters(self):
FILE: models/loss_function.py
function smooth_loss (line 5) | def smooth_loss(pred_map):
FILE: models/networks.py
function get_norm_layer (line 11) | def get_norm_layer(norm_type='instance'):
function get_scheduler (line 23) | def get_scheduler(optimizer, opt):
function init_weights (line 42) | def init_weights(net, init_type='normal', gain=0.02):
function init_net (line 66) | def init_net(net, init_type='normal', init_gain=0.02, gpu_ids=[]):
function define_vgg (line 74) | def define_vgg(num_input,num_classes,init_type='normal', init_gain=0.02,...
function define_G (line 85) | def define_G(input_nc, output_nc, ngf, netG, norm='batch', use_dropout=F...
function define_D (line 115) | def define_D(input_nc, ndf, netD,
class GANLoss (line 142) | class GANLoss(nn.Module):
method __init__ (line 143) | def __init__(self, use_lsgan=True, target_real_label=1.0, target_fake_...
method get_target_tensor (line 152) | def get_target_tensor(self, input, target_is_real):
method __call__ (line 159) | def __call__(self, input, target_is_real):
class ResnetGenerator (line 168) | class ResnetGenerator(nn.Module):
method __init__ (line 169) | def __init__(self, input_nc, output_nc, ngf=64, norm_layer=nn.BatchNor...
method forward (line 212) | def forward(self, input):
class ResnetBlock (line 217) | class ResnetBlock(nn.Module):
method __init__ (line 218) | def __init__(self, dim, padding_type, norm_layer, use_dropout, use_bias):
method build_conv_block (line 222) | def build_conv_block(self, dim, padding_type, norm_layer, use_dropout,...
method forward (line 254) | def forward(self, x):
class UnetGenerator (line 263) | class UnetGenerator(nn.Module):
method __init__ (line 264) | def __init__(self, input_nc, output_nc, num_downs, ngf=64,
method forward (line 279) | def forward(self, input):
class UnetSkipConnectionBlock (line 286) | class UnetSkipConnectionBlock(nn.Module):
method __init__ (line 287) | def __init__(self, outer_nc, inner_nc, input_nc=None,
method forward (line 332) | def forward(self, x):
class NLayerDiscriminator (line 340) | class NLayerDiscriminator(nn.Module):
method __init__ (line 341) | def __init__(self, input_nc, ndf=64, n_layers=3, norm_layer=nn.BatchNo...
method forward (line 383) | def forward(self, input):
class PixelDiscriminator (line 387) | class PixelDiscriminator(nn.Module):
method __init__ (line 388) | def __init__(self, input_nc, ndf=64, norm_layer=nn.BatchNorm2d, use_si...
method forward (line 408) | def forward(self, input):
FILE: models/resnet.py
function conv3x3 (line 27) | def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
function conv1x1 (line 33) | def conv1x1(in_planes, out_planes, stride=1):
class BasicBlock (line 38) | class BasicBlock(nn.Module):
method __init__ (line 41) | def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
method forward (line 59) | def forward(self, x):
class Bottleneck (line 78) | class Bottleneck(nn.Module):
method __init__ (line 87) | def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
method forward (line 104) | def forward(self, x):
class ResNet (line 127) | class ResNet(nn.Module):
method my_load_state_dict (line 128) | def my_load_state_dict(self,state_dict):
method __init__ (line 142) | def __init__(self, block, layers, num_classes=1000, zero_init_residual...
method _make_layer (line 193) | def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
method _forward_impl (line 217) | def _forward_impl(self, x):
method forward (line 235) | def forward(self, x):
function _resnet (line 239) | def _resnet(arch, block, layers, pretrained, progress, **kwargs):
function resnet18 (line 248) | def resnet18(pretrained=False, progress=True, **kwargs):
function resnet34 (line 260) | def resnet34(pretrained=False, progress=True, **kwargs):
function resnet50 (line 272) | def resnet50(pretrained=False, progress=True, **kwargs):
function resnet101 (line 284) | def resnet101(pretrained=False, progress=True, **kwargs):
function resnet152 (line 296) | def resnet152(pretrained=False, progress=True, **kwargs):
function resnext50_32x4d (line 308) | def resnext50_32x4d(pretrained=False, progress=True, **kwargs):
function resnext101_32x8d (line 322) | def resnext101_32x8d(pretrained=False, progress=True, **kwargs):
function wide_resnet50_2 (line 336) | def wide_resnet50_2(pretrained=False, progress=True, **kwargs):
function wide_resnet101_2 (line 354) | def wide_resnet101_2(pretrained=False, progress=True, **kwargs):
FILE: models/vgg.py
function set_parameter_requires_grad (line 14) | def set_parameter_requires_grad(model, feature_extracting):
function create_vgg (line 19) | def create_vgg(num_ic,num_classes,use_pretrained=False,feature_extract=F...
FILE: options/PAMI_options.py
class TrainOptions (line 4) | class TrainOptions(BaseOptions):
method initialize (line 5) | def initialize(self, parser):
FILE: options/base_options.py
class BaseOptions (line 9) | class BaseOptions():
method __init__ (line 10) | def __init__(self):
method initialize (line 13) | def initialize(self, parser):
method gather_options (line 62) | def gather_options(self):
method print_options (line 87) | def print_options(self, opt):
method parse (line 107) | def parse(self):
FILE: options/test_options.py
class TestOptions (line 4) | class TestOptions(BaseOptions):
method initialize (line 5) | def initialize(self, parser):
FILE: options/train_options.py
class TrainOptions (line 4) | class TrainOptions(BaseOptions):
method initialize (line 5) | def initialize(self, parser):
FILE: util/get_data.py
class GetData (line 11) | class GetData(object):
method __init__ (line 29) | def __init__(self, technique='cyclegan', verbose=True):
method _print (line 37) | def _print(self, text):
method _get_options (line 42) | def _get_options(r):
method _present_options (line 48) | def _present_options(self):
method _download_data (line 58) | def _download_data(self, dataset_url, save_path):
method get (line 81) | def get(self, save_path, dataset=None):
FILE: util/html.py
class HTML (line 6) | class HTML:
method __init__ (line 7) | def __init__(self, web_dir, title, reflesh=0):
method get_image_dir (line 22) | def get_image_dir(self):
method add_header (line 25) | def add_header(self, str):
method add_table (line 29) | def add_table(self, border=1):
method add_images (line 33) | def add_images(self, ims, txts, links, width=400):
method save (line 45) | def save(self):
FILE: util/image_pool.py
class ImagePool (line 5) | class ImagePool():
method __init__ (line 6) | def __init__(self, pool_size):
method query (line 12) | def query(self, images):
FILE: util/util.py
function sdmkdir (line 8) | def sdmkdir(dir_name):
function tensor2im (line 13) | def tensor2im(input_image, imtype=np.uint8,scale=None):
function tensor2im_logc (line 31) | def tensor2im_logc(image_tensor, imtype=np.uint8,scale=255):
function diagnose_network (line 42) | def diagnose_network(net, name='network'):
function save_image (line 55) | def save_image(image_numpy, image_path):
function print_numpy (line 60) | def print_numpy(x, val=True, shp=False):
function mkdirs (line 70) | def mkdirs(paths):
function mkdir (line 78) | def mkdir(path):
FILE: util/visualizer.py
class Visualizer (line 8) | class Visualizer():
method __init__ (line 9) | def __init__(self, opt):
method display_current_results (line 33) | def display_current_results(self, visuals, epoch):
method plot_current_losses (line 99) | def plot_current_losses(self, epoch, counter_ratio, opt, errors):
method plot_test_errors (line 114) | def plot_test_errors(self, epoch, counter_ratio, opt, errors):
method print_current_errors (line 137) | def print_current_errors(self, epoch, i, errors, t):
method save_images (line 147) | def save_images(self, webpage, visuals, image_path):
Condensed preview — 33 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (296K chars).
[
{
"path": "OE_eval.py",
"chars": 4022,
"preview": "from collections import OrderedDict\nfrom options.train_options import TrainOptions\nfrom data import CreateDataLoader\nfro"
},
{
"path": "OE_eval.sh",
"chars": 1467,
"preview": "#!/bin/bash\n\nMYGIT=/mnt/nvme/zcq/git\nREPO_PATH=/home/fulan/shadow_removal/exposure-fusion-shadow-removal\n# DATA_PATH=${M"
},
{
"path": "OE_train.py",
"chars": 5969,
"preview": "from collections import OrderedDict\nfrom options.train_options import TrainOptions\nfrom data import CreateDataLoader\nfro"
},
{
"path": "OE_train.sh",
"chars": 1497,
"preview": "#!/bin/bash\n\n\nMYGIT=/mnt/nvme/zcq/git\nREPO_PATH=/home/fulan/shadow_removal\n# DATA_PATH=${MYGIT}/shadow_removal/data/SRD\n"
},
{
"path": "README.md",
"chars": 3793,
"preview": "# Auto-exposure fusion for single-image shadow removal\nWe propose a new method for effective shadow removal by regarding"
},
{
"path": "data/__init__.py",
"chars": 2394,
"preview": "import importlib\nimport torch.utils.data\nfrom data.base_data_loader import BaseDataLoader\nfrom data.base_dataset import "
},
{
"path": "data/base_data_loader.py",
"chars": 171,
"preview": "class BaseDataLoader():\n def __init__(self):\n pass\n\n def initialize(self, opt):\n self.opt = opt\n "
},
{
"path": "data/base_dataset.py",
"chars": 3397,
"preview": "import torch.utils.data as data\nfrom PIL import Image\nimport torchvision.transforms as transforms\n\n\nclass BaseDataset(da"
},
{
"path": "data/expo_param_dataset.py",
"chars": 6166,
"preview": "import os.path\nimport torchvision.transforms as transforms\nfrom data.base_dataset import BaseDataset, get_transform\nfrom"
},
{
"path": "data/image_folder.py",
"chars": 2003,
"preview": "###############################################################################\n# Code from\n# https://github.com/pytorch"
},
{
"path": "data/srd_dataset.py",
"chars": 6171,
"preview": "import os.path\nimport torchvision.transforms as transforms\nfrom data.base_dataset import BaseDataset, get_transform\nfrom"
},
{
"path": "data_processing/compute_params.ipynb",
"chars": 140399,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 48,\n \"metadata\": {\n \"scrolled\": true\n },\n \"outp"
},
{
"path": "data_processing/test_mask_generation.py",
"chars": 1031,
"preview": "import os\n\nimport cv2\nimport matplotlib.pyplot as plt\n\nimport PIL.Image as Image\nfrom skimage.filters import threshold_o"
},
{
"path": "models/Fusion_model.py",
"chars": 12738,
"preview": "import torch\nfrom collections import OrderedDict\nimport time\nimport numpy as np\nimport os\nimport torch.nn.functional as "
},
{
"path": "models/Refine_model.py",
"chars": 13028,
"preview": "import torch\nfrom collections import OrderedDict\nimport time\nimport numpy as np\nimport os\nimport torch.nn.functional as "
},
{
"path": "models/__init__.py",
"chars": 1267,
"preview": "import importlib\nfrom models.base_model import BaseModel\n\n\ndef find_model_using_name(model_name):\n # Given the option"
},
{
"path": "models/base_model.py",
"chars": 8667,
"preview": "import os\nimport time\nimport torch\nfrom collections import OrderedDict\nfrom . import networks\nimport util.util as util\ni"
},
{
"path": "models/distangle_model.py",
"chars": 4765,
"preview": "import torch\nfrom util.image_pool import ImagePool\nfrom .base_model import BaseModel\nfrom . import networks\nimport util."
},
{
"path": "models/loss_function.py",
"chars": 481,
"preview": "import torch\nfrom torch import nn\nimport torch.nn.functional as F\n\ndef smooth_loss(pred_map):\n def gradient(pred):\n "
},
{
"path": "models/networks.py",
"chars": 16977,
"preview": "import torch\nimport torch.nn as nn\nfrom torch.nn import init\nimport functools\nfrom torch.optim import lr_scheduler\n#####"
},
{
"path": "models/resnet.py",
"chars": 14957,
"preview": "import torch\nimport torch.nn as nn\n#from .utils import load_state_dict_from_url\ntry:\n from torch.hub import load_stat"
},
{
"path": "models/vgg.py",
"chars": 1287,
"preview": "from __future__ import print_function\nfrom __future__ import division\nimport torch\nimport torch.nn as nn\nimport torch.op"
},
{
"path": "options/PAMI_options.py",
"chars": 2993,
"preview": "from .base_options import BaseOptions\n\n\nclass TrainOptions(BaseOptions):\n def initialize(self, parser):\n parse"
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{
"path": "options/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "options/base_options.py",
"chars": 7396,
"preview": "import argparse\nimport os\nfrom util import util\nimport torch\nimport models\nimport data\n\n\nclass BaseOptions():\n def __"
},
{
"path": "options/test_options.py",
"chars": 1086,
"preview": "from .base_options import BaseOptions\n\n\nclass TestOptions(BaseOptions):\n def initialize(self, parser):\n parser"
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{
"path": "options/train_options.py",
"chars": 3626,
"preview": "from .base_options import BaseOptions\n\n\nclass TrainOptions(BaseOptions):\n def initialize(self, parser):\n parse"
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{
"path": "util/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "util/get_data.py",
"chars": 3511,
"preview": "from __future__ import print_function\nimport os\nimport tarfile\nimport requests\nfrom warnings import warn\nfrom zipfile im"
},
{
"path": "util/html.py",
"chars": 1912,
"preview": "import dominate\nfrom dominate.tags import *\nimport os\n\n\nclass HTML:\n def __init__(self, web_dir, title, reflesh=0):\n "
},
{
"path": "util/image_pool.py",
"chars": 1072,
"preview": "import random\nimport torch\n\n\nclass ImagePool():\n def __init__(self, pool_size):\n self.pool_size = pool_size\n "
},
{
"path": "util/util.py",
"chars": 2357,
"preview": "from __future__ import print_function\nimport torch\nimport numpy as np\nfrom PIL import Image\nimport os\n\n\ndef sdmkdir(dir_"
},
{
"path": "util/visualizer.py",
"chars": 7287,
"preview": "import numpy as np\nimport os\nimport ntpath\nimport time\nfrom . import util\nfrom . import html\nfrom pdb import set_trace a"
}
]
About this extraction
This page contains the full source code of the tsingqguo/exposure-fusion-shadow-removal GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 33 files (277.2 KB), approximately 103.1k tokens, and a symbol index with 219 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.