Commit 55e29afc authored by Rakesh MEHTA's avatar Rakesh MEHTA
Browse files

Variable name change

parent 3f17cb4d
......@@ -83,13 +83,13 @@ class lbp_feature():
# extract the specific feature from the image
if self.ftype == 'lbp':
feature_map = self.lbp(coord, fmap_dimx, fmap_dimy, block_sum)
feature_map = self.lbp(coord, feature_map_dimx, feature_map_dimy, block_sum)
elif self.ftype == 'tlbp':
feature_map = self.tlbp(coord, fmap_dimx, fmap_dimy, block_sum)
feature_map = self.tlbp(coord, feature_map_dimx, feature_map_dimy, block_sum)
elif self.ftype == 'dlbp':
feature_map = self.dlbp(coord, fmap_dimx, fmap_dimy, block_sum)
feature_map = self.dlbp(coord, feature_map_dimx, feature_map_dimy, block_sum)
elif self.ftype == 'mlbp':
feature_map = self.mlbp(coord, fmap_dimx, fmap_dimy, block_sum)
feature_map = self.mlbp(coord, feature_map_dimx, feature_map_dimy, block_sum)
# reshape feature image into vector
temp_vector = numpy.reshape(feature_map,feature_map.shape[0]*fmap.shape[1],1)
......@@ -100,79 +100,79 @@ class lbp_feature():
def lbp(self, coord, fmap_dimx, fmap_dimy, block_sum):
def lbp(self, coord, feature_map_dimx, feature_map_dimy, block_sum):
"""Function to compute the LBP for a image at single scale.
The LBP features of the given image is computed and the feature map is returned
Inputs:
coord: The coordinates specify the neighbour to be considered.
fmap_dimx: feature map's dimension along the columns.
fmap_dimy: Feature maps dimension along the rows.
feature_map_dimx: feature map's dimension along the columns.
feature_map_dimy: Feature maps dimension along the rows.
Return:
feature_map: The lbp feature map
"""
num_neighbours = 8
blk_center = block_sum[1:1+fmap_dimy,1:1+fmap_dimx]
feature_map = numpy.zeros([fmap_dimy, fmap_dimx])
blk_center = block_sum[1:1+feature_map_dimy,1:1+feature_map_dimx]
feature_map = numpy.zeros([feature_map_dimy, feature_map_dimx])
for ind in range(num_neighbours):
feature_map = feature_map + (2**ind)*(block_sum[coord[ind][0]:coord[ind][0] + fmap_dimy,coord[ind][1]:coord[ind][1] + fmap_dimx]>= blk_center)
feature_map = feature_map + (2**ind)*(block_sum[coord[ind][0]:coord[ind][0] + feature_map_dimy,coord[ind][1]:coord[ind][1] + feature_map_dimx]>= blk_center)
return feature_map
def tlbp(self, coord, fmap_dimx, fmap_dimy, block_sum):
def tlbp(self, coord, feature_map_dimx, feature_map_dimy, block_sum):
"""Function to compute the tLBP for a image at single scale.
The tLBP features of the given image is computed and the feature map is returned
Inputs:
coord: The coordinates specify the neighbour to be considered.
fmap_dimx: feature map's dimension along the columns.
fmap_dimy: Feature maps dimension along the rows.
feature_map_dimx: feature map's dimension along the columns.
feature_map_dimy: Feature maps dimension along the rows.
Return:
feature_map: The lbp feature map
"""
feature_map = numpy.zeros([fmap_dimy, fmap_dimx])
feature_map = numpy.zeros([feature_map_dimy, feature_map_dimx])
num_neighbour = 8
""" Compute the feature map for the tLBP features. """
for ind in range(num_neighbours):
"""The comparison of pixel is done with the adjacent neighbours."""
comparing_img = block_sum[coord[(ind+1)%num_neighbour][0]:coord[(ind+1)%num_neighbour][0] + fmap_dimy,coord[(ind+1)%num_neighbour][1]:coord[(ind+1)%num_neighbour][1] + fmap_dimx]
comparing_img = block_sum[coord[(ind+1)%num_neighbour][0]:coord[(ind+1)%num_neighbour][0] + feature_map_dimy,coord[(ind+1)%num_neighbour][1]:coord[(ind+1)%num_neighbour][1] + feature_map_dimx]
""" Compare the neighbours and increment the feature map. """
feature_map = feature_map + (2**ind)*(block_sum[coord[ind][0]:coord[ind][0] + fmap_dimy,coord[ind][1]:coord[ind][1] + fmap_dimx]>= comparing_img)
feature_map = feature_map + (2**ind)*(block_sum[coord[ind][0]:coord[ind][0] + feature_map_dimy,coord[ind][1]:coord[ind][1] + feature_map_dimx]>= comparing_img)
return feature_map
def dlbp(self, coord, fmap_dimx, fmap_dimy, block_sum):
def dlbp(self, coord, feature_map_dimx, feature_map_dimy, block_sum):
"""Function to compute the dLBP for a image at single scale.
The dLBP features of the given image is computed and the feature map is returned
Inputs:
coord: The coordinates specify the neighbour to be considered.
fmap_dimx: feature map's dimension along the columns.
fmap_dimy: Feature maps dimension along the rows.
feature_map_dimx: feature map's dimension along the columns.
feature_map_dimy: Feature maps dimension along the rows.
Return:
feature_map: The lbp feature map
"""
pc = block_sum[1:1+fmap_dimy,1:1+fmap_dimx]
pc = block_sum[1:1+feature_map_dimy,1:1+feature_map_dimx]
num_neighbours = 8
fmap = numpy.zeros([fmap_dimy,fmap_dimx])
fmap = numpy.zeros([feature_map_dimy,feature_map_dimx])
for ind in range(num_neighbours/2):
"""The comparison of pixel is done with the diagonal neighbours."""
pi = block_sum[coord[ind][0]:coord[ind][0]+ fmap_dimy,coord[ind][1]:coord[ind][1] + fmap_dimx]
pi4 = block_sum[coord[ind+4][0]:coord[ind+4][0]+ fmap_dimy,coord[ind+4][1]:coord[ind+4][1] + fmap_dimx]
pi = block_sum[coord[ind][0]:coord[ind][0]+ feature_map_dimy,coord[ind][1]:coord[ind][1] + feature_map_dimx]
pi4 = block_sum[coord[ind+4][0]:coord[ind+4][0]+ feature_map_dimy,coord[ind+4][1]:coord[ind+4][1] + feature_map_dimx]
""" Compare the neighbours and increment the feature map. """
fmap = fmap + (2**ind)*((pi-pc)*(pi4 - pc) > 0) + (4**ind)*(abs(pi - pc) >= abs(pi4 -pc))
......@@ -181,33 +181,33 @@ class lbp_feature():
def mlbp(self, coord, fmap_dimx, fmap_dimy, block_sum):
def mlbp(self, coord, feature_map_dimx, feature_map_dimy, block_sum):
"""Function to compute the mLBP for a image at single scale.
The mLBP features of the given image is computed and the feature map is returned.
Inputs:
coord: The coordinates specify the neighbour to be considered.
fmap_dimx: feature map's dimension along the columns.
fmap_dimy: Feature maps dimension along the rows.
feature_map_dimx: feature map's dimension along the columns.
feature_map_dimy: Feature maps dimension along the rows.
Return:
feature_map: The lbp feature map
"""
num_neighbours = 8
pm = numpy.zeros([fmap_dimy,fmap_dimx])
pm = numpy.zeros([feature_map_dimy,feature_map_dimx])
"""The comparison of pixel is done with the average of the neighbours and central pixel."""
for ind in range(num_neighbours):
pm = pm + block_sum[coord[ind][0]:coord[ind][0]+ fmap_dimy,coord[ind][1]:coord[ind][1] + fmap_dimx]
pm = pm + block_sum[coord[ind][0]:coord[ind][0]+ feature_map_dimy,coord[ind][1]:coord[ind][1] + feature_map_dimx]
pm = pm/num_neighbours
feature_map = numpy.zeros([fmap_dimy,fmap_dimx])
feature_map = numpy.zeros([feature_map_dimy,feature_map_dimx])
for ind in range(num_neighbours):
""" Select the value of the current neighbour."""
pi = block_sum[coord[ind][0]:coord[ind][0]+ fmap_dimy,coord[ind][1]:coord[ind][1] + fmap_dimx]
pi = block_sum[coord[ind][0]:coord[ind][0]+ feature_map_dimy,coord[ind][1]:coord[ind][1] + feature_map_dimx]
""" Compare the neighbours and increment the feature map. """
feature_map = feature_map + (2**ind)*(pi >= pm)
......
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