Create app.py

app.py

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+import torch
+import numpy as np
+from PIL import Image, ImageFilter
+from transformers import AutoImageProcessor, AutoModelForDepthEstimation
+import streamlit as st
+
+# Load the depth estimation model
+processor = AutoImageProcessor.from_pretrained("depth-anything/Depth-Anything-V2-Small-hf")
+model = AutoModelForDepthEstimation.from_pretrained("depth-anything/Depth-Anything-V2-Small-hf")
+
+# Streamlit app title
+st.title("Depth-Based Gaussian Blur App")
+st.markdown("Upload a photo to apply depth-based blurring and adjust blur intensity.")
+
+# File uploader
+uploaded_file = st.file_uploader("Upload an image", type=["jpg", "png", "jpeg"])
+
+if uploaded_file:
+    # Load and preprocess the image
+    input_image = Image.open(uploaded_file).convert("RGB")  # Ensure image is RGB format
+    input_image = input_image.resize((512, 512))  # Resize image for processing
+
+    # Display the uploaded image
+    st.image(input_image, caption="Uploaded Image", use_column_width=True)
+
+    # Depth map generation
+    inputs = processor(images=input_image, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model(**inputs)
+        depth = outputs.predicted_depth.squeeze().cpu().numpy()
+
+    # Normalize depth map
+    depth_min, depth_max = np.min(depth), np.max(depth)
+    normalized_depth = (depth - depth_min) / (depth_max - depth_min)
+    adjusted_depth = np.clip(normalized_depth, 0, 1)
+
+    # Blur intensity slider
+    max_blur = st.slider("Max Blur Intensity", min_value=1, max_value=10, value=3)
+
+    # Invert depth for blur
+    depth_array = ((1 - adjusted_depth) * max_blur).astype(np.uint8)
+
+    # Create multiple blurred images
+    blurred_images = [input_image.filter(ImageFilter.GaussianBlur(i)) for i in range(max_blur + 1)]
+    final_image = Image.new("RGB", input_image.size)
+
+    # Apply depth-based blur pixel by pixel
+    for y in range(input_image.height):
+        for x in range(input_image.width):
+            blur_level = min(max_blur, max(0, depth_array[y, x]))
+            final_image.putpixel((x, y), blurred_images[blur_level].getpixel((x, y)))
+
+    # Display the blurred image
+    st.image(final_image, caption="Depth-Based Blurred Image", use_column_width=True)