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import cv2
import paddlehub as hub
import gradio as gr
module = hub.Module(name="lseg")
def segment(image, labels):
long_size = max(image.shape[:2])
if long_size > 512:
f = 512 / long_size
image = cv2.resize(image, (0, 0), fx=f, fy=f)
results = module.segment(
image=image[..., ::-1],
labels=[item for item in labels.split('\n') if item != '']
)
return [
results['color'][..., ::-1],
results['mix'][..., ::-1],
*[v[..., ::-1] for v in results['classes'].values()]
]
gr.Interface(
title='LSeg: Language-driven Semantic Segmentation',
fn=segment,
inputs=[
gr.Image(),
gr.Textbox(placeholder='other\ncat', lines=5, max_lines=50),
],
outputs=[
gr.Gallery().style(grid=[2, 3], height="auto")
],
article='''## More
* There are more interesting models in [PaddleHub](https://github.com/PaddlePaddle/PaddleHub), you can star [PaddleHub](https://github.com/PaddlePaddle/PaddleHub) to follow.
* Besides, you can use free GPU resourses in [AIStudio](https://aistudio.baidu.com/aistudio/projectdetail/4580391) to enjoy more cases, have fun.
[](https://github.com/PaddlePaddle/PaddleHub/stargazers)
## References
* Paper: [Language-driven Semantic Segmentation](https://arxiv.org/abs/2201.03546)
* Offical Code: [isl-org/lang-seg](https://github.com/isl-org/lang-seg)
''',
examples=[['cat.jpeg', 'other\ncat']],
cache_examples=True
).launch()
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