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import gradio as gr
from PIL import Image, ImageFilter
import numpy as np
import torch
from transformers import (
SegformerFeatureExtractor, SegformerForSemanticSegmentation,
DPTFeatureExtractor, DPTForDepthEstimation
)
import cv2
import os, json
# ————————————————
# load segmentation model
seg_model_name = "nvidia/segformer-b1-finetuned-ade-512-512"
seg_fe = SegformerFeatureExtractor.from_pretrained(seg_model_name)
seg_model = SegformerForSemanticSegmentation.from_pretrained(seg_model_name)
# load depth model
depth_model_name = "Intel/dpt-hybrid-midas"
depth_fe = DPTFeatureExtractor.from_pretrained(depth_model_name)
depth_model = DPTForDepthEstimation.from_pretrained(depth_model_name)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
seg_model.to(device)
depth_model.to(device)
def process_image(image: Image.Image):
# 1) prep
image = image.convert("RGB").resize((512,512))
# 2) segmentation → binary mask
seg_inputs = seg_fe(images=image, return_tensors="pt").to(device)
with torch.no_grad():
seg_logits = seg_model(**seg_inputs).logits
seg_map = torch.argmax(seg_logits, dim=1)[0].cpu().numpy()
mask = (seg_map > 0).astype(np.uint8) * 255
mask = Image.fromarray(mask).resize((512,512))
# 3) gaussian-blur background
bg_blur = image.filter(ImageFilter.GaussianBlur(15))
output_blur = Image.composite(image, bg_blur, mask)
# 4) depth estimation
depth_inputs = depth_fe(images=image, return_tensors="pt").to(device)
with torch.no_grad():
depth_pred = depth_model(**depth_inputs).predicted_depth.squeeze().cpu().numpy()
# normalize & resize
dmin, dmax = depth_pred.min(), depth_pred.max()
depth_norm = (depth_pred - dmin) / (dmax - dmin + 1e-8)
depth_norm = cv2.resize(depth_norm, (512,512))
# 5) vectorized depth-based blur
img_np = np.array(image).astype(np.float32)
# two extremes: no blur for near, heavy blur for far
near_blur = img_np
far_blur = cv2.GaussianBlur(img_np, (81,81), 20)
# invert so 0 -> near, 1 -> far
inv_d = 1.0 - depth_norm
alpha = inv_d[...,None]
# now near (inv_d≈1) stays sharp, far (inv_d≈0) becomes far_blur
combined = img_np * alpha + far_blur * (1.0 - alpha)
lens_blur = Image.fromarray(np.clip(combined,0,255).astype(np.uint8))
# 6) composite to keep foreground sharp
mask_np = np.array(mask)
inv_mask = (mask_np == 0).astype(np.uint8) * 255
bg_mask = Image.fromarray(inv_mask)
final_lens_blur = Image.composite(lens_blur, image, mask=bg_mask)
iface = gr.Interface(
fn=process_image,
inputs=gr.Image(type="pil", label="Upload Image"),
outputs=[
gr.Image(type="pil", label="Original"),
gr.Image(type="pil", label="Gaussian Blur"),
gr.Image(type="pil", label="Depth-Based Lens Blur"),
],
title="Image Blurring with CLAHE + Depth-Based Blur",
description="Upload a selfie to see background blur and depth-based lens blur."
)
if __name__ == "__main__":
iface.launch(share=True)
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