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import gradio as gr
from transformers import pipeline
from PIL import Image, ImageFilter
import numpy as np
# Load models from Hugging Face
segmentation_model = pipeline("image-segmentation", model="nvidia/segformer-b1-finetuned-cityscapes-1024-1024")
depth_estimator = pipeline("depth-estimation", model="Intel/zoedepth-nyu-kitti")
def process_image(image, blur_type, sigma):
# Step 1: Perform segmentation
segmentation_results = segmentation_model(image)
foreground_mask = segmentation_results[-1]["mask"]
# Step 2: Apply Gaussian blur to background
blurred_background = image.filter(ImageFilter.GaussianBlur(sigma))
segmented_output = Image.composite(image, blurred_background, foreground_mask)
# Step 3: Perform depth estimation
depth_results = depth_estimator(image)
depth_map = depth_results["depth"]
# Step 4: Normalize depth map values
depth_array = np.array(depth_map)
normalized_depth = (depth_array - np.min(depth_array)) / (np.max(depth_array) - np.min(depth_array)) * 255
normalized_depth_image = Image.fromarray(normalized_depth.astype('uint8'))
# Step 5: Apply variable Gaussian blur based on depth map (Lens Blur)
if blur_type == "Lens Blur":
variable_blur_image = image.copy()
for x in range(variable_blur_image.width):
for y in range(variable_blur_image.height):
blur_intensity = normalized_depth[y, x] / 255 * sigma # Scale blur intensity by depth value
pixel_value = image.getpixel((x, y))
variable_blur_image.putpixel((x, y), tuple(int(p * blur_intensity) for p in pixel_value))
output_image = variable_blur_image
else:
output_image = segmented_output
return segmented_output, normalized_depth_image, output_image
# Create Gradio interface
app = gr.Interface(
fn=process_image,
inputs=[
gr.Image(type="pil", label="Upload Image"),
gr.Radio(["Gaussian Blur", "Lens Blur"], label="Blur Type", value="Gaussian Blur"),
gr.Slider(0, 50, step=1, label="Blur Intensity (Sigma)", value=15)
],
outputs=[
gr.Image(type="pil", label="Segmented Output with Background Blur"),
gr.Image(type="pil", label="Depth Map Visualization"),
gr.Image(type="pil", label="Final Output with Selected Blur")
],
title="Vision Transformer Segmentation & Depth-Based Blur Effects",
description="Upload an image and select the type of blur effect (Gaussian or Lens). Adjust the blur intensity using the slider."
)
app.launch() |