Update app.py

app.py

@@ -1,5 +1,6 @@
 import streamlit as st
 import numpy as np
+import matplotlib.pyplot as plt
 import scipy.fftpack as fp
 import cv2
 from PIL import Image
@@ -18,7 +19,7 @@ def ideal_filter(rows, cols, D0, filtr):
     if filtr == "High Pass":
         H = 1-H
     #H = H*255
-    cv2.imwrite('./filter.jpg',np.abs(H*255))
+    cv2.imwrite('filter.jpg',np.abs(H*255))
     return H
 
 def butterworth_filter(rows, cols, n_order, D0, filtr):
@@ -31,12 +32,12 @@ def butterworth_filter(rows, cols, n_order, D0, filtr):
     if filtr == "High Pass":
         H = 1-H
     
-    cv2.imwrite('./filter.jpg',np.abs(H*255))
+    cv2.imwrite('filter.jpg',np.abs(H*255))
     
     return H
 
 def gaussian_filter(rows, cols, filtr):
-    #import pdb;pdb.set_trace()
+
     H = np.zeros(shape = (rows, cols))
     for i in range(rows):
         for j in range(cols):
@@ -45,44 +46,62 @@ def gaussian_filter(rows, cols, filtr):
     if filtr == "High Pass":
         H = 1-H
     #H = H*255
-    cv2.imwrite('./filter.jpg',np.abs(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)
 
-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)
 
-if uploaded_file is not None:
-    img = Image.open(uploaded_file)
-    img.save("./read_image.jpg")
-    st.write("Source Image")
-    st.image("./read_image.jpg", width = 300)
-    img = cv2.imread("./read_image.jpg", 0)
-    rows, cols = img.shape
-    if kernel == "Ideal":
-        H = ideal_filter(rows, cols, D0, filtr)
-    elif kernel == "Gaussian":
-        H = gaussian_filter(rows, cols, filtr)
-    elif kernel == "Butterworth":
-        H = butterworth_filter(rows, cols, n_order, D0, filtr)
-    
-    H = fp.fft2(fp.ifftshift(H)) # 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
 
-    inv_img = fp.ifft2(conv_img).real
+        output_img = ((inv_img - np.min(inv_img))/np.max(inv_img))*255
 
-    output_img = ((inv_img - np.min(inv_img))/np.max(inv_img))*255
+        #output_img = fp.ifft2(conv_img)
 
-    #plt.imshow(output_img, cmap='gray')
-    st.write("Kernel")
-    st.image("./filter.jpg", width = 300)
-    cv2.imwrite('./output_image.jpg',output_img)
-    st.write(f"Target Image with {filtr} {kernel} Filter")
-    st.image("./output_image.jpg", width = 300)
+        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()