def lbp(image, radius, points, ignore_zeros=False):
'''
features = lbp(image, radius, points, ignore_zeros=False)
Compute Linear Binary Patterns
Parameters
----------
image : ndarray
input image (2-D numpy ndarray)
radius : number (integer or floating point)
radius (in pixels)
points : integer
nr of points to consider
ignore_zeros : boolean, optional
whether to ignore zeros (default: False)
Returns
-------
features : 1-D numpy ndarray
histogram of features
Reference
---------
Gray Scale and Rotation Invariant Texture Classification with Local Binary Patterns
Ojala, T. Pietikainen, M. Maenpaa, T. LECTURE NOTES IN COMPUTER SCIENCE (Springer)
2000, ISSU 1842, pages 404-420
'''
from scipy.ndimage.interpolation import map_coordinates
import mahotas._lbp
from mahotas.histogram import fullhistogram
if ignore_zeros:
Y,X = np.nonzero(image)
pixels = image[Y,X].ravel()
else:
Y,X = np.indices(image.shape)
pixels = image.ravel()
angles = np.linspace(0, 2*np.pi, points+1)[:-1]
coordinates = np.empty((2, points, Y.size), float)
for i,(dy,dx) in enumerate(zip(radius * np.sin(angles), radius * np.cos(angles))):
coordinates[0][i] = Y.ravel()
coordinates[1][i] = X.ravel()
coordinates[0][i] += dy
coordinates[1][i] += dx
data = map_coordinates(image, coordinates.reshape((2,-1)), order=1).reshape((Y.size, -1))
codes = (data.T > pixels).sum(0)
codes = mahotas._lbp.map(codes.astype(np.uint32), points)
final = fullhistogram(codes.astype(np.uint32))
codes = np.arange(2**points, dtype=np.uint32)
iters = codes.copy()
codes = mahotas._lbp.map(codes.astype(np.uint32), points)
pivots = (codes == iters)
npivots = np.sum(pivots)
compressed = final[pivots[:len(final)]]
compressed = np.concatenate((compressed, [0]*(npivots - len(compressed))))
return compressed
def lbp(image, radius, points, ignore_zeros=False):
"""
features = lbp(image, radius, points, ignore_zeros=False)
Compute Linear Binary Patterns
Parameters
----------
image : ndarray
input image (2-D numpy ndarray)
radius : number (integer or floating point)
radius (in pixels)
points : integer
nr of points to consider
ignore_zeros : boolean, optional
whether to ignore zeros (default: False)
Returns
-------
features : 1-D numpy ndarray
histogram of features
Reference
---------
Gray Scale and Rotation Invariant Texture Classification with Local Binary Patterns
Ojala, T. Pietikainen, M. Maenpaa, T. LECTURE NOTES IN COMPUTER SCIENCE (Springer)
2000, ISSU 1842, pages 404-420
"""
from .interpolate import shift
import mahotas._lbp
from mahotas.histogram import fullhistogram
if ignore_zeros:
Y, X = np.nonzero(image)
def select(im):
return im[Y, X].ravel()
pixels = image[Y, X].ravel()
else:
select = np.ravel
pixels = image.ravel()
image = image.astype(np.float64)
angles = np.linspace(0, 2 * np.pi, points + 1)[:-1]
data = []
for dy, dx in zip(np.sin(angles), np.cos(angles)):
data.append(select(shift(image, [radius * dy, radius * dx], order=1)))
data = np.array(data)
codes = (data > pixels).astype(np.int32)
codes *= 2 ** np.arange(points)[:, np.newaxis]
codes = codes.sum(0)
codes = mahotas._lbp.map(codes.astype(np.uint32), points)
final = fullhistogram(codes.astype(np.uint32))
codes = np.arange(2 ** points, dtype=np.uint32)
iters = codes.copy()
codes = mahotas._lbp.map(codes.astype(np.uint32), points)
pivots = codes == iters
npivots = np.sum(pivots)
compressed = final[pivots[: len(final)]]
compressed = np.concatenate((compressed, [0] * (npivots - len(compressed))))
return compressed
