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Create app.py
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app.py
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import streamlit as st
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import requests
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from PIL import Image
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import torch
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from transformers import DepthProImageProcessorFast, DepthProForDepthEstimation
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import numpy as np
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import io
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# Check if CUDA is available
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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# Load model and processor
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image_processor = DepthProImageProcessorFast.from_pretrained("apple/DepthPro-hf")
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model = DepthProForDepthEstimation.from_pretrained("apple/DepthPro-hf").to(device)
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# Streamlit App UI
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st.title("Interactive Depth-based AR Painting App")
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# Upload image through Streamlit UI
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uploaded_file = st.file_uploader("Upload an Image", type=["jpg", "jpeg", "png"])
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if uploaded_file is not None:
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image = Image.open(uploaded_file)
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st.image(image, caption="Uploaded Image", use_column_width=True)
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# Process image with DepthPro for depth estimation
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inputs = image_processor(images=image, return_tensors="pt").to(device)
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with torch.no_grad():
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outputs = model(**inputs)
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# Post-process depth output
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post_processed_output = image_processor.post_process_depth_estimation(
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outputs, target_sizes=[(image.height, image.width)],
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)
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depth = post_processed_output[0]["predicted_depth"]
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depth = (depth - depth.min()) / (depth.max() - depth.min())
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depth = depth * 255.
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depth = depth.detach().cpu().numpy()
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depth_image = Image.fromarray(depth.astype("uint8"))
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st.subheader("Depth Map")
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st.image(depth_image, caption="Estimated Depth Map", use_column_width=True)
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# Colorize the depth map to make it more visible
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colormap = depth_image.convert("RGB")
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st.subheader("Colorized Depth Map")
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st.image(colormap, caption="Colorized Depth Map", use_column_width=True)
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# Option to save depth image
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if st.button('Save Depth Image'):
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depth_image.save('depth_image.png')
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st.success("Depth image saved successfully!")
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# Option for interactive painting (Placeholder)
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st.subheader("Interactive Depth-based Painting (Demo Placeholder)")
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st.write("This feature will allow users to paint on surfaces based on depth. For now, we can show the depth and its effects.")
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# Placeholder for future interactive painting functionality.
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# This could be extended with AR-based libraries or Unity integration in a full-scale app.
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