def setUpClass(cls):
super(_TestKeypoints, cls).setUpClass()
if ocl:
cls.ctx = ocl.create_context()
if logger.getEffectiveLevel() <= logging.INFO:
cls.PROFILE = True
cls.queue = pyopencl.CommandQueue(cls.ctx, properties=pyopencl.command_queue_properties.PROFILING_ENABLE)
else:
cls.PROFILE = False
cls.queue = pyopencl.CommandQueue(cls.ctx)
device = cls.ctx.devices[0]
device_id = device.platform.get_devices().index(device)
platform_id = pyopencl.get_platforms().index(device.platform)
cls.maxwg = ocl.platforms[platform_id].devices[device_id].max_work_group_size
def setUpClass(cls):
super(TestKeypoints, cls).setUpClass()
if ocl:
cls.ctx = ocl.create_context()
if logger.getEffectiveLevel() <= logging.INFO:
cls.PROFILE = True
cls.queue = pyopencl.CommandQueue(cls.ctx, properties=pyopencl.command_queue_properties.PROFILING_ENABLE)
else:
cls.PROFILE = False
cls.queue = pyopencl.CommandQueue(cls.ctx)
device = cls.ctx.devices[0]
device_id = device.platform.get_devices().index(device)
platform_id = pyopencl.get_platforms().index(device.platform)
cls.maxwg = ocl.platforms[platform_id].devices[device_id].max_work_group_size
def __init__(self,
image,
devicetype="CPU",
profile=False,
device=None,
max_workgroup_size=None,
ROI=None,
extra=0,
context=None,
init_sigma=None):
"""
Constructor of the class
:param image: reference image on which other image should be aligned
:param devicetype: Kind of preferred devce
:param profile:collect profiling information ?
:param device: 2-tuple of integer. see clinfo
:param max_workgroup_size: limit the workgroup size
:param ROI: Region of interest: to be implemented
:param extra: extra space around the image, can be an integer, or a 2 tuple in YX convention: TODO!
:param init_sigma: bluring width, you should have good reasons to modify the 1.6 default value...
"""
self.profile = bool(profile)
self.events = []
self.program = None
self.ref = numpy.ascontiguousarray(image, numpy.float32)
self.buffers = {}
self.shape = image.shape
if len(self.shape) == 3:
self.RGB = True
self.shape = self.shape[:2]
elif len(self.shape) == 2:
self.RGB = False
else:
raise RuntimeError("Unable to process image of shape %s" %
(tuple(self.shape, )))
if "__len__" not in dir(extra):
self.extra = (int(extra), int(extra))
else:
self.extra = extra[:2]
self.outshape = tuple(i + 2 * j
for i, j in zip(self.shape, self.extra))
self.ROI = ROI
if context:
self.ctx = context
device_name = self.ctx.devices[0].name.strip()
platform_name = self.ctx.devices[0].platform.name.strip()
platform = ocl.get_platform(platform_name)
device = platform.get_device(device_name)
self.device = platform.id, device.id
else:
if device is None:
self.device = ocl.select_device(type=devicetype, best=True)
if self.device is None:
raise RuntimeError(
"No suitable OpenCL device found with given constrains"
)
else:
self.device = device
self.ctx = pyopencl.Context(devices=[
pyopencl.get_platforms()[self.device[0]].get_devices()[
self.device[1]]
])
ocldevice = ocl.platforms[self.device[0]].devices[self.device[1]]
self.devicetype = ocldevice.type
if max_workgroup_size:
self.max_workgroup_size = min(int(max_workgroup_size),
ocldevice.max_work_group_size)
else:
self.max_workgroup_size = ocldevice.max_work_group_size
self.kernels = {}
for k, v in self.__class__.kernels.items():
self.kernels[k] = min(v, self.max_workgroup_size)
if self.RGB:
target = (4, 8, 4)
self.wg = tuple(
min(t, i, self.max_workgroup_size) for t, i in zip(
target, self.ctx.devices[0].max_work_item_sizes))
else:
target = (8, 4)
self.wg = tuple(
min(t, i, self.max_workgroup_size) for t, i in zip(
target, self.ctx.devices[0].max_work_item_sizes))
self.sift = SiftPlan(template=image,
context=self.ctx,
profile=self.profile,
max_workgroup_size=self.max_workgroup_size,
init_sigma=init_sigma)
self.ref_kp = self.sift.keypoints(image)
if self.ROI is not None:
kpx = numpy.round(self.ref_kp.x).astype(numpy.int32)
kpy = numpy.round(self.ref_kp.y).astype(numpy.int32)
masked = self.ROI[(kpy, kpx)].astype(bool)
logger.warning(
"Reducing keypoint list from %i to %i because of the ROI" %
(self.ref_kp.size, masked.sum()))
self.ref_kp = self.ref_kp[masked]
self.match = MatchPlan(context=self.ctx,
profile=self.profile,
max_workgroup_size=self.max_workgroup_size)
# Allocate reference keypoints on the GPU within match context:
self.buffers["ref_kp_gpu"] = pyopencl.array.to_device(
self.match.queue, self.ref_kp)
# TODO optimize match so that the keypoint2 can be optional
self.fill_value = 0
# print self.ctx.devices[0]
if self.profile:
self.queue = pyopencl.CommandQueue(
self.ctx,
properties=pyopencl.command_queue_properties.PROFILING_ENABLE)
else:
self.queue = pyopencl.CommandQueue(self.ctx)
self._compile_kernels()
self._allocate_buffers()
self.sem = Semaphore()
self.relative_transfo = None
def __init__(
self,
shape=None,
dtype=None,
devicetype="CPU",
template=None,
profile=False,
device=None,
PIX_PER_KP=None,
max_workgroup_size=None,
context=None,
init_sigma=None,
):
"""
Constructor of the class
:param shape: shape of the input image
:param dtype: data type of the input image
:param devicetype: can be 'CPU' or 'GPU'
:param template: extract shape and dtype from an image
:param profile: collect timing info
:param device: 2-tuple of integers
:param PIX_PER_KP: number of keypoint pre-allocated: 1 for 10 pixel
:param max_workgroup_size: set to 1 under macosX on CPU
:param context: provide an external context
"""
if init_sigma is None:
init_sigma = par.InitSigma
# no test on the values, just make sure it is a float
self._init_sigma = float(init_sigma)
self.buffers = {}
self.programs = {}
if template is not None:
self.shape = template.shape
self.dtype = template.dtype
else:
self.shape = shape
self.dtype = numpy.dtype(dtype)
if len(self.shape) == 3:
self.RGB = True
self.shape = self.shape[:2]
elif len(self.shape) == 2:
self.RGB = False
else:
raise RuntimeError("Unable to process image of shape %s" % (tuple(self.shape)))
if PIX_PER_KP:
self.PIX_PER_KP = int(PIX_PER_KP)
self.profile = bool(profile)
self.events = []
self._sem = threading.Semaphore()
self.scales = [] # in XY order
self.procsize = [] # same as procsize but with dimension in (X,Y) not (slow, fast)
self.wgsize = []
self.kpsize = None
self.memory = None
self.octave_max = None
self.red_size = None
self._calc_scales()
self.max_workgroup_size = max_workgroup_size or 4096
self._calc_memory()
self.LOW_END = 0
if context:
self.ctx = context
device_name = self.ctx.devices[0].name.strip()
platform_name = self.ctx.devices[0].platform.name.strip()
platform = ocl.get_platform(platform_name)
device = platform.get_device(device_name)
self.device = platform.id, device.id
else:
if device is None:
self.device = ocl.select_device(type=devicetype, memory=self.memory, best=True)
if self.device is None:
self.device = ocl.select_device(memory=self.memory, best=True)
logger.warning("Unable to find suitable device, selecting device: %s,%s" % self.device)
else:
self.device = device
self.ctx = pyopencl.Context(
devices=[pyopencl.get_platforms()[self.device[0]].get_devices()[self.device[1]]]
)
if profile:
self.queue = pyopencl.CommandQueue(self.ctx, properties=pyopencl.command_queue_properties.PROFILING_ENABLE)
else:
self.queue = pyopencl.CommandQueue(self.ctx)
ocldevice = ocl.platforms[self.device[0]].devices[self.device[1]]
self._calc_workgroups()
self._compile_kernels()
self._allocate_buffers()
self.debug = []
self.cnt = numpy.empty(1, dtype=numpy.int32)
self.devicetype = ocldevice.type
if self.devicetype == "CPU":
self.USE_CPU = True
else:
self.USE_CPU = False
if "HD Graphics" in ocldevice.name:
self.LOW_END = 2
def __init__(self, size=16384, devicetype="CPU", profile=False, device=None, max_workgroup_size=None, roi=None, context=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 max_workgroup_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)
"""
self.profile = bool(profile)
self.events = []
self.kpsize = size
self.buffers = {}
self.programs = {}
self.memory = None
self.octave_max = None
self.red_size = None
if context:
self.ctx = context
device_name = self.ctx.devices[0].name.strip()
platform_name = self.ctx.devices[0].platform.name.strip()
platform = ocl.get_platform(platform_name)
device = platform.get_device(device_name)
self.device = platform.id, device.id
else:
if device is None:
self.device = ocl.select_device(type=devicetype, memory=self.memory, best=True)
else:
self.device = device
self.ctx = pyopencl.Context(devices=[pyopencl.get_platforms()[self.device[0]].get_devices()[self.device[1]]])
if profile:
self.queue = pyopencl.CommandQueue(self.ctx, properties=pyopencl.command_queue_properties.PROFILING_ENABLE)
else:
self.queue = pyopencl.CommandQueue(self.ctx)
# self._calc_workgroups()
self._compile_kernels()
self._allocate_buffers()
self.debug = []
self._sem = threading.Semaphore()
ocldevice = ocl.platforms[self.device[0]].devices[self.device[1]]
if max_workgroup_size:
self.max_workgroup_size = min(int(max_workgroup_size), ocldevice.max_work_group_size)
else:
self.max_workgroup_size = ocldevice.max_work_group_size
self.kernels = {}
for k, v in self.__class__.kernels.items():
self.kernels[k] = min(v, self.max_workgroup_size)
self.devicetype = ocldevice.type
if (self.devicetype == "CPU"):
self.USE_CPU = True
self.matching_kernel = "matching_cpu"
else:
self.USE_CPU = False
self.matching_kernel = "matching_gpu"
self.roi = None
if roi:
self.set_roi(roi)
def __init__(self,
size=16384,
devicetype="ALL",
profile=False,
device=None,
max_workgroup_size=None,
roi=None,
context=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 max_workgroup_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)
"""
self.profile = bool(profile)
self.events = []
self.kpsize = size
self.buffers = {}
self.programs = {}
self.memory = None
self.octave_max = None
self.red_size = None
if context:
self.ctx = context
device_name = self.ctx.devices[0].name.strip()
platform_name = self.ctx.devices[0].platform.name.strip()
platform = ocl.get_platform(platform_name)
device = platform.get_device(device_name)
self.device = platform.id, device.id
else:
if device is None:
self.device = ocl.select_device(type=devicetype,
memory=self.memory,
best=True)
else:
self.device = device
self.ctx = pyopencl.Context(devices=[
pyopencl.get_platforms()[self.device[0]].get_devices()[
self.device[1]]
])
if profile:
self.queue = pyopencl.CommandQueue(
self.ctx,
properties=pyopencl.command_queue_properties.PROFILING_ENABLE)
else:
self.queue = pyopencl.CommandQueue(self.ctx)
# self._calc_workgroups()
self._compile_kernels()
self._allocate_buffers()
self.debug = []
self._sem = threading.Semaphore()
ocldevice = ocl.platforms[self.device[0]].devices[self.device[1]]
if max_workgroup_size:
self.max_workgroup_size = min(int(max_workgroup_size),
ocldevice.max_work_group_size)
else:
self.max_workgroup_size = ocldevice.max_work_group_size
self.kernels = {}
for k, v in self.__class__.kernels.items():
self.kernels[k] = min(v, self.max_workgroup_size)
self.devicetype = ocldevice.type
if (self.devicetype == "CPU"):
self.USE_CPU = True
self.matching_kernel = "matching_cpu"
else:
self.USE_CPU = False
self.matching_kernel = "matching_gpu"
self.roi = None
if roi:
self.set_roi(roi)
def __init__(self,
shape=None,
dtype=None,
devicetype="ALL",
template=None,
profile=False,
device=None,
PIX_PER_KP=None,
max_workgroup_size=None,
context=None,
init_sigma=None):
"""
Constructor of the class
:param shape: shape of the input image
:param dtype: data type of the input image
:param devicetype: can be 'CPU' or 'GPU'
:param template: extract shape and dtype from an image
:param profile: collect timing info
:param device: 2-tuple of integers
:param PIX_PER_KP: number of keypoint pre-allocated: 1 for 10 pixel
:param max_workgroup_size: set to 1 under macosX on CPU
:param context: provide an external context
"""
if init_sigma is None:
init_sigma = par.InitSigma
# no test on the values, just make sure it is a float
self._init_sigma = float(init_sigma)
self.buffers = {}
self.programs = {}
if template is not None:
self.shape = template.shape
self.dtype = template.dtype
else:
self.shape = shape
self.dtype = numpy.dtype(dtype)
if len(self.shape) == 3:
self.RGB = True
self.shape = self.shape[:2]
elif len(self.shape) == 2:
self.RGB = False
else:
raise RuntimeError("Unable to process image of shape %s" %
(tuple(self.shape, )))
if PIX_PER_KP:
self.PIX_PER_KP = int(PIX_PER_KP)
self.profile = bool(profile)
self.events = []
self._sem = threading.Semaphore()
self.scales = [] # in XY order
self.procsize = [
] # same as procsize but with dimension in (X,Y) not (slow, fast)
self.wgsize = []
self.kpsize = None
self.memory = None
self.octave_max = None
self.red_size = None
self._calc_scales()
self.max_workgroup_size = max_workgroup_size or 4096
self._calc_memory()
self.LOW_END = 0
if context:
self.ctx = context
device_name = self.ctx.devices[0].name.strip()
platform_name = self.ctx.devices[0].platform.name.strip()
platform = ocl.get_platform(platform_name)
device = platform.get_device(device_name)
self.device = platform.id, device.id
else:
if device is None:
self.device = ocl.select_device(type=devicetype,
memory=self.memory,
best=True)
if self.device is None:
self.device = ocl.select_device(memory=self.memory,
best=True)
if self.device:
logger.warning(
'Unable to find suitable device. Selecting device: %s, %s'
% self.device)
if self.device is None:
raise RuntimeError(
"No suitable OpenCL device found with given constrains"
)
else:
self.device = device
self.ctx = pyopencl.Context(devices=[
pyopencl.get_platforms()[self.device[0]].get_devices()[
self.device[1]]
])
if profile:
self.queue = pyopencl.CommandQueue(
self.ctx,
properties=pyopencl.command_queue_properties.PROFILING_ENABLE)
else:
self.queue = pyopencl.CommandQueue(self.ctx)
ocldevice = ocl.platforms[self.device[0]].devices[self.device[1]]
self._calc_workgroups()
self._compile_kernels()
self._allocate_buffers()
self.debug = []
self.cnt = numpy.empty(1, dtype=numpy.int32)
self.devicetype = ocldevice.type
if (self.devicetype == "CPU"):
self.USE_CPU = True
else:
self.USE_CPU = False
if "HD Graphics" in ocldevice.name:
self.LOW_END = 2
def __init__(self, image, devicetype="CPU", profile=False, device=None, max_workgroup_size=None,
ROI=None, extra=0, context=None, init_sigma=None):
"""
Constructor of the class
:param image: reference image on which other image should be aligned
:param devicetype: Kind of preferred devce
:param profile:collect profiling information ?
:param device: 2-tuple of integer. see clinfo
:param max_workgroup_size: set to 1 for macOSX on CPU
:param ROI: Region of interest: to be implemented
:param extra: extra space around the image, can be an integer, or a 2 tuple in YX convention: TODO!
:param init_sigma: bluring width, you should have good reasons to modify the 1.6 default value...
"""
self.profile = bool(profile)
self.events = []
self.program = None
self.ref = numpy.ascontiguousarray(image, numpy.float32)
self.buffers = {}
self.shape = image.shape
if max_workgroup_size:
self.max_workgroup_size = int(max_workgroup_size)
self.kernels = {}
for k, v in self.__class__.kernels.items():
self.kernels[k] = min(v, self.max_workgroup_size)
else:
self.max_workgroup_size = None
if len(self.shape) == 3:
self.RGB = True
self.shape = self.shape[:2]
elif len(self.shape) == 2:
self.RGB = False
else:
raise RuntimeError("Unable to process image of shape %s" % (tuple(self.shape,)))
if "__len__" not in dir(extra):
self.extra = (int(extra), int(extra))
else:
self.extra = extra[:2]
self.outshape = tuple(i + 2 * j for i, j in zip(self.shape, self.extra))
self.ROI = ROI
if context:
self.ctx = context
device_name = self.ctx.devices[0].name.strip()
platform_name = self.ctx.devices[0].platform.name.strip()
platform = ocl.get_platform(platform_name)
device = platform.get_device(device_name)
self.device = platform.id, device.id
else:
if device is None:
self.device = ocl.select_device(type=devicetype, best=True)
else:
self.device = device
self.ctx = pyopencl.Context(devices=[pyopencl.get_platforms()[self.device[0]].get_devices()[self.device[1]]])
self.devicetype = ocl.platforms[self.device[0]].devices[self.device[1]].type
if (self.devicetype == "CPU"):
self.USE_CPU = True
if sys.platform == "darwin":
logger.warning("MacOSX computer working on CPU: limiting workgroup size to 1 !")
self.max_workgroup_size = 1
self.kernels = {}
for k, v in self.__class__.kernels.items():
if isinstance(v, int):
self.kernels[k] = 1
else:
self.kernels[k] = tuple([1 for i in v])
if self.RGB:
if self.max_workgroup_size == 1:
self.wg = (1, 1, 1)
else:
self.wg = (4, 8, 4)
else:
if self.max_workgroup_size == 1:
self.wg = (1, 1)
else:
self.wg = (8, 4)
self.sift = SiftPlan(template=image, context=self.ctx, profile=self.profile,
max_workgroup_size=self.max_workgroup_size, init_sigma=init_sigma)
self.ref_kp = self.sift.keypoints(image)
if self.ROI is not None:
kpx = numpy.round(self.ref_kp.x).astype(numpy.int32)
kpy = numpy.round(self.ref_kp.y).astype(numpy.int32)
masked = self.ROI[(kpy, kpx)].astype(bool)
logger.warning("Reducing keypoint list from %i to %i because of the ROI" % (self.ref_kp.size, masked.sum()))
self.ref_kp = self.ref_kp[masked]
self.match = MatchPlan(context=self.ctx, profile=self.profile, max_workgroup_size=self.max_workgroup_size)
# Allocate reference keypoints on the GPU within match context:
self.buffers["ref_kp_gpu"] = pyopencl.array.to_device(self.match.queue, self.ref_kp)
# TODO optimize match so that the keypoint2 can be optional
self.fill_value = 0
# print self.ctx.devices[0]
if self.profile:
self.queue = pyopencl.CommandQueue(self.ctx, properties=pyopencl.command_queue_properties.PROFILING_ENABLE)
else:
self.queue = pyopencl.CommandQueue(self.ctx)
self._compile_kernels()
self._allocate_buffers()
self.sem = Semaphore()
self.relative_transfo = None
