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import cv2
import numpy as np
import gradio as gr
from PIL import Image
# Dummy segmentation function: replace with your actual segmentation model inference if available.
def segment_foreground(img):
# Convert input image to a NumPy array
np_img = np.array(img.convert("RGB"))
h, w, _ = np_img.shape
# Create a circular mask as a dummy example
mask = np.zeros((h, w), dtype=np.uint8)
center = (w // 2, h // 2)
radius = min(center) - 10
cv2.circle(mask, center, radius, (255), thickness=-1)
return mask
# Function to apply Gaussian blur to the background using the segmentation mask.
def gaussian_blur_background(img, sigma=15):
mask = segment_foreground(img)
np_img = np.array(img.convert("RGB"))
# Apply Gaussian blur to the entire image
blurred = cv2.GaussianBlur(np_img, (0, 0), sigma)
# Prepare the mask in 3 channels
mask_3d = np.stack([mask] * 3, axis=-1) / 255.0
# Combine the original (foreground) with the blurred (background)
combined = np_img * mask_3d + blurred * (1 - mask_3d)
return Image.fromarray(combined.astype(np.uint8))
# Dummy depth estimation function: replace with your actual depth estimation inference.
def estimate_depth(img):
np_img = np.array(img.convert("RGB"))
h, w, _ = np_img.shape
# Create a gradient depth map: top of the image is close (0), bottom is far (1)
depth = np.tile(np.linspace(0, 1, h)[:, None], (1, w))
return depth
# Function to apply depth-based lens blur.
def depth_based_blur(img, max_sigma=20):
depth = estimate_depth(img)
np_img = np.array(img.convert("RGB"))
output = np.zeros_like(np_img)
# Normalize the depth map to [0, 1]
depth_norm = (depth - depth.min()) / (depth.max() - depth.min() + 1e-8)
# Apply a variable Gaussian blur to each row based on the depth value (using the first column as representative)
for i in range(np_img.shape[0]):
sigma = max_sigma * depth_norm[i, 0]
row = cv2.GaussianBlur(np_img[i:i+1, :, :], (0, 0), sigma)
output[i, :, :] = row
return Image.fromarray(output.astype(np.uint8))
# Function that dispatches the processing based on user selection.
def process_image(img, effect):
if effect == "Gaussian Blur Background":
return gaussian_blur_background(img)
elif effect == "Depth-based Lens Blur":
return depth_based_blur(img)
else:
return img
# Create the Gradio interface with an image input and a radio button to select the effect.
iface = gr.Interface(
fn=process_image,
inputs=[
gr.inputs.Image(type="pil", label="Input Image"),
gr.inputs.Radio(["Gaussian Blur Background", "Depth-based Lens Blur"], label="Select Effect")
],
outputs=gr.outputs.Image(type="pil", label="Output Image"),
title="Blur Effects Demo",
description="Upload an image and choose an effect to apply either a Gaussian Blur to the background or a Depth-based Lens Blur."
)
if __name__ == "__main__":
iface.launch()
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