singan-seg-polyp
If you want to train your own SinGAN-Seg models, please follow the repository here: https://github.com/vlbthambawita/singan-seg
Install pip package
pip install singan-seg-polypImport required packages
from singan_seg_polyp import generate_data, prepare_requirementsPrepare checkpoints
Help on function prepare_checkpoints in module singan_seg_polyp.prepare_requirements:
prepare_checkpoints(path_to_checkpoints: str, link_keys=['link1', 'link2', 'link3', 'link4'], real_data=True, *args, **kwargs) -> str
'''
The main function preparing checkpoints for pre-trained SinGANs of Polyp images.
Parameters
-----------
path_to_checkpoints: str
A directory path to download checkpoints.
link_keys: list
A list of link keys: link1, link2, link3, link4. One or multiple link keys can be put in this list.
real_data: bool
If True, the real images and masks used to train SinGANs will be downloaded to the checkpoint directory.
Return
------
checkpoint_paths_list, real_image_mask_pair_list
A sorted list of paths to downloaded checkpoints.
A sorted (image_path, mask_path) tuple list.
'''out_paths = prepare_requirements.prepare_checkpoints("./singan_polyp_checkpoints", link_keys=["link1", "link2", "link3", "link4"])Parameters
-
path_to_checkpoints -- A path to save checkpoints. The prepare_checkpoints() function checks the availability of pre-downloaded checkpoints. So, the use can run the save command twice without any downlaod overhead.
-
link_keys -- A list of pre-defined keys to download links. To download the full checkpoint list, use link_keys=["link1", "link2", "link3", "link4"]. If the user needs only a half of check points, then, the user can use only a half of link_keys. For example, link_keys= ["link1", "link2"]
-
real_data -- If this is True, real images and masks used to train SinGANs will be downloaded into the checkpoint folder.
Return
This function returns a list of paths to all downloaded checkpoints to use with other functions of singan_polyp_aug. If the all checkpoints are used, then the function returns 1000 different sinGAN checkpoint paths which are dirrecting to pre-trained SinGAN checkpoints of the 1000 polyp images introduced in Hyper-kvasir dataset.
Generate synthetic polyps and corresponding mask
generate_data.generate_from_single_checkpoint(out_dir:str,
checkpoint_path:str,
num_samples:int=1,
gen_start_scale:int=5,
mask_post_processing:bool=True) -> None:
''' A function to generate synthetic polyp and correspondign mask from a given checkpoint path.
Parameters
----------
out_dir: str
A path to save output data.
checkpoint_path: str
A path to a downloaded checkpoint. To get paths, you have to run prepare_requirements.prepare_checkpoints() function.
num_samples: int
Number of random samples to generate from the given checkpoint. Default=1.
gen_start_scale: int
Predefined scales used in SinGAN training. You can use values between 0-9. Value 0 generates more random samples and value 9 generate sampels which are
very close to the training image.
mask_post_processing: bool
Whether the generated mask should be post processed or not. If True, generates mask is cleaned to have only 0 and 255.
Returns
------
None
This function does not have a return.
'''generate_data.generate_from_multiple_checkpoints(out_dir:str, checkpoint_paths:list, *args, **kwargs)-> None:
''' A function to generate synthetic polyp and correspondign mask from a given list of checkpoint paths.
Parameters
----------
out_dir: str
A path to save output data.
checkpoint_paths: list
A path list to downloaded checkpoints. To get paths, you have to run prepare_requirements.prepare_checkpoints() function.
num_samples: int
Number of random samples to generate from the given checkpoint. Default=1.
gen_start_scale: int
Predefined scales used in SinGAN training. You can use values between 0-9. Value 0 generates more random samples and value 9 generate sampels which are
very close to the training image.
mask_post_processing: bool
Whether the generated mask should be post processed or not. If True, generates mask is cleaned to have only 0 and 255.
Returns
------
None
This function does not have a return.
'''generate_data.generate_simple(out_dir:str, chk_dir:str, *args, **kwargs)-> None:
''' A function to generate synthetic polyp and correspondign mask from all downloaded checkpoint paths.
Parameters
----------
out_dir: str
A path to save output data.
chk_dir: str
The path to checkpoint directory. If the directory does not have downloaded checkpoints, this function will download them.
num_samples: int
Number of random samples to generate from the given checkpoint. Default=1.
gen_start_scale: int
Predefined scales used in SinGAN training. You can use values between 0-9. Value 0 generates more random samples and value 9 generate sampels which are
very close to the training image.
mask_post_processing: bool
Whether the generated mask should be post processed or not. If True, generates mask is cleaned to have only 0 and 255.
Returns
------
None
This function does not have a return.
'''Transfering style from real to fake
help(style_transfer.transfer_style)
transfer_style(content_img_path: str, style_img_path: str, num_epochs: int, content_weight: int, style_weight: int, device: torch.device, vgg_model: str, verbose=False, tqdm_position=0, tqdm_leave=True, *args, **kwargs) -> 'PIL.Image'
Trnsfering style from a source image to a target image.
Parameters
==========
content_img_path: str
A path to an image to which the style is going to be transfered.
style_img: str
A path to an image which has the required style to be transferred.
num_epochs: int
Number of epoch to iterate for transfering style to content image.
content_weight: int
Weight to keep the content of the destination image.
style_weight: int
Weight to transfer style from the source image.
device: torch.device
Torch device object, either "CPU" or "CUDA". Refer Pytoch documentation for more detials.
vgg_model: str
A model to extract features.
verbose: bool
If true, loss values will be printed to stdout.
Return
=======
PIL.Image
Style transferred image.Citation:
@article{thambawita2021singan,
title={SinGAN-Seg: Synthetic Training Data Generation for Medical Image Segmentation},
author={Thambawita, Vajira and Salehi, Pegah and Sheshkal, Sajad Amouei and Hicks, Steven A and Hammer, Hugo L and Parasa, Sravanthi and de Lange, Thomas and Halvorsen, P{\aa}l and Riegler, Michael A},
journal={arXiv preprint arXiv:2107.00471},
year={2021}
}References:
@article{cite-key,
da = {2020/08/28},
date-added = {2021-03-27 01:08:18 +0100},
date-modified = {2021-03-27 01:08:18 +0100},
doi = {10.1038/s41597-020-00622-y},
id = {Borgli2020},
isbn = {2052-4463},
journal = {Scientific Data},
number = {1},
pages = {283},
title = {HyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy},
ty = {JOUR},
url = {https://doi.org/10.1038/s41597-020-00622-y},
volume = {7},
year = {2020},
Bdsk-Url-1 = {https://doi.org/10.1038/s41597-020-00622-y}}
@inproceedings{shaham2019singan,
title={Singan: Learning a generative model from a single natural image},
author={Shaham, Tamar Rott and Dekel, Tali and Michaeli, Tomer},
booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
pages={4570--4580},
year={2019}
}
