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81 lines
3.0 KiB
81 lines
3.0 KiB
import six
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import numpy as np
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class Roi(object):
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"""
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A ND2 ROI representation.
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Coordinates are the center coordinates of the ROI in (x, y, z) order in micron.
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Sizes are the sizes of the ROI in (x, y, z) order in micron.
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Shapes are represented by numbers, defined by constants in this class.
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All these properties can be set for multiple time points (in ms).
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"""
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SHAPE_RECTANGLE = 3
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SHAPE_CIRCLE = 9
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TYPE_BACKGROUND = 2
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def __init__(self, raw_roi_dict, metadata):
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"""
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:param raw_roi_dict:
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:param metadata
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"""
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self.timepoints = []
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self.positions = []
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self.sizes = []
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self.shape = self.SHAPE_CIRCLE
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self.type = self.TYPE_BACKGROUND
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self._img_width_micron = metadata.width * metadata.pixel_microns
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self._img_height_micron = metadata.height * metadata.pixel_microns
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self._pixel_microns = metadata.pixel_microns
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self._extract_vect_anims(raw_roi_dict)
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def _extract_vect_anims(self, raw_roi_dict):
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"""
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Extract the vector animation parameters from the ROI.
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This includes the position and size at the given timepoints.
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:param raw_roi_dict:
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:return:
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"""
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number_of_timepoints = raw_roi_dict[six.b('m_vectAnimParams_Size')]
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for i in range(number_of_timepoints):
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self._parse_vect_anim(raw_roi_dict[six.b('m_vectAnimParams_%d') % i])
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self.shape = raw_roi_dict[six.b('m_sInfo')][six.b('m_uiShapeType')]
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self.type = raw_roi_dict[six.b('m_sInfo')][six.b('m_uiInterpType')]
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# convert to NumPy arrays
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self.timepoints = np.array(self.timepoints, dtype=np.float)
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self.positions = np.array(self.positions, dtype=np.float)
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self.sizes = np.array(self.sizes, dtype=np.float)
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def _parse_vect_anim(self, animation_dict):
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"""
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Parses a ROI vector animation object and adds it to the global list of timepoints and positions.
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:param animation_dict:
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:return:
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"""
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self.timepoints.append(animation_dict[six.b('m_dTimeMs')])
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# positions are taken from the center of the image as a fraction of the half width/height of the image
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position = np.array((0.5 * self._img_width_micron * (1 + animation_dict[six.b('m_dCenterX')]),
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0.5 * self._img_height_micron * (1 + animation_dict[six.b('m_dCenterY')]),
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animation_dict[six.b('m_dCenterZ')]))
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self.positions.append(position)
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size_dict = animation_dict[six.b('m_sBoxShape')]
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# sizes are fractions of the half width/height of the image
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self.sizes.append((size_dict[six.b('m_dSizeX')] * 0.25 * self._img_width_micron,
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size_dict[six.b('m_dSizeY')] * 0.25 * self._img_height_micron,
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size_dict[six.b('m_dSizeZ')]))
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def is_circle(self):
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return self.shape == self.SHAPE_CIRCLE
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def is_rectangle(self):
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return self.shape == self.SHAPE_RECTANGLE
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