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import numpy as np
from tensorflow.keras.models import load_model
from PIL import Image
import gradio as gr
# Load model
model = load_model("unet_model.h5", compile=False)
# Preprocess function
def preprocess_image(image, target_size=(192, 176)):
image = image.resize((target_size[1], target_size[0])) # width, height
image = np.array(image) / 255.0
if image.ndim == 2:
image = np.expand_dims(image, axis=-1)
return np.expand_dims(image, axis=0)
# Prediction function for Gradio
def predict(img):
# Convert to grayscale
img = img.convert("L")
# Preprocess
input_data = preprocess_image(img)
# Model prediction
pred = model.predict(input_data)[0]
# Remove channel
if pred.ndim == 3 and pred.shape[-1] == 1:
pred = np.squeeze(pred, axis=-1)
# Convert to image
pred_img = (pred * 255).astype(np.uint8)
pred_img = Image.fromarray(pred_img)
return pred_img
# Gradio UI
interface = gr.Interface(
fn=predict,
inputs=gr.Image(type="pil", label="Upload Image"),
outputs=gr.Image(type="pil", label="Denoised Output"),
title="U-Net Image Denoising",
description="Upload a grayscale image and get the denoised result using a U-Net model."
)
interface.launch() |