Upload app.py

app.py

@@ -0,0 +1,59 @@
+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)