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super_resolution.py
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super_resolution.py
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import streamlit as st
import pandas as pd
import io
from streamlit_option_menu import option_menu
import tensorflow as tf
from keras import backend as K
import matplotlib.pyplot as plt
from skimage.measure import compare_ssim
import cv2
import numpy
from skimage import io
from htbuilder import HtmlElement, div, br, hr, a, p, img, styles
from htbuilder.units import percent,px
def image(src_as_string, **style):
return img(src=src_as_string, style=styles(**style))
def link(link, text, **style):
return a(_href=link, _target="_blank", style=styles(**style))(text)
def layout(*args):
style = """
<style>
# MainMenu {visibility: hidden;}
footer {visibility: hidden;}
</style>
"""
style_div = styles(
left=0,
bottom=0,
margin=px(0, 0, 0, 0),
width=percent(100),
text_align="center",
height="60px",
opacity=0.5
)
style_hr = styles(
)
body = p()
foot = div(style=style_div)(hr(style=style_hr), body)
st.markdown(style, unsafe_allow_html=True)
for arg in args:
if isinstance(arg, str):
body(arg)
elif isinstance(arg, HtmlElement):
body(arg)
st.markdown(str(foot), unsafe_allow_html=True)
def footer():
myargs = [
"<b> Designed by : ❤️ ",
link("https://www.linkedin.com/in/ossamajali/", "Ossama Majali"),
br(),
]
layout(*myargs)
def MSEloss(y_true, y_pred):
return tf.reduce_mean(tf.square(tf.subtract(y_true, y_pred)))
def dssimloss(y_true, y_pred):
ssim2 = tf.image.ssim(y_true, y_pred, 1.0)
return K.mean(1 - ssim2)
def tf_log10(x):
numerator = tf.math.log(x)
denominator = tf.math.log(tf.constant(10, dtype=numerator.dtype))
return numerator / denominator
def PSNR(y_true, y_pred):
max_pixel = 1.0
return 10.0 * tf_log10((max_pixel ** 2) / (K.mean(K.square(y_pred - y_true))))
def SSIM(y_true, y_pred):
return tf.image.ssim(y_true, y_pred, 1.0)
def SR(image,model,scale):
fig = plt.figure(figsize=(23, 23))
ax1 = fig.add_subplot(1,3,1)
ax1.set_title('Original' , fontsize=20, color= '#C8AD7F', fontweight='bold')
ax2 = fig.add_subplot(1,3,2)
ax2.set_title('Bicubic (Input)' , fontsize=20, color= '#C8AD7F', fontweight='bold')
ax3 = fig.add_subplot(1,3,3)
ax3.set_title('Super Resolution (Ouput)' , fontsize=20, color= '#C8AD7F', fontweight='bold')
# original image
original = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
eval_img1 = original
ax1.imshow(cv2.cvtColor(original, cv2.COLOR_BGR2RGB))
img = cv2.cvtColor(original, cv2.COLOR_BGR2YCrCb)
shape = img.shape
# Bicubic image
Y_img = cv2.resize(img[:, :, 0], (int(shape[1] / scale), int(shape[0] / scale)), cv2.INTER_CUBIC)
Y_img = cv2.resize(Y_img, (shape[1], shape[0]), cv2.INTER_CUBIC)
img[:, :, 0] = Y_img
img = cv2.cvtColor(img, cv2.COLOR_YCrCb2BGR)
ax2.imshow( cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
eval_img2 = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
Y = numpy.zeros((1, img.shape[0], img.shape[1], 1), dtype=float)
Y[0, :, :, 0] = Y_img.astype(float) / 255.
pre = model.predict(Y, batch_size=1) * 255.
pre[pre[:] > 255] = 255
pre[pre[:] < 0] = 0
pre = pre.astype(numpy.uint8)
img = cv2.cvtColor(img, cv2.COLOR_BGR2YCrCb)
img[:, :, 0] = pre[0, :, :, 0]
img = cv2.cvtColor(img, cv2.COLOR_YCrCb2BGR)
ax3.imshow( cv2.cvtColor(img,cv2.COLOR_BGR2RGB))
eval_img3 = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
st.pyplot(fig)
evalPSNR(eval_img1,eval_img2,eval_img3)
def log10(x):
numerator = tf.math.log(x)
denominator = tf.math.log(tf.constant(10, dtype=numerator.dtype))
return numerator / denominator
def psnr(im1, im2):
img_arr1 = numpy.array(im1).astype('float32')
img_arr2 = numpy.array(im2).astype('float32')
mse = tf.math.reduce_mean(tf.math.squared_difference(img_arr1, img_arr2))
psnr = tf.constant(255 ** 2, dtype=tf.float32) / mse
result = tf.constant(10, dtype=tf.float32) * log10(psnr)
return result
def evalPSNR(imp1,imp2,imp3):
im1 = cv2.cvtColor(imp1, cv2.COLOR_BGR2YCrCb)[:, :, 0]
im2 = cv2.cvtColor(imp2, cv2.COLOR_BGR2YCrCb)[:, :, 0]
im3 = cv2.cvtColor(imp3, cv2.COLOR_BGR2YCrCb)[:, :, 0]
LR = psnr(im1, im2)
SR = psnr(im1, im3)
df = pd.DataFrame()
header('Quality measures (PSNR / SSIM)')
df = df.append({'Type': 'Low resolution', 'PSNR': tf.get_static_value("%.2f"%LR), 'SSIM': compare_ssim(im1, im2)}, ignore_index = True)
df = df.append({'Type': 'Super resolution', 'PSNR': tf.get_static_value("%.2f"%SR), 'SSIM': compare_ssim(im1, im3)}, ignore_index = True)
st.table(df)
def header(url):
st.markdown(f'<p style="color:#C8AD7F;font-size:24px; font-weight: bold">{url}</p>', unsafe_allow_html=True)
if __name__ == '__main__':
menu_id = option_menu(
menu_title=None, # required
options=["Home", "Super-Resolution"], # required
icons=["house", "bi-badge-hd"], # optional
menu_icon="cast", # optional
default_index=0, # optional
orientation="horizontal",
styles={
"nav-link-selected": {"background-color": "#C8AD7F"},
},
)
hide_streamlit_style = """
<style>
#MainMenu {visibility: hidden;}
footer {visibility: hidden;}
Header {visibility: hidden;}
.stSpinner > div > div {border-top-color: #C8AD7F;}
thead tr th:first-child {display:none}
tbody th {display:none}
</style>
"""
st.markdown(hide_streamlit_style, unsafe_allow_html=True)
if menu_id =='Home':
st.title("Deep Learning-based super resolut-ion image reconstruction")
st.write("Super Resolution is the process of improving the quality of a image by enhancing its apparent resolution. Having an algorithm that effectively imagines the detail that would be present if the image was at a higher resolution.")
st.image("Images/sr.png")
header("References")
st.markdown('<p style="font-style: italic; font-family: \'Times New Roman\', monospace;">Chao Dong, Chen Change Loy, Kaiming He, and Xiaoou Tang. Learning a deep convolutional network for image super-resolution. In European conference on computer vision, pages 184-199. Springer, 2014.</p>', unsafe_allow_html=True)
elif menu_id =="Super-Resolution":
header("Select an image")
img_file = st.file_uploader("Upload File", type=['png', 'jpg', 'jpeg','bmp'])
col1, col2, col3 = st.columns(3)
col1.write(' ')
col3.write(' ')
if img_file is not None:
up_img = io.imread(img_file)
header('Pre-processing')
#col2.image(up_img)
listSearch= ['x2','x3','x4','x8']
__1,__2,__3=st.columns((3))
opt_tick=__2.selectbox("Select the upscale factor",options=listSearch)
if opt_tick == 'x2':
scale = 2
s = str(opt_tick)
elif opt_tick == 'x3':
scale = 3
s = str(opt_tick)
elif opt_tick == 'x4':
scale = 4
s = str(opt_tick)
elif opt_tick == 'x8':
scale = 8
s = str(opt_tick)
else:
scale = 2
s = "x2"
model = tf.keras.models.load_model("models/model-100epoch-"+s+".h5", custom_objects={'dssimloss': dssimloss, 'PSNR': PSNR, 'SSIM': SSIM})
with st.spinner('Wait image processing in progress ..'):
SR(up_img,model,scale)
footer()