Update edge.py

corrected space/tab errors... all indentation are now tabs...

mahotas/edge.py

@@ -20,10 +20,10 @@
     [ 1,  2,  1]])/8.
 
 __all__ = [
-    'sobel',
-    'sujoy',
-    'dog',
-    ]
+	'sobel',
+	'sujoy',
+	'dog',
+	]
 
 def sobel(img, just_filter=False):
     '''
@@ -76,56 +76,56 @@ def sujoy(img, kernel_nhood=0, just_filter=False):
 	edges = sujoy(img, kernel_nhood=0, just_filter=False)
     
 	Compute edges using Sujoy's algorithm
-    
-    `edges` is a binary image of edges computed according to Sujoy's algorithm.
-	https://www.ijert.org/research/a-better-first-derivative-approach-for-edge-detection-IJERTV2IS110616.pdf
 	
-    Parameters
-    ----------
-    img : Any 2D-ndarray
+	`edges` is a binary image of edges computed according to Sujoy's algorithm.
+	Paper link: https://www.ijert.org/research/a-better-first-derivative-approach-for-edge-detection-IJERTV2IS110616.pdf
+	
+	Parameters
+	----------
+	img : Any 2D-ndarray
 	kernel_nhood : 0(default) or 1
 		if 0, kernel is based on 4-neighborhood
 		else , kernel is based on 8-neighborhood
-    just_filter : boolean, optional
-        If true, then return the result of filtering the image with the Sujoy's
-        filters, but do not threshold (default is False).
-        
-    Returns
-    -------
-    edges : ndarray
-        Binary image of edges, unless `just_filter`, in which case it will be
-        an array of floating point values.
-    '''
+	just_filter : boolean, optional
+		If true, then return the result of filtering the image with the Sujoy's
+		filters, but do not threshold (default is False).
+	
+	Returns
+	-------
+	edges : ndarray
+		Binary image of edges, unless `just_filter`, in which case it will be
+		an array of floating point values.
+	'''
 	# This is similar to 'sobel" implementation above
-    img = np.array(img, dtype=np.float)
-    if img.ndim != 2:
-        raise ValueError('mahotas.sujoy: Only available for 2-dimensional images')
-    img -= img.min()
-    ptp = img.ptp()
-    if ptp == 0:
-        return img
-    img /= ptp
-
-    if kernel_nhood:
-	    krnl_h = np.array([[0,-1,-1,-1,0],[0,-1,-1,-1,0],[0,0,0,0,0],[0,1,1,1,0],[0,1,1,1,0]])/12.
-	    krnl_v = np.array([[0,0,0,0,0],[-1,-1,0,1,1],[-1,-1,0,1,1],[-1,-1,0,1,1],[0,0,0,0,0]])/12.
-    else:
-        krnl_h = np.array([[0,0,-1,0,0],[0,-1,-1,-1,0],[0,0,0,0,0],[0,1,1,1,0],[0,0,1,0,0]])/8.
-        krnl_v = np.array([[0,0,0,0,0],[0,-1,0,1,0],[-1,-1,0,1,1],[0,-1,0,1,0],[0,0,0,0,0]])/8.
-
-    grad_h = convolve(img, krnl_h, mode='nearest')
-    grad_v = convolve(img, krnl_v, mode='nearest')
-
-    grad_h **=2
-    grad_v **=2
-
-    grad = grad_h
-    grad += grad_v
-    if just_filter:
-	    return grad
-    t = 2*np.sqrt(grad.mean())
-
-    return mh.regmax(grad)*(np.sqrt(grad)>t)
+	img = np.array(img, dtype=np.float)
+	if img.ndim != 2:
+		raise ValueError('mahotas.sujoy: Only available for 2-dimensional images')
+	img -= img.min()
+	ptp = img.ptp()
+	if ptp == 0:
+		return img
+	img /= ptp
+
+	if kernel_nhood:
+		krnl_h = np.array([[0,-1,-1,-1,0],[0,-1,-1,-1,0],[0,0,0,0,0],[0,1,1,1,0],[0,1,1,1,0]])/12.
+		krnl_v = np.array([[0,0,0,0,0],[-1,-1,0,1,1],[-1,-1,0,1,1],[-1,-1,0,1,1],[0,0,0,0,0]])/12.
+	else:
+		krnl_h = np.array([[0,0,-1,0,0],[0,-1,-1,-1,0],[0,0,0,0,0],[0,1,1,1,0],[0,0,1,0,0]])/8.
+		krnl_v = np.array([[0,0,0,0,0],[0,-1,0,1,0],[-1,-1,0,1,1],[0,-1,0,1,0],[0,0,0,0,0]])/8.
+
+	grad_h = convolve(img, krnl_h, mode='nearest')
+	grad_v = convolve(img, krnl_v, mode='nearest')
+
+	grad_h **=2
+	grad_v **=2
+
+	grad = grad_h
+	grad += grad_v
+	if just_filter:
+		return grad
+	t = 2*np.sqrt(grad.mean())
+
+	return mh.regmax(grad)*(np.sqrt(grad)>t)
 
 
 def dog(img, sigma1 = 2, multiplier = 1.001, just_filter = False):