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Update app.py
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app.py
CHANGED
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@@ -38,10 +38,8 @@ def postprocess(output, prompt):
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right = (i + 1) * slice_width if i < n - 1 else w
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cropped_img = image.crop((left, 0, right, h))
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# 生成 caption
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caption = prompt[i]
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# 存入列表
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result.append((cropped_img, caption))
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return result
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@@ -256,9 +254,9 @@ def run_demo_server():
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with gr.Row():
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gr.Markdown('The results of semantic segmentation may be unstable because:')
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with gr.Row():
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gr.Markdown('
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with gr.Row():
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gr.Markdown('
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with gr.Row():
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gr.Markdown('However, we are still able to produce some high-quality semantic segmentation results, strongly demonstrating the potential of our approach.')
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with gr.Row():
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@@ -280,22 +278,10 @@ def run_demo_server():
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undo_button = gr.Button('Undo point')
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matting_image_reset_btn = gr.Button(value="Reset")
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# with gr.Row():
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# img_clear_button = gr.Button("Clear Cache")
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with gr.Column():
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# matting_image_output = gr.Image(label='Output')
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# matting_image_output = gr.Image(label='Results')
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matting_image_output = gr.Gallery(label="Results")
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# label="Matting Output",
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# type="filepath",
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# show_download_button=True,
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# show_share_button=True,
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# interactive=False,
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# elem_classes="slider",
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# position=0.25,
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# )
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@@ -308,7 +294,6 @@ def run_demo_server():
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preprocess=False,
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queue=False,
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).success(
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# fn=process_pipe_matting,
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fn=inf,
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inputs=[original_image, checkbox_group, selected_points, semantic_input],
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outputs=[matting_image_output],
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@@ -376,13 +361,6 @@ def run_demo_server():
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cache_examples=False,
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)
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# examples.dataset.click(
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# fn=dummy
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# ).success(
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# fn=set_point, # Now run the actual function after inputs are populated
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# inputs=[input_image, checkbox_group, selected_points_tmp, semantic_input],
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# outputs=[input_image, selected_points]
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# )
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demo.queue(
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api_open=False,
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right = (i + 1) * slice_width if i < n - 1 else w
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cropped_img = image.crop((left, 0, right, h))
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caption = prompt[i]
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result.append((cropped_img, caption))
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return result
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with gr.Row():
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gr.Markdown('The results of semantic segmentation may be unstable because:')
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with gr.Row():
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gr.Markdown('- We only trained on COCO, whose quality and quantity are insufficient to meet the requirements.')
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with gr.Row():
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gr.Markdown('- Semantic segmentation is more complex than other tasks, as it requires accurately learning the relationship between semantics and objects.')
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with gr.Row():
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gr.Markdown('However, we are still able to produce some high-quality semantic segmentation results, strongly demonstrating the potential of our approach.')
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with gr.Row():
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undo_button = gr.Button('Undo point')
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matting_image_reset_btn = gr.Button(value="Reset")
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with gr.Column():
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matting_image_output = gr.Gallery(label="Results")
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preprocess=False,
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queue=False,
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).success(
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fn=inf,
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inputs=[original_image, checkbox_group, selected_points, semantic_input],
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outputs=[matting_image_output],
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cache_examples=False,
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)
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demo.queue(
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api_open=False,
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