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| import streamlit as st | |
| import time | |
| import cv2 | |
| import pandas | |
| from PIL import Image | |
| import numpy as np | |
| import tensorflow as tf | |
| from tensorflow.keras.applications.resnet50 import preprocess_input | |
| from tensorflow.keras.preprocessing.image import img_to_array | |
| st.title('Palm Identification') | |
| st.markdown("This is a Deep Learning application to identify if a satellite image clip contains Palm trees.\n") | |
| st.markdown('The predicting result will be "Palm", or "Others".') | |
| st.markdown('You can click "Browse files" multiple times until adding all images before generating prediction.\n') | |
| img_height = 224 | |
| img_width = 224 | |
| class_names = ['Palm', 'Others'] | |
| model = tf.keras.models.load_model('model') | |
| state = st.session_state | |
| if 'file_uploader_key' not in state: | |
| state['file_uploader_key'] = 0 | |
| if "uploaded_files" not in state: | |
| state["uploaded_files"] = [] | |
| uploaded_files = st.file_uploader( | |
| "Upload images", | |
| type="jpg" or 'jpeg' or 'bmp' or 'png' or 'tif', | |
| accept_multiple_files=True, | |
| key=state['file_uploader_key']) | |
| if uploaded_files: | |
| state["uploaded_files"] = uploaded_files | |
| if st.button("Clear all"): | |
| state["file_uploader_key"] += 1 | |
| time.sleep(.5) | |
| st.experimental_rerun() | |
| if st.button("Generate prediction"): | |
| for file in uploaded_files: | |
| img = Image.open(file) | |
| img_array = img_to_array(img) | |
| img_array = tf.expand_dims(img_array, axis = 0) # Create a batch | |
| processed_image = preprocess_input(img_array) | |
| predictions = model.predict(processed_image) | |
| score = predictions[0] | |
| st.markdown("Predicted class of the image {} is : {}".format(file, class_names[np.argmax(score)])) | |