def __init__(self, size=[160, 120], neighborarea=[8, 8]):
self.size = size
shape = size
nsensels = shape[0] * shape[1]
# for each sensel, create an area
lengths = np.array(neighborarea)
neighbor_coords = [None] * nsensels
self.neighbor_indices_flat = [None] * nsensels
flattening = Flattening.by_rows(tuple(shape))
cmg = cmap(lengths)
self.cmg = cmg
for coord in coords_iterate(shape):
k = flattening.cell2index[coord]
cm = cmg.copy()
cm[:, :, 0] += coord[0]
cm[:, :, 1] += coord[1]
cm[:, :, 0] = cm[:, :, 0] % shape[0]
cm[:, :, 1] = cm[:, :, 1] % shape[1]
neighbor_coords[k] = cm
indices = np.zeros(lengths, 'int32')
for a, b in coords_iterate(indices.shape):
c = tuple(cm[a, b, :])
indices[a, b] = flattening.cell2index[c]
self.neighbor_indices_flat[k] = np.array(indices.flat)
def init_structures(self, shape):
self.shape = shape
self.nsensels = shape[0] * shape[1]
self.ydd = np.zeros(shape, dtype='float32')
# for each sensel, create an area
self.lengths = np.ceil(self.max_displ *
np.array(self.shape)).astype('int32')
# print(' Field Shape: %s' % str(self.shape))
# print(' Fraction: %s' % str(self.max_displ))
# print(' Search area: %s' % str(self.lengths))
self.neighbor_coords = [None] * self.nsensels
self.neighbor_indices = [None] * self.nsensels
self.neighbor_indices_flat = [None] * self.nsensels
self.neighbor_similarity_flat = [None] * self.nsensels
self.neighbor_similarity_best = np.zeros(self.nsensels,
dtype='float32')
self.neighbor_argsort_flat = [None] * self.nsensels
self.neighbor_num_bestmatch_flat = [None] * self.nsensels
self.flattening = Flattening.by_rows(shape)
logger.info('Creating structure shape %s lengths %s' %
(self.shape, self.lengths))
cmg = cmap(self.lengths)
for coord in coords_iterate(self.shape):
k = self.flattening.cell2index[coord]
cm = cmg.copy()
cm[:, :, 0] += coord[0]
cm[:, :, 1] += coord[1]
cm[:, :, 0] = cm[:, :, 0] % self.shape[0]
cm[:, :, 1] = cm[:, :, 1] % self.shape[1]
self.neighbor_coords[k] = cm
indices = np.zeros(self.lengths, 'int32')
for a, b in coords_iterate(indices.shape):
c = tuple(cm[a, b, :])
indices[a, b] = self.flattening.cell2index[c]
self.neighbor_indices[k] = indices
self.neighbor_indices_flat[k] = np.array(indices.flat)
self.neighbor_similarity_flat[k] = np.zeros(
indices.size, 'float32')
self.neighbor_argsort_flat[k] = np.zeros(indices.size, 'float32')
self.neighbor_num_bestmatch_flat[k] = np.zeros(
indices.size, 'uint')
def init_structures(self, shape):
self.shape = shape
self.nsensels = shape[0] * shape[1]
self.ydd = np.zeros(shape, dtype='float32')
# for each sensel, create an area
self.lengths = np.ceil(self.max_displ *
np.array(self.shape)).astype('int32')
# print(' Field Shape: %s' % str(self.shape))
# print(' Fraction: %s' % str(self.max_displ))
# print(' Search area: %s' % str(self.lengths))
self.neighbor_coords = [None] * self.nsensels
self.neighbor_indices = [None] * self.nsensels
self.neighbor_indices_flat = [None] * self.nsensels
self.neighbor_similarity_flat = [None] * self.nsensels
self.neighbor_similarity_best = np.zeros(self.nsensels, dtype='float32')
self.neighbor_argsort_flat = [None] * self.nsensels
self.neighbor_num_bestmatch_flat = [None] * self.nsensels
self.flattening = Flattening.by_rows(shape)
logger.info('Creating structure shape %s lengths %s' %
(self.shape, self.lengths))
cmg = cmap(self.lengths)
for coord in coords_iterate(self.shape):
k = self.flattening.cell2index[coord]
cm = cmg.copy()
cm[:, :, 0] += coord[0]
cm[:, :, 1] += coord[1]
cm[:, :, 0] = cm[:, :, 0] % self.shape[0]
cm[:, :, 1] = cm[:, :, 1] % self.shape[1]
self.neighbor_coords[k] = cm
indices = np.zeros(self.lengths, 'int32')
for a, b in coords_iterate(indices.shape):
c = tuple(cm[a, b, :])
indices[a, b] = self.flattening.cell2index[c]
self.neighbor_indices[k] = indices
self.neighbor_indices_flat[k] = np.array(indices.flat)
self.neighbor_similarity_flat[k] = np.zeros(indices.size, 'float32')
self.neighbor_argsort_flat[k] = np.zeros(indices.size, 'float32')
self.neighbor_num_bestmatch_flat[k] = np.zeros(indices.size, 'uint')