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# -*- coding: utf-8 -*-
#cython: embedsignature=True, language_level=3
#cython: boundscheck=False, wraparound=False, cdivision=True, initializedcheck=False,
## This is for developping
## cython: profile=True, warn.undeclared=True, warn.unused=True, warn.unused_result=False, warn.unused_arg=True
#
# Project: Fast Azimuthal integration
# https://github.com/silx-kit/pyFAI
#
# Copyright (C) 2014-2021 European Synchrotron Radiation Facility, France
#
# Principal author: Jérôme Kieffer (Jerome.Kieffer@ESRF.eu)
#
# 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.
"""Cython module to reconstruct the masked values of an image.
It's a simple inpainting module for reconstructing the missing part of an
image (masked) to be able to use more common algorithms.
"""
__author__ = "Jerome Kieffer"
__contact__ = "Jerome.kieffer@esrf.fr"
__date__ = "14/01/2021"
__status__ = "stable"
__license__ = "MIT"
import cython
import numpy
from libc.math cimport sqrt, fabs
from cython.parallel import prange
from libc.stdint cimport int8_t, uint8_t, int16_t, uint16_t, \
int32_t, uint32_t, int64_t, uint64_t
cdef float invert_distance(size_t i0, size_t i1, size_t p0, size_t p1) nogil:
return 1. / sqrt(<float> ((i0 - p0) ** 2 + (i1 - p1) ** 2))
cdef inline float processPoint(float[:, ::1] data,
int8_t[:, ::1] mask,
size_t p0,
size_t p1,
size_t d0,
size_t d1)nogil:
cdef:
size_t dist = 0, i = 0
float sum = 0.0, count = 0.0, invdst = 0.0
bint found = 0
size_t start0 = p0, stop0 = p0, start1 = p1, stop1 = p1
while not found:
dist += 1
if start0 > 0:
start0 = p0 - dist
else:
start0 = 0
if stop0 < d0 - 1:
stop0 = p0 + dist
else:
stop0 = d0 - 1
if start1 > 0:
start1 = p1 - dist
else:
start1 = 0
if stop1 < d1 - 1:
stop1 = p1 + dist
else:
stop1 = d1 - 1
for i in range(start0, stop0 + 1):
if mask[i, start1] == 0:
invdst = invert_distance(i, start1, p0, p1)
count += invdst
sum += invdst * data[i, start1]
if mask[i, stop1] == 0:
invdst = invert_distance(i, stop1, p0, p1)
count += invdst
sum += invdst * data[i, stop1]
for i in range(start1 + 1, stop1):
if mask[start0, i] == 0:
invdst = invert_distance(start0, i, p0, p1)
count += invdst
sum += invdst * data[start0, i]
if mask[stop0, i] == 0:
invdst = invert_distance(stop0, i, p0, p1)
count += invdst
sum += invdst * data[stop0, i]
if count > 0:
found = 1
return sum / count
def reconstruct(data,
mask=None,
dummy=None,
delta_dummy=None):
"""
reconstruct missing part of an image (tries to be continuous)
:param data: the input image
:param mask: where data should be reconstructed.
:param dummy: value of the dummy (masked out) data
:param delta_dummy: precision for dummy values
:return: reconstructed image.
"""
assert data.ndim == 2, "data.ndim == 2"
cdef:
ssize_t d0 = data.shape[0]
ssize_t d1 = data.shape[1]
ssize_t p0, p1
float[:, ::1] cdata
int8_t[:, ::1] cmask
bint is_masked, do_dummy
float cdummy, cddummy, value
float[:, ::1] out = numpy.zeros_like(data)
cdata = numpy.ascontiguousarray(data, dtype=numpy.float32)
if mask is not None:
cmask = numpy.ascontiguousarray(mask, dtype=numpy.int8)
else:
cmask = numpy.zeros((d0, d1), dtype=numpy.int8)
assert d0 == mask.shape[0], "mask.shape[0]"
assert d1 == mask.shape[1], "mask.shape[1]"
if dummy is not None:
do_dummy = True
cdummy = <float> dummy
if delta_dummy is None:
cddummy = 0.0
else:
cddummy = <float> delta_dummy
# Nota: this has to go in 2 passes, one to mark, one to reconstruct
for p0 in prange(d0, nogil=True, schedule="guided"):
for p1 in range(d1):
is_masked = cmask[p0, p1]
if not is_masked and do_dummy:
value = cdata[p0, p1]
if cddummy == 0.0:
cmask[p0, p1] += (value == cdummy)
elif (fabs(value - cdummy) <= cddummy):
cmask[p0, p1] += 1
# Reconstruction phase
for p0 in prange(d0, nogil=True, schedule="guided"):
for p1 in range(d1):
if cmask[p0, p1]:
out[p0, p1] += processPoint(cdata, cmask, p0, p1, d0, d1)
else:
out[p0, p1] += cdata[p0, p1]
return numpy.asarray(out)
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