Did I find the right examples for you? yes no      Crawl my project      Python Jobs

All Samples(31)  |  Call(31)  |  Derive(0)  |  Import(0)

src/a/n/antispoofing.lbptop-1.0.4/antispoofing/lbptop/spoof/calclbptop.py   antispoofing.lbptop(Download)
  #XY
  if(nXY==4):
    lbp_XY = bob.ip.LBP(neighbors = 4, radius=rX, circular=cXY, uniform=uniformXY, rotation_invariant=riu2XY, to_average=mctXY, elbp_type=elbps[elbptypeXY])
    lbp_XY.radius2 = rY
  elif(nXY==8):
    lbp_XY = bob.ip.LBP(neighbors = 8, radius=rX, circular=cXY, uniform=uniformXY, rotation_invariant=riu2XY, to_average=mctXY,elbp_type=elbps[elbptypeXY])
    lbp_XY.radius2 = rY
  elif(nXY==16):
    lbp_XY = bob.ip.LBP(neighbors = 16, radius=rX, circular=cXY, uniform=uniformXY, rotation_invariant=riu2XY, to_average=mctXY,elbp_type=elbps[elbptypeXY])
  #XT
  if(nXT==4):
    lbp_XT = bob.ip.LBP(neighbors = 4, radius=rX, circular=cXT, uniform=uniformXT, rotation_invariant=riu2XT, to_average=mctXT, elbp_type=elbps[elbptypeXT])
    lbp_XT.radius2 = rT
  elif(nXT==8):
    lbp_XT = bob.ip.LBP(neighbors = 8, radius=rX, circular=cXT, uniform=uniformXT, rotation_invariant=riu2XT, to_average=mctXT,elbp_type=elbps[elbptypeXT])

src/m/a/maskattack.lbp-1.0.3/maskattack/lbp/spoof/calclbp.py   maskattack.lbp(Download)
  if lbptype == 'uniform':
    if neighbors==16:
      lbp = bob.ip.LBP(neighbors=16, uniform=True, circular=circ, radius=rad, to_average=mct, elbp_type=elbps[elbptype])
    else: # we assume neighbors==8 in this case
      lbp = bob.ip.LBP(neighbors=8, uniform=True, circular=circ, radius=rad, to_average=mct, elbp_type=elbps[elbptype])
  elif lbptype == 'riu2':
    if neighbors==16:
      lbp = bob.ip.LBP(neighbors=16, uniform=True, rotation_invariant=True, radius=rad, circular=circ, to_average=mct, elbp_type=elbps[elbptype])
    else: # we assume neighbors==8 in this case
      lbp = bob.ip.LBP(neighbors=8, uniform=True, rotation_invariant=True, radius=rad, circular=circ, to_average=mct, elbp_type=elbps[elbptype])
  else: # regular LBP
    if neighbors==16:
      lbp = bob.ip.LBP(neighbors=16, circular=circ, radius=rad, to_average=mct, elbp_type=elbps[elbptype])
    else: # we assume neighbors==8 in this case
      lbp = bob.ip.LBP(neighbors=16, circular=circ, radius=rad, to_average=mct, elbp_type=elbps[elbptype])

src/a/n/antispoofing.lbp-1.3.0/antispoofing/lbp/spoof/calclbp.py   antispoofing.lbp(Download)
  if lbptype == 'uniform':
    if neighbors==16:
      lbp = bob.ip.LBP(neighbors=16, uniform=True, circular=circ, radius=rad, to_average=mct, elbp_type=elbps[elbptype])
    else: # we assume neighbors==8 in this case
      lbp = bob.ip.LBP(neighbors=8, uniform=True, circular=circ, radius=rad, to_average=mct, elbp_type=elbps[elbptype])
  elif lbptype == 'riu2':
    if neighbors==16:
      lbp = bob.ip.LBP(neighbors=16, uniform=True, rotation_invariant=True, radius=rad, circular=circ, to_average=mct, elbp_type=elbps[elbptype])
    else: # we assume neighbors==8 in this case
      lbp = bob.ip.LBP(neighbors=8, uniform=True, rotation_invariant=True, radius=rad, circular=circ, to_average=mct, elbp_type=elbps[elbptype])
  else: # regular LBP
    if neighbors==16:
      lbp = bob.ip.LBP(neighbors=16, circular=circ, radius=rad, to_average=mct, elbp_type=elbps[elbptype])
    else: # we assume neighbors==8 in this case
      lbp = bob.ip.LBP(neighbors=16, circular=circ, radius=rad, to_average=mct, elbp_type=elbps[elbptype])

src/m/a/maskattack.study-1.0.0/maskattack/study/antispoof/lbp.py   maskattack.study(Download)
  if lbptype != 'maatta11':
    if lbptype != 'modified':
      lbp = bob.ip.LBP(8,1,1,uniform=True,elbp_type=elbptype[lbptype]) 
    else:
      lbp = bob.ip.LBP(8,1,1,uniform=True,to_average=True)
  else:
    hist1 = numpy.array([]) 
    lbp1 = bob.ip.LBP(8,1,1,uniform=True)
    lbpimage = numpy.ndarray(lbp1.get_lbp_shape(img), 'uint16') # allocating the image with lbp codes
    lbp1(img, lbpimage) # calculating the lbp image
        hist = hist / sum(hist) # histogram normalization
      hist1 = numpy.append(hist1, hist) # concatenate the subblocks' already normalized histograms     
    lbp2 = bob.ip.LBP(8,2,2,uniform=True,circular=True)
    lbpimage = numpy.ndarray(lbp2.get_lbp_shape(img), 'uint16') # allocating the image with lbp codes  
    lbp2(img, lbpimage) # calculating the lbp image
    hist2 = bob.ip.histogram(lbpimage, 0, lbp2.max_label-1, lbp2.max_label)   
    lbp3 = bob.ip.LBP(16,2,2,uniform=True,circular=True)

src/e/s/estimate.gender-0.4/estimate/gender/estimateGender.py   estimate.gender(Download)
  finalhist = numpy.array([])
  if lbptype != 'mod':
    lbp = bob.ip.LBP(num_points,radius,radius,uniform=True,elbp_type=elbptype[lbptype]) 
  else:
    lbp = bob.ip.LBP(num_points,radius,radius,uniform=True,to_average=True)

src/m/a/maskattack.study-1.0.0/maskattack/study/analyze/lbp.py   maskattack.study(Download)
def lbphist(img):
  norm = False
  finalhist = numpy.array([])
  lbp = bob.ip.LBP(8,2,2,uniform=True) 
  lbpimage = numpy.ndarray(lbp.get_lbp_shape(img), 'uint16') # allocating the image with lbp codes

src/f/a/facereclib-1.2.1/facereclib/preprocessing/INormLBP.py   facereclib(Download)
 
    # lbp extraction
    self.m_lgb_extractor = bob.ip.LBP(8, radius, is_circular, compare_to_average, add_average_bit, is_uniform, is_rotation_invariant, bob.ip.ELBPType.REGULAR)
    if self.m_perform_image_cropping:
      self.m_i_norm_image = numpy.ndarray([size - 2*radius for size in self.m_cropped_image.shape], numpy.uint16)

src/f/a/facereclib-HEAD/facereclib/preprocessing/INormLBP.py   facereclib(Download)
 
    # lbp extraction
    self.m_lgb_extractor = bob.ip.LBP(8, radius, is_circular, compare_to_average, add_average_bit, is_uniform, is_rotation_invariant, bob.ip.ELBPType.REGULAR)
    if self.m_perform_image_cropping:
      self.m_i_norm_image = numpy.ndarray([size - 2*radius for size in self.m_cropped_image.shape], numpy.uint16)