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| import streamlit as st | |
| # import fastai | |
| from pathlib import Path | |
| import pathlib | |
| temp = pathlib.PosixPath | |
| # pathlib.PosixPath = pathlib.WindowsPath | |
| from fastai.learner import load_learner | |
| from PIL import Image | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| path = Path('/content/') | |
| def label_func(x): | |
| num = x.stem.split('_')[1] | |
| return path/'labels'/f'label_{num}.png' | |
| st.title('Flood Segmentation App') | |
| uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg"]) | |
| print(uploaded_file) | |
| model = load_learner('fastai_tfl_253img.pkl', cpu = True) | |
| if uploaded_file is not None: | |
| img = Image.open(uploaded_file) | |
| x = np.asarray(img) | |
| tm, tb, tbp = model.predict(x) | |
| # img = img.resize((300,300)) | |
| # print(img) | |
| # img = image.load_img(im, target_size=(300,300)) | |
| # x = image.img_to_array(img) | |
| st.image(img, caption='Uploaded Image.', use_column_width=True) | |
| st.write("") | |
| st.write("Generating Segmentation Mask...") | |
| # x = np.expand_dims(x, axis=0) | |
| # prob = model.predict(x) | |
| # print(tm.numpy()) | |
| fig, ax = plt.subplots() | |
| plt.axis('off') | |
| ax.imshow(tm) | |
| # mask = Image.fromarray(np.uint8(tm)) | |
| # st.image(mask, caption='Segmentation Mask', use_column_width=True) | |
| st.pyplot(fig) | |