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- .gitattributes +2 -35
- .idea/.gitignore +8 -0
- .idea/CR-Net.iml +12 -0
- .idea/deployment.xml +14 -0
- .idea/inspectionProfiles/Project_Default.xml +166 -0
- .idea/inspectionProfiles/profiles_settings.xml +6 -0
- .idea/modules.xml +8 -0
- .idea/vcs.xml +6 -0
- .idea/workspace.xml +87 -0
- LICENSE +22 -0
- README.md +140 -3
- data/__init__.py +45 -0
- data/__pycache__/__init__.cpython-310.pyc +0 -0
- data/__pycache__/base_dataset.cpython-310.pyc +0 -0
- data/__pycache__/image_folder.cpython-310.pyc +0 -0
- data/__pycache__/n2h_dataset.cpython-310.pyc +0 -0
- data/__pycache__/pix2pix_dataset.cpython-310.pyc +0 -0
- data/__pycache__/single_folder_dataset.cpython-310.pyc +0 -0
- data/__pycache__/summer2winteryosemite_dataset.cpython-310.pyc +0 -0
- data/__pycache__/unaligned_day_night_dataset.cpython-310.pyc +0 -0
- data/ade20k_dataset.py +48 -0
- data/base_dataset.py +125 -0
- data/cityscapes_dataset.py +46 -0
- data/custom_dataset.py +45 -0
- data/day2night_dataset.py +43 -0
- data/image_folder.py +93 -0
- data/n2h_dataset.py +86 -0
- data/photo2art_dataset.py +43 -0
- data/pix2pix_dataset.py +107 -0
- data/single_folder_dataset.py +51 -0
- data/summer2winteryosemite_dataset.py +42 -0
- data/sunny2diffweathers_dataset.py +54 -0
- data/unaligned_day_night_dataset.py +53 -0
- datasets/bdd100k_lists/day2night/day_test.txt +1764 -0
- datasets/bdd100k_lists/day2night/day_train.txt +0 -0
- datasets/bdd100k_lists/day2night/night_test.txt +0 -0
- datasets/bdd100k_lists/day2night/night_train.txt +0 -0
- datasets/bdd100k_lists/sunny2diffweathers/cloudy_test.txt +50 -0
- datasets/bdd100k_lists/sunny2diffweathers/cloudy_train.txt +0 -0
- datasets/bdd100k_lists/sunny2diffweathers/night_test.txt +50 -0
- datasets/bdd100k_lists/sunny2diffweathers/night_train.txt +0 -0
- datasets/bdd100k_lists/sunny2diffweathers/rainy_test.txt +50 -0
- datasets/bdd100k_lists/sunny2diffweathers/rainy_train.txt +0 -0
- datasets/bdd100k_lists/sunny2diffweathers/snowy_test.txt +50 -0
- datasets/bdd100k_lists/sunny2diffweathers/snowy_train.txt +0 -0
- datasets/bdd100k_lists/sunny2diffweathers/sunny_test.txt +200 -0
- datasets/bdd100k_lists/sunny2diffweathers/sunny_train.txt +0 -0
- datasets/prepare_ade20k.sh +7 -0
- datasets/prepare_bdd100k.sh +8 -0
- datasets/prepare_cityscapes.sh +7 -0
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LICENSE
ADDED
|
@@ -0,0 +1,22 @@
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|
|
|
| 1 |
+
Copyright (c) 2025 Vision and Learning Laboratory.
|
| 2 |
+
|
| 3 |
+
Permission is hereby granted, free of charge, to any person
|
| 4 |
+
obtaining a copy of this software and associated documentation
|
| 5 |
+
files (the "Software"), to deal in the Software without
|
| 6 |
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restriction, including without limitation the rights to use,
|
| 7 |
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copy, modify, merge, publish, distribute, sublicense, and/or sell
|
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copies of the Software, and to permit persons to whom the
|
| 9 |
+
Software is furnished to do so, subject to the following
|
| 10 |
+
conditions:
|
| 11 |
+
|
| 12 |
+
The above copyright notice and this permission notice shall be
|
| 13 |
+
included in all copies or substantial portions of the Software.
|
| 14 |
+
|
| 15 |
+
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
|
| 16 |
+
EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
|
| 17 |
+
OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
|
| 18 |
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NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
|
| 19 |
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HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
|
| 20 |
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WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
|
| 21 |
+
FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
|
| 22 |
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OTHER DEALINGS IN THE SOFTWARE.
|
README.md
CHANGED
|
@@ -1,3 +1,140 @@
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|
| 1 |
-
|
| 2 |
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|
| 3 |
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
# CR-Net: A Continuous Rendering Network for Enhancing Processing in Low-Light Environments
|
| 2 |
+
|
| 3 |
+
<p align="center">
|
| 4 |
+
📄 <a href="link-to-your-paper"><b>Paper</b></a> |
|
| 5 |
+
💻 <a href="https://github.com/val-utehy/CR-Net"><b>Source Code</b></a> |
|
| 6 |
+
🤗 <a href="https://huggingface.co/datasets/datnguyentien204/CR-Net"><b>Hugging Face</b></a>
|
| 7 |
+
</p>
|
| 8 |
+
|
| 9 |
+
<p align="center">
|
| 10 |
+
<img src="preview/structures.jpg" width="800"/>
|
| 11 |
+
<p>
|
| 12 |
+
|
| 13 |
+
<p align="center">
|
| 14 |
+
<em>Architecture of the CR-Net model.</em>
|
| 15 |
+
<p>
|
| 16 |
+
|
| 17 |
+
## Introduction
|
| 18 |
+
|
| 19 |
+
**CR-Net** is a model enhance the quality of images and videos captured under low-light conditions.
|
| 20 |
+
By learning a continuous rendering process, CR-Net effectively improves brightness, producing natural and sharp results even in challenging dark environments.
|
| 21 |
+
To learn more about CR-Net, feel free to read our documentation [English](../README.md) | [Tiếng Việt](preview/README-vi.md) | [中文](preview/README-zh.md).
|
| 22 |
+
|
| 23 |
+
|
| 24 |
+
### Key Features
|
| 25 |
+
|
| 26 |
+
* **Low-light image/video enhancement:** Significantly improves brightness and contrast for images and videos captured in dim lighting.
|
| 27 |
+
* **Continuous rendering network:** Employs a novel architecture to deliver smoother and more natural results compared to traditional methods.
|
| 28 |
+
* **Flexible applications:** Supports both video processing and directories containing multiple still images.
|
| 29 |
+
|
| 30 |
+
## Demo
|
| 31 |
+
|
| 32 |
+
|
| 33 |
+
|
| 34 |
+
## Installation and Requirements
|
| 35 |
+
|
| 36 |
+
To run this model, you need the proper environment. We recommend the following versions:
|
| 37 |
+
|
| 38 |
+
* **Python:** `Python >= 3.10` (Recommended `Python 3.10`)
|
| 39 |
+
* **PyTorch:** `PyTorch >= 1.12` (Recommended `PyTorch 2.1.2`)
|
| 40 |
+
|
| 41 |
+
**Step 1: Clone the repository**
|
| 42 |
+
|
| 43 |
+
```shell
|
| 44 |
+
git clone https://github.com/val-utehy/CR-Net.git
|
| 45 |
+
cd CR-Net
|
| 46 |
+
```
|
| 47 |
+
**Step 2: Install dependencies**
|
| 48 |
+
|
| 49 |
+
```shell
|
| 50 |
+
pip install -r requirements.txt
|
| 51 |
+
```
|
| 52 |
+
|
| 53 |
+
> [!NOTE]
|
| 54 |
+
> Make sure you have installed the compatible versions of **torch** and **torchvision** with your **CUDA driver** to leverage GPU.
|
| 55 |
+
## Pretrained Models
|
| 56 |
+
You can download the pretrained models from this [link](https://huggingface.co/datasets/datnguyentien204/CR-Net).
|
| 57 |
+
You can use latest checkpoint `latest_net_G.pth` for inference.
|
| 58 |
+
> [!NOTE]
|
| 59 |
+
> Please ensure your path to the checkpoint is correct in the script files before running.
|
| 60 |
+
|
| 61 |
+
## Usage Guide
|
| 62 |
+
|
| 63 |
+
### 1. Model Training
|
| 64 |
+
|
| 65 |
+
Training file will be updated soon!
|
| 66 |
+
|
| 67 |
+
[//]: # (To train the CR-Net model on your own dataset, follow these steps:)
|
| 68 |
+
|
| 69 |
+
[//]: # ()
|
| 70 |
+
[//]: # (**a. Configure the training script file:**)
|
| 71 |
+
|
| 72 |
+
[//]: # ()
|
| 73 |
+
[//]: # (Open and edit the file `train_scripts/ast_n2h.sh`. In this file, you need to specify important paths such as the dataset path and the checkpoint saving directory.)
|
| 74 |
+
|
| 75 |
+
[//]: # ()
|
| 76 |
+
[//]: # (**b. Run the training script:**)
|
| 77 |
+
|
| 78 |
+
[//]: # ()
|
| 79 |
+
[//]: # (After finishing the configuration, navigate to the project’s root directory and execute the following command:)
|
| 80 |
+
|
| 81 |
+
[//]: # ()
|
| 82 |
+
[//]: # (```shell)
|
| 83 |
+
|
| 84 |
+
[//]: # ( bash train_scripts/ast_n2h_dat.sh)
|
| 85 |
+
|
| 86 |
+
[//]: # (```)
|
| 87 |
+
### 2. Testing and Inference
|
| 88 |
+
|
| 89 |
+
**a. Video Processing:**
|
| 90 |
+
|
| 91 |
+
#### 1. Configure the script file:
|
| 92 |
+
Open and edit the file `test_scripts/ast_inference_video.sh`. Here, you need to provide the path to the trained checkpoint and the input/output video paths.
|
| 93 |
+
|
| 94 |
+
#### 2. Run the video processing script:
|
| 95 |
+
After completing the configuration, navigate to the project’s root directory and execute the following command:
|
| 96 |
+
|
| 97 |
+
```shell
|
| 98 |
+
bash test_scripts/ast_inference_video.sh
|
| 99 |
+
```
|
| 100 |
+
|
| 101 |
+
**b. Image Directory Processing:**
|
| 102 |
+
#### 1. Configure the script file:
|
| 103 |
+
Open and edit the file `test_scripts/ast_n2h_dat.sh`. Here, you need to provide the path to the trained checkpoint and the input/output image directory paths.
|
| 104 |
+
|
| 105 |
+
#### 2. Run the image directory processing script:
|
| 106 |
+
After completing the configuration, navigate to the project’s root directory and execute the following command:
|
| 107 |
+
|
| 108 |
+
```shell
|
| 109 |
+
bash test_scripts/ast_n2h.sh
|
| 110 |
+
```
|
| 111 |
+
|
| 112 |
+
## Citation
|
| 113 |
+
|
| 114 |
+
|
| 115 |
+
[//]: # (```bibtex)
|
| 116 |
+
|
| 117 |
+
[//]: # (@article{crnet2025,)
|
| 118 |
+
|
| 119 |
+
[//]: # ( title={CR-Net: A Continuous Rendering Network for Improving Robustness to Low-illumination},)
|
| 120 |
+
|
| 121 |
+
[//]: # ( author={},)
|
| 122 |
+
|
| 123 |
+
[//]: # ( journal={},)
|
| 124 |
+
|
| 125 |
+
[//]: # ( year={2025})
|
| 126 |
+
|
| 127 |
+
[//]: # (})
|
| 128 |
+
|
| 129 |
+
[//]: # (```)
|
| 130 |
+
## References
|
| 131 |
+
|
| 132 |
+
1. https://github.com/EndlessSora/TSIT
|
| 133 |
+
|
| 134 |
+
2. https://github.com/astra-vision/CoMoGAN
|
| 135 |
+
|
| 136 |
+
3. https://github.com/AlienZhang1996/S2WAT
|
| 137 |
+
|
| 138 |
+
|
| 139 |
+
## License
|
| 140 |
+
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
|
data/__init__.py
ADDED
|
@@ -0,0 +1,45 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
# --- START OF FILE data/__init__.py ---
|
| 2 |
+
import importlib
|
| 3 |
+
import torch.utils.data
|
| 4 |
+
from data.base_dataset import BaseDataset
|
| 5 |
+
|
| 6 |
+
|
| 7 |
+
def find_dataset_using_name(dataset_name):
|
| 8 |
+
dataset_filename = "data." + dataset_name + "_dataset"
|
| 9 |
+
|
| 10 |
+
datasetlib = importlib.import_module(dataset_filename)
|
| 11 |
+
|
| 12 |
+
dataset = None
|
| 13 |
+
target_dataset_name = dataset_name.replace('_', '') + 'dataset'
|
| 14 |
+
for name, cls in datasetlib.__dict__.items():
|
| 15 |
+
if name.lower() == target_dataset_name.lower() \
|
| 16 |
+
and issubclass(cls, BaseDataset):
|
| 17 |
+
dataset = cls
|
| 18 |
+
|
| 19 |
+
if dataset is None:
|
| 20 |
+
raise ValueError("In %s.py, there should be a subclass of BaseDataset "
|
| 21 |
+
"with class name that matches %s in lowercase." %
|
| 22 |
+
(dataset_filename, target_dataset_name))
|
| 23 |
+
|
| 24 |
+
return dataset
|
| 25 |
+
|
| 26 |
+
|
| 27 |
+
def get_option_setter(dataset_name):
|
| 28 |
+
dataset_class = find_dataset_using_name(dataset_name)
|
| 29 |
+
return dataset_class.modify_commandline_options
|
| 30 |
+
|
| 31 |
+
|
| 32 |
+
def create_dataloader(opt):
|
| 33 |
+
dataset = find_dataset_using_name(opt.dataset_mode)
|
| 34 |
+
instance = dataset(opt)
|
| 35 |
+
|
| 36 |
+
print("dataset [%s] of size %d was created" %
|
| 37 |
+
(type(instance).__name__, len(instance)))
|
| 38 |
+
dataloader = torch.utils.data.DataLoader(
|
| 39 |
+
instance,
|
| 40 |
+
batch_size=opt.batchSize,
|
| 41 |
+
shuffle=not opt.serial_batches,
|
| 42 |
+
num_workers=int(opt.nThreads),
|
| 43 |
+
drop_last=opt.isTrain
|
| 44 |
+
)
|
| 45 |
+
return dataloader
|
data/__pycache__/__init__.cpython-310.pyc
ADDED
|
Binary file (1.42 kB). View file
|
|
|
data/__pycache__/base_dataset.cpython-310.pyc
ADDED
|
Binary file (4.49 kB). View file
|
|
|
data/__pycache__/image_folder.cpython-310.pyc
ADDED
|
Binary file (2.81 kB). View file
|
|
|
data/__pycache__/n2h_dataset.cpython-310.pyc
ADDED
|
Binary file (2.78 kB). View file
|
|
|
data/__pycache__/pix2pix_dataset.cpython-310.pyc
ADDED
|
Binary file (3.32 kB). View file
|
|
|
data/__pycache__/single_folder_dataset.cpython-310.pyc
ADDED
|
Binary file (1.89 kB). View file
|
|
|
data/__pycache__/summer2winteryosemite_dataset.cpython-310.pyc
ADDED
|
Binary file (1.65 kB). View file
|
|
|
data/__pycache__/unaligned_day_night_dataset.cpython-310.pyc
ADDED
|
Binary file (2.21 kB). View file
|
|
|
data/ade20k_dataset.py
ADDED
|
@@ -0,0 +1,48 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
from data.pix2pix_dataset import Pix2pixDataset
|
| 2 |
+
from data.image_folder import make_dataset
|
| 3 |
+
|
| 4 |
+
|
| 5 |
+
class ADE20KDataset(Pix2pixDataset):
|
| 6 |
+
|
| 7 |
+
@staticmethod
|
| 8 |
+
def modify_commandline_options(parser, is_train):
|
| 9 |
+
parser = Pix2pixDataset.modify_commandline_options(parser, is_train)
|
| 10 |
+
parser.set_defaults(preprocess_mode='resize_and_crop')
|
| 11 |
+
if is_train:
|
| 12 |
+
parser.set_defaults(load_size=286)
|
| 13 |
+
else:
|
| 14 |
+
parser.set_defaults(load_size=256)
|
| 15 |
+
parser.set_defaults(crop_size=256)
|
| 16 |
+
parser.set_defaults(display_winsize=256)
|
| 17 |
+
parser.set_defaults(label_nc=150)
|
| 18 |
+
parser.set_defaults(contain_dontcare_label=True)
|
| 19 |
+
parser.set_defaults(cache_filelist_read=False)
|
| 20 |
+
parser.set_defaults(cache_filelist_write=False)
|
| 21 |
+
parser.set_defaults(no_instance=True)
|
| 22 |
+
return parser
|
| 23 |
+
|
| 24 |
+
def get_paths(self, opt):
|
| 25 |
+
root = opt.croot
|
| 26 |
+
phase = 'val' if opt.phase == 'test' else 'train'
|
| 27 |
+
|
| 28 |
+
all_images = make_dataset(root, recursive=True, read_cache=False, write_cache=False)
|
| 29 |
+
image_paths = []
|
| 30 |
+
label_paths = []
|
| 31 |
+
for p in all_images:
|
| 32 |
+
if '_%s_' % phase not in p:
|
| 33 |
+
continue
|
| 34 |
+
if p.endswith('.jpg'):
|
| 35 |
+
image_paths.append(p)
|
| 36 |
+
elif p.endswith('.png'):
|
| 37 |
+
label_paths.append(p)
|
| 38 |
+
|
| 39 |
+
instance_paths = [] # don't use instance map for ade20k
|
| 40 |
+
|
| 41 |
+
return label_paths, image_paths, instance_paths
|
| 42 |
+
|
| 43 |
+
# In ADE20k, 'unknown' label is of value 0.
|
| 44 |
+
# Change the 'unknown' label to the last label to match other datasets.
|
| 45 |
+
def postprocess(self, input_dict):
|
| 46 |
+
label = input_dict['label']
|
| 47 |
+
label = label - 1
|
| 48 |
+
label[label == -1] = self.opt.label_nc
|
data/base_dataset.py
ADDED
|
@@ -0,0 +1,125 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import torch.utils.data as data
|
| 2 |
+
from PIL import Image
|
| 3 |
+
import torchvision.transforms as transforms
|
| 4 |
+
import numpy as np
|
| 5 |
+
import random
|
| 6 |
+
|
| 7 |
+
|
| 8 |
+
class BaseDataset(data.Dataset):
|
| 9 |
+
def __init__(self,opt=None):
|
| 10 |
+
super(BaseDataset, self).__init__()
|
| 11 |
+
if opt is not None:
|
| 12 |
+
self.opt = opt
|
| 13 |
+
|
| 14 |
+
@staticmethod
|
| 15 |
+
def modify_commandline_options(parser, is_train):
|
| 16 |
+
return parser
|
| 17 |
+
|
| 18 |
+
def initialize(self, opt):
|
| 19 |
+
pass
|
| 20 |
+
|
| 21 |
+
|
| 22 |
+
def get_params(opt, size):
|
| 23 |
+
w, h = size
|
| 24 |
+
new_h = h
|
| 25 |
+
new_w = w
|
| 26 |
+
if opt.preprocess_mode == 'resize_and_crop':
|
| 27 |
+
new_h = new_w = opt.load_size
|
| 28 |
+
elif opt.preprocess_mode == 'scale_width_and_crop':
|
| 29 |
+
new_w = opt.load_size
|
| 30 |
+
new_h = opt.load_size * h // w
|
| 31 |
+
elif opt.preprocess_mode == 'scale_shortside_and_crop':
|
| 32 |
+
ss, ls = min(w, h), max(w, h) # shortside and longside
|
| 33 |
+
width_is_shorter = w == ss
|
| 34 |
+
ls = int(opt.load_size * ls / ss)
|
| 35 |
+
new_w, new_h = (ss, ls) if width_is_shorter else (ls, ss)
|
| 36 |
+
|
| 37 |
+
x = random.randint(0, np.maximum(0, new_w - opt.crop_size))
|
| 38 |
+
y = random.randint(0, np.maximum(0, new_h - opt.crop_size))
|
| 39 |
+
|
| 40 |
+
flip = random.random() > 0.5
|
| 41 |
+
return {'crop_pos': (x, y), 'flip': flip}
|
| 42 |
+
|
| 43 |
+
|
| 44 |
+
def get_transform(opt, params, method=Image.BICUBIC, normalize=True, toTensor=True):
|
| 45 |
+
transform_list = []
|
| 46 |
+
if 'resize' in opt.preprocess_mode:
|
| 47 |
+
osize = [opt.load_size, opt.load_size]
|
| 48 |
+
transform_list.append(transforms.Resize(osize, interpolation=method))
|
| 49 |
+
elif 'scale_width' in opt.preprocess_mode:
|
| 50 |
+
transform_list.append(transforms.Lambda(lambda img: __scale_width(img, opt.load_size, method)))
|
| 51 |
+
elif 'scale_shortside' in opt.preprocess_mode:
|
| 52 |
+
transform_list.append(transforms.Lambda(lambda img: __scale_shortside(img, opt.load_size, method)))
|
| 53 |
+
|
| 54 |
+
if 'crop' in opt.preprocess_mode:
|
| 55 |
+
transform_list.append(transforms.Lambda(lambda img: __crop(img, params['crop_pos'], opt.crop_size)))
|
| 56 |
+
|
| 57 |
+
if opt.preprocess_mode == 'none':
|
| 58 |
+
base = 32
|
| 59 |
+
transform_list.append(transforms.Lambda(lambda img: __make_power_2(img, base, method)))
|
| 60 |
+
|
| 61 |
+
if opt.preprocess_mode == 'fixed':
|
| 62 |
+
w = opt.crop_size
|
| 63 |
+
h = round(opt.crop_size / opt.aspect_ratio)
|
| 64 |
+
transform_list.append(transforms.Lambda(lambda img: __resize(img, w, h, method)))
|
| 65 |
+
|
| 66 |
+
if opt.isTrain and not opt.no_flip:
|
| 67 |
+
transform_list.append(transforms.Lambda(lambda img: __flip(img, params['flip'])))
|
| 68 |
+
|
| 69 |
+
if toTensor:
|
| 70 |
+
transform_list += [transforms.ToTensor()]
|
| 71 |
+
|
| 72 |
+
if normalize:
|
| 73 |
+
transform_list += [transforms.Normalize((0.5, 0.5, 0.5),
|
| 74 |
+
(0.5, 0.5, 0.5))]
|
| 75 |
+
return transforms.Compose(transform_list)
|
| 76 |
+
|
| 77 |
+
|
| 78 |
+
def normalize():
|
| 79 |
+
return transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
|
| 80 |
+
|
| 81 |
+
|
| 82 |
+
def __resize(img, w, h, method=Image.BICUBIC):
|
| 83 |
+
return img.resize((w, h), method)
|
| 84 |
+
|
| 85 |
+
|
| 86 |
+
def __make_power_2(img, base, method=Image.BICUBIC):
|
| 87 |
+
ow, oh = img.size
|
| 88 |
+
h = int(round(oh / base) * base)
|
| 89 |
+
w = int(round(ow / base) * base)
|
| 90 |
+
if (h == oh) and (w == ow):
|
| 91 |
+
return img
|
| 92 |
+
return img.resize((w, h), method)
|
| 93 |
+
|
| 94 |
+
|
| 95 |
+
def __scale_width(img, target_width, method=Image.BICUBIC):
|
| 96 |
+
ow, oh = img.size
|
| 97 |
+
if (ow == target_width):
|
| 98 |
+
return img
|
| 99 |
+
w = target_width
|
| 100 |
+
h = int(target_width * oh / ow)
|
| 101 |
+
return img.resize((w, h), method)
|
| 102 |
+
|
| 103 |
+
|
| 104 |
+
def __scale_shortside(img, target_width, method=Image.BICUBIC):
|
| 105 |
+
ow, oh = img.size
|
| 106 |
+
ss, ls = min(ow, oh), max(ow, oh) # shortside and longside
|
| 107 |
+
width_is_shorter = ow == ss
|
| 108 |
+
if (ss == target_width):
|
| 109 |
+
return img
|
| 110 |
+
ls = int(target_width * ls / ss)
|
| 111 |
+
nw, nh = (ss, ls) if width_is_shorter else (ls, ss)
|
| 112 |
+
return img.resize((nw, nh), method)
|
| 113 |
+
|
| 114 |
+
|
| 115 |
+
def __crop(img, pos, size):
|
| 116 |
+
ow, oh = img.size
|
| 117 |
+
x1, y1 = pos
|
| 118 |
+
tw = th = size
|
| 119 |
+
return img.crop((x1, y1, x1 + tw, y1 + th))
|
| 120 |
+
|
| 121 |
+
|
| 122 |
+
def __flip(img, flip):
|
| 123 |
+
if flip:
|
| 124 |
+
return img.transpose(Image.FLIP_LEFT_RIGHT)
|
| 125 |
+
return img
|
data/cityscapes_dataset.py
ADDED
|
@@ -0,0 +1,46 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import os
|
| 2 |
+
from data.pix2pix_dataset import Pix2pixDataset
|
| 3 |
+
from data.image_folder import make_dataset
|
| 4 |
+
|
| 5 |
+
|
| 6 |
+
class CityscapesDataset(Pix2pixDataset):
|
| 7 |
+
|
| 8 |
+
@staticmethod
|
| 9 |
+
def modify_commandline_options(parser, is_train):
|
| 10 |
+
parser = Pix2pixDataset.modify_commandline_options(parser, is_train)
|
| 11 |
+
parser.set_defaults(preprocess_mode='fixed')
|
| 12 |
+
parser.set_defaults(load_size=512)
|
| 13 |
+
parser.set_defaults(crop_size=512)
|
| 14 |
+
parser.set_defaults(display_winsize=512)
|
| 15 |
+
parser.set_defaults(label_nc=35)
|
| 16 |
+
parser.set_defaults(aspect_ratio=2.0)
|
| 17 |
+
parser.set_defaults(batchSize=16)
|
| 18 |
+
opt, _ = parser.parse_known_args()
|
| 19 |
+
if hasattr(opt, 'num_upsampling_layers'):
|
| 20 |
+
parser.set_defaults(num_upsampling_layers='more')
|
| 21 |
+
return parser
|
| 22 |
+
|
| 23 |
+
def get_paths(self, opt):
|
| 24 |
+
root = opt.croot
|
| 25 |
+
phase = 'val' if opt.phase == 'test' else 'train'
|
| 26 |
+
|
| 27 |
+
label_dir = os.path.join(root, 'gtFine', phase)
|
| 28 |
+
label_paths_all = make_dataset(label_dir, recursive=True)
|
| 29 |
+
label_paths = [p for p in label_paths_all if p.endswith('_labelIds.png')]
|
| 30 |
+
|
| 31 |
+
image_dir = os.path.join(root, 'leftImg8bit', phase)
|
| 32 |
+
image_paths = make_dataset(image_dir, recursive=True)
|
| 33 |
+
|
| 34 |
+
if not opt.no_instance:
|
| 35 |
+
instance_paths = [p for p in label_paths_all if p.endswith('_instanceIds.png')]
|
| 36 |
+
else:
|
| 37 |
+
instance_paths = []
|
| 38 |
+
|
| 39 |
+
return label_paths, image_paths, instance_paths
|
| 40 |
+
|
| 41 |
+
def paths_match(self, path1, path2):
|
| 42 |
+
name1 = os.path.basename(path1)
|
| 43 |
+
name2 = os.path.basename(path2)
|
| 44 |
+
# compare the first 3 components, [city]_[id1]_[id2]
|
| 45 |
+
return '_'.join(name1.split('_')[:3]) == \
|
| 46 |
+
'_'.join(name2.split('_')[:3])
|
data/custom_dataset.py
ADDED
|
@@ -0,0 +1,45 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
from data.pix2pix_dataset import Pix2pixDataset
|
| 2 |
+
from data.image_folder import make_dataset
|
| 3 |
+
|
| 4 |
+
|
| 5 |
+
class CustomDataset(Pix2pixDataset):
|
| 6 |
+
""" Dataset that loads images from directories
|
| 7 |
+
Use option --label_dir, --image_dir, --instance_dir to specify the directories.
|
| 8 |
+
The images in the directories are sorted in alphabetical order and paired in order.
|
| 9 |
+
"""
|
| 10 |
+
|
| 11 |
+
@staticmethod
|
| 12 |
+
def modify_commandline_options(parser, is_train):
|
| 13 |
+
parser = Pix2pixDataset.modify_commandline_options(parser, is_train)
|
| 14 |
+
parser.set_defaults(preprocess_mode='resize_and_crop')
|
| 15 |
+
load_size = 286 if is_train else 256
|
| 16 |
+
parser.set_defaults(load_size=load_size)
|
| 17 |
+
parser.set_defaults(crop_size=256)
|
| 18 |
+
parser.set_defaults(display_winsize=256)
|
| 19 |
+
parser.set_defaults(label_nc=13)
|
| 20 |
+
parser.set_defaults(contain_dontcare_label=False)
|
| 21 |
+
|
| 22 |
+
parser.add_argument('--label_dir', type=str, required=True,
|
| 23 |
+
help='path to the directory that contains label images')
|
| 24 |
+
parser.add_argument('--image_dir', type=str, required=True,
|
| 25 |
+
help='path to the directory that contains photo images')
|
| 26 |
+
parser.add_argument('--instance_dir', type=str, default='',
|
| 27 |
+
help='path to the directory that contains instance maps. Leave black if not exists')
|
| 28 |
+
return parser
|
| 29 |
+
|
| 30 |
+
def get_paths(self, opt):
|
| 31 |
+
label_dir = opt.label_dir
|
| 32 |
+
label_paths = make_dataset(label_dir, recursive=False, read_cache=True)
|
| 33 |
+
|
| 34 |
+
image_dir = opt.image_dir
|
| 35 |
+
image_paths = make_dataset(image_dir, recursive=False, read_cache=True)
|
| 36 |
+
|
| 37 |
+
if len(opt.instance_dir) > 0:
|
| 38 |
+
instance_dir = opt.instance_dir
|
| 39 |
+
instance_paths = make_dataset(instance_dir, recursive=False, read_cache=True)
|
| 40 |
+
else:
|
| 41 |
+
instance_paths = []
|
| 42 |
+
|
| 43 |
+
assert len(label_paths) == len(image_paths), "The #images in %s and %s do not match. Is there something wrong?"
|
| 44 |
+
|
| 45 |
+
return label_paths, image_paths, instance_paths
|
data/day2night_dataset.py
ADDED
|
@@ -0,0 +1,43 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import os
|
| 2 |
+
from data.pix2pix_dataset import Pix2pixDataset
|
| 3 |
+
|
| 4 |
+
|
| 5 |
+
class Day2NightDataset(Pix2pixDataset):
|
| 6 |
+
|
| 7 |
+
@staticmethod
|
| 8 |
+
def modify_commandline_options(parser, is_train):
|
| 9 |
+
parser = Pix2pixDataset.modify_commandline_options(parser, is_train)
|
| 10 |
+
parser.set_defaults(preprocess_mode='fixed')
|
| 11 |
+
parser.set_defaults(load_size=512)
|
| 12 |
+
parser.set_defaults(crop_size=512)
|
| 13 |
+
parser.set_defaults(display_winsize=512)
|
| 14 |
+
parser.set_defaults(aspect_ratio=2.0)
|
| 15 |
+
opt, _ = parser.parse_known_args()
|
| 16 |
+
if hasattr(opt, 'num_upsampling_layers'):
|
| 17 |
+
parser.set_defaults(num_upsampling_layers='more')
|
| 18 |
+
return parser
|
| 19 |
+
|
| 20 |
+
def get_paths(self, opt):
|
| 21 |
+
croot = opt.croot
|
| 22 |
+
sroot = opt.sroot
|
| 23 |
+
|
| 24 |
+
with open(os.path.join(croot, 'bdd100k_lists/day2night/day_%s.txt' % opt.phase)) as c_list:
|
| 25 |
+
c_image_paths_read = c_list.read().splitlines()
|
| 26 |
+
c_image_paths = [os.path.join(croot, p) for p in c_image_paths_read if p != '']
|
| 27 |
+
|
| 28 |
+
with open(os.path.join(sroot, 'bdd100k_lists/day2night/night_%s.txt' % opt.phase)) as s_list:
|
| 29 |
+
s_image_paths_read = s_list.read().splitlines()
|
| 30 |
+
s_image_paths = [os.path.join(sroot, p) for p in s_image_paths_read if p != '']
|
| 31 |
+
|
| 32 |
+
if opt.phase == 'train':
|
| 33 |
+
c_image_paths = c_image_paths + c_image_paths
|
| 34 |
+
|
| 35 |
+
instance_paths = []
|
| 36 |
+
|
| 37 |
+
length = min(len(c_image_paths), len(s_image_paths))
|
| 38 |
+
c_image_paths = c_image_paths[:length]
|
| 39 |
+
s_image_paths = s_image_paths[:length]
|
| 40 |
+
return c_image_paths, s_image_paths, instance_paths
|
| 41 |
+
|
| 42 |
+
def paths_match(self, path1, path2):
|
| 43 |
+
return True
|
data/image_folder.py
ADDED
|
@@ -0,0 +1,93 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
###############################################################################
|
| 2 |
+
# Code from
|
| 3 |
+
# https://github.com/pytorch/vision/blob/master/torchvision/datasets/folder.py
|
| 4 |
+
# Modified the original code so that it also loads images from the current
|
| 5 |
+
# directory as well as the subdirectories
|
| 6 |
+
###############################################################################
|
| 7 |
+
import torch.utils.data as data
|
| 8 |
+
from PIL import Image
|
| 9 |
+
import os
|
| 10 |
+
|
| 11 |
+
IMG_EXTENSIONS = [
|
| 12 |
+
'.jpg', '.JPG', '.jpeg', '.JPEG',
|
| 13 |
+
'.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP', '.tiff', '.webp'
|
| 14 |
+
]
|
| 15 |
+
|
| 16 |
+
|
| 17 |
+
def is_image_file(filename):
|
| 18 |
+
return any(filename.endswith(extension) for extension in IMG_EXTENSIONS)
|
| 19 |
+
|
| 20 |
+
|
| 21 |
+
def make_dataset_rec(dir, images):
|
| 22 |
+
assert os.path.isdir(dir), '%s is not a valid directory' % dir
|
| 23 |
+
|
| 24 |
+
for root, dnames, fnames in sorted(os.walk(dir, followlinks=True)):
|
| 25 |
+
for fname in fnames:
|
| 26 |
+
if is_image_file(fname):
|
| 27 |
+
path = os.path.join(root, fname)
|
| 28 |
+
images.append(path)
|
| 29 |
+
|
| 30 |
+
|
| 31 |
+
def make_dataset(dir, recursive=False, read_cache=False, write_cache=False):
|
| 32 |
+
images = []
|
| 33 |
+
|
| 34 |
+
if read_cache:
|
| 35 |
+
possible_filelist = os.path.join(dir, 'files.list')
|
| 36 |
+
if os.path.isfile(possible_filelist):
|
| 37 |
+
with open(possible_filelist, 'r') as f:
|
| 38 |
+
images = f.read().splitlines()
|
| 39 |
+
return images
|
| 40 |
+
|
| 41 |
+
if recursive:
|
| 42 |
+
make_dataset_rec(dir, images)
|
| 43 |
+
else:
|
| 44 |
+
assert os.path.isdir(dir) or os.path.islink(dir), '%s is not a valid directory' % dir
|
| 45 |
+
|
| 46 |
+
for root, dnames, fnames in sorted(os.walk(dir)):
|
| 47 |
+
for fname in fnames:
|
| 48 |
+
if is_image_file(fname):
|
| 49 |
+
path = os.path.join(root, fname)
|
| 50 |
+
images.append(path)
|
| 51 |
+
|
| 52 |
+
if write_cache:
|
| 53 |
+
filelist_cache = os.path.join(dir, 'files.list')
|
| 54 |
+
with open(filelist_cache, 'w') as f:
|
| 55 |
+
for path in images:
|
| 56 |
+
f.write("%s\n" % path)
|
| 57 |
+
print('wrote filelist cache at %s' % filelist_cache)
|
| 58 |
+
|
| 59 |
+
return images
|
| 60 |
+
|
| 61 |
+
|
| 62 |
+
def default_loader(path):
|
| 63 |
+
return Image.open(path).convert('RGB')
|
| 64 |
+
|
| 65 |
+
|
| 66 |
+
class ImageFolder(data.Dataset):
|
| 67 |
+
|
| 68 |
+
def __init__(self, root, transform=None, return_paths=False,
|
| 69 |
+
loader=default_loader):
|
| 70 |
+
imgs = make_dataset(root)
|
| 71 |
+
if len(imgs) == 0:
|
| 72 |
+
raise(RuntimeError("Found 0 images in: " + root + "\n"
|
| 73 |
+
"Supported image extensions are: " +
|
| 74 |
+
",".join(IMG_EXTENSIONS)))
|
| 75 |
+
|
| 76 |
+
self.root = root
|
| 77 |
+
self.imgs = imgs
|
| 78 |
+
self.transform = transform
|
| 79 |
+
self.return_paths = return_paths
|
| 80 |
+
self.loader = loader
|
| 81 |
+
|
| 82 |
+
def __getitem__(self, index):
|
| 83 |
+
path = self.imgs[index]
|
| 84 |
+
img = self.loader(path)
|
| 85 |
+
if self.transform is not None:
|
| 86 |
+
img = self.transform(img)
|
| 87 |
+
if self.return_paths:
|
| 88 |
+
return img, path
|
| 89 |
+
else:
|
| 90 |
+
return img
|
| 91 |
+
|
| 92 |
+
def __len__(self):
|
| 93 |
+
return len(self.imgs)
|
data/n2h_dataset.py
ADDED
|
@@ -0,0 +1,86 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
# --- START OF FILE data/n2h_dataset.py (Sửa lỗi AttributeError) ---
|
| 2 |
+
import os
|
| 3 |
+
from data.pix2pix_dataset import Pix2pixDataset
|
| 4 |
+
from data.image_folder import make_dataset
|
| 5 |
+
from PIL import Image
|
| 6 |
+
import random
|
| 7 |
+
from data.base_dataset import get_params, get_transform
|
| 8 |
+
|
| 9 |
+
|
| 10 |
+
class N2HDataset(Pix2pixDataset):
|
| 11 |
+
|
| 12 |
+
def __init__(self, opt):
|
| 13 |
+
"""Initialize this dataset class.
|
| 14 |
+
|
| 15 |
+
A_paths and B_paths are defined here, and we call the initialize
|
| 16 |
+
method of the parent class (Pix2pixDataset) to set up the rest.
|
| 17 |
+
"""
|
| 18 |
+
# Gọi __init__ của lớp cha gần nhất (Pix2pixDataset)
|
| 19 |
+
# Pix2pixDataset không có __init__, nên nó sẽ gọi BaseDataset.__init__(self, opt)
|
| 20 |
+
# Điều này là đúng với bản sửa lỗi trước của chúng ta.
|
| 21 |
+
super().__init__(opt)
|
| 22 |
+
|
| 23 |
+
# Gọi hàm initialize của lớp cha để thiết lập self.label_paths, self.image_paths, và self.dataset_size
|
| 24 |
+
self.initialize(opt)
|
| 25 |
+
|
| 26 |
+
@staticmethod
|
| 27 |
+
def modify_commandline_options(parser, is_train):
|
| 28 |
+
parser = Pix2pixDataset.modify_commandline_options(parser, is_train)
|
| 29 |
+
parser.set_defaults(preprocess_mode='resize_and_crop')
|
| 30 |
+
parser.set_defaults(load_size=286)
|
| 31 |
+
parser.set_defaults(crop_size=256)
|
| 32 |
+
parser.set_defaults(display_winsize=256)
|
| 33 |
+
parser.set_defaults(aspect_ratio=1.0)
|
| 34 |
+
opt, _ = parser.parse_known_args()
|
| 35 |
+
if hasattr(opt, 'num_upsampling_layers'):
|
| 36 |
+
parser.set_defaults(num_upsampling_layers='more')
|
| 37 |
+
return parser
|
| 38 |
+
|
| 39 |
+
def get_paths(self, opt):
|
| 40 |
+
croot = opt.croot
|
| 41 |
+
sroot = opt.sroot
|
| 42 |
+
|
| 43 |
+
# Logic này giả định cấu trúc thư mục là croot/trainA, sroot/trainB
|
| 44 |
+
c_image_dir = os.path.join(croot, opt.phase + 'A')
|
| 45 |
+
s_image_dir = os.path.join(sroot, opt.phase + 'B')
|
| 46 |
+
|
| 47 |
+
if not os.path.isdir(c_image_dir):
|
| 48 |
+
raise FileNotFoundError(f"Content directory not found: {c_image_dir}")
|
| 49 |
+
if not os.path.isdir(s_image_dir):
|
| 50 |
+
raise FileNotFoundError(f"Style directory not found: {s_image_dir}")
|
| 51 |
+
|
| 52 |
+
c_image_paths = sorted(make_dataset(c_image_dir, recursive=True))
|
| 53 |
+
s_image_paths = sorted(make_dataset(s_image_dir, recursive=True))
|
| 54 |
+
|
| 55 |
+
if opt.phase == 'train' and len(c_image_paths) > 0 and len(s_image_paths) > 0:
|
| 56 |
+
if len(c_image_paths) > len(s_image_paths):
|
| 57 |
+
s_image_paths = s_image_paths * (len(c_image_paths) // len(s_image_paths) + 1)
|
| 58 |
+
elif len(s_image_paths) > len(c_image_paths):
|
| 59 |
+
c_image_paths = c_image_paths * (len(s_image_paths) // len(c_image_paths) + 1)
|
| 60 |
+
|
| 61 |
+
instance_paths = []
|
| 62 |
+
|
| 63 |
+
return c_image_paths, s_image_paths, instance_paths
|
| 64 |
+
|
| 65 |
+
def __getitem__(self, index):
|
| 66 |
+
# Lấy ảnh Day (ảnh A - content)
|
| 67 |
+
# self.label_paths được gán bằng c_image_paths trong Pix2pixDataset.initialize()
|
| 68 |
+
day_path = self.label_paths[index % len(self.label_paths)]
|
| 69 |
+
|
| 70 |
+
# Lấy ảnh Night (ảnh B - style) ngẫu nhiên
|
| 71 |
+
# self.image_paths được gán bằng s_image_paths trong Pix2pixDataset.initialize()
|
| 72 |
+
night_path = self.image_paths[random.randint(0, len(self.image_paths) - 1)]
|
| 73 |
+
|
| 74 |
+
day_img = Image.open(day_path).convert('RGB')
|
| 75 |
+
night_img = Image.open(night_path).convert('RGB')
|
| 76 |
+
|
| 77 |
+
params = get_params(self.opt, day_img.size)
|
| 78 |
+
transform = get_transform(self.opt, params)
|
| 79 |
+
|
| 80 |
+
day_tensor = transform(day_img)
|
| 81 |
+
night_tensor = transform(night_img)
|
| 82 |
+
|
| 83 |
+
return {'day': day_tensor, 'night': night_tensor, 'cpath': day_path, 'spath_night': night_path}
|
| 84 |
+
|
| 85 |
+
def paths_match(self, path1, path2):
|
| 86 |
+
return True
|
data/photo2art_dataset.py
ADDED
|
@@ -0,0 +1,43 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import os
|
| 2 |
+
from data.pix2pix_dataset import Pix2pixDataset
|
| 3 |
+
from data.image_folder import make_dataset
|
| 4 |
+
|
| 5 |
+
|
| 6 |
+
class Photo2ArtDataset(Pix2pixDataset):
|
| 7 |
+
|
| 8 |
+
@staticmethod
|
| 9 |
+
def modify_commandline_options(parser, is_train):
|
| 10 |
+
parser = Pix2pixDataset.modify_commandline_options(parser, is_train)
|
| 11 |
+
parser.set_defaults(preprocess_mode='fixed')
|
| 12 |
+
parser.set_defaults(load_size=256)
|
| 13 |
+
parser.set_defaults(crop_size=256)
|
| 14 |
+
parser.set_defaults(display_winsize=256)
|
| 15 |
+
parser.set_defaults(aspect_ratio=1.0)
|
| 16 |
+
opt, _ = parser.parse_known_args()
|
| 17 |
+
if hasattr(opt, 'num_upsampling_layers'):
|
| 18 |
+
parser.set_defaults(num_upsampling_layers='more')
|
| 19 |
+
return parser
|
| 20 |
+
|
| 21 |
+
def get_paths(self, opt):
|
| 22 |
+
croot = opt.croot
|
| 23 |
+
sroot = opt.sroot
|
| 24 |
+
|
| 25 |
+
c_image_dir = os.path.join(croot, '%sA' % opt.phase)
|
| 26 |
+
c_image_paths = sorted(make_dataset(c_image_dir, recursive=True))
|
| 27 |
+
|
| 28 |
+
s_image_dir = os.path.join(sroot, '%sB' % opt.phase)
|
| 29 |
+
s_image_paths = sorted(make_dataset(s_image_dir, recursive=True))
|
| 30 |
+
|
| 31 |
+
if opt.phase == 'train':
|
| 32 |
+
for i in range(2):
|
| 33 |
+
s_image_paths = s_image_paths + s_image_paths
|
| 34 |
+
|
| 35 |
+
instance_paths = []
|
| 36 |
+
|
| 37 |
+
length = min(len(c_image_paths), len(s_image_paths))
|
| 38 |
+
c_image_paths = c_image_paths[:length]
|
| 39 |
+
s_image_paths = s_image_paths[:length]
|
| 40 |
+
return c_image_paths, s_image_paths, instance_paths
|
| 41 |
+
|
| 42 |
+
def paths_match(self, path1, path2):
|
| 43 |
+
return True
|
data/pix2pix_dataset.py
ADDED
|
@@ -0,0 +1,107 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
from data.base_dataset import BaseDataset, get_params, get_transform
|
| 2 |
+
from PIL import Image
|
| 3 |
+
import util.util as util
|
| 4 |
+
import os
|
| 5 |
+
|
| 6 |
+
|
| 7 |
+
class Pix2pixDataset(BaseDataset):
|
| 8 |
+
@staticmethod
|
| 9 |
+
def modify_commandline_options(parser, is_train):
|
| 10 |
+
parser.add_argument('--no_pairing_check', action='store_true',
|
| 11 |
+
help='If specified, skip sanity check of correct label-image file pairing')
|
| 12 |
+
return parser
|
| 13 |
+
|
| 14 |
+
def initialize(self, opt):
|
| 15 |
+
self.opt = opt
|
| 16 |
+
|
| 17 |
+
label_paths, image_paths, instance_paths = self.get_paths(opt)
|
| 18 |
+
|
| 19 |
+
util.natural_sort(label_paths)
|
| 20 |
+
util.natural_sort(image_paths)
|
| 21 |
+
if not opt.no_instance:
|
| 22 |
+
util.natural_sort(instance_paths)
|
| 23 |
+
|
| 24 |
+
label_paths = label_paths[:opt.max_dataset_size]
|
| 25 |
+
image_paths = image_paths[:opt.max_dataset_size]
|
| 26 |
+
instance_paths = instance_paths[:opt.max_dataset_size]
|
| 27 |
+
|
| 28 |
+
if not opt.no_pairing_check:
|
| 29 |
+
for path1, path2 in zip(label_paths, image_paths):
|
| 30 |
+
assert self.paths_match(path1, path2), \
|
| 31 |
+
"The label-image pair (%s, %s) do not look like the right pair because the filenames are quite different. Are you sure about the pairing? Please see data/pix2pix_dataset.py to see what is going on, and use --no_pairing_check to bypass this." % (path1, path2)
|
| 32 |
+
|
| 33 |
+
self.label_paths = label_paths
|
| 34 |
+
self.image_paths = image_paths
|
| 35 |
+
self.instance_paths = instance_paths
|
| 36 |
+
|
| 37 |
+
size = len(self.label_paths)
|
| 38 |
+
self.dataset_size = size
|
| 39 |
+
|
| 40 |
+
def get_paths(self, opt):
|
| 41 |
+
label_paths = []
|
| 42 |
+
image_paths = []
|
| 43 |
+
instance_paths = []
|
| 44 |
+
assert False, "A subclass of Pix2pixDataset must override self.get_paths(self, opt)"
|
| 45 |
+
return label_paths, image_paths, instance_paths
|
| 46 |
+
|
| 47 |
+
def paths_match(self, path1, path2):
|
| 48 |
+
filename1_without_ext = os.path.splitext(os.path.basename(path1))[0]
|
| 49 |
+
filename2_without_ext = os.path.splitext(os.path.basename(path2))[0]
|
| 50 |
+
return filename1_without_ext == filename2_without_ext
|
| 51 |
+
|
| 52 |
+
def __getitem__(self, index):
|
| 53 |
+
# Label (Content) Image
|
| 54 |
+
label_path = self.label_paths[index]
|
| 55 |
+
label = Image.open(label_path)
|
| 56 |
+
if self.opt.task != 'SIS':
|
| 57 |
+
label = label.convert('RGB')
|
| 58 |
+
params = get_params(self.opt, label.size)
|
| 59 |
+
|
| 60 |
+
if self.opt.task != 'SIS':
|
| 61 |
+
transform_label = get_transform(self.opt, params)
|
| 62 |
+
label_tensor = transform_label(label)
|
| 63 |
+
else:
|
| 64 |
+
transform_label = get_transform(self.opt, params, method=Image.NEAREST, normalize=False)
|
| 65 |
+
label_tensor = transform_label(label) * 255.0
|
| 66 |
+
label_tensor[label_tensor == 255] = self.opt.label_nc # 'unknown' is opt.label_nc
|
| 67 |
+
|
| 68 |
+
# Real (Style) Image
|
| 69 |
+
image_path = self.image_paths[index]
|
| 70 |
+
assert self.paths_match(label_path, image_path), \
|
| 71 |
+
"The label_path %s and image_path %s don't match." % \
|
| 72 |
+
(label_path, image_path)
|
| 73 |
+
image = Image.open(image_path)
|
| 74 |
+
image = image.convert('RGB')
|
| 75 |
+
|
| 76 |
+
transform_image = get_transform(self.opt, params)
|
| 77 |
+
image_tensor = transform_image(image)
|
| 78 |
+
|
| 79 |
+
# if using instance maps
|
| 80 |
+
if self.opt.no_instance:
|
| 81 |
+
instance_tensor = 0
|
| 82 |
+
else:
|
| 83 |
+
instance_path = self.instance_paths[index]
|
| 84 |
+
instance = Image.open(instance_path)
|
| 85 |
+
if instance.mode == 'L':
|
| 86 |
+
instance_tensor = transform_label(instance) * 255
|
| 87 |
+
instance_tensor = instance_tensor.long()
|
| 88 |
+
else:
|
| 89 |
+
instance_tensor = transform_label(instance)
|
| 90 |
+
|
| 91 |
+
input_dict = {'label': label_tensor,
|
| 92 |
+
'instance': instance_tensor,
|
| 93 |
+
'image': image_tensor,
|
| 94 |
+
'path': image_path,
|
| 95 |
+
'cpath': label_path
|
| 96 |
+
}
|
| 97 |
+
|
| 98 |
+
# Give subclasses a chance to modify the final output
|
| 99 |
+
self.postprocess(input_dict)
|
| 100 |
+
|
| 101 |
+
return input_dict
|
| 102 |
+
|
| 103 |
+
def postprocess(self, input_dict):
|
| 104 |
+
return input_dict
|
| 105 |
+
|
| 106 |
+
def __len__(self):
|
| 107 |
+
return self.dataset_size
|
data/single_folder_dataset.py
ADDED
|
@@ -0,0 +1,51 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
# --- START OF FILE data/single_folder_dataset.py ---
|
| 2 |
+
from data.base_dataset import BaseDataset, get_transform, get_params
|
| 3 |
+
from data.image_folder import make_dataset
|
| 4 |
+
from PIL import Image
|
| 5 |
+
|
| 6 |
+
|
| 7 |
+
class SingleFolderDataset(BaseDataset):
|
| 8 |
+
"""
|
| 9 |
+
A dataset class for loading images from a single folder.
|
| 10 |
+
Used for testing where only content images are needed.
|
| 11 |
+
"""
|
| 12 |
+
|
| 13 |
+
@staticmethod
|
| 14 |
+
def modify_commandline_options(parser, is_train):
|
| 15 |
+
# is_train sẽ là False khi chạy test.py
|
| 16 |
+
parser.add_argument('--image_dir', type=str, required=True,
|
| 17 |
+
help='path to the directory that contains images')
|
| 18 |
+
# Khi test, chúng ta thường không muốn crop ngẫu nhiên,
|
| 19 |
+
# nên preprocess_mode='resize' hoặc 'scale_width' là phù hợp.
|
| 20 |
+
# Hoặc có thể giữ 'resize_and_crop' và dùng crop_pos=(0,0)
|
| 21 |
+
parser.set_defaults(preprocess_mode='resize_and_crop', load_size=256, crop_size=256, no_flip=True)
|
| 22 |
+
return parser
|
| 23 |
+
|
| 24 |
+
def __init__(self, opt):
|
| 25 |
+
super().__init__()
|
| 26 |
+
self.opt = opt
|
| 27 |
+
self.image_paths = sorted(make_dataset(opt.image_dir, recursive=True))
|
| 28 |
+
# <<< THAY ĐỔI: Xóa dòng self.transform ở đây >>>
|
| 29 |
+
|
| 30 |
+
def __getitem__(self, index):
|
| 31 |
+
path = self.image_paths[index]
|
| 32 |
+
img = Image.open(path).convert('RGB')
|
| 33 |
+
|
| 34 |
+
# <<< THAY ĐỔI: Tạo params và transform cho mỗi ảnh >>>
|
| 35 |
+
# Khi test, chúng ta không muốn augmentation ngẫu nhiên.
|
| 36 |
+
# Đặt crop_pos=(0,0) và flip=False để đảm bảo tính nhất quán.
|
| 37 |
+
params = get_params(self.opt, img.size)
|
| 38 |
+
if not self.opt.isTrain:
|
| 39 |
+
params['crop_pos'] = (0, 0)
|
| 40 |
+
params['flip'] = False
|
| 41 |
+
|
| 42 |
+
transform = get_transform(self.opt, params, normalize=True)
|
| 43 |
+
img_tensor = transform(img)
|
| 44 |
+
# <<< KẾT THÚC THAY ĐỔI >>>
|
| 45 |
+
|
| 46 |
+
# Trả về một dict tương thích với những gì model mong đợi ở bước test
|
| 47 |
+
# 'day' sẽ được dùng làm content_image.
|
| 48 |
+
return {'day': img_tensor, 'cpath': path}
|
| 49 |
+
|
| 50 |
+
def __len__(self):
|
| 51 |
+
return len(self.image_paths)
|
data/summer2winteryosemite_dataset.py
ADDED
|
@@ -0,0 +1,42 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import os
|
| 2 |
+
from data.pix2pix_dataset import Pix2pixDataset
|
| 3 |
+
from data.image_folder import make_dataset
|
| 4 |
+
|
| 5 |
+
|
| 6 |
+
class Summer2WinterYosemiteDataset(Pix2pixDataset):
|
| 7 |
+
|
| 8 |
+
@staticmethod
|
| 9 |
+
def modify_commandline_options(parser, is_train):
|
| 10 |
+
parser = Pix2pixDataset.modify_commandline_options(parser, is_train)
|
| 11 |
+
parser.set_defaults(preprocess_mode='fixed')
|
| 12 |
+
parser.set_defaults(load_size=256)
|
| 13 |
+
parser.set_defaults(crop_size=256)
|
| 14 |
+
parser.set_defaults(display_winsize=256)
|
| 15 |
+
parser.set_defaults(aspect_ratio=1.0)
|
| 16 |
+
opt, _ = parser.parse_known_args()
|
| 17 |
+
if hasattr(opt, 'num_upsampling_layers'):
|
| 18 |
+
parser.set_defaults(num_upsampling_layers='more')
|
| 19 |
+
return parser
|
| 20 |
+
|
| 21 |
+
def get_paths(self, opt):
|
| 22 |
+
croot = opt.croot
|
| 23 |
+
sroot = opt.sroot
|
| 24 |
+
|
| 25 |
+
c_image_dir = os.path.join(croot, '%sA' % opt.phase)
|
| 26 |
+
c_image_paths = sorted(make_dataset(c_image_dir, recursive=True))
|
| 27 |
+
|
| 28 |
+
s_image_dir = os.path.join(sroot, '%sB' % opt.phase)
|
| 29 |
+
s_image_paths = sorted(make_dataset(s_image_dir, recursive=True))
|
| 30 |
+
|
| 31 |
+
if opt.phase == 'train':
|
| 32 |
+
s_image_paths = s_image_paths + s_image_paths
|
| 33 |
+
|
| 34 |
+
instance_paths = []
|
| 35 |
+
|
| 36 |
+
length = min(len(c_image_paths), len(s_image_paths))
|
| 37 |
+
c_image_paths = c_image_paths[:length]
|
| 38 |
+
s_image_paths = s_image_paths[:length]
|
| 39 |
+
return c_image_paths, s_image_paths, instance_paths
|
| 40 |
+
|
| 41 |
+
def paths_match(self, path1, path2):
|
| 42 |
+
return True
|
data/sunny2diffweathers_dataset.py
ADDED
|
@@ -0,0 +1,54 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import os
|
| 2 |
+
from data.pix2pix_dataset import Pix2pixDataset
|
| 3 |
+
|
| 4 |
+
|
| 5 |
+
class Sunny2DiffWeathersDataset(Pix2pixDataset):
|
| 6 |
+
|
| 7 |
+
@staticmethod
|
| 8 |
+
def modify_commandline_options(parser, is_train):
|
| 9 |
+
parser = Pix2pixDataset.modify_commandline_options(parser, is_train)
|
| 10 |
+
parser.add_argument('--test_mode', type=str, default='all',
|
| 11 |
+
help='specify style mode to control multi-modal image synthesis (MMIS) during test phase:'
|
| 12 |
+
'night | cloudy | rainy | snowy | all')
|
| 13 |
+
parser.set_defaults(preprocess_mode='fixed')
|
| 14 |
+
parser.set_defaults(load_size=512)
|
| 15 |
+
parser.set_defaults(crop_size=512)
|
| 16 |
+
parser.set_defaults(display_winsize=512)
|
| 17 |
+
parser.set_defaults(aspect_ratio=2.0)
|
| 18 |
+
opt, _ = parser.parse_known_args()
|
| 19 |
+
if hasattr(opt, 'num_upsampling_layers'):
|
| 20 |
+
parser.set_defaults(num_upsampling_layers='more')
|
| 21 |
+
return parser
|
| 22 |
+
|
| 23 |
+
|
| 24 |
+
def get_paths(self, opt):
|
| 25 |
+
croot = opt.croot
|
| 26 |
+
sroot = opt.sroot
|
| 27 |
+
|
| 28 |
+
with open(os.path.join(croot, 'bdd100k_lists/sunny2diffweathers/sunny_%s.txt' % opt.phase)) as c_list:
|
| 29 |
+
c_image_paths_read = c_list.read().splitlines()
|
| 30 |
+
c_image_paths = [os.path.join(croot, p) for p in c_image_paths_read if p != '']
|
| 31 |
+
|
| 32 |
+
if opt.phase == 'train' or opt.test_mode == 'all':
|
| 33 |
+
mode_list = ['night', 'cloudy', 'rainy', 'snowy']
|
| 34 |
+
else:
|
| 35 |
+
mode_list = [opt.test_mode]
|
| 36 |
+
s_image_paths = []
|
| 37 |
+
for mode in mode_list:
|
| 38 |
+
with open(os.path.join(sroot, 'bdd100k_lists/sunny2diffweathers/%s_%s.txt' % (mode, opt.phase))) as s_list:
|
| 39 |
+
s_image_paths_read = s_list.read().splitlines()
|
| 40 |
+
s_image_paths_mode = [os.path.join(sroot, p) for p in s_image_paths_read if p != '']
|
| 41 |
+
s_image_paths.extend(s_image_paths_mode)
|
| 42 |
+
|
| 43 |
+
while len(s_image_paths) < len(c_image_paths):
|
| 44 |
+
s_image_paths = s_image_paths + s_image_paths
|
| 45 |
+
|
| 46 |
+
instance_paths = []
|
| 47 |
+
|
| 48 |
+
length = min(len(c_image_paths), len(s_image_paths))
|
| 49 |
+
c_image_paths = c_image_paths[:length]
|
| 50 |
+
s_image_paths = s_image_paths[:length]
|
| 51 |
+
return c_image_paths, s_image_paths, instance_paths
|
| 52 |
+
|
| 53 |
+
def paths_match(self, path1, path2):
|
| 54 |
+
return True
|
data/unaligned_day_night_dataset.py
ADDED
|
@@ -0,0 +1,53 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import os.path
|
| 2 |
+
from data.base_dataset import BaseDataset, get_transform, get_params
|
| 3 |
+
from data.image_folder import make_dataset
|
| 4 |
+
from PIL import Image
|
| 5 |
+
import random
|
| 6 |
+
|
| 7 |
+
class UnalignedDayNightDataset(BaseDataset):
|
| 8 |
+
@staticmethod
|
| 9 |
+
def modify_commandline_options(parser, is_train):
|
| 10 |
+
parser.add_argument('--dataroot', required=True,
|
| 11 |
+
help='path to images (should have subfolders train/val containing day/night)')
|
| 12 |
+
parser.set_defaults(preprocess_mode='resize_and_crop', load_size=286, crop_size=256)
|
| 13 |
+
if not is_train:
|
| 14 |
+
parser.set_defaults(no_flip=True)
|
| 15 |
+
return parser
|
| 16 |
+
|
| 17 |
+
def __init__(self, opt):
|
| 18 |
+
BaseDataset.__init__(self)
|
| 19 |
+
self.opt = opt
|
| 20 |
+
|
| 21 |
+
root = opt.dataroot
|
| 22 |
+
phase = opt.phase
|
| 23 |
+
|
| 24 |
+
self.dir_A = os.path.join(root, phase, 'day')
|
| 25 |
+
self.dir_B = os.path.join(root, phase, 'night')
|
| 26 |
+
|
| 27 |
+
self.A_paths = sorted(make_dataset(self.dir_A, recursive=True))
|
| 28 |
+
self.B_paths = sorted(make_dataset(self.dir_B, recursive=True))
|
| 29 |
+
|
| 30 |
+
self.A_size = len(self.A_paths)
|
| 31 |
+
self.B_size = len(self.B_paths)
|
| 32 |
+
|
| 33 |
+
if self.A_size == 0 or self.B_size == 0:
|
| 34 |
+
raise (RuntimeError(f"Found 0 images in one of the data directories: {self.dir_A} or {self.dir_B}"))
|
| 35 |
+
|
| 36 |
+
def __getitem__(self, index):
|
| 37 |
+
A_path = self.A_paths[index % self.A_size]
|
| 38 |
+
index_B = random.randint(0, self.B_size - 1)
|
| 39 |
+
B_path = self.B_paths[index_B]
|
| 40 |
+
|
| 41 |
+
A_img = Image.open(A_path).convert('RGB')
|
| 42 |
+
B_img = Image.open(B_path).convert('RGB')
|
| 43 |
+
|
| 44 |
+
params = get_params(self.opt, A_img.size)
|
| 45 |
+
transform = get_transform(self.opt, params)
|
| 46 |
+
|
| 47 |
+
A = transform(A_img)
|
| 48 |
+
B = transform(B_img)
|
| 49 |
+
|
| 50 |
+
return {'day': A, 'night': B, 'cpath': A_path, 'spath_night': B_path}
|
| 51 |
+
|
| 52 |
+
def __len__(self):
|
| 53 |
+
return max(self.A_size, self.B_size)
|
datasets/bdd100k_lists/day2night/day_test.txt
ADDED
|
@@ -0,0 +1,1764 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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datasets/bdd100k_lists/sunny2diffweathers/cloudy_test.txt
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datasets/bdd100k_lists/sunny2diffweathers/cloudy_train.txt
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datasets/bdd100k_lists/sunny2diffweathers/night_train.txt
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datasets/bdd100k_lists/sunny2diffweathers/rainy_test.txt
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datasets/bdd100k_lists/sunny2diffweathers/rainy_train.txt
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datasets/bdd100k_lists/sunny2diffweathers/snowy_test.txt
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datasets/bdd100k_lists/sunny2diffweathers/snowy_train.txt
ADDED
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datasets/bdd100k_lists/sunny2diffweathers/sunny_test.txt
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datasets/bdd100k_lists/sunny2diffweathers/sunny_train.txt
ADDED
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|
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|
datasets/prepare_ade20k.sh
ADDED
|
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|
| 1 |
+
#!/usr/bin/env bash
|
| 2 |
+
|
| 3 |
+
set -x
|
| 4 |
+
|
| 5 |
+
DATAROOT=$1
|
| 6 |
+
|
| 7 |
+
ln -s $DATAROOT ./datasets
|
datasets/prepare_bdd100k.sh
ADDED
|
@@ -0,0 +1,8 @@
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
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|
| 1 |
+
#!/usr/bin/env bash
|
| 2 |
+
|
| 3 |
+
set -x
|
| 4 |
+
|
| 5 |
+
DATAROOT=$1
|
| 6 |
+
|
| 7 |
+
cp -r ./datasets/bdd100k_lists $DATAROOT
|
| 8 |
+
ln -s $DATAROOT ./datasets
|
datasets/prepare_cityscapes.sh
ADDED
|
@@ -0,0 +1,7 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
| 1 |
+
#!/usr/bin/env bash
|
| 2 |
+
|
| 3 |
+
set -x
|
| 4 |
+
|
| 5 |
+
DATAROOT=$1
|
| 6 |
+
|
| 7 |
+
ln -s $DATAROOT ./datasets
|