# coding: utf-8
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"""This module define core objects for profile tools.
"""
__authors__ = ["V.A. Sole", "T. Vincent", "P. Knobel", "H. Payno", "V. Valls"]
__license__ = "MIT"
__date__ = "17/04/2020"
import collections
import numpy
import weakref
from silx.image.bilinear import BilinearImage
from silx.gui import qt
CurveProfileData = collections.namedtuple(
'CurveProfileData', [
"coords",
"profile",
"title",
"xLabel",
"yLabel",
])
RgbaProfileData = collections.namedtuple(
'RgbaProfileData', [
"coords",
"profile",
"profile_r",
"profile_g",
"profile_b",
"profile_a",
"title",
"xLabel",
"yLabel",
])
ImageProfileData = collections.namedtuple(
'ImageProfileData', [
'coords',
'profile',
'title',
'xLabel',
'yLabel',
'colormap',
])
[docs]class ProfileRoiMixIn:
"""Base mix-in for ROI which can be used to select a profile.
This mix-in have to be applied to a :class:`~silx.gui.plot.items.roi.RegionOfInterest`
in order to be usable by a :class:`~silx.gui.plot.tools.profile.manager.ProfileManager`.
"""
ITEM_KIND = None
"""Define the plot item which can be used with this profile ROI"""
sigProfilePropertyChanged = qt.Signal()
"""Emitted when a property of this profile have changed"""
sigPlotItemChanged = qt.Signal()
"""Emitted when the plot item linked to this profile have changed"""
def __init__(self, parent=None):
self.__profileWindow = None
self.__profileManager = None
self.__plotItem = None
self.setName("Profile")
self.setEditable(True)
self.setSelectable(True)
[docs] def invalidateProfile(self):
"""Must be called by the implementation when the profile have to be
recomputed."""
profileManager = self.getProfileManager()
if profileManager is not None:
profileManager.requestUpdateProfile(self)
[docs] def invalidateProperties(self):
"""Must be called when a property of the profile have changed."""
self.sigProfilePropertyChanged.emit()
def _setPlotItem(self, plotItem):
"""Specify the plot item to use with this profile
:param `~silx.gui.plot.items.item.Item` plotItem: A plot item
"""
previousPlotItem = self.getPlotItem()
if previousPlotItem is plotItem:
return
self.__plotItem = weakref.ref(plotItem)
self.sigPlotItemChanged.emit()
[docs] def getPlotItem(self):
"""Returns the plot item used by this profile
:rtype: `~silx.gui.plot.items.item.Item`
"""
if self.__plotItem is None:
return None
plotItem = self.__plotItem()
if plotItem is None:
self.__plotItem = None
return plotItem
def _setProfileManager(self, profileManager):
self.__profileManager = profileManager
[docs] def getProfileManager(self):
"""
Returns the profile manager connected to this ROI.
:rtype: ~silx.gui.plot.tools.profile.manager.ProfileManager
"""
return self.__profileManager
[docs] def getProfileWindow(self):
"""
Returns the windows associated to this ROI, else None.
:rtype: ProfileWindow
"""
return self.__profileWindow
[docs] def setProfileWindow(self, profileWindow):
"""
Associate a window to this ROI. Can be None.
:param ProfileWindow profileWindow: A main window
to display the profile.
"""
if profileWindow is self.__profileWindow:
return
if self.__profileWindow is not None:
self.__profileWindow.sigClose.disconnect(self.__profileWindowAboutToClose)
self.__profileWindow.setRoiProfile(None)
self.__profileWindow = profileWindow
if self.__profileWindow is not None:
self.__profileWindow.sigClose.connect(self.__profileWindowAboutToClose)
self.__profileWindow.setRoiProfile(self)
def __profileWindowAboutToClose(self):
profileManager = self.getProfileManager()
roiManager = profileManager.getRoiManager()
try:
roiManager.removeRoi(self)
except ValueError:
pass
[docs] def computeProfile(self, item):
"""
Compute the profile which will be displayed.
This method is not called from the main Qt thread, but from a thread
pool.
:param ~silx.gui.plot.items.Item item: A plot item
:rtype: Union[CurveProfileData,ImageProfileData]
"""
raise NotImplementedError()
def _alignedFullProfile(data, origin, scale, position, roiWidth, axis, method):
"""Get a profile along one axis on a stack of images
:param numpy.ndarray data: 3D volume (stack of 2D images)
The first dimension is the image index.
:param origin: Origin of image in plot (ox, oy)
:param scale: Scale of image in plot (sx, sy)
:param float position: Position of profile line in plot coords
on the axis orthogonal to the profile direction.
:param int roiWidth: Width of the profile in image pixels.
:param int axis: 0 for horizontal profile, 1 for vertical.
:param str method: method to compute the profile. Can be 'mean' or 'sum' or
'none'
:return: profile image + effective ROI area corners in plot coords
"""
assert axis in (0, 1)
assert len(data.shape) == 3
assert method in ('mean', 'sum', 'none')
# Convert from plot to image coords
imgPos = int((position - origin[1 - axis]) / scale[1 - axis])
if axis == 1: # Vertical profile
# Transpose image to always do a horizontal profile
data = numpy.transpose(data, (0, 2, 1))
nimages, height, width = data.shape
roiWidth = min(height, roiWidth) # Clip roi width to image size
# Get [start, end[ coords of the roi in the data
start = int(int(imgPos) + 0.5 - roiWidth / 2.)
start = min(max(0, start), height - roiWidth)
end = start + roiWidth
if method == 'none':
profile = None
else:
if start < height and end > 0:
if method == 'mean':
fct = numpy.mean
elif method == 'sum':
fct = numpy.sum
else:
raise ValueError('method not managed')
profile = fct(data[:, max(0, start):min(end, height), :], axis=1).astype(numpy.float32)
else:
profile = numpy.zeros((nimages, width), dtype=numpy.float32)
# Compute effective ROI in plot coords
profileBounds = numpy.array(
(0, width, width, 0),
dtype=numpy.float32) * scale[axis] + origin[axis]
roiBounds = numpy.array(
(start, start, end, end),
dtype=numpy.float32) * scale[1 - axis] + origin[1 - axis]
if axis == 0: # Horizontal profile
area = profileBounds, roiBounds
else: # vertical profile
area = roiBounds, profileBounds
return profile, area
def _alignedPartialProfile(data, rowRange, colRange, axis, method):
"""Mean of a rectangular region (ROI) of a stack of images
along a given axis.
Returned values and all parameters are in image coordinates.
:param numpy.ndarray data: 3D volume (stack of 2D images)
The first dimension is the image index.
:param rowRange: [min, max[ of ROI rows (upper bound excluded).
:type rowRange: 2-tuple of int (min, max) with min < max
:param colRange: [min, max[ of ROI columns (upper bound excluded).
:type colRange: 2-tuple of int (min, max) with min < max
:param int axis: The axis along which to take the profile of the ROI.
0: Sum rows along columns.
1: Sum columns along rows.
:param str method: method to compute the profile. Can be 'mean' or 'sum'
:return: Profile image along the ROI as the mean of the intersection
of the ROI and the image.
"""
assert axis in (0, 1)
assert len(data.shape) == 3
assert rowRange[0] < rowRange[1]
assert colRange[0] < colRange[1]
assert method in ('mean', 'sum')
nimages, height, width = data.shape
# Range aligned with the integration direction
profileRange = colRange if axis == 0 else rowRange
profileLength = abs(profileRange[1] - profileRange[0])
# Subset of the image to use as intersection of ROI and image
rowStart = min(max(0, rowRange[0]), height)
rowEnd = min(max(0, rowRange[1]), height)
colStart = min(max(0, colRange[0]), width)
colEnd = min(max(0, colRange[1]), width)
if method == 'mean':
_fct = numpy.mean
elif method == 'sum':
_fct = numpy.sum
else:
raise ValueError('method not managed')
imgProfile = _fct(data[:, rowStart:rowEnd, colStart:colEnd], axis=axis + 1,
dtype=numpy.float32)
# Profile including out of bound area
profile = numpy.zeros((nimages, profileLength), dtype=numpy.float32)
# Place imgProfile in full profile
offset = - min(0, profileRange[0])
profile[:, offset:offset + imgProfile.shape[1]] = imgProfile
return profile
def createProfile(roiInfo, currentData, origin, scale, lineWidth, method):
"""Create the profile line for the the given image.
:param roiInfo: information about the ROI: start point, end point and
type ("X", "Y", "D")
:param numpy.ndarray currentData: the 2D image or the 3D stack of images
on which we compute the profile.
:param origin: (ox, oy) the offset from origin
:type origin: 2-tuple of float
:param scale: (sx, sy) the scale to use
:type scale: 2-tuple of float
:param int lineWidth: width of the profile line
:param str method: method to compute the profile. Can be 'mean' or 'sum'
or 'none': to compute everything except the profile
:return: `coords, profile, area, profileName, xLabel`, where:
- coords is the X coordinate to use to display the profile
- profile is a 2D array of the profiles of the stack of images.
For a single image, the profile is a curve, so this parameter
has a shape *(1, len(curve))*
- area is a tuple of two 1D arrays with 4 values each. They represent
the effective ROI area corners in plot coords.
- profileName is a string describing the ROI, meant to be used as
title of the profile plot
- xLabel the label for X in the profile window
:rtype: tuple(ndarray,ndarray,(ndarray,ndarray),str)
"""
if currentData is None or roiInfo is None or lineWidth is None:
raise ValueError("createProfile called with invalide arguments")
# force 3D data (stack of images)
if len(currentData.shape) == 2:
currentData3D = currentData.reshape((1,) + currentData.shape)
elif len(currentData.shape) == 3:
currentData3D = currentData
roiWidth = max(1, lineWidth)
roiStart, roiEnd, lineProjectionMode = roiInfo
if lineProjectionMode == 'X': # Horizontal profile on the whole image
profile, area = _alignedFullProfile(currentData3D,
origin, scale,
roiStart[1], roiWidth,
axis=0,
method=method)
if method == 'none':
coords = None
else:
coords = numpy.arange(len(profile[0]), dtype=numpy.float32)
coords = coords * scale[0] + origin[0]
yMin, yMax = min(area[1]), max(area[1]) - 1
if roiWidth <= 1:
profileName = '{ylabel} = %g' % yMin
else:
profileName = '{ylabel} = [%g, %g]' % (yMin, yMax)
xLabel = '{xlabel}'
elif lineProjectionMode == 'Y': # Vertical profile on the whole image
profile, area = _alignedFullProfile(currentData3D,
origin, scale,
roiStart[0], roiWidth,
axis=1,
method=method)
if method == 'none':
coords = None
else:
coords = numpy.arange(len(profile[0]), dtype=numpy.float32)
coords = coords * scale[1] + origin[1]
xMin, xMax = min(area[0]), max(area[0]) - 1
if roiWidth <= 1:
profileName = '{xlabel} = %g' % xMin
else:
profileName = '{xlabel} = [%g, %g]' % (xMin, xMax)
xLabel = '{ylabel}'
else: # Free line profile
# Convert start and end points in image coords as (row, col)
startPt = ((roiStart[1] - origin[1]) / scale[1],
(roiStart[0] - origin[0]) / scale[0])
endPt = ((roiEnd[1] - origin[1]) / scale[1],
(roiEnd[0] - origin[0]) / scale[0])
if (int(startPt[0]) == int(endPt[0]) or
int(startPt[1]) == int(endPt[1])):
# Profile is aligned with one of the axes
# Convert to int
startPt = int(startPt[0]), int(startPt[1])
endPt = int(endPt[0]), int(endPt[1])
# Ensure startPt <= endPt
if startPt[0] > endPt[0] or startPt[1] > endPt[1]:
startPt, endPt = endPt, startPt
if startPt[0] == endPt[0]: # Row aligned
rowRange = (int(startPt[0] + 0.5 - 0.5 * roiWidth),
int(startPt[0] + 0.5 + 0.5 * roiWidth))
colRange = startPt[1], endPt[1] + 1
if method == 'none':
profile = None
else:
profile = _alignedPartialProfile(currentData3D,
rowRange, colRange,
axis=0,
method=method)
else: # Column aligned
rowRange = startPt[0], endPt[0] + 1
colRange = (int(startPt[1] + 0.5 - 0.5 * roiWidth),
int(startPt[1] + 0.5 + 0.5 * roiWidth))
if method == 'none':
profile = None
else:
profile = _alignedPartialProfile(currentData3D,
rowRange, colRange,
axis=1,
method=method)
# Convert ranges to plot coords to draw ROI area
area = (
numpy.array(
(colRange[0], colRange[1], colRange[1], colRange[0]),
dtype=numpy.float32) * scale[0] + origin[0],
numpy.array(
(rowRange[0], rowRange[0], rowRange[1], rowRange[1]),
dtype=numpy.float32) * scale[1] + origin[1])
else: # General case: use bilinear interpolation
# Ensure startPt <= endPt
if (startPt[1] > endPt[1] or (
startPt[1] == endPt[1] and startPt[0] > endPt[0])):
startPt, endPt = endPt, startPt
if method == 'none':
profile = None
else:
profile = []
for slice_idx in range(currentData3D.shape[0]):
bilinear = BilinearImage(currentData3D[slice_idx, :, :])
profile.append(bilinear.profile_line(
(startPt[0] - 0.5, startPt[1] - 0.5),
(endPt[0] - 0.5, endPt[1] - 0.5),
roiWidth,
method=method))
profile = numpy.array(profile)
# Extend ROI with half a pixel on each end, and
# Convert back to plot coords (x, y)
length = numpy.sqrt((endPt[0] - startPt[0]) ** 2 +
(endPt[1] - startPt[1]) ** 2)
dRow = (endPt[0] - startPt[0]) / length
dCol = (endPt[1] - startPt[1]) / length
# Extend ROI with half a pixel on each end
roiStartPt = startPt[0] - 0.5 * dRow, startPt[1] - 0.5 * dCol
roiEndPt = endPt[0] + 0.5 * dRow, endPt[1] + 0.5 * dCol
# Rotate deltas by 90 degrees to apply line width
dRow, dCol = dCol, -dRow
area = (
numpy.array((roiStartPt[1] - 0.5 * roiWidth * dCol,
roiStartPt[1] + 0.5 * roiWidth * dCol,
roiEndPt[1] + 0.5 * roiWidth * dCol,
roiEndPt[1] - 0.5 * roiWidth * dCol),
dtype=numpy.float32) * scale[0] + origin[0],
numpy.array((roiStartPt[0] - 0.5 * roiWidth * dRow,
roiStartPt[0] + 0.5 * roiWidth * dRow,
roiEndPt[0] + 0.5 * roiWidth * dRow,
roiEndPt[0] - 0.5 * roiWidth * dRow),
dtype=numpy.float32) * scale[1] + origin[1])
# Convert start and end points back to plot coords
y0 = startPt[0] * scale[1] + origin[1]
x0 = startPt[1] * scale[0] + origin[0]
y1 = endPt[0] * scale[1] + origin[1]
x1 = endPt[1] * scale[0] + origin[0]
if startPt[1] == endPt[1]:
profileName = '{xlabel} = %g; {ylabel} = [%g, %g]' % (x0, y0, y1)
if method == 'none':
coords = None
else:
coords = numpy.arange(len(profile[0]), dtype=numpy.float32)
coords = coords * scale[1] + y0
xLabel = '{ylabel}'
elif startPt[0] == endPt[0]:
profileName = '{ylabel} = %g; {xlabel} = [%g, %g]' % (y0, x0, x1)
if method == 'none':
coords = None
else:
coords = numpy.arange(len(profile[0]), dtype=numpy.float32)
coords = coords * scale[0] + x0
xLabel = '{xlabel}'
else:
m = (y1 - y0) / (x1 - x0)
b = y0 - m * x0
profileName = '{ylabel} = %g * {xlabel} %+g' % (m, b)
if method == 'none':
coords = None
else:
coords = numpy.linspace(x0, x1, len(profile[0]),
endpoint=True,
dtype=numpy.float32)
xLabel = '{xlabel}'
return coords, profile, area, profileName, xLabel
