Project 3

imageprocessing.py

@@ -0,0 +1,44 @@
+import numpy as np
+import math as mt
+
+def sum_squared_error(D1, D2):
+    if(D1.shape == D2.shape):
+        return np.sum((((D1-np.mean(D1))/np.std(D1)) - ((D2-np.mean(D2))/np.std(D2)))**2)
+    else:
+        return np.inf
+
+def gauss_kernal(size, var):
+       '''Returns a gauss kernal with the given size and variance'''
+
+       kernel = np.zeros(shape=(size,size))
+       for i in range(size):
+              for j in range(size):
+                     kernel[i][j] = mt.exp( -((i - (size-1)/2)**2 + (j - (size-1)/2)**2 )/(2*var*var))
+
+              kernel = kernel / kernel.sum()
+
+       return kernel
+
+def convolve(g,h):
+       '''Convolves image g with kernal h'''
+
+       I_gray_copy = g.copy()
+       x,y = h.shape
+       xl = int(x/2)
+       yl = int(y/2)
+       for i in range(xl,len(g[:,1])-xl):
+              for j in range(yl, len(g[i,:])-yl):
+
+                     f = g[i-xl:i+(xl+1), j-yl:j+(yl+1)]
+
+                     total = h*f
+
+                     I_gray_copy[i][j] = sum(sum(total))
+
+       return I_gray_copy
+
+def gauss_blur(image):
+       '''Returns the image blurred with a gauss kernel of variance 1'''
+       g_kernal = gauss_kernal(3,1)
+       g_blur = convolve(image, g_kernal)
+       return g_blur

testing.py

@@ -0,0 +1,6 @@
+import matplotlib.pyplot as plt
+import numpy as snp
+
+image = plt.imread("waldo_1.jpg")
+plt.imshow(image)
+plt.show()