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99cd6e8
1
Parent(s):
2feecfe
Update app.py
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app.py
CHANGED
@@ -47,13 +47,8 @@ def lowlight(image_path):
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if __name__ == '__main__':
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# test_images
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with torch.no_grad():
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filePath = 'data/test_data/'
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-
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file_list = os.listdir(filePath)
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test_list = glob.glob(filePath+file_name+"/*")
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for image in test_list:
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# image = image
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print(image)
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lowlight(image)
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@@ -63,7 +58,7 @@ title = "Compound Multi-branch Feature Fusion for Image Restoration (Deblur)"
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description = "Gradio demo for CMFNet. CMFNet achieves competitive performance on three tasks: image deblurring, image dehazing and image deraindrop. Here, we provide a demo for image deblur. To use it, simply upload your image, or click one of the examples to load them. Reference from: https://huggingface.co/akhaliq"
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article = "<p style='text-align: center'><a href='https://' target='_blank'>Compound Multi-branch Feature Fusion for Real Image Restoration</a> | <a href='https://github.com/FanChiMao/CMFNet' target='_blank'>Github Repo</a></p> <center><img src='https://visitor-badge.glitch.me/badge?page_id=52Hz_CMFNet_deblurring' alt='visitor badge'></center>"
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examples = [['
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gr.Interface(
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inference,
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[gr.inputs.Image(type="pil", label="Input")],
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if __name__ == '__main__':
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# test_images
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with torch.no_grad():
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for image
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# image = image
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print(image)
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lowlight(image)
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description = "Gradio demo for CMFNet. CMFNet achieves competitive performance on three tasks: image deblurring, image dehazing and image deraindrop. Here, we provide a demo for image deblur. To use it, simply upload your image, or click one of the examples to load them. Reference from: https://huggingface.co/akhaliq"
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article = "<p style='text-align: center'><a href='https://' target='_blank'>Compound Multi-branch Feature Fusion for Real Image Restoration</a> | <a href='https://github.com/FanChiMao/CMFNet' target='_blank'>Github Repo</a></p> <center><img src='https://visitor-badge.glitch.me/badge?page_id=52Hz_CMFNet_deblurring' alt='visitor badge'></center>"
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examples = [['01.jpg'], ['02.jpg'],]
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gr.Interface(
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inference,
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[gr.inputs.Image(type="pil", label="Input")],
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