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Running
on
Zero
Running
on
Zero
Create app.py
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app.py
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import gradio as gr
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from PIL import Image
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import depth_pro
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import numpy as np
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import matplotlib.pyplot as plt
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# Load model and preprocessing transform
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model, transform = depth_pro.create_model_and_transforms()
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model.eval()
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def predict_depth(input_image):
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# Preprocess the image
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result = depth_pro.load_rgb(input_image.name)
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image = result[0]
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f_px = result[-1] # Assuming f_px is the last item in the returned tuple
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image = transform(image)
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# Run inference
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prediction = model.infer(image, f_px=f_px)
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depth = prediction["depth"] # Depth in [m]
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focallength_px = prediction["focallength_px"] # Focal length in pixels
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# Normalize depth for visualization
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depth_normalized = (depth - np.min(depth)) / (np.max(depth) - np.min(depth))
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# Create a color map
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plt.figure(figsize=(10, 10))
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plt.imshow(depth_normalized, cmap='viridis')
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plt.colorbar(label='Depth')
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plt.title('Predicted Depth Map')
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plt.axis('off')
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# Save the plot to a file
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output_path = "depth_map.png"
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plt.savefig(output_path)
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plt.close()
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return output_path, f"Focal length: {focallength_px:.2f} pixels"
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# Create Gradio interface
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iface = gr.Interface(
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fn=predict_depth,
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inputs=gr.Image(type="filepath"),
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outputs=[gr.Image(type="filepath", label="Depth Map"), gr.Textbox(label="Focal Length")],
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title="Depth Prediction Demo",
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description="Upload an image to predict its depth map and focal length."
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)
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# Launch the interface
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iface.launch()
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