Upload New File - Includes bivariate distribution of Gaussian and skewed t (attempt)

extraction/bivariate.py

+import numpy as np
+import matplotlib.pyplot as plt
+import argparse
+import math
+from scipy.stats import multivariate_t
+from convolution import convolution
+
+def gauss2D(shape=(3,3),sigma=0.5):
+    """
+    2D gaussian mask - should give the same result as MATLAB's
+    fspecial('gaussian',[shape],[sigma])
+    """
+    m,n = [(ss-1.)/2. for ss in shape]
+    y,x = np.ogrid[-m:m+1,-n:n+1]
+    h = np.exp( -(x*x + y*y) / (2.*sigma*sigma) )/(sigma*np.sqrt(2*np.pi))
+    h[ h < np.finfo(h.dtype).eps*h.max() ] = 0
+    sumh = h.sum()
+    if sumh != 0:
+        h /= sumh
+    return h
+
+def attempt_skewed2D(shape=(3,3),upsilon=2):
+    """
+    2D skewed mask - attempt
+    """
+    m1,n1 = [(ss-1.)/2. for ss in shape]
+    y1,x1 = np.mgrid[0:2*m1+1:1, 0:2*n1+1:1]
+    pos = np.dstack((x1, y1))
+    
+    rv = multivariate_t([1*(2*m1+1)/5, 3*(2*n1+1)/5],[[1, 0], [0, 1]], df=upsilon)
+    tile = np.array(rv.pdf(pos))
+    m,n = [(ss-1.)/2. for ss in shape]
+    y,x = np.ogrid[-m:m+1,-n:n+1]
+    h = tile
+    h[ h < np.finfo(h.dtype).eps*h.max() ] = 0
+    sumh = h.sum()
+    if sumh != 0:
+        h /= sumh
+    return h
+
+def gaussian_blur(image, verbose=False):
+    kernel = gauss2D((5,5),1)
+    return convolution(image, kernel, average=False, verbose=verbose)
+
+def skew_blur(image, verbose=False):
+    kernel = attempt_skewed2D((5,5),1)
+    return convolution(image, kernel, average=False, verbose=verbose)
+
+if __name__ == '__main__':
+    ap = argparse.ArgumentParser()
+    ap.add_argument("-i", "--image", required=True, help="Path to the image")
+    args = vars(ap.parse_args())
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