Dropping bob.ip.base and bob.ip.color from this package

bob/bio/vein/database/putvein.py

@@ -8,9 +8,35 @@ framework).
 """
 
 from bob.bio.base.database import BioFile, BioDatabase
-import bob.ip.color
 import numpy as np
 
+#TODO: I know this is not DRY recommended, but that's life
+# I might move this to a proper package.
+def rgb_to_gray(image):
+    """
+    Converts an RGB image to a grayscale image.
+    The formula is:
+    GRAY = 0.299 * R + 0.587 * G + 0.114 * B
+    
+
+    Parameters
+    ----------
+
+    image : numpy.ndarray
+        An image in RGB format (channels first): For an ND array (N >= 3),
+
+
+    """
+
+    assert image.ndim == 3, "The image should have 3 dimensions"
+
+    R = image[0, :, :]
+    G = image[1, :, :]
+    B = image[2, :, :]
+
+    return 0.299 * R + 0.587 * G + 0.114 * B
+
+
 
 class File(BioFile):
     """
@@ -38,7 +64,7 @@ class File(BioFile):
         """
         color_image = self.f.load(directory=directory,
                                   extension=extension)
-        grayscale_image = bob.ip.color.rgb_to_gray(color_image)
+        grayscale_image = rgb_to_gray(color_image)
         grayscale_image = np.rot90(grayscale_image, k=3)
         return grayscale_image
 

bob/bio/vein/extractor/LocalBinaryPatterns.py

-#!/usr/bin/env python
-# vim: set fileencoding=utf-8 :
-
-import numpy
-
-import bob.ip.base
-import bob.io.base
-
-from bob.bio.base.extractor import Extractor
-
-
-class LocalBinaryPatterns (Extractor):
-  """LBP feature extractor
-
-  Parameters fixed based on L. Mirmohamadsadeghi and A. Drygajlo. Palm vein
-  recognition using local texture patterns, IET Biometrics, pp. 1-9, 2013.
-  """
-
-  def __init__(
-      self,
-      # Block setup
-      block_size = 59,    # one or two parameters for block size
-      block_overlap = 15, # one or two parameters for block overlap
-      # LBP parameters
-      lbp_radius = 7,
-      lbp_neighbor_count = 16,
-      lbp_uniform = True,
-      lbp_circular = True,
-      lbp_rotation_invariant = False,
-      lbp_compare_to_average = False,
-      lbp_add_average = False,
-      # histogram options
-      sparse_histogram = False,
-      split_histogram = None,
-      ):
-
-    # call base class constructor
-    Extractor.__init__(
-        self,
-        # Block setup
-        block_size = block_size,
-        block_overlap = block_overlap,
-        #LBP parameters
-        lbp_radius = lbp_radius,
-        lbp_neighbor_count = lbp_neighbor_count,
-        lbp_uniform = lbp_uniform,
-        lbp_circular = lbp_circular,
-        lbp_rotation_invariant = lbp_rotation_invariant,
-        lbp_compare_to_average = lbp_compare_to_average,
-        lbp_add_average = lbp_add_average,
-        sparse_histogram = sparse_histogram,
-        split_histogram = split_histogram,
-        )
-
-    # block parameters
-    self.m_block_size = block_size if isinstance(block_size, (tuple, list)) else (block_size, block_size)
-    self.m_block_overlap = block_overlap if isinstance(block_overlap, (tuple, list)) else (block_overlap, block_overlap)
-    if self.m_block_size[0] < self.m_block_overlap[0] or self.m_block_size[1] < self.m_block_overlap[1]:
-      raise ValueError("The overlap is bigger than the block size. This won't work. Please check your setup!")
-
-    self.m_lbp = bob.ip.base.LBP(
-        neighbors = lbp_neighbor_count,
-        radius = float(lbp_radius),
-        circular = lbp_circular,
-        to_average = lbp_compare_to_average,
-        add_average_bit = lbp_add_average,
-        uniform = lbp_uniform,
-        rotation_invariant = lbp_rotation_invariant,
-        border_handling = 'wrap',
-        )
-
-
-    self.m_split = split_histogram
-    self.m_sparse = sparse_histogram
-    if self.m_sparse and self.m_split:
-      raise ValueError("Sparse histograms cannot be split! Check your setup.")
-
-
-  def __fill__(self, lbphs_array, lbphs_blocks, j):
-    """Copies the given array into the given blocks"""
-
-    # fill array in the desired shape
-    for b in range(self.m_n_blocks):
-      lbphs_array[b * self.m_n_bins : (b+1) * self.m_n_bins] = lbphs_blocks[b][:]
-
-
-  def lbp_features(self, finger_image, mask):
-    """Computes and returns the LBP features for the given input fingervein
-    image"""
-
-    finger_image = finger_image.astype(numpy.float64)
-
-    finger_mask = numpy.zeros(mask.shape)
-    finger_mask[mask == True] = 1
-
-    # Mask the vein image with the finger region
-    finger_image = finger_image*finger_mask
-
-    # Computes LBP histograms
-    abs_blocks = bob.ip.base.lbphs(finger_image, self.m_lbp, self.m_block_size, self.m_block_overlap)
-
-    # Converts to Blitz array (of different dimensionalities)
-    self.m_n_bins = abs_blocks.shape[1]
-    self.m_n_blocks = abs_blocks.shape[0]
-
-    shape = self.m_n_bins * self.m_n_blocks
-
-    # create new array
-    lbphs_array = numpy.zeros(shape, 'float64')
-
-    # fill the array with the absolute values of the Gabor wavelet transform
-    self.__fill__(lbphs_array, abs_blocks, 0)
-
-    # return the concatenated list of all histograms
-    return lbphs_array
-
-
-  def __call__(self, image):
-    """Reads the input image, extract the features based on LBP of the fingervein image, and writes the resulting template"""
-    #For debugging
-
-    finger_image = image[0]    #Normalized image with histogram equalization
-    finger_mask = image[1]
-
-    return self.lbp_features(finger_image, finger_mask)

bob/bio/vein/extractor/__init__.py

-from .LocalBinaryPatterns import LocalBinaryPatterns
 from .NormalisedCrossCorrelation import NormalisedCrossCorrelation
 from .PrincipalCurvature import PrincipalCurvature
 from .RepeatedLineTracking import RepeatedLineTracking

conda/meta.yaml

@@ -35,9 +35,6 @@ requirements:
     - bob.extension
     - bob.core
     - bob.io.base
-    - bob.io.image
-    - bob.ip.base
-    - bob.ip.color
     - bob.bio.base
 
   run:

requirements.txt

@@ -9,7 +9,4 @@ matplotlib
 bob.extension
 bob.core
 bob.io.base
-bob.io.image
-bob.ip.base
-bob.ip.color
 bob.bio.base