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SAMScore: A Semantic Structural Similarity Metric for Image Translation Evaluation
Yunxiang Li, Meixu Chen, Wenxuan Yang, Kai Wang, Jun Ma, Alan C. Bovik, You Zhang.
<div> <a href="https://arxiv.org/pdf/2305.15367.pdf"><img src="https://info.arxiv.org/brand/images/brand-logo-primary.jpg" alt="Arxiv" width=85></a> <br> <a href="https://colab.research.google.com/github/Kent0n-Li/SAMScore/blob/main/SAMScore.ipynb#scrollTo=mCidlfXu88UY"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> </div> <img src='imgs/overview.jpg' width=1200>Quick start
Run pip install samscore.
pip install git+https://github.com/ChaoningZhang/MobileSAM.git
pip install timm
pip install samscore
pip install git+https://github.com/facebookresearch/segment-anything.git
The following Python code is all you need.
import requests
import os
import samscore
def download_image(url, save_path):
response = requests.get(url)
response.raise_for_status() # Raise an exception if the request was unsuccessful
with open(save_path, 'wb') as file:
file.write(response.content)
os.makedirs('imgs', exist_ok=True)
# Example usage
image_url = 'https://i.ibb.co/yFFg5pn/n02381460-20-real.png'
save_location = 'imgs/real.png'
download_image(image_url, save_location)
image_url = 'https://i.ibb.co/GCQ2jQy/n02381460-20-fake.png'
save_location = 'imgs/fake.png'
download_image(image_url, save_location)
## Initializing the model
# You can choose "vit_t", "vit_l", "vit_b", "vit_h"
SAMScore_Evaluation = samscore.SAMScore(model_type = "vit_b" )
samscore_result = SAMScore_Evaluation.evaluation_from_path(source_image_path='imgs/real.png', generated_image_path='imgs/fake.png')
print('SAMScore: %.4f'%samscore_result)
Use it on pytorch
import requests
import os
import samscore
import cv2
import torch
def download_image(url, save_path):
response = requests.get(url)
response.raise_for_status() # Raise an exception if the request was unsuccessful
with open(save_path, 'wb') as file:
file.write(response.content)
os.makedirs('imgs', exist_ok=True)
# Example usage
image_url = 'https://i.ibb.co/yFFg5pn/n02381460-20-real.png'
save_location = 'imgs/real.png'
download_image(image_url, save_location)
image_url = 'https://i.ibb.co/GCQ2jQy/n02381460-20-fake.png'
save_location = 'imgs/fake.png'
download_image(image_url, save_location)
# You can choose "vit_t", "vit_l", "vit_b", "vit_h"
SAMScore_Evaluation = samscore.SAMScore(model_type = "vit_b" )
source_cv2 = cv2.imread('imgs/real.png')
source = torch.from_numpy(source_cv2.transpose(2, 0, 1)).unsqueeze(0).float()
source = torch.cat((source,source,source),dim=0)
generated_cv2 = cv2.imread('imgs/fake.png')
generated = torch.from_numpy(generated_cv2.transpose(2, 0, 1)).unsqueeze(0).float()
generated = torch.cat((generated,generated,generated),dim=0)
samscore_result = SAMScore_Evaluation.evaluation_from_torch(source, generated)
print('SAMScore:',samscore_result)
Citation
If you find this repository useful for your research, please use the following.
@article{li2023samscore,
title={SAMScore: A Semantic Structural Similarity Metric for Image Translation Evaluation},
author={Li, Yunxiang and Chen, Meixu and Yang, Wenxuan and Wang, Kai and Ma, Jun and Bovik, Alan C and Zhang, You},
journal={arXiv preprint arXiv:2305.15367},
year={2023}
}