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import tensorflow as tf
from simple_unet_model import simple_unet_model
from tensorflow.keras.utils import normalize
import os
from PIL import Image, ImageOps
import numpy as np
import gradio as gr
#Loading Model
def get_model():
return simple_unet_model(256, 256, 1)
model = get_model()
model.load_weights('mitochondria.hdf5')
def predict(input_image):
img = Image.fromarray(input_image)
gray_img = ImageOps.grayscale(img)
resized_img = gray_img.resize((256,256))
img = np.array(resized_img)
img = np.expand_dims(img, axis = (0,3))
img = normalize(img, axis=1)
mask = model.predict(img)[0,:,:,0]
return mask
def load_examples():
files = os.listdir()
img_list = []
for file in files:
if '.jpg' in file:
img_list.append(file)
return img_list
examples = load_examples()
demo = gr.Interface(fn=predict,
inputs="image",
outputs=gr.Image(shape=(256, 256)),
title = "Mitochondria Detection",
examples =examples )
demo.launch() |