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import gradio as gr
import numpy as np
import torch
from PIL import Image
import cv2
import requests
from transformers import pipeline
# Load the depth estimation pipeline
pipe = pipeline(task="depth-estimation", model="depth-anything/Depth-Anything-V2-Small-hf")
def apply_depth_aware_blur_inverse(
image,
foreground_blur,
midground_blur,
background_blur,
foreground_threshold,
midground_lower,
midground_upper,
background_threshold,
):
original_image = Image.fromarray(image).convert("RGB")
original_image = original_image.resize((512, 512))
image_np = np.array(original_image)
# Inference
depth = pipe(original_image)["depth"]
depth = np.array(depth) # Convert to numpy array
depth = cv2.resize(depth, (512, 512), interpolation=cv2.INTER_CUBIC) # Resize depth map
# Normalize the depth map
normalized_depth_map = (depth - np.min(depth)) / (np.max(depth) - np.min(depth))
# Create masks (Inverted Logic)
foreground_mask = (normalized_depth_map >= foreground_threshold).astype(np.uint8) * 255
midground_mask = ((normalized_depth_map < foreground_threshold) & (normalized_depth_map >= background_threshold)).astype(np.uint8) * 255
background_mask = (normalized_depth_map < background_threshold).astype(np.uint8) * 255
blurred_image = np.copy(np.array(original_image))
# Apply blur, ensuring kernel size is valid (Inverted Blur)
if foreground_blur > 0 and foreground_blur % 2 == 1:
blurred_image = np.where(
(foreground_mask[..., None] == 255),
cv2.GaussianBlur(blurred_image, (foreground_blur, foreground_blur), 10),
blurred_image,
)
if midground_blur > 0 and midground_blur % 2 == 1:
blurred_image = np.where(
(midground_mask[..., None] == 255),
cv2.GaussianBlur(blurred_image, (midground_blur, midground_blur), 8),
blurred_image,
)
if background_blur > 0 and background_blur % 2 == 1:
blurred_image = np.where(
(background_mask[..., None] == 255),
cv2.GaussianBlur(blurred_image, (background_blur, background_blur), 20),
blurred_image,
)
return Image.fromarray(blurred_image.astype(np.uint8))
# Example input values (including defaults)
example_image = np.zeros((512, 512, 3), dtype=np.uint8) # Placeholder for an image
example_inputs = [
example_image,
35, # foreground_blur
7, # midground_blur
15, # background_blur (default)
0.6, # foreground_threshold (default)
0.6, # midground_lower (default)
0.2, # midground_upper (default)
0.2, # background_threshold (default)
]
iface = gr.Interface(
fn=apply_depth_aware_blur_inverse, # Changed function name
inputs=[
gr.Image(label="Input Image"),
gr.Slider(1, 51, step=2, label="Foreground Blur Kernel Size"),
gr.Slider(1, 51, step=2, label="Midground Blur Kernel Size"),
gr.Slider(1, 51, step=2, label="Background Blur Kernel Size"),
gr.Slider(0, 1, label="Foreground Threshold"),
gr.Slider(0, 1, label="Midground Lower Threshold"),
gr.Slider(0, 1, label="Midground Upper Threshold"),
gr.Slider(0, 1, label="Background Threshold"),
],
outputs=gr.Image(label="Blurred Image"),
title="Inverse Depth-Aware Lens Blur App", # Changed title
description="Apply inverse depth-based blur to uploaded images using Depth Anything V2. Closer objects are blurred, farther objects are sharper.", # Changed description
examples=[example_inputs], # Provide example inputs
)
if __name__ == "__main__":
iface.launch() |