savu.plugins.centering.centering360.Centering360.executive_summary() - Code Metrics - DiamondLightSource/Savu - Measure and Improve Code Quality continuously with Scrutinizer

Centering360.executive_summary() A
last analyzed 2022-08-17 15:44 UTC

↳ Parent: savu.plugins.centering.centering360

Complexity

Conditions

Size

Total Lines	11
Code Lines	10

Duplication

Lines	11
Ratio	100 %

Importance

Changes

Metric	Value
cc	3
eloc	10
nop	1
dl	11
loc	11
rs	9.9
c	0
b	0
f	0

# Copyright 2014 Diamond Light Source Ltd.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
.. module:: centering360
   :platform: Unix
   :synopsis: A plugin to find the center of rotation per frame
.. moduleauthor:: Nghia Vo, <[email protected]>
"""
from savu.plugins.driver.cpu_plugin import CpuPlugin
from savu.plugins.utils import register_plugin
from savu.plugins.filters.base_filter import BaseFilter
import savu.core.utils as cu

import logging
import numpy as np
import scipy.ndimage as ndi
from scipy import stats

@register_plugin
class Centering360(BaseFilter, CpuPlugin):

    def __init__(self):
        super(Centering360, self).__init__("Centering360")

    def find_overlap(self, mat1, mat2, win_width, side=None, denoise=True, norm=False,
                     use_overlap=False):
        """
        Find the overlap area and overlap side between two images (Ref. [1]) where
        the overlap side referring to the first image.

        Parameters
        ----------
        mat1 : array_like
            2D array. Projection image or sinogram image.
        mat2 :  array_like
            2D array. Projection image or sinogram image.
        win_width : int
            Width of the searching window.
        side : {None, 0, 1}, optional
            Only there options: None, 0, or 1. "None" corresponding to fully
            automated determination. "0" corresponding to the left side. "1"
            corresponding to the right side.
        denoise : bool, optional
            Apply the Gaussian filter if True.
        norm : bool, optional
            Apply the normalization if True.
        use_overlap : bool, optional
            Use the combination of images in the overlap area for calculating
            correlation coefficients if True.

        Returns
        -------
        overlap : float
            Width of the overlap area between two images.
        side : int
            Overlap side between two images.
        overlap_position : float
            Position of the window in the first image giving the best
            correlation metric.

        References
        ----------
        .. [1] https://doi.org/10.1364/OE.418448
        """
        (_, ncol1) = mat1.shape
        (_, ncol2) = mat2.shape
        win_width = np.int16(np.clip(win_width, 6, min(ncol1, ncol2) // 2))
        if side == 1:
            (list_metric, offset) = self.search_overlap(mat1, mat2, win_width, side,
                                                   denoise, norm, use_overlap)
            (_, overlap_position) = self.calculate_curvature(list_metric)
            overlap_position = overlap_position + offset
            overlap = ncol1 - overlap_position + win_width // 2
        elif side == 0:
            (list_metric, offset) = self.search_overlap(mat1, mat2, win_width, side,
                                                   denoise, norm, use_overlap)
            (_, overlap_position) = self.calculate_curvature(list_metric)
            overlap_position = overlap_position + offset
            overlap = overlap_position + win_width // 2
        else:
            (list_metric1, offset1) = self.search_overlap(mat1, mat2, win_width, 1, norm,
                                                     denoise, use_overlap)
            (list_metric2, offset2) = self.search_overlap(mat1, mat2, win_width, 0, norm,
                                                     denoise, use_overlap)
            (curvature1, overlap_position1) = self.calculate_curvature(list_metric1)
            overlap_position1 = overlap_position1 + offset1
            (curvature2, overlap_position2) = self.calculate_curvature(list_metric2)
            overlap_position2 = overlap_position2 + offset2
            if curvature1 > curvature2:
                side = 1
                overlap_position = overlap_position1
                overlap = ncol1 - overlap_position + win_width // 2
            else:
                side = 0
                overlap_position = overlap_position2
                overlap = overlap_position + win_width // 2
        return overlap, side, overlap_position

    def search_overlap(self, mat1, mat2, win_width, side, denoise=True, norm=False,
                       use_overlap=False):
        """
        Calculate the correlation metrics between a rectangular region, defined
        by the window width, on the utmost left/right side of image 2 and the
        same size region in image 1 where the region is slided across image 1.

        Parameters
        ----------
        mat1 : array_like
            2D array. Projection image or sinogram image.
        mat2 : array_like
            2D array. Projection image or sinogram image.
        win_width : int
            Width of the searching window.
        side : {0, 1}
            Only two options: 0 or 1. It is used to indicate the overlap side
            respects to image 1. "0" corresponds to the left side. "1" corresponds
            to the right side.
        denoise : bool, optional
            Apply the Gaussian filter if True.
        norm : bool, optional
            Apply the normalization if True.
        use_overlap : bool, optional
            Use the combination of images in the overlap area for calculating
            correlation coefficients if True.

        Returns
        -------
        list_metric : array_like
            1D array. List of the correlation metrics.
        offset : int
            Initial position of the searching window where the position
            corresponds to the center of the window.
        """
        if denoise is True:
            mat1 = ndi.gaussian_filter(mat1, (2, 2), mode='reflect')
            mat2 = ndi.gaussian_filter(mat2, (2, 2), mode='reflect')
        (nrow1, ncol1) = mat1.shape
        (nrow2, ncol2) = mat2.shape
        if nrow1 != nrow2:
            raise ValueError("Two images are not at the same height!!!")
        win_width = np.int16(np.clip(win_width, 6, min(ncol1, ncol2) // 2 - 1))
        offset = win_width // 2
        win_width = 2 * offset  # Make it even
        ramp_down = np.linspace(1.0, 0.0, win_width)
        ramp_up = 1.0 - ramp_down
        wei_down = np.tile(ramp_down, (nrow1, 1))
        wei_up = np.tile(ramp_up, (nrow1, 1))
        if side == 1:
            mat2_roi = mat2[:, 0:win_width]
            mat2_roi_wei = mat2_roi * wei_up
        else:
            mat2_roi = mat2[:, ncol2 - win_width:]
            mat2_roi_wei = mat2_roi * wei_down
        list_mean2 = np.mean(np.abs(mat2_roi), axis=1)
        list_pos = np.arange(offset, ncol1 - offset)
        num_metric = len(list_pos)
        list_metric = np.ones(num_metric, dtype=np.float32)
        for i, pos in enumerate(list_pos):
            mat1_roi = mat1[:, pos - offset:pos + offset]
            if use_overlap is True:
                if side == 1:
                    mat1_roi_wei = mat1_roi * wei_down
                else:
                    mat1_roi_wei = mat1_roi * wei_up
            if norm is True:
                list_mean1 = np.mean(np.abs(mat1_roi), axis=1)
                list_fact = list_mean2 / list_mean1
                mat_fact = np.transpose(np.tile(list_fact, (win_width, 1)))
                mat1_roi = mat1_roi * mat_fact
                if use_overlap is True:
                    mat1_roi_wei = mat1_roi_wei * mat_fact

            if use_overlap is True:
                mat_comb = mat1_roi_wei + mat2_roi_wei
                list_metric[i] = (self.correlation_metric(mat1_roi, mat2_roi)
                                  + self.correlation_metric(mat1_roi, mat_comb)
                                  + self.correlation_metric(mat2_roi, mat_comb)) / 3.0
            else:
                list_metric[i] = self.correlation_metric(mat1_roi, mat2_roi)
        min_metric = np.min(list_metric)
        if min_metric != 0.0:
            list_metric = list_metric / min_metric
        return list_metric, offset

    def correlation_metric(self, mat1, mat2):
        """
        Calculate the correlation metric. Smaller metric corresponds to better
        correlation.

        Parameters
        ---------
        mat1 : array_like
        mat2 : array_like

        Returns
        -------
        float
            Correlation metric.
        """
        metric = np.abs(
            1.0 - stats.pearsonr(mat1.flatten('F'), mat2.flatten('F'))[0])
        return metric

    def calculate_curvature(self, list_metric):
        """
        Calculate the curvature of a fitted curve going through the minimum
        value of a metric list.

        Parameters
        ----------
        list_metric : array_like
            1D array. List of metrics.

        Returns
        -------
        curvature : float
            Quadratic coefficient of the parabola fitting.
        min_pos : float
            Position of the minimum value with sub-pixel accuracy.
        """
        radi = 2
        num_metric = len(list_metric)
        min_pos = np.clip(
            np.argmin(list_metric), radi, num_metric - radi - 1)
        list1 = list_metric[min_pos - radi:min_pos + radi + 1]
        (afact1, _, _) = np.polyfit(np.arange(0, 2 * radi + 1), list1, 2)
        list2 = list_metric[min_pos - 1:min_pos + 2]
        (afact2, bfact2, _) = np.polyfit(
            np.arange(min_pos - 1, min_pos + 2), list2, 2)
        curvature = np.abs(afact1)
        if afact2 != 0.0:
            num = - bfact2 / (2 * afact2)
            if (num >= min_pos - 1) and (num <= min_pos + 1):
                min_pos = num
        return curvature, np.float32(min_pos)

    def _downsample(self, image, dsp_fact0, dsp_fact1):

        """Downsample an image by averaging.

        Parameters
        ----------
            image : 2D array.
            dsp_fact0 : downsampling factor along axis 0.
            dsp_fact1 : downsampling factor along axis 1.

        Returns
        ---------
            image_dsp : Downsampled image.
        """
        (height, width) = image.shape
        dsp_fact0 = np.clip(np.int16(dsp_fact0), 1, height // 2)
        dsp_fact1 = np.clip(np.int16(dsp_fact1), 1, width // 2)
        height_dsp = height // dsp_fact0
        width_dsp = width // dsp_fact1
        if dsp_fact0 == 1 and dsp_fact1 == 1:
            image_dsp = image
        else:
            image_dsp = image[0:dsp_fact0 * height_dsp, 0:dsp_fact1 * width_dsp]
            image_dsp = image_dsp.reshape(
                height_dsp, dsp_fact0, width_dsp, dsp_fact1).mean(-1).mean(1)
        return image_dsp

    def pre_process(self):
        self.win_width = np.int16(self.parameters['win_width'])
        self.side = self.parameters['side']
        self.denoise = self.parameters['denoise']
        self.norm = self.parameters['norm']
        self.use_overlap = self.parameters['use_overlap']

        self.broadcast_method = str(self.parameters['broadcast_method'])
        self.error_msg_1 = ""
        self.error_msg_2 = ""
        self.error_msg_3 = ""
        if not ((self.broadcast_method == 'mean')

                or (self.broadcast_method == 'median')
                or (self.broadcast_method == 'linear_fit')
                or (self.broadcast_method == 'nearest')):
            self.error_msg_3 = "!!!WARNING!!! Selected broadcasting method" \
                               " is out of the list. Use the default option:" \
                               " 'median'"
            logging.warning(self.error_msg_3)
            cu.user_message(self.error_msg_3)
            self.broadcast_method = 'median'
        in_pData = self.get_plugin_in_datasets()[0]
        data = self.get_in_datasets()[0]
        starts, stops, steps = data.get_preview().get_starts_stops_steps()[0:3]
        start_ind = starts[1]
        stop_ind = stops[1]
        step_ind = steps[1]
        name = data.get_name()
        pre_start = self.exp.meta_data.get(name + '_preview_starts')[1]
        pre_stop = self.exp.meta_data.get(name + '_preview_stops')[1]
        pre_step = self.exp.meta_data.get(name + '_preview_steps')[1]
        self.origin_prev = np.arange(pre_start, pre_stop, pre_step)
        self.plugin_prev = self.origin_prev[start_ind:stop_ind:step_ind]
        num_sino = len(self.plugin_prev)
        if num_sino > 20:
            warning_msg = "\n!!!WARNING!!! You selected to calculate the " \
                          "center-of-rotation using '{}' sinograms.\n" \
                          "This is computationally expensive. Considering to " \
                          "adjust the preview parameter to use\na smaller " \
                          "number of sinograms (< 20).\n".format(num_sino)
            logging.warning(warning_msg)
            cu.user_message(warning_msg)

    def process_frames(self, data):
        """
        Find the center-of-rotation (COR) in a 360-degree scan with offset COR use
        the method presented in Ref. [1].

        Parameters
        ----------
        data : array_like
            2D array. 360-degree sinogram.

        Returns
        -------
        cor : float
            Center-of-rotation.

        References
        ----------
        .. [1] https://doi.org/10.1364/OE.418448
        """
        sino = data[0]
        (nrow, ncol) = sino.shape
        nrow_180 = nrow // 2 + 1
        sino_top = sino[0:nrow_180, :]
        sino_bot = np.fliplr(sino[-nrow_180:, :])
        overlap, side, overlap_position =\
            self.find_overlap(sino_top, sino_bot, self.win_width, self.side,
                              self.denoise, self.norm, self.use_overlap)
        #overlap : Width of the overlap area between two halves
        #           of the sinogram.
        # side : Overlap side between two halves of the sinogram.
        # overlap_position : Position of the window in the first
        #           image giving the best correlation metric."""
        if side == 0:
            cor = overlap / 2.0 - 1.0
        else:
            cor = ncol - overlap / 2.0 - 1.0
        return [np.array([cor]), np.array([cor])]

    def post_process(self):

        in_datasets, out_datasets = self.get_datasets()
        cor_prev = out_datasets[0].data[...]
        cor_broad = out_datasets[1].data[...]
        cor_broad[:] = np.median(np.squeeze(cor_prev))
        self.cor_for_executive_summary = np.median(cor_broad[:])
        if self.broadcast_method == 'mean':
            cor_broad[:] = np.mean(np.squeeze(cor_prev))
            self.cor_for_executive_summary = np.mean(cor_broad[:])
        if (self.broadcast_method == 'linear_fit') and (len(cor_prev) > 1):
            afact, bfact = np.polyfit(self.plugin_prev, cor_prev[:, 0], 1)
            list_cor = self.origin_prev * afact + bfact
            cor_broad[:, 0] = list_cor
            self.cor_for_executive_summary = cor_broad[:]
        if (self.broadcast_method == 'nearest') and (len(cor_prev) > 1):
            for i, pos in enumerate(self.origin_prev):
                minpos = np.argmin(np.abs(pos - self.plugin_prev))
                cor_broad[i, 0] = cor_prev[minpos, 0]
            self.cor_for_executive_summary = cor_broad[:]
        out_datasets[1].data[:] = cor_broad[:]
        self.populate_meta_data('cor_preview', np.squeeze(cor_prev))
        self.populate_meta_data('centre_of_rotation',
                                out_datasets[1].data[:].squeeze(axis=1))

    def populate_meta_data(self, key, value):
        datasets = self.parameters['datasets_to_populate']
        in_meta_data = self.get_in_meta_data()[0]
        in_meta_data.set(key, value)
        for name in datasets:
            self.exp.index['in_data'][name].meta_data.set(key, value)

    def setup(self):
        self.exp.log(self.name + " Start calculating center of rotation")
        # set up the output dataset that is created by the plugin
        in_dataset, out_dataset = self.get_datasets()
        in_pData, out_pData = self.get_plugin_datasets()
        in_pData[0].plugin_data_setup('SINOGRAM', self.get_max_frames())
        slice_dirs = list(in_dataset[0].get_slice_dimensions())
        self.orig_full_shape = in_dataset[0].get_shape()

        # reduce the data as per data_subset parameter
        self.set_preview(in_dataset[0], self.parameters['preview'])
        total_frames = \
            self._calc_total_frames(in_dataset[0].get_preview(), slice_dirs)

        # copy all required information from in_dataset[0]
        fullData = in_dataset[0]
        new_shape = (np.prod(np.array(fullData.get_shape())[slice_dirs]), 1)
        self.orig_shape = \
            (np.prod(np.array(self.orig_full_shape)[slice_dirs]), 1)
        out_dataset[0].create_dataset(shape=new_shape,
                                      axis_labels=['x.pixels', 'y.pixels'],
                                      remove=True,
                                      transport='hdf5')
        out_dataset[0].add_pattern("METADATA", core_dims=(1,), slice_dims=(0,))

        out_dataset[1].create_dataset(shape=self.orig_shape,
                                      axis_labels=['x.pixels', 'y.pixels'],
                                      remove=True,
                                      transport='hdf5')
        out_dataset[1].add_pattern("METADATA", core_dims=(1,), slice_dims=(0,))
        out_pData[0].plugin_data_setup('METADATA', self.get_max_frames())
        out_pData[1].plugin_data_setup('METADATA', self.get_max_frames())
        out_pData[1].meta_data.set('fix_total_frames', total_frames)
        self.exp.log(self.name + " End")

    def _calc_total_frames(self, preview, slice_dims):
        starts, stops, steps, _ = preview.get_starts_stops_steps()
        lengths = [len(np.arange(starts[i], stops[i], steps[i]))
                   for i in range(len(starts))]
        return np.prod([lengths[i] for i in slice_dims])

    def nOutput_datasets(self):
        return 2

    def get_max_frames(self):
        return 'single'

    def fix_transport(self):
        return 'hdf5'

    def executive_summary(self):

        if ((self.error_msg_1 == "")
                and (self.error_msg_2 == "")):
            msg = "Centre of rotation is : %s" % (
                str(self.cor_for_executive_summary))
        else:
            msg = "\n" + self.error_msg_1 + "\n" + self.error_msg_2
            msg2 = "(Not well) estimated centre of rotation is : %s" % (str(
                self.cor_for_executive_summary))
            cu.user_message(msg2)
        return [msg]


1		# Copyright 2014 Diamond Light Source Ltd.
2		#
3		# Licensed under the Apache License, Version 2.0 (the "License");
4		# you may not use this file except in compliance with the License.
5		# You may obtain a copy of the License at
6		#
7		# http://www.apache.org/licenses/LICENSE-2.0
8		#
9		# Unless required by applicable law or agreed to in writing, software
10		# distributed under the License is distributed on an "AS IS" BASIS,
11		# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12		# See the License for the specific language governing permissions and
13		# limitations under the License.
14		"""
15		.. module:: centering360
16		:platform: Unix
17		:synopsis: A plugin to find the center of rotation per frame
18		.. moduleauthor:: Nghia Vo, <[email protected]>
19		"""
20		from savu.plugins.driver.cpu_plugin import CpuPlugin
21		from savu.plugins.utils import register_plugin
22		from savu.plugins.filters.base_filter import BaseFilter
23		import savu.core.utils as cu
24
25		import logging
26		import numpy as np
27		import scipy.ndimage as ndi
28		from scipy import stats
29
30		@register_plugin
31		class Centering360(BaseFilter, CpuPlugin):
32
33		def __init__(self):
34		super(Centering360, self).__init__("Centering360")
35
36		def find_overlap(self, mat1, mat2, win_width, side=None, denoise=True, norm=False,
37		use_overlap=False):
38		"""
39		Find the overlap area and overlap side between two images (Ref. [1]) where
40		the overlap side referring to the first image.
41
42		Parameters
43		----------
44		mat1 : array_like
45		2D array. Projection image or sinogram image.
46		mat2 : array_like
47		2D array. Projection image or sinogram image.
48		win_width : int
49		Width of the searching window.
50		side : {None, 0, 1}, optional
51		Only there options: None, 0, or 1. "None" corresponding to fully
52		automated determination. "0" corresponding to the left side. "1"
53		corresponding to the right side.
54		denoise : bool, optional
55		Apply the Gaussian filter if True.
56		norm : bool, optional
57		Apply the normalization if True.
58		use_overlap : bool, optional
59		Use the combination of images in the overlap area for calculating
60		correlation coefficients if True.
61
62		Returns
63		-------
64		overlap : float
65		Width of the overlap area between two images.
66		side : int
67		Overlap side between two images.
68		overlap_position : float
69		Position of the window in the first image giving the best
70		correlation metric.
71
72		References
73		----------
74		.. [1] https://doi.org/10.1364/OE.418448
75		"""
76		(_, ncol1) = mat1.shape
77		(_, ncol2) = mat2.shape
78		win_width = np.int16(np.clip(win_width, 6, min(ncol1, ncol2) // 2))
79		if side == 1:
80		(list_metric, offset) = self.search_overlap(mat1, mat2, win_width, side,
81		denoise, norm, use_overlap)
82		(_, overlap_position) = self.calculate_curvature(list_metric)
83		overlap_position = overlap_position + offset
84		overlap = ncol1 - overlap_position + win_width // 2
85		elif side == 0:
86		(list_metric, offset) = self.search_overlap(mat1, mat2, win_width, side,
87		denoise, norm, use_overlap)
88		(_, overlap_position) = self.calculate_curvature(list_metric)
89		overlap_position = overlap_position + offset
90		overlap = overlap_position + win_width // 2
91		else:
92		(list_metric1, offset1) = self.search_overlap(mat1, mat2, win_width, 1, norm,
93		denoise, use_overlap)
94		(list_metric2, offset2) = self.search_overlap(mat1, mat2, win_width, 0, norm,
95		denoise, use_overlap)
96		(curvature1, overlap_position1) = self.calculate_curvature(list_metric1)
97		overlap_position1 = overlap_position1 + offset1
98		(curvature2, overlap_position2) = self.calculate_curvature(list_metric2)
99		overlap_position2 = overlap_position2 + offset2
100		if curvature1 > curvature2:
101		side = 1
102		overlap_position = overlap_position1
103		overlap = ncol1 - overlap_position + win_width // 2
104		else:
105		side = 0
106		overlap_position = overlap_position2
107		overlap = overlap_position + win_width // 2
108		return overlap, side, overlap_position
109
110		def search_overlap(self, mat1, mat2, win_width, side, denoise=True, norm=False,
111		use_overlap=False):
112		"""
113		Calculate the correlation metrics between a rectangular region, defined
114		by the window width, on the utmost left/right side of image 2 and the
115		same size region in image 1 where the region is slided across image 1.
116
117		Parameters
118		----------
119		mat1 : array_like
120		2D array. Projection image or sinogram image.
121		mat2 : array_like
122		2D array. Projection image or sinogram image.
123		win_width : int
124		Width of the searching window.
125		side : {0, 1}
126		Only two options: 0 or 1. It is used to indicate the overlap side
127		respects to image 1. "0" corresponds to the left side. "1" corresponds
128		to the right side.
129		denoise : bool, optional
130		Apply the Gaussian filter if True.
131		norm : bool, optional
132		Apply the normalization if True.
133		use_overlap : bool, optional
134		Use the combination of images in the overlap area for calculating
135		correlation coefficients if True.
136
137		Returns
138		-------
139		list_metric : array_like
140		1D array. List of the correlation metrics.
141		offset : int
142		Initial position of the searching window where the position
143		corresponds to the center of the window.
144		"""
145		if denoise is True:
146		mat1 = ndi.gaussian_filter(mat1, (2, 2), mode='reflect')
147		mat2 = ndi.gaussian_filter(mat2, (2, 2), mode='reflect')
148		(nrow1, ncol1) = mat1.shape
149		(nrow2, ncol2) = mat2.shape
150		if nrow1 != nrow2:
151		raise ValueError("Two images are not at the same height!!!")
152		win_width = np.int16(np.clip(win_width, 6, min(ncol1, ncol2) // 2 - 1))
153		offset = win_width // 2
154		win_width = 2 * offset # Make it even
155		ramp_down = np.linspace(1.0, 0.0, win_width)
156		ramp_up = 1.0 - ramp_down
157		wei_down = np.tile(ramp_down, (nrow1, 1))
158		wei_up = np.tile(ramp_up, (nrow1, 1))
159		if side == 1:
160		mat2_roi = mat2[:, 0:win_width]
161		mat2_roi_wei = mat2_roi * wei_up
162		else:
163		mat2_roi = mat2[:, ncol2 - win_width:]
164		mat2_roi_wei = mat2_roi * wei_down
165		list_mean2 = np.mean(np.abs(mat2_roi), axis=1)
166		list_pos = np.arange(offset, ncol1 - offset)
167		num_metric = len(list_pos)
168		list_metric = np.ones(num_metric, dtype=np.float32)
169		for i, pos in enumerate(list_pos):
170		mat1_roi = mat1[:, pos - offset:pos + offset]
171		if use_overlap is True:
172		if side == 1:
173		mat1_roi_wei = mat1_roi * wei_down
174		else:
175		mat1_roi_wei = mat1_roi * wei_up
176		if norm is True:
177		list_mean1 = np.mean(np.abs(mat1_roi), axis=1)
178		list_fact = list_mean2 / list_mean1
179		mat_fact = np.transpose(np.tile(list_fact, (win_width, 1)))
180		mat1_roi = mat1_roi * mat_fact
181		if use_overlap is True:
182		mat1_roi_wei = mat1_roi_wei * mat_fact
		0 ignored issues – show introduced 2021-12-01 12:02 UTC by Report Bug Copy Issue Report The variable `mat1_roi_wei` does not seem to be defined for all execution paths. Loading history...
183		if use_overlap is True:
184		mat_comb = mat1_roi_wei + mat2_roi_wei
185		list_metric[i] = (self.correlation_metric(mat1_roi, mat2_roi)
186		+ self.correlation_metric(mat1_roi, mat_comb)
187		+ self.correlation_metric(mat2_roi, mat_comb)) / 3.0
188		else:
189		list_metric[i] = self.correlation_metric(mat1_roi, mat2_roi)
190		min_metric = np.min(list_metric)
191		if min_metric != 0.0:
192		list_metric = list_metric / min_metric
193		return list_metric, offset
194
195		def correlation_metric(self, mat1, mat2):
196		"""
197		Calculate the correlation metric. Smaller metric corresponds to better
198		correlation.
199
200		Parameters
201		---------
202		mat1 : array_like
203		mat2 : array_like
204
205		Returns
206		-------
207		float
208		Correlation metric.
209		"""
210		metric = np.abs(
211		1.0 - stats.pearsonr(mat1.flatten('F'), mat2.flatten('F'))[0])
212		return metric
213
214		def calculate_curvature(self, list_metric):
215		"""
216		Calculate the curvature of a fitted curve going through the minimum
217		value of a metric list.
218
219		Parameters
220		----------
221		list_metric : array_like
222		1D array. List of metrics.
223
224		Returns
225		-------
226		curvature : float
227		Quadratic coefficient of the parabola fitting.
228		min_pos : float
229		Position of the minimum value with sub-pixel accuracy.
230		"""
231		radi = 2
232		num_metric = len(list_metric)
233		min_pos = np.clip(
234		np.argmin(list_metric), radi, num_metric - radi - 1)
235		list1 = list_metric[min_pos - radi:min_pos + radi + 1]
236		(afact1, _, _) = np.polyfit(np.arange(0, 2 * radi + 1), list1, 2)
237		list2 = list_metric[min_pos - 1:min_pos + 2]
238		(afact2, bfact2, _) = np.polyfit(
239		np.arange(min_pos - 1, min_pos + 2), list2, 2)
240		curvature = np.abs(afact1)
241		if afact2 != 0.0:
242		num = - bfact2 / (2 * afact2)
243		if (num >= min_pos - 1) and (num <= min_pos + 1):
244		min_pos = num
245		return curvature, np.float32(min_pos)
246
247	View Code Duplication	def _downsample(self, image, dsp_fact0, dsp_fact1):
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248		"""Downsample an image by averaging.
249
250		Parameters
251		----------
252		image : 2D array.
253		dsp_fact0 : downsampling factor along axis 0.
254		dsp_fact1 : downsampling factor along axis 1.
255
256		Returns
257		---------
258		image_dsp : Downsampled image.
259		"""
260		(height, width) = image.shape
261		dsp_fact0 = np.clip(np.int16(dsp_fact0), 1, height // 2)
262		dsp_fact1 = np.clip(np.int16(dsp_fact1), 1, width // 2)
263		height_dsp = height // dsp_fact0
264		width_dsp = width // dsp_fact1
265		if dsp_fact0 == 1 and dsp_fact1 == 1:
266		image_dsp = image
267		else:
268		image_dsp = image[0:dsp_fact0 * height_dsp, 0:dsp_fact1 * width_dsp]
269		image_dsp = image_dsp.reshape(
270		height_dsp, dsp_fact0, width_dsp, dsp_fact1).mean(-1).mean(1)
271		return image_dsp
272
273		def pre_process(self):
274		self.win_width = np.int16(self.parameters['win_width'])
275		self.side = self.parameters['side']
276		self.denoise = self.parameters['denoise']
277		self.norm = self.parameters['norm']
278		self.use_overlap = self.parameters['use_overlap']
279
280		self.broadcast_method = str(self.parameters['broadcast_method'])
281		self.error_msg_1 = ""
282		self.error_msg_2 = ""
283		self.error_msg_3 = ""
284	View Code Duplication	if not ((self.broadcast_method == 'mean')
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285		or (self.broadcast_method == 'median')
286		or (self.broadcast_method == 'linear_fit')
287		or (self.broadcast_method == 'nearest')):
288		self.error_msg_3 = "!!!WARNING!!! Selected broadcasting method" \
289		" is out of the list. Use the default option:" \
290		" 'median'"
291		logging.warning(self.error_msg_3)
292		cu.user_message(self.error_msg_3)
293		self.broadcast_method = 'median'
294		in_pData = self.get_plugin_in_datasets()[0]
295		data = self.get_in_datasets()[0]
296		starts, stops, steps = data.get_preview().get_starts_stops_steps()[0:3]
297		start_ind = starts[1]
298		stop_ind = stops[1]
299		step_ind = steps[1]
300		name = data.get_name()
301		pre_start = self.exp.meta_data.get(name + '_preview_starts')[1]
302		pre_stop = self.exp.meta_data.get(name + '_preview_stops')[1]
303		pre_step = self.exp.meta_data.get(name + '_preview_steps')[1]
304		self.origin_prev = np.arange(pre_start, pre_stop, pre_step)
305		self.plugin_prev = self.origin_prev[start_ind:stop_ind:step_ind]
306		num_sino = len(self.plugin_prev)
307		if num_sino > 20:
308		warning_msg = "\n!!!WARNING!!! You selected to calculate the " \
309		"center-of-rotation using '{}' sinograms.\n" \
310		"This is computationally expensive. Considering to " \
311		"adjust the preview parameter to use\na smaller " \
312		"number of sinograms (< 20).\n".format(num_sino)
313		logging.warning(warning_msg)
314		cu.user_message(warning_msg)
315
316		def process_frames(self, data):
317		"""
318		Find the center-of-rotation (COR) in a 360-degree scan with offset COR use
319		the method presented in Ref. [1].
320
321		Parameters
322		----------
323		data : array_like
324		2D array. 360-degree sinogram.
325
326		Returns
327		-------
328		cor : float
329		Center-of-rotation.
330
331		References
332		----------
333		.. [1] https://doi.org/10.1364/OE.418448
334		"""
335		sino = data[0]
336		(nrow, ncol) = sino.shape
337		nrow_180 = nrow // 2 + 1
338		sino_top = sino[0:nrow_180, :]
339		sino_bot = np.fliplr(sino[-nrow_180:, :])
340		overlap, side, overlap_position =\
341		self.find_overlap(sino_top, sino_bot, self.win_width, self.side,
342		self.denoise, self.norm, self.use_overlap)
343		#overlap : Width of the overlap area between two halves
344		# of the sinogram.
345		# side : Overlap side between two halves of the sinogram.
346		# overlap_position : Position of the window in the first
347		# image giving the best correlation metric."""
348		if side == 0:
349		cor = overlap / 2.0 - 1.0
350		else:
351		cor = ncol - overlap / 2.0 - 1.0
352		return [np.array([cor]), np.array([cor])]
353
354	View Code Duplication	def post_process(self):
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355		in_datasets, out_datasets = self.get_datasets()
356		cor_prev = out_datasets[0].data[...]
357		cor_broad = out_datasets[1].data[...]
358		cor_broad[:] = np.median(np.squeeze(cor_prev))
359		self.cor_for_executive_summary = np.median(cor_broad[:])
360		if self.broadcast_method == 'mean':
361		cor_broad[:] = np.mean(np.squeeze(cor_prev))
362		self.cor_for_executive_summary = np.mean(cor_broad[:])
363		if (self.broadcast_method == 'linear_fit') and (len(cor_prev) > 1):
364		afact, bfact = np.polyfit(self.plugin_prev, cor_prev[:, 0], 1)
365		list_cor = self.origin_prev * afact + bfact
366		cor_broad[:, 0] = list_cor
367		self.cor_for_executive_summary = cor_broad[:]
368		if (self.broadcast_method == 'nearest') and (len(cor_prev) > 1):
369		for i, pos in enumerate(self.origin_prev):
370		minpos = np.argmin(np.abs(pos - self.plugin_prev))
371		cor_broad[i, 0] = cor_prev[minpos, 0]
372		self.cor_for_executive_summary = cor_broad[:]
373		out_datasets[1].data[:] = cor_broad[:]
374		self.populate_meta_data('cor_preview', np.squeeze(cor_prev))
375		self.populate_meta_data('centre_of_rotation',
376		out_datasets[1].data[:].squeeze(axis=1))
377
378		def populate_meta_data(self, key, value):
379		datasets = self.parameters['datasets_to_populate']
380		in_meta_data = self.get_in_meta_data()[0]
381		in_meta_data.set(key, value)
382		for name in datasets:
383		self.exp.index['in_data'][name].meta_data.set(key, value)
384
385		def setup(self):
386		self.exp.log(self.name + " Start calculating center of rotation")
387		# set up the output dataset that is created by the plugin
388		in_dataset, out_dataset = self.get_datasets()
389		in_pData, out_pData = self.get_plugin_datasets()
390		in_pData[0].plugin_data_setup('SINOGRAM', self.get_max_frames())
391		slice_dirs = list(in_dataset[0].get_slice_dimensions())
392		self.orig_full_shape = in_dataset[0].get_shape()
393
394		# reduce the data as per data_subset parameter
395		self.set_preview(in_dataset[0], self.parameters['preview'])
396		total_frames = \
397		self._calc_total_frames(in_dataset[0].get_preview(), slice_dirs)
398
399		# copy all required information from in_dataset[0]
400		fullData = in_dataset[0]
401		new_shape = (np.prod(np.array(fullData.get_shape())[slice_dirs]), 1)
402		self.orig_shape = \
403		(np.prod(np.array(self.orig_full_shape)[slice_dirs]), 1)
404		out_dataset[0].create_dataset(shape=new_shape,
405		axis_labels=['x.pixels', 'y.pixels'],
406		remove=True,
407		transport='hdf5')
408		out_dataset[0].add_pattern("METADATA", core_dims=(1,), slice_dims=(0,))
409
410		out_dataset[1].create_dataset(shape=self.orig_shape,
411		axis_labels=['x.pixels', 'y.pixels'],
412		remove=True,
413		transport='hdf5')
414		out_dataset[1].add_pattern("METADATA", core_dims=(1,), slice_dims=(0,))
415		out_pData[0].plugin_data_setup('METADATA', self.get_max_frames())
416		out_pData[1].plugin_data_setup('METADATA', self.get_max_frames())
417		out_pData[1].meta_data.set('fix_total_frames', total_frames)
418		self.exp.log(self.name + " End")
419
420		def _calc_total_frames(self, preview, slice_dims):
421		starts, stops, steps, _ = preview.get_starts_stops_steps()
422		lengths = [len(np.arange(starts[i], stops[i], steps[i]))
423		for i in range(len(starts))]
424		return np.prod([lengths[i] for i in slice_dims])
425
426		def nOutput_datasets(self):
427		return 2
428
429		def get_max_frames(self):
430		return 'single'
431
432		def fix_transport(self):
433		return 'hdf5'
434
435	View Code Duplication	def executive_summary(self):
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436		if ((self.error_msg_1 == "")
437		and (self.error_msg_2 == "")):
438		msg = "Centre of rotation is : %s" % (
439		str(self.cor_for_executive_summary))
440		else:
441		msg = "\n" + self.error_msg_1 + "\n" + self.error_msg_2
442		msg2 = "(Not well) estimated centre of rotation is : %s" % (str(
443		self.cor_for_executive_summary))
444		cu.user_message(msg2)
445		return [msg]
446

DiamondLightSource / Savu

Centering360.executive_summary() A last analyzed 2022-08-17 15:44 UTC

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Centering360.executive_summary() A
last analyzed 2022-08-17 15:44 UTC