Commit c35479d3 authored by André Anjos's avatar André Anjos 💬

[preproc] Improve finger post-processing (heq); Fix warnings

parent bc0745df
......@@ -26,10 +26,10 @@ class FingerCrop (Preprocessor):
In this implementation, the finger image is (in this order):
1. Padded
2. The mask is extracted
3. The finger is normalized (made horizontal)
3. (optionally) Post processed
1. Padded
2. The mask is extracted
3. The finger is normalized (made horizontal)
4. (optionally) Post processed
Parameters:
......@@ -220,15 +220,15 @@ class FingerCrop (Preprocessor):
# Right region has always the finger ending, crop the padding with the
# meadian
finger_mask[:,numpy.median(y_rg)+img_filt_rg.shape[1]:] = False
finger_mask[:,int(numpy.median(y_rg)+img_filt_rg.shape[1]):] = False
# Extract y-position of finger edges
edges = numpy.zeros((2,img_w))
edges[0,:] = y_up
edges[0,0:round(numpy.mean(y_lf))+1] = edges[0,round(numpy.mean(y_lf))+1]
edges[0,0:int(round(numpy.mean(y_lf))+1)] = edges[0,int(round(numpy.mean(y_lf))+1)]
edges[1,:] = numpy.round(y_lo + img_filt_lo.shape[0])
edges[1,0:round(numpy.mean(y_lf))+1] = edges[1,round(numpy.mean(y_lf))+1]
edges[1,0:int(round(numpy.mean(y_lf))+1)] = edges[1,int(round(numpy.mean(y_lf))+1)]
return finger_mask, edges
......@@ -401,8 +401,13 @@ class FingerCrop (Preprocessor):
return (image_norm, mask_norm)
def __HE__(self, image):
"""Applies histogram equalization on the input image
def __HE__(self, image, mask):
"""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 NumPy.
Parameters:
......@@ -410,6 +415,10 @@ class FingerCrop (Preprocessor):
image (numpy.ndarray): raw image to be filtered, as 2D array of
unsigned 8-bit integers
mask (numpy.ndarray): mask of the same size of the image, but composed
of boolean values indicating which values should be considered for
the histogram equalization
Returns:
......@@ -418,9 +427,19 @@ class FingerCrop (Preprocessor):
"""
#Umbralization based on the pixels non zero
retval = numpy.zeros(image.shape, dtype=numpy.uint8)
bob.ip.base.histogram_equalization(image, retval)
image_histogram, bins = numpy.histogram(image[mask], 256, normed=True)
cdf = image_histogram.cumsum() # cumulative distribution function
cdf = 255 * cdf / cdf[-1] # normalize
# use linear interpolation of cdf to find new pixel values
image_equalized = numpy.interp(image.flatten(), bins[:-1], cdf)
image_equalized = image_equalized.reshape(image.shape)
# normalized image to be returned is a composition of the original image
# (background) and the equalized image (finger area)
retval = image.copy()
retval[mask] = image_equalized[mask]
return retval
......@@ -521,32 +540,33 @@ class FingerCrop (Preprocessor):
"""Reads the input image, extract the mask of the fingervein, postprocesses
"""
import ipdb; ipdb.set_trace()
# 1. Pads the input image if any padding should be added
image = numpy.pad(image, self.padding_width, 'constant',
constant_values = self.padding_constant)
## Finger edges and contour extraction:
if self.fingercontour == 'leemaskMatlab':
finger_mask, finger_edges = self.__leemaskMatlab__(image) #for UTFVP
mask, edges = self.__leemaskMatlab__(image) #for UTFVP
elif self.fingercontour == 'leemaskMod':
finger_mask, finger_edges = self.__leemaskMod__(image) #for VERA
mask, edges = self.__leemaskMod__(image) #for VERA
elif self.fingercontour == 'konomask':
finger_mask, finger_edges = self.__konomask__(image, sigma=5)
mask, edges = self.__konomask__(image, sigma=5)
## Finger region normalization:
image_norm, finger_mask_norm = self.__huangnormalization__(image,
finger_mask, finger_edges)
image_norm, mask_norm = self.__huangnormalization__(image, mask, edges)
## veins enhancement:
if self.postprocessing == 'HE':
image_norm = self.__HE__(image_norm)
image_norm = self.__HE__(image_norm, mask_norm)
elif self.postprocessing == 'HFE':
image_norm = self.__HFE__(image_norm)
elif self.postprocessing == 'CircGabor':
image_norm = self.__circularGabor__(image_norm, 1.12, 5)
## returns the normalized image and the finger mask
return image_norm, finger_mask_norm
return image_norm, mask_norm
def write_data(self, data, filename):
......
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