Merge branch 'master' of github.com:taddeus/licenseplates

src/FilterNoise.py

@@ -1,29 +0,0 @@
-from scipy.ndimage import convolve1d
-from pylab import ceil, zeros, pi, e, exp, sqrt, array
-
-def f(x, s):
-    """Return the value of a 1D Gaussian function for a given x and scale."""
-    return exp(-(x ** 2 / (2 * s ** 2))) / (sqrt(2 * pi) * s)
-
-                                                             
-def gauss1(s, order=0):
-    """Sample a one-dimensional Gaussian function of scale s."""
-    s = float(s)
-    r = int(ceil(3 * s))
-    size = 2 * r + 1
-    W = zeros(size)
-
-    # Sample the Gaussian function
-    W = array([f(x - r, s) for x in xrange(size)])
-
-    if not order:
-        # Make sure that the sum of all kernel values is equal to one
-        W /= W.sum()
-
-    return W
-    
-def filterNoise(image, s):
-    '''Apply a gaussian blur to an image, to suppress noise.'''
-    filt = gauss1(s)
-    image = convolve1d(image.data, filt, axis=0, mode='nearest')
-    return convolve1d(image, filt, axis=1, mode='nearest')

src/FilterNoiseTest.py

@@ -1,11 +0,0 @@
-from FilterNoise import filterNoise
-from GrayscaleImage import GrayscaleImage
-
-# Get the image
-image = GrayscaleImage('../images/plate.png')
-
-output_image = filterNoise(image, 1.4)
-
-# Show the licenseplate                 
-output_image = GrayscaleImage(None, output_image)
-output_image.show()

src/GaussianFilter.py

@@ -0,0 +1,39 @@
+from GrayscaleImage import GrayscaleImage
+from scipy.ndimage import convolve1d
+from pylab import ceil, zeros, pi, e, exp, sqrt, array
+
+class GaussianFilter:
+
+    def __init__(self, scale):
+        self.scale = scale
+        
+    def gaussian(self, x):
+        '''Return the value of a 1D Gaussian function for a given x and scale'''
+        return exp(-(x ** 2 / (2 * self.scale ** 2))) / (sqrt(2 * pi) * self.scale)
+
+    def get_1d_gaussian_kernel(self):
+        '''Sample a one-dimensional Gaussian function of scale s'''
+        radius = int(ceil(3 * self.scale))
+        size = 2 * radius + 1
+        
+        result = zeros(size)
+        # Sample the Gaussian function    
+        result = array([self.gaussian(x - radius) for x in xrange(size)])
+        # The sum of all kernel values is equal to one
+        result /= result.sum() 
+
+        return result
+
+    def get_filtered_copy(self, image):
+        '''Apply a gaussian blur to an image, to suppress noise.'''
+        kernel = self.get_1d_gaussian_kernel()
+        image = convolve1d(image.data, kernel, axis=0, mode='nearest')
+        return GrayscaleImage(None, convolve1d(image, kernel, axis=1, mode='nearest'))
+        
+    def get_scale(self):
+      return self.scale
+      
+    def set_scale(self, scale):
+        self.scale = float(scale)
+
+    scale = property(get_scale, set_scale)

src/GaussianFilterTest.py

@@ -0,0 +1,9 @@
+from FilterNoise import GaussianFilter
+from GrayscaleImage import GrayscaleImage
+
+image = GrayscaleImage('../images/plate.png')
+
+filter = GaussianFilter(1.4)
+output_image = filter.get_filtered_copy(image)
+
+output_image.show()