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import streamlit as st
import numpy as np
import matplotlib.pyplot as plt
from astropy.io import fits
from astropy.wcs import WCS
from astropy.nddata import Cutout2D
from tensorflow.keras.models import load_model
st.set_option('deprecation.showPyplotGlobalUse', False)
st.title("Cavity Detection Tool")
model = load_model("CADET.hdf5")
# Define function to plot the uploaded image
def plot_image(image_array, pred):
plt.figure(figsize=(10, 5))
plt.subplot(1, 2, 1)
plt.imshow(image_array, origin="lower")
plt.axis('off')
plt.subplot(1, 2, 2)
plt.imshow(pred, origin="lower")
plt.axis('off')
st.pyplot()
def cut(data0, wcs0, scale=1):
shape = data0.shape[0]
x0 = shape / 2
size = 128 * scale
cutout = Cutout2D(data0, (x0, x0), (size, size), wcs=wcs0)
data, wcs = cutout.data, cutout.wcs
# REGRID DATA
factor = size // 128
data = data.reshape(128, factor, 128, factor).mean(-1).mean(1)
# REGIRD WCS
ra, dec = wcs.wcs_pix2world(np.array([[63, 63]]),0)[0]
wcs.wcs.cdelt[0] = wcs.wcs.cdelt[0] * factor
wcs.wcs.cdelt[1] = wcs.wcs.cdelt[1] * factor
wcs.wcs.crval[0] = ra
wcs.wcs.crval[1] = dec
wcs.wcs.crpix[0] = 64 / factor
wcs.wcs.crpix[1] = 64 / factor
return data, wcs
# Create file uploader widget
uploaded_file = st.file_uploader("Choose a FITS file", type=['fits'])
# Add a slider to change the scale
scale = st.slider("Scale", 1, 4, 1, 1)
# If file is uploaded, read in the data and plot it
if uploaded_file is not None:
with fits.open(uploaded_file) as hdul:
data = hdul[0].data
wcs = WCS(hdul[0].header)
data, wcs = cut(data, wcs, scale=scale)
image_data = np.log10(data+1)
pred = model.predict(image_data.reshape(1, 128, 128, 1)).reshape(128 ,128)
ccd = CCDData(pred, unit="adu", wcs=wcs)
ccd.write(f"predicted.fits", overwrite=True)
plot_image(image_data, pred)
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