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import streamlit as st
import numpy as np
import matplotlib.pyplot as plt
import scipy.fftpack as fp
import cv2
from PIL import Image
# ideal filter
def ideal_filter(rows, cols, D0, filtr):
H = np.zeros(shape = (rows, cols))
for i in range(rows):
for j in range(cols):
# euclidean distance from u,v to origin of frequency
Duv = np.sqrt(np.power(i - rows/2, 2) + np.power(j - cols/2, 2))
if Duv < D0:
H[i,j] = 1.0
if filtr == "High Pass":
H = 1-H
#H = H*255
cv2.imwrite('filter.jpg',np.abs(H*255))
return H
def butterworth_filter(rows, cols, n_order, D0, filtr):
H = np.zeros(shape = (rows, cols))
for i in range(rows):
for j in range(cols):
Duv = np.sqrt(np.power(i - rows/2, 2) + np.power(j - cols/2, 2))
H[i,j] = 1/(1+((Duv/D0)**(2*n_order)))
if filtr == "High Pass":
H = 1-H
cv2.imwrite('filter.jpg',np.abs(H*255))
return H
def gaussian_filter(rows, cols, filtr):
H = np.zeros(shape = (rows, cols))
for i in range(rows):
for j in range(cols):
Duv = np.sqrt(np.power(i - rows/2, 2) + np.power(j - cols/2, 2))
H[i,j] = np.exp(-((Duv**2)/(2*(D0**2))))
if filtr == "High Pass":
H = 1-H
#H = H*255
cv2.imwrite('filter.jpg',np.abs(H*255))
return H
def calculate_distance(rows, cols):
dist =np.zeros((rows,cols))
u=np.arange(0, rows, 1)
v=np.arange(0, cols, 1)
for i in range(rows):
for j in range(cols):
dist[i,j]=np.sqrt(((u[i]-rows/2)**2)+((v[j]-cols/2)**2))
dist = np.float32(dist)
return dist
if __name__ == "__main__":
#image = Image.open(file).convert("L")
st.set_option('deprecation.showPyplotGlobalUse', False)
uploaded_file = st.sidebar.file_uploader("Upload image", type = ["jpeg", "jpg", "png"])
filtr = st.sidebar.radio("Filters", ("Low Pass", "High Pass"))
kernel = st.sidebar.radio("Kernels", ("Ideal", "Butterworth", "Gaussian"))
D0 = st.sidebar.slider("Cutoff Frequency", min_value = 0, max_value = 120)
n_order = st.sidebar.number_input(label = "Order", min_value = 0, max_value = 5)
if uploaded_file is not None:
img = Image.open(uploaded_file)
img.save("read_image.jpg")
st.subheader("Source Image")
st.image("read_image.jpg", width = 300)
img = cv2.imread("read_image.jpg", 0)
rows, cols = img.shape
if kernel == "Ideal":
k = ideal_filter(rows, cols, D0, filtr)
elif kernel == "Gaussian":
k = gaussian_filter(rows, cols, filtr)
elif kernel == "Butterworth":
k = butterworth_filter(rows, cols, n_order, D0, filtr)
H = fp.fft2(fp.ifftshift(k)) # fast fourier transform
f_img = fp.fft2(img) # fast fourier transform
conv_img = np.multiply(H, f_img)
inv_img = fp.ifft2(conv_img).real
output_img = ((inv_img - np.min(inv_img))/np.max(inv_img))*255
#output_img = fp.ifft2(conv_img)
cv2.imwrite('output_image.jpg',output_img)
st.subheader(f"Target Image with {filtr} {kernel} Filter, and Filter itself")
st.image(["filter.jpg", "output_image.jpg"], width = 320)
dist = calculate_distance(rows, cols)
st.subheader("Graph of Distance against Function")
plt.plot(dist.ravel(), k.ravel())
plt.xlabel('Distance from the Center')
plt.ylabel('Filter Function')
st.pyplot()
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