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-rw-r--r--silx/math/medianfilter/medianfilter.pyx145
1 files changed, 98 insertions, 47 deletions
diff --git a/silx/math/medianfilter/medianfilter.pyx b/silx/math/medianfilter/medianfilter.pyx
index c7c9497..4d0a1e9 100644
--- a/silx/math/medianfilter/medianfilter.pyx
+++ b/silx/math/medianfilter/medianfilter.pyx
@@ -35,7 +35,6 @@ 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
@@ -43,7 +42,10 @@ ctypedef unsigned int uint32
ctypedef unsigned short uint16
-def medfilt1d(data, kernel_size=3, bool conditional=False):
+MODES = {'nearest':0, 'reflect':1, 'mirror':2, 'shrink':3}
+
+
+def medfilt1d(data, kernel_size=3, bool conditional=False, mode='nearest'):
"""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).
@@ -57,10 +59,10 @@ def medfilt1d(data, kernel_size=3, bool conditional=False):
:returns: the array with the median value for each pixel.
"""
- return medfilt(data, kernel_size, conditional)
+ return medfilt(data, kernel_size, conditional, mode)
-def medfilt2d(image, kernel_size=3, bool conditional=False):
+def medfilt2d(image, kernel_size=3, bool conditional=False, mode='nearest'):
"""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).
@@ -75,10 +77,10 @@ def medfilt2d(image, kernel_size=3, bool conditional=False):
:returns: the array with the median value for each pixel.
"""
- return medfilt(image, kernel_size, conditional)
+ return medfilt(image, kernel_size, conditional, mode)
-def medfilt(data, kernel_size=3, bool conditional=False):
+def medfilt(data, kernel_size=3, bool conditional=False, mode='nearest'):
"""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).
@@ -90,9 +92,15 @@ def medfilt(data, kernel_size=3, bool conditional=False):
a list of (kernel_height, kernel_width)
:param bool conditional: True if we want to apply a conditional median
filtering.
+ :param str mode: the algorithm used to determine how values at borders
+ are determined.
:returns: the array with the median value for each pixel.
"""
+ if mode not in MODES:
+ err = 'Requested mode %s is unknowed.' % mode
+ raise ValueError(err)
+
reshaped = False
if len(data.shape) == 1:
data = data.reshape(data.shape[0], 1)
@@ -134,7 +142,8 @@ def medfilt(data, kernel_size=3, bool conditional=False):
medfilterfc(input_buffer=data,
output_buffer=output_buffer,
kernel_size=ker_dim,
- conditional=conditional)
+ conditional=conditional,
+ mode=MODES[mode])
if reshaped:
data = data.reshape(data.shape[0])
@@ -170,27 +179,55 @@ def check(input_buffer, output_buffer):
@cython.boundscheck(False)
@cython.wraparound(False)
@cython.initializedcheck(False)
+def reflect(int index, int length_max):
+ """find the correct index into [0, length_max-1] for index in reflect mode
+
+ :param int index: the index to move into [0, length_max-1] in reflect mode
+ :param int length_max: the higher bound limit
+ """
+ return median_filter.reflect(index, length_max)
+
+
+@cython.cdivision(True)
+@cython.boundscheck(False)
+@cython.wraparound(False)
+@cython.initializedcheck(False)
+def mirror(int index, int length_max):
+ """find the correct index into [0, length_max-1] for index in mirror mode
+
+ :param int index: the index to move into [0, length_max-1] in mirror mode
+ :param int length_max: the higher bound limit
+ """
+ return median_filter.mirror(index, length_max)
+
+
+@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):
+ bool conditional,
+ int mode):
cdef:
- int x = 0
+ int y = 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):
+ for y 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,
+ y,
0,
image_dim,
- conditional)
+ conditional,
+ mode)
@cython.cdivision(True)
@@ -200,24 +237,26 @@ def _median_filter_float32(float[:, ::1] input_buffer not None,
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):
+ bool conditional,
+ int mode):
cdef:
- int x = 0
+ int y = 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):
+ for y 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,
+ y,
0,
image_dim,
- conditional)
+ conditional,
+ mode)
@cython.cdivision(True)
@@ -227,24 +266,26 @@ def _median_filter_float64(double[:, ::1] input_buffer not None,
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):
+ bool conditional,
+ int mode):
cdef:
- int x = 0
+ int y = 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):
+ for y 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,
+ y,
0,
image_dim,
- conditional);
+ conditional,
+ mode)
@cython.cdivision(True)
@cython.boundscheck(False)
@@ -254,24 +295,26 @@ 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):
+ bool conditional,
+ int mode):
cdef:
- int x = 0
+ int y = 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):
+ for y 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,
+ y,
0,
image_dim,
- conditional)
+ conditional,
+ mode)
@cython.cdivision(True)
@@ -281,24 +324,26 @@ def _median_filter_uint64(
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):
+ bool conditional,
+ int mode):
cdef:
- int x = 0
+ int y = 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):
+ for y 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,
+ y,
0,
image_dim,
- conditional)
+ conditional,
+ mode)
@cython.cdivision(True)
@@ -308,24 +353,26 @@ def _median_filter_int32(cnumpy.int32_t[:, ::1] input_buffer not None,
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):
+ bool conditional,
+ int mode):
cdef:
- int x = 0
+ int y = 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):
+ for y 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,
+ y,
0,
image_dim,
- conditional)
+ conditional,
+ mode)
@cython.cdivision(True)
@@ -335,24 +382,26 @@ def _median_filter_uint32(cnumpy.uint32_t[:, ::1] input_buffer not None,
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):
+ bool conditional,
+ int mode):
cdef:
- int x = 0
+ int y = 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):
+ for y 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,
+ y,
0,
image_dim,
- conditional)
+ conditional,
+ mode)
@cython.cdivision(True)
@@ -363,21 +412,23 @@ 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):
+ bool conditional,
+ int mode):
cdef:
- int x = 0
+ int y = 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):
+ for y 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,
+ y,
0,
image_dim,
- conditional)
+ conditional,
+ mode)
generated by cgit v1.2.3 (git 2.25.1) at 2024-04-26 08:13:31 +0000