More preprocessor changes

bob/bio/vein/preprocessor/filters.py

+#!/usr/bin/env python
+# vim: set fileencoding=utf-8 :
+
+'''Base utilities for post-filtering vein images'''
+
+import numpy
+
+
+class Filter(object):
+  '''Objects of this class filter the input image'''
+
+
+  def __init__(self):
+    pass
+
+
+  def __call__(self, image, mask):
+    '''Inputs image and mask and outputs a filtered version of the image
+
+
+    Parameters:
+
+      image (numpy.ndarray): raw image to filter as 2D array of unsigned
+          8-bit integers
+
+      mask (numpy.ndarray): mask to normalize as 2D array of booleans
+
+
+    Returns:
+
+      numpy.ndarray: A 2D boolean array with the same shape and data type of
+      the input image representing the filtered image.
+
+    '''
+
+    raise NotImplemented('You must implement the __call__ slot')
+
+
+class NoFilter(Filter):
+  '''Applies no filtering on the input image, returning it without changes'''
+
+  def __init__(self):
+    pass
+
+
+  def __call__(self, image, mask):
+    '''Inputs image and mask and outputs the image, without changes
+
+
+    Parameters:
+
+      image (numpy.ndarray): raw image to filter as 2D array of unsigned
+          8-bit integers
+
+      mask (numpy.ndarray): mask to normalize as 2D array of booleans
+
+
+    Returns:
+
+      numpy.ndarray: A 2D boolean array with the same shape and data type of
+      the input image representing the filtered image.
+
+    '''
+
+    return image
+
+
+class HistogramEqualization(Filter):
+  '''Applies histogram equalization on the input image inside the mask.
+
+  In this implementation, only the pixels that lie inside the mask will be
+  used to calculate the histogram equalization parameters. Because of this
+  particularity, we don't use Bob's implementation for histogram equalization
+  and have one based exclusively on scikit-image.
+  '''
+
+
+  def __init__(self):
+    pass
+
+
+  def __call__(self, image, mask):
+    '''Applies histogram equalization on the input image, returns filtered
+
+
+    Parameters:
+
+      image (numpy.ndarray): raw image to filter as 2D array of unsigned
+          8-bit integers
+
+      mask (numpy.ndarray): mask to normalize as 2D array of booleans
+
+
+    Returns:
+
+      numpy.ndarray: A 2D boolean array with the same shape and data type of
+      the input image representing the filtered image.
+
+    '''
+
+    from skimage.exposure import equalize_hist
+    from skimage.exposure import rescale_intensity
+
+    retval = rescale_intensity(equalize_hist(image, mask=mask), out_range = (0, 255))
+
+    # make the parts outside the mask totally black
+    retval[~mask] = 0
+
+    return retval