#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
#
# Project: Sift implementation in Python + OpenCL
# https://github.com/silx-kit/silx
#
# Copyright (C) 2013-2018 European Synchrotron Radiation Facility, Grenoble, France
#
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# included in all copies or substantial portions of the Software.
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"""
Contains a class for creating a matching plan, allocating arrays,
compiling kernels and other things like that
"""
from __future__ import division, print_function, with_statement
__authors__ = ["Jérôme Kieffer", "Pierre Paleo"]
__contact__ = "jerome.kieffer@esrf.eu"
__license__ = "MIT"
__copyright__ = "European Synchrotron Radiation Facility, Grenoble, France"
__date__ = "17/01/2018"
__status__ = "production"
import logging
import numpy
from .param import par
from ..common import pyopencl, kernel_workgroup_size
from .utils import calc_size
from ..processing import OpenclProcessing, BufferDescription
logger = logging.getLogger(__name__)
[docs]class MatchPlan(OpenclProcessing):
"""Plan to compare sets of SIFT keypoint and find common ones.
.. code-block:: python
siftp = sift.MatchPlan(devicetype="ALL")
commonkp = siftp.match(kp1,kp2)
where kp1, kp2 is a n x 132 array. the second dimension is composed of x,y,
scale and angle as well as 128 floats describing the keypoint.
commonkp is mx2 array of matching keypoints
"""
kernels_size = {"matching_gpu": 64,
"matching_cpu": 16}
dtype_kp = numpy.dtype([('x', numpy.float32),
('y', numpy.float32),
('scale', numpy.float32),
('angle', numpy.float32),
('desc', (numpy.uint8, 128))
])
def __init__(self, size=16384, devicetype="ALL", profile=False, device=None,
block_size=None, roi=None, ctx=None):
"""Constructor of the class:
:param size: size of the input keypoint-list alocated on the GPU.
:param devicetype: can be CPU or GPU
:param profile: set to true to activate profiling information collection
:param device: 2-tuple of integer, see clinfo
:param block_size: CPU on MacOS, limit to 1. None by default to use default ones (max=128).
:param roi: Region Of Interest: TODO
:param context: Use an external context (discard devicetype and device options)
"""
OpenclProcessing.__init__(self, ctx=ctx, devicetype=devicetype,
block_size=block_size, profile=profile)
self.kpsize = size
self.octave_max = None
self.red_size = None
self.debug = []
devicetype = self.device.type
if (devicetype == "CPU"):
self.USE_CPU = True
matching_kernel = "matching_cpu"
else:
self.USE_CPU = False
matching_kernel = "matching_gpu"
wg_size = self.__class__.kernels_size[matching_kernel]
self.compile_kernels(kernel_files=["sift/sift",
"sift/memset",
"sift/" + matching_kernel],
compile_options='-D WORKGROUP_SIZE=%s' % wg_size)
self.roi = None
if roi:
self.set_roi(roi)
buffers = [ # BufferDescription"name", "size", "dtype", "flags"
BufferDescription("Kp_1", self.kpsize, self.dtype_kp, flags=None),
BufferDescription("Kp_2", self.kpsize, dtype=self.dtype_kp, flags=None),
BufferDescription("match", (self.kpsize, 2), dtype=numpy.int32, flags=None),
BufferDescription("cnt", 1, numpy.int32, flags=None)]
self.allocate_buffers(buffers, use_array=True)
self.kernel_size = {}
for name, kernel in self.kernels.get_kernels().items():
self.kernel_size[name] = kernel_workgroup_size(self.program, kernel)
[docs] def match(self, nkp1, nkp2, raw_results=False):
"""Calculate the matching of 2 keypoint list
:param nkp1: numpy 1D recarray of keypoints or equivalent GPU buffer
:param nkp2: numpy 1D recarray of keypoints or equivalent GPU buffer
:param raw_results: if true return the 2D array of indexes of matching keypoints (not the actual keypoints)
TODO: implement the ROI ...
"""
assert len(nkp1.shape) == 1 # Nota: nkp1.ndim is not valid for gpu_arrays
assert len(nkp2.shape) == 1
valid_types = (numpy.ndarray, numpy.core.records.recarray, pyopencl.array.Array)
assert isinstance(nkp1, valid_types)
assert isinstance(nkp2, valid_types)
result = None
with self.sem:
if isinstance(nkp1, pyopencl.array.Array):
kpt1_gpu = nkp1
else:
if nkp1.size > self.cl_mem["Kp_1"].size:
logger.warning("increasing size of keypoint vector 1 to %i" % nkp1.size)
self.cl_mem["Kp_1"] = pyopencl.array.empty(self.queue, (nkp1.size,), dtype=self.dtype_kp)
kpt1_gpu = self.cl_mem["Kp_1"]
self._reset_buffer1()
evt1 = pyopencl.enqueue_copy(self.queue, kpt1_gpu.data, nkp1)
if self.profile:
self.events.append(("copy H->D KP_1", evt1))
if isinstance(nkp2, pyopencl.array.Array):
kpt2_gpu = nkp2
else:
if nkp2.size > self.cl_mem["Kp_2"].size:
logger.warning("increasing size of keypoint vector 2 to %i" % nkp2.size)
self.cl_mem["Kp_2"] = pyopencl.array.empty(self.queue, (nkp2.size,), dtype=self.dtype_kp)
kpt2_gpu = self.cl_mem["Kp_2"]
self._reset_buffer2()
evt2 = pyopencl.enqueue_copy(self.queue, kpt2_gpu.data, nkp2)
if self.profile:
self.events.append(("copy H->D KP_2", evt2))
if min(kpt1_gpu.size, kpt2_gpu.size) > self.cl_mem["match"].shape[0]:
self.kpsize = min(kpt1_gpu.size, kpt2_gpu.size)
self.cl_mem["match"] = pyopencl.array.empty(self.queue, (self.kpsize, 2), dtype=numpy.int32)
self._reset_output()
wg = self.kernel_size["matching"]
size = calc_size((nkp1.size,), (wg,))
evt = self.kernels.matching(self.queue, size, (wg,),
kpt1_gpu.data,
kpt2_gpu.data,
self.cl_mem["match"].data,
self.cl_mem["cnt"].data,
numpy.int32(self.kpsize),
numpy.float32(par.MatchRatio * par.MatchRatio),
numpy.int32(nkp1.size),
numpy.int32(nkp2.size))
if self.profile:
self.events.append(("matching", evt))
size = self.cl_mem["cnt"].get()[0]
match = numpy.empty(shape=(size, 2), dtype=numpy.int32)
if size > 0:
cpyD2H = pyopencl.enqueue_copy(self.queue, match, self.cl_mem["match"].data)
if self.profile:
self.events.append(("copy D->H match", cpyD2H))
if raw_results:
result = match
else:
result = numpy.recarray(shape=(size, 2), dtype=self.dtype_kp)
result[:, 0] = nkp1[match[:size, 0]]
result[:, 1] = nkp2[match[:size, 1]]
return result
__call__ = match
def _reset_buffer(self):
"""Reseet all buffers"""
self._reset_buffer1()
self._reset_buffer2()
self._reset_output()
def _reset_buffer1(self):
wg = self.kernel_size["memset_kp"]
size = calc_size((self.cl_mem["Kp_1"].size,), (wg,))
ev1 = self.kernels.memset_kp(self.queue, size, (wg,),
self.cl_mem["Kp_1"].data, numpy.float32(-1.0), numpy.uint8(0), numpy.int32(self.cl_mem["Kp_1"].size))
if self.profile:
self.events.append(("memset Kp1", ev1))
def _reset_buffer2(self):
wg = self.kernel_size["memset_kp"]
size = calc_size((self.cl_mem["Kp_2"].size,), (wg,))
ev2 = self.kernels.memset_kp(self.queue, size, (wg,),
self.cl_mem["Kp_2"].data, numpy.float32(-1.0), numpy.uint8(0), numpy.int32(self.cl_mem["Kp_2"].size))
if self.profile:
self.events.append(("memset Kp2", ev2))
def _reset_output(self):
ev3 = self.kernels.memset_int(self.queue, calc_size((self.cl_mem["match"].size,), (self.kernel_size["memset_int"],)), (self.kernel_size["memset_int"],),
self.cl_mem["match"].data, numpy.int32(-1), numpy.int32(self.cl_mem["match"].size))
ev4 = self.kernels.memset_int(self.queue, (1,), (1,),
self.cl_mem["cnt"].data, numpy.int32(0), numpy.int32(1))
if self.profile:
self.events += [("memset match", ev3),
("memset cnt", ev4), ]
reset_timer = OpenclProcessing.reset_log
[docs] def set_roi(self, roi):
"""Defines the region of interest
:param roi: region of interest as 2D numpy array with non zero where
valid pixels are
"""
with self.sem:
self.roi = numpy.ascontiguousarray(roi, numpy.int8)
self.cl_mem["ROI"] = pyopencl.array.to_device(self.queue, self.roi)
[docs] def unset_roi(self):
"""Unset the region of interest
"""
with self.sem:
self.roi = None
self.cl_mem["ROI"] = None
[docs]def match_py(nkp1, nkp2, raw_results=False):
"""Pure numpy implementation of match:
:param nkp1, nkp2: Numpy record array of keypoints with descriptors
:param raw_results: return the indices of valid indexes instead of
:return: (2,n) 2D array of matching keypoints.
"""
assert len(nkp1.shape) == 1
assert len(nkp2.shape) == 1
valid_types = (numpy.ndarray, numpy.core.records.recarray)
assert isinstance(nkp1, valid_types)
assert isinstance(nkp2, valid_types)
result = None
desc1 = nkp1.desc
desc2 = nkp2.desc
big1 = desc1.astype(int)[:, numpy.newaxis, :]
big2 = desc2.astype(int)[numpy.newaxis, :, :]
big = abs(big1 - big2).sum(axis=-1)
maxi = big.max(axis=-1)
mini = big.min(axis=-1)
amin = big.argmin(axis=-1)
patched = big.copy()
patched[numpy.arange(big.shape[0]), amin] = maxi
mini2 = patched.min(axis=-1)
ratio = mini.astype(float) / mini2
ratio[mini2 == 0] = 1.0
match_mask = ratio < (par.MatchRatio * par.MatchRatio)
size = match_mask.sum()
match = numpy.empty((size, 2), dtype=int)
match[:, 0] = numpy.arange(nkp1.size)[match_mask]
match[:, 1] = amin[match_mask]
if raw_results:
result = match
else:
result = numpy.recarray(shape=(size, 2), dtype=MatchPlan.dtype_kp)
result[:, 0] = nkp1[match[:, 0]]
result[:, 1] = nkp2[match[:, 1]]
return result
def demo():
import scipy.misc
from .plan import SiftPlan
if hasattr(scipy.misc, "ascent"):
img1 = scipy.misc.ascent()
else:
img1 = scipy.misc.lena()
splan = SiftPlan(template=img1)
kp1 = splan(img1)
img2 = numpy.zeros_like(img1)
img2[5:, 8:] = img1[:-5, :-8]
kp2 = splan(img2)
mp = MatchPlan()
match = mp(kp1, kp2)
print(match.shape)
print(numpy.median(match[:, 0].x - match[:, 1].x))
print(numpy.median(match[:, 0].y - match[:, 1].y))
if __name__ == "__main__":
demo()
