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# coding: utf-8
# /*##########################################################################
#
# Copyright (c) 2015-2017 European Synchrotron Radiation Facility
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
#
# ###########################################################################*/
"""This module provides median filter function for 1D and 2D arrays.
"""
__authors__ = ["H. Payno", "J. Kieffer"]
__license__ = "MIT"
__date__ = "02/05/2017"
from cython.parallel import prange
cimport cython
cimport median_filter
import numpy
cimport numpy as cnumpy
cdef Py_ssize_t size = 10
from libcpp cimport bool
ctypedef unsigned long uint64
ctypedef unsigned int uint32
ctypedef unsigned short uint16
def medfilt1d(data, kernel_size=3, bool conditional=False):
"""Function computing the median filter of the given input.
Behavior at boundaries: the algorithm is reducing the size of the
window/kernel for pixels at boundaries (there is no mirroring).
:param numpy.ndarray data: the array for which we want to apply
the median filter. Should be 1d.
:param kernel_size: the dimension of the kernel.
:type kernel_size: int
:param bool conditional: True if we want to apply a conditional median
filtering.
:returns: the array with the median value for each pixel.
"""
return medfilt(data, kernel_size, conditional)
def medfilt2d(image, kernel_size=3, bool conditional=False):
"""Function computing the median filter of the given input.
Behavior at boundaries: the algorithm is reducing the size of the
window/kernel for pixels at boundaries (there is no mirroring).
:param numpy.ndarray data: the array for which we want to apply
the median filter. Should be 2d.
:param kernel_size: the dimension of the kernel.
:type kernel_size: For 1D should be an int for 2D should be a tuple or
a list of (kernel_height, kernel_width)
:param bool conditional: True if we want to apply a conditional median
filtering.
:returns: the array with the median value for each pixel.
"""
return medfilt(image, kernel_size, conditional)
def medfilt(data, kernel_size=3, bool conditional=False):
"""Function computing the median filter of the given input.
Behavior at boundaries: the algorithm is reducing the size of the
window/kernel for pixels at boundaries (there is no mirroring).
:param numpy.ndarray data: the array for which we want to apply
the median filter. Should be 1d or 2d.
:param kernel_size: the dimension of the kernel.
:type kernel_size: For 1D should be an int for 2D should be a tuple or
a list of (kernel_height, kernel_width)
:param bool conditional: True if we want to apply a conditional median
filtering.
:returns: the array with the median value for each pixel.
"""
reshaped = False
if len(data.shape) == 1:
data = data.reshape(data.shape[0], 1)
reshaped = True
elif len(data.shape) > 2:
raise ValueError("Invalid data shape. Dimemsion of the arary should be 1 or 2")
# simple median filter apply into a 2D buffer
output_buffer = numpy.zeros_like(data)
check(data, output_buffer)
if type(kernel_size) in (tuple, list):
if(len(kernel_size) == 1):
ker_dim = numpy.array(1, [kernel_size[0]], dtype=numpy.int32)
else:
ker_dim = numpy.array(kernel_size, dtype=numpy.int32)
else:
ker_dim = numpy.array([kernel_size, kernel_size], dtype=numpy.int32)
if data.dtype == numpy.float64:
medfilterfc = _median_filter_float64
elif data.dtype == numpy.float32:
medfilterfc = _median_filter_float32
elif data.dtype == numpy.int64:
medfilterfc = _median_filter_int64
elif data.dtype == numpy.uint64:
medfilterfc = _median_filter_uint64
elif data.dtype == numpy.int32:
medfilterfc = _median_filter_int32
elif data.dtype == numpy.uint32:
medfilterfc = _median_filter_uint32
elif data.dtype == numpy.int16:
medfilterfc = _median_filter_int16
elif data.dtype == numpy.uint16:
medfilterfc = _median_filter_uint16
else:
raise ValueError("%s type is not managed by the median filter" % data.dtype)
medfilterfc(input_buffer=data,
output_buffer=output_buffer,
kernel_size=ker_dim,
conditional=conditional)
if reshaped:
data = data.reshape(data.shape[0])
output_buffer = output_buffer.reshape(data.shape[0])
return output_buffer
def check(input_buffer, output_buffer):
"""Simple check on the two buffers to make sure we can apply the median filter
"""
if (input_buffer.flags['C_CONTIGUOUS'] is False):
raise ValueError('<input_buffer> must be a C_CONTIGUOUS numpy array.')
if (output_buffer.flags['C_CONTIGUOUS'] is False):
raise ValueError('<output_buffer> must be a C_CONTIGUOUS numpy array.')
if not (len(input_buffer.shape) <= 2):
raise ValueError('<input_buffer> dimension must mo higher than 2.')
if not (len(output_buffer.shape) <= 2):
raise ValueError('<output_buffer> dimension must mo higher than 2.')
if not(input_buffer.dtype == output_buffer.dtype):
raise ValueError('input buffer and output_buffer must be of the same type')
if not (input_buffer.shape == output_buffer.shape):
raise ValueError('input buffer and output_buffer must be of the same dimension and same dimension')
######### implementations of the include/median_filter.hpp function ############
@cython.cdivision(True)
@cython.boundscheck(False)
@cython.wraparound(False)
@cython.initializedcheck(False)
def _median_filter_float32(float[:, ::1] input_buffer not None,
float[:, ::1] output_buffer not None,
cnumpy.int32_t[::1] kernel_size not None,
bool conditional):
cdef:
int x = 0
int image_dim = input_buffer.shape[1] - 1
int[2] buffer_shape
buffer_shape[0] = input_buffer.shape[0]
buffer_shape[1] = input_buffer.shape[1]
for x in prange(input_buffer.shape[0], nogil=True):
median_filter.median_filter[float](<float*> & input_buffer[0,0],
<float*> & output_buffer[0,0],
<int*>& kernel_size[0],
<int*>buffer_shape,
x,
0,
image_dim,
conditional)
@cython.cdivision(True)
@cython.boundscheck(False)
@cython.wraparound(False)
@cython.initializedcheck(False)
def _median_filter_float64(double[:, ::1] input_buffer not None,
double[:, ::1] output_buffer not None,
cnumpy.int32_t[::1] kernel_size not None,
bool conditional):
cdef:
int x = 0
int image_dim = input_buffer.shape[1] - 1
int[2] buffer_shape
buffer_shape[0] = input_buffer.shape[0]
buffer_shape[1] = input_buffer.shape[1]
for x in prange(input_buffer.shape[0], nogil=True):
median_filter.median_filter[double](<double*> & input_buffer[0, 0],
<double*> & output_buffer[0, 0],
<int*>&kernel_size[0],
<int*>buffer_shape,
x,
0,
image_dim,
conditional)
@cython.cdivision(True)
@cython.boundscheck(False)
@cython.wraparound(False)
@cython.initializedcheck(False)
def _median_filter_int64(cnumpy.int64_t[:, ::1] input_buffer not None,
cnumpy.int64_t[:, ::1] output_buffer not None,
cnumpy.int32_t[::1] kernel_size not None,
bool conditional):
cdef:
int x = 0
int image_dim = input_buffer.shape[1] - 1
int[2] buffer_shape
buffer_shape[0] = input_buffer.shape[0]
buffer_shape[1] = input_buffer.shape[1]
for x in prange(input_buffer.shape[0], nogil=True):
median_filter.median_filter[long](<long*> & input_buffer[0,0],
<long*> & output_buffer[0, 0],
<int*>&kernel_size[0],
<int*>buffer_shape,
x,
0,
image_dim,
conditional);
@cython.cdivision(True)
@cython.boundscheck(False)
@cython.wraparound(False)
@cython.initializedcheck(False)
def _median_filter_uint64(
cnumpy.uint64_t[:, ::1] input_buffer not None,
cnumpy.uint64_t[:, ::1] output_buffer not None,
cnumpy.int32_t[::1] kernel_size not None,
bool conditional):
cdef:
int x = 0
int image_dim = input_buffer.shape[1] - 1
int[2] buffer_shape
buffer_shape[0] = input_buffer.shape[0]
buffer_shape[1] = input_buffer.shape[1]
for x in prange(input_buffer.shape[0], nogil=True):
median_filter.median_filter[uint64](<uint64*> & input_buffer[0,0],
<uint64*> & output_buffer[0, 0],
<int*>&kernel_size[0],
<int*>buffer_shape,
x,
0,
image_dim,
conditional)
@cython.cdivision(True)
@cython.boundscheck(False)
@cython.wraparound(False)
@cython.initializedcheck(False)
def _median_filter_int32(cnumpy.int32_t[:, ::1] input_buffer not None,
cnumpy.int32_t[:, ::1] output_buffer not None,
cnumpy.int32_t[::1] kernel_size not None,
bool conditional):
cdef:
int x = 0
int image_dim = input_buffer.shape[1] - 1
int[2] buffer_shape
buffer_shape[0] = input_buffer.shape[0]
buffer_shape[1] = input_buffer.shape[1]
for x in prange(input_buffer.shape[0], nogil=True):
median_filter.median_filter[int](<int*> & input_buffer[0,0],
<int*> & output_buffer[0, 0],
<int*>&kernel_size[0],
<int*>buffer_shape,
x,
0,
image_dim,
conditional)
@cython.cdivision(True)
@cython.boundscheck(False)
@cython.wraparound(False)
@cython.initializedcheck(False)
def _median_filter_uint32(cnumpy.uint32_t[:, ::1] input_buffer not None,
cnumpy.uint32_t[:, ::1] output_buffer not None,
cnumpy.int32_t[::1] kernel_size not None,
bool conditional):
cdef:
int x = 0
int image_dim = input_buffer.shape[1] - 1
int[2] buffer_shape
buffer_shape[0] = input_buffer.shape[0]
buffer_shape[1] = input_buffer.shape[1]
for x in prange(input_buffer.shape[0], nogil=True):
median_filter.median_filter[uint32](<uint32*> & input_buffer[0,0],
<uint32*> & output_buffer[0, 0],
<int*>&kernel_size[0],
<int*>buffer_shape,
x,
0,
image_dim,
conditional)
@cython.cdivision(True)
@cython.boundscheck(False)
@cython.wraparound(False)
@cython.initializedcheck(False)
def _median_filter_int16(cnumpy.int16_t[:, ::1] input_buffer not None,
cnumpy.int16_t[:, ::1] output_buffer not None,
cnumpy.int32_t[::1] kernel_size not None,
bool conditional):
cdef:
int x = 0
int image_dim = input_buffer.shape[1] - 1
int[2] buffer_shape
buffer_shape[0] = input_buffer.shape[0]
buffer_shape[1] = input_buffer.shape[1]
for x in prange(input_buffer.shape[0], nogil=True):
median_filter.median_filter[short](<short*> & input_buffer[0,0],
<short*> & output_buffer[0, 0],
<int*>&kernel_size[0],
<int*>buffer_shape,
x,
0,
image_dim,
conditional)
@cython.cdivision(True)
@cython.boundscheck(False)
@cython.wraparound(False)
@cython.initializedcheck(False)
def _median_filter_uint16(
cnumpy.ndarray[cnumpy.uint16_t, ndim=2, mode='c'] input_buffer not None,
cnumpy.ndarray[cnumpy.uint16_t, ndim=2, mode='c'] output_buffer not None,
cnumpy.ndarray[cnumpy.int32_t, ndim=1, mode='c'] kernel_size not None,
bool conditional):
cdef:
int x = 0
int image_dim = input_buffer.shape[1] - 1
int[2] buffer_shape,
buffer_shape[0] = input_buffer.shape[0]
buffer_shape[1] = input_buffer.shape[1]
for x in prange(input_buffer.shape[0], nogil=True):
median_filter.median_filter[uint16](<uint16*> & input_buffer[0, 0],
<uint16*> & output_buffer[0, 0],
<int*>&kernel_size[0],
<int*>buffer_shape,
x,
0,
image_dim,
conditional)
|
