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MCP: refactored to use typed memoryviews

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The arrays are stored on the mcp object, so that they can
be used in methods as well. Will use that to make MCP more
flexibel.
This commit is contained in:
Almar Klein
2013-12-13 15:13:24 +01:00
parent d1329488e6
commit 280fadff39
2 changed files with 59 additions and 49 deletions
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skimage/graph/_mcp.pxd
+17 -9
View File
@@ -9,22 +9,30 @@ cimport numpy as cnp
ctypedef heap.BOOL_T BOOL_T
ctypedef unsigned char DIM_T
ctypedef cnp.float64_t FLOAT_T
ctypedef cnp.intp_t INDEX_T
ctypedef cnp.int8_t EDGE_T
ctypedef cnp.int8_t OFFSET_T
ctypedef cnp.int16_t OFFSETS_INDEX_T
cdef class MCP:
cdef heap.FastUpdateBinaryHeap costs_heap
cdef object costs_shape
cdef DIM_T dim
cdef object flat_costs
cdef object flat_cumulative_costs
cdef object traceback_offsets
cdef object flat_pos_edge_map
cdef object flat_neg_edge_map
cdef readonly object offsets
cdef object flat_offsets
cdef object offset_lengths
cdef BOOL_T dirty
cdef BOOL_T use_start_cost
# if use_start_cost is true, the cost of the starting element is added to
# the cost of the path. Set to true by default in the base class...
# Arrays used during front propagation
cdef FLOAT_T [:] flat_costs
cdef FLOAT_T [:] flat_cumulative_costs
cdef OFFSETS_INDEX_T [:] traceback_offsets
cdef EDGE_T [:,:] flat_pos_edge_map
cdef EDGE_T [:,:] flat_neg_edge_map
cdef OFFSET_T [:,:] offsets
cdef INDEX_T [:] flat_offsets
cdef FLOAT_T [:] offset_lengths
# Methods
cdef FLOAT_T _travel_cost(self, FLOAT_T old_cost, FLOAT_T new_cost, FLOAT_T offset_length)
skimage/graph/_mcp.pyx
+42 -40
View File
@@ -39,13 +39,9 @@ import heap
cimport numpy as cnp
cimport heap
ctypedef cnp.int8_t OFFSET_T
OFFSET_D = np.int8
ctypedef cnp.int16_t OFFSETS_INDEX_T
OFFSETS_INDEX_D = np.int16
ctypedef cnp.int8_t EDGE_T
EDGE_D = np.int8
ctypedef cnp.intp_t INDEX_T
INDEX_D = np.intp
FLOAT_D = np.float64
@@ -274,6 +270,7 @@ cdef class MCP:
returned by the find_costs() method.
"""
def __init__(self, costs, offsets=None, fully_connected=True,
sampling=None):
"""__init__(costs, offsets=None, fully_connected=True, sampling=None)
@@ -301,7 +298,7 @@ cdef class MCP:
self.flat_costs = costs.astype(FLOAT_D).flatten('F')
size = self.flat_costs.shape[0]
self.flat_cumulative_costs = np.empty(size, dtype=FLOAT_D)
self.flat_cumulative_costs.fill(np.inf)
self.flat_cumulative_costs[...] = np.inf
self.dim = len(costs.shape)
self.costs_shape = costs.shape
self.costs_heap = heap.FastUpdateBinaryHeap(initial_capacity=128,
@@ -311,7 +308,7 @@ cdef class MCP:
# array (see below) that leads to that point from the
# predecessor point.
self.traceback_offsets = np.empty(size, dtype=OFFSETS_INDEX_D)
self.traceback_offsets.fill(-1)
self.traceback_offsets[...] = -1
# The offsets are a list of relative offsets from a central
# point to each point in the relevant neighborhood. (e.g. (-1,
@@ -343,21 +340,24 @@ cdef class MCP:
np.sum((sampling*self.offsets)**2, axis=1)).astype(FLOAT_D)
self.dirty = 0
self.use_start_cost = 1
def _reset(self):
"""_reset()
Clears paths found by find_costs().
"""
self.costs_heap.reset()
self.traceback_offsets.fill(-1)
self.flat_cumulative_costs.fill(np.inf)
self.traceback_offsets[...] = -1
self.flat_cumulative_costs[...] = np.inf
self.dirty = 0
cdef FLOAT_T _travel_cost(self, FLOAT_T old_cost,
FLOAT_T new_cost, FLOAT_T offset_length):
return new_cost
def find_costs(self, starts, ends=None, find_all_ends=True):
"""
Find the minimum-cost path from the given starting points.
@@ -406,7 +406,7 @@ cdef class MCP:
cdef BOOL_T use_ends = 0
cdef INDEX_T num_ends
cdef BOOL_T all_ends = find_all_ends
cdef cnp.ndarray[INDEX_T, ndim=1] flat_ends
cdef INDEX_T [:] flat_ends
starts = _normalize_indices(starts, self.costs_shape)
if starts is None:
raise ValueError('start points must all be within the costs array')
@@ -422,32 +422,31 @@ cdef class MCP:
if self.dirty:
self._reset()
# lookup and array-ify object attributes for fast use
# Get shorter names for arrays
cdef FLOAT_T [:] flat_costs = self.flat_costs
cdef FLOAT_T [:] flat_cumulative_costs = self.flat_cumulative_costs
cdef OFFSETS_INDEX_T [:] traceback_offsets = self.traceback_offsets
cdef EDGE_T [:,:] flat_pos_edge_map = self.flat_pos_edge_map
cdef EDGE_T [:,:] flat_neg_edge_map = self.flat_neg_edge_map
cdef OFFSET_T [:,:] offsets = self.offsets
cdef INDEX_T [:] flat_offsets = self.flat_offsets
cdef FLOAT_T [:] offset_lengths = self.offset_lengths
# Short names for other attributes
cdef heap.FastUpdateBinaryHeap costs_heap = self.costs_heap
cdef cnp.ndarray[FLOAT_T, ndim=1] flat_costs = self.flat_costs
cdef cnp.ndarray[FLOAT_T, ndim=1] flat_cumulative_costs = \
self.flat_cumulative_costs
cdef cnp.ndarray[OFFSETS_INDEX_T, ndim=1] traceback_offsets = \
self.traceback_offsets
cdef cnp.ndarray[EDGE_T, ndim=2] flat_pos_edge_map = \
self.flat_pos_edge_map
cdef cnp.ndarray[EDGE_T, ndim=2] flat_neg_edge_map = \
self.flat_neg_edge_map
cdef cnp.ndarray[OFFSET_T, ndim=2] offsets = self.offsets
cdef cnp.ndarray[INDEX_T, ndim=1] flat_offsets = self.flat_offsets
cdef cnp.ndarray[FLOAT_T, ndim=1] offset_lengths = self.offset_lengths
cdef DIM_T dim = self.dim
cdef int num_offsets = len(flat_offsets)
# push each start point into the heap. Note that we use flat indexing!
cdef INDEX_T start
for start in _ravel_index_fortran(starts, self.costs_shape):
if self.use_start_cost:
costs_heap.push_fast(flat_costs[start], start)
else:
costs_heap.push_fast(0, start)
# Variables used during front propagation
cdef FLOAT_T cost, new_cost, cumcost, new_cumcost
cdef INDEX_T index, new_index
cdef BOOL_T is_at_edge, use_offset
@@ -475,7 +474,7 @@ cdef class MCP:
# Record the cost we found to this point
flat_cumulative_costs[index] = cumcost
if use_ends:
# If we're only tracing out a path to one or more
# endpoints, check to see if this is an endpoint, and
@@ -489,7 +488,7 @@ cdef class MCP:
# if we've found one or all of the end points (as
# requested), stop searching
break
# Look into the edge map to see if this point is at an
# edge along any axis
is_at_edge = 0
@@ -557,12 +556,15 @@ cdef class MCP:
traceback_offsets[new_index] = i
# Un-flatten the costs and traceback arrays for human consumption.
cumulative_costs = flat_cumulative_costs.reshape(self.costs_shape,
cumulative_costs = np.asarray(flat_cumulative_costs)
cumulative_costs = cumulative_costs.reshape(self.costs_shape,
order='F')
traceback = traceback_offsets.reshape(self.costs_shape, order='F')
traceback = np.asarray(traceback_offsets)
traceback = traceback.reshape(self.costs_shape, order='F')
self.dirty = 1
return cumulative_costs, traceback
def traceback(self, end):
"""traceback(end)
@@ -602,13 +604,13 @@ cdef class MCP:
if self.flat_cumulative_costs[flat_position] == np.inf:
raise ValueError('no minimum-cost path was found '
'to the specified end point')
cdef cnp.ndarray[INDEX_T, ndim=1] position = \
np.array(ends[0], dtype=INDEX_D)
cdef cnp.ndarray[OFFSETS_INDEX_T, ndim=1] traceback_offsets = \
self.traceback_offsets
cdef cnp.ndarray[OFFSET_T, ndim=2] offsets = self.offsets
cdef cnp.ndarray[INDEX_T, ndim=1] flat_offsets = self.flat_offsets
# Short names for arrays
cdef OFFSETS_INDEX_T [:] traceback_offsets = self.traceback_offsets
cdef OFFSET_T [:,:] offsets = self.offsets
cdef INDEX_T [:] flat_offsets = self.flat_offsets
# New array
cdef INDEX_T [:] position = np.array(ends[0], dtype=INDEX_D)
cdef OFFSETS_INDEX_T offset
cdef DIM_T d