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import streamlit as st
from zipfile import ZipFile
from PIL import Image
import numpy as np
from tensorflow import keras
from keras.models import load_model
from keras import backend as K
import cv2
import tempfile
st.title("Satellite Image Segmentation with Dense-UNet")
@st.cache(allow_output_mutation=True)
def loading_model():
model = load_model('satellitesegment.h5')
#model._make_predict_function()
#model.summary()
session = K.get_session()
return model,session
@st.cache
def upload_img(image):
img_npy = np.array(image)
#img_npy = img_npy.reshape((1,512,512,3))
return img_npy
uploaded_file = st.file_uploader("Choose an image...", type=['tif'])
if uploaded_file is not None:
tfile = tempfile.NamedTemporaryFile(delete=False)
tfile.write(uploaded_file.read())
t_img = Image.open(tfile.name)
image = cv2.imread(tfile.name,-1)
st.image(t_img, caption='Uploaded Image.', use_column_width=False)
button = st.button("Let's Predict Image")
if button:
t = st.empty()
t.markdown('## İmage is segmenting...')
#t.markdown(f'{image.shape}')
model,session = loading_model()
K.set_session(session)
image = np.array(image,dtype='uint16').reshape((1,512,512,3))
result_img = model.predict(image)
result_img = result_img[:,:,:,:]>0.5
result_img = result_img[0,:,:,0]
result_img = Image.fromarray(result_img)
t.markdown('## Segmentation result: ')
st.image(result_img, caption='Predicted Image.', use_column_width=False)
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