Spaces:
Running
on
Zero
Running
on
Zero
Update app.py
Browse files
app.py
CHANGED
@@ -83,9 +83,9 @@ description = """... **...**"""
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# return model.infer_image(image)
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@spaces.GPU
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def predict_depth(image
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with torch.no_grad():
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pipe_out = pipe(image, denoising_steps=1, ensemble_size=1, noise="zeros", normals=False, processing_res=
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pred = pipe_out.depth_np
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pred_colored = pipe_out.depth_colored
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return pred, pred_colored
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@@ -115,14 +115,14 @@ with gr.Blocks(css=css) as demo:
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cmap = matplotlib.colormaps.get_cmap('Spectral_r')
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def on_submit(image):
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if image is None:
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print("No image uploaded.")
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return None
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pil_image = Image.fromarray(image.astype('uint8'))
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depth_npy, depth_colored = predict_depth(pil_image)
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# Save the npy data (raw depth map)
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# tmp_npy_depth = tempfile.NamedTemporaryFile(suffix='.npy', delete=False)
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@@ -163,7 +163,7 @@ with gr.Blocks(css=css) as demo:
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example_files.sort()
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example_files = [os.path.join('assets/examples', filename) for filename in example_files]
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example_files = [[image, 768] for image in example_files]
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examples = gr.Examples(examples=example_files, inputs=[input_image], outputs=[depth_image_slider, gray_depth_file, raw_file], fn=on_submit)
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if __name__ == '__main__':
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# return model.infer_image(image)
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@spaces.GPU
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def predict_depth(image, processing_res_choice):
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with torch.no_grad():
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pipe_out = pipe(image, denoising_steps=1, ensemble_size=1, noise="zeros", normals=False, processing_res=processing_res_choice, match_input_res=True)
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pred = pipe_out.depth_np
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pred_colored = pipe_out.depth_colored
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return pred, pred_colored
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cmap = matplotlib.colormaps.get_cmap('Spectral_r')
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def on_submit(image, processing_res_choice):
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if image is None:
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print("No image uploaded.")
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return None
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pil_image = Image.fromarray(image.astype('uint8'))
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depth_npy, depth_colored = predict_depth(pil_image, processing_res_choice)
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# Save the npy data (raw depth map)
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# tmp_npy_depth = tempfile.NamedTemporaryFile(suffix='.npy', delete=False)
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example_files.sort()
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example_files = [os.path.join('assets/examples', filename) for filename in example_files]
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example_files = [[image, 768] for image in example_files]
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examples = gr.Examples(examples=example_files, inputs=[input_image, processing_res_choice], outputs=[depth_image_slider, gray_depth_file, raw_file], fn=on_submit)
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if __name__ == '__main__':
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