temp.py

import gradio as gr
from PIL import Image, ImageFilter
import torch
from transformers import DepthProImageProcessorFast, DepthProForDepthEstimation
import numpy as np

# Load the device (use CPU or GPU)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Initialize the model and processor
image_processor = DepthProImageProcessorFast.from_pretrained("apple/DepthPro-hf")
model = DepthProForDepthEstimation.from_pretrained("apple/DepthPro-hf").to(device)

# Function to apply background blur based on depth
def apply_background_blur(image: Image):
    # Convert the uploaded image to RGB if necessary
    image = image.convert("RGB")
    
    # Process the image with DepthPro model
    inputs = image_processor(images=image, return_tensors="pt").to(device)
    
    with torch.no_grad():
        outputs = model(**inputs)
    
    post_processed_output = image_processor.post_process_depth_estimation(
        outputs, target_sizes=[(image.height, image.width)],
    )

    # Get the predicted depth and normalize it
    depth = post_processed_output[0]["predicted_depth"]
    depth_np = depth.detach().cpu().numpy().squeeze()
    depth_normalized = (depth_np - depth_np.min()) / (depth_np.max() - depth_np.min())

    # Create a blurred image
    blurred_image = image.copy()

    # Apply variable Gaussian blur based on depth
    blur_strength = 20  # You can adjust this for overall blur strength
    blur_map = (depth_normalized * blur_strength).astype(int)

    for radius in range(1, blur_strength + 1):
        mask = (blur_map == radius)
        if np.any(mask):
            temp_image = image.copy()
            temp_image = temp_image.filter(ImageFilter.GaussianBlur(radius))
            blurred_image = Image.composite(temp_image, blurred_image, Image.fromarray((mask * 255).astype(np.uint8)))
    
    return blurred_image

# Create Gradio interface
def create_interface():
    # Gradio interface with image upload input and output for processed image
    gr.Interface(
        fn=apply_background_blur,
        inputs=gr.Image(type="pil", label="Upload Image"),
        outputs=gr.Image(type="pil", label="Blurred Image"),
        live=True
    ).launch()

# Start the app
if __name__ == "__main__":
    create_interface()