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import os
from huggingface_hub import login
import gradio as gr
import numpy as np
import torch
from PIL import Image
from transformers import AutoModel, AutoImageProcessor
import matplotlib.pyplot as plt
from weights import W, b
# Load DINOv3
token = os.environ.get("DINO_TOKEN")
if token:
login(token)
MODEL_ID = "facebook/dinov3-vitl16-pretrain-lvd1689m"
processor = AutoImageProcessor.from_pretrained(MODEL_ID)
model = AutoModel.from_pretrained(MODEL_ID)
model.eval()
# Check for register tokens
num_reg = getattr(model.config, "num_register_tokens", 0)
def analyze(image: Image.Image):
# Resize to 224x224
image = image.convert("RGB").resize((224, 224), Image.BICUBIC)
# Extract DINO features
inputs = processor(image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
# Get patch features (skip CLS and register tokens)
features = outputs.last_hidden_state[0, 1+num_reg:, :].numpy() # (196, 1024)
features = features.reshape(14, 14, -1) # (14, 14, 1024)
# EFIQA adapter: 1x1 conv
logits = features @ W + b # (14, 14)
probs = 1 / (1 + np.exp(-logits)) # sigmoid
# Visualize
fig, axes = plt.subplots(1, 2, figsize=(10, 5))
axes[0].imshow(image)
axes[0].set_title("Input Fundus Image")
axes[0].axis("off")
im = axes[1].imshow(probs, cmap="magma", vmin=0, vmax=1)
axes[1].set_title("Quality Map (bright = degraded)")
axes[1].axis("off")
plt.colorbar(im, ax=axes[1], fraction=0.046)
plt.tight_layout()
return fig
demo = gr.Interface(
fn=analyze,
inputs=gr.Image(type="pil", label="Upload Fundus Image"),
outputs=gr.Plot(label="Quality Assessment"),
title="EFIQA: Explainable Fundus Image Quality Assessment",
description="Upload a color fundus photograph to get a spatial quality map. Bright regions indicate areas with degraded quality (missing anatomical structures).",
article="Paper: *EFIQA: Explainable Fundus Image Quality Assessment via Anatomical Priors* (MIDL 2026)",
)
if __name__ == "__main__":
demo.launch()
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