Add ROI type and shape

nd2reader/model/roi.py

@@ -5,19 +5,31 @@ import numpy as np
 class Roi(object):
     """
     A ND2 ROI representation.
-    Coordinates are the center coordinates of the ROI in (x, y, z) order.
-    Sizes are the sizes of the ROI in (x, y, z) order.
+    Coordinates are the center coordinates of the ROI in (x, y, z) order in micron.
+    Sizes are the sizes of the ROI in (x, y, z) order in micron.
     Shapes are represented by numbers, defined by constants in this class.
-    All these properties can be set for multiple timepoints.
+    All these properties can be set for multiple time points (in ms).
     """
     SHAPE_RECTANGLE = 3
     SHAPE_CIRCLE = 9
 
-    def __init__(self, raw_roi_dict):
+    TYPE_BACKGROUND = 2
+
+    def __init__(self, raw_roi_dict, metadata):
+        """
+
+        :param raw_roi_dict:
+        :param metadata
+        """
         self.timepoints = []
         self.positions = []
         self.sizes = []
-        self.shapes = []
+        self.shape = self.SHAPE_CIRCLE
+        self.type = self.TYPE_BACKGROUND
+
+        self._width_micron = metadata.width * metadata.pixel_microns
+        self._height_micron = metadata.height * metadata.pixel_microns
+        self._pixel_microns = metadata.pixel_microns
 
         self._extract_vect_anims(raw_roi_dict)
 
@@ -31,33 +43,37 @@ class Roi(object):
         number_of_timepoints = raw_roi_dict[six.b('m_vectAnimParams_Size')]
 
         for i in range(number_of_timepoints):
-            shape = raw_roi_dict[six.b('m_sInfo')][six.b('m_uiShapeType')]
-            self._parse_vect_anim(raw_roi_dict[six.b('m_vectAnimParams_%d') % i], shape)
+            self._parse_vect_anim(raw_roi_dict[six.b('m_vectAnimParams_%d') % i])
+
+        self.shape = raw_roi_dict[six.b('m_sInfo')][six.b('m_uiShapeType')]
+        self.type = raw_roi_dict[six.b('m_sInfo')][six.b('m_uiInterpType')]
 
         # convert to NumPy arrays
         self.timepoints = np.array(self.timepoints, dtype=np.float)
         self.positions = np.array(self.positions, dtype=np.float)
         self.sizes = np.array(self.sizes, dtype=np.float)
-        self.shapes = np.array(self.shapes, dtype=np.uint)
 
-    def _parse_vect_anim(self, animation_dict, shape):
+    def _parse_vect_anim(self, animation_dict):
         """
         Parses a ROI vector animation object and adds it to the global list of timepoints and positions.
         :param animation_dict:
         :return:
         """
         self.timepoints.append(animation_dict[six.b('m_dTimeMs')])
-        self.positions.append((animation_dict[six.b('m_dCenterX')],
-                               animation_dict[six.b('m_dCenterY')],
-                               animation_dict[six.b('m_dCenterZ')]))
+
+        position = np.array((self._width_micron / 2.0 + animation_dict[six.b('m_dCenterX')],
+                             self._height_micron / 2.0 + animation_dict[six.b('m_dCenterY')],
+                             animation_dict[six.b('m_dCenterZ')]))
+        self.positions.append(position)
+
         size_dict = animation_dict[six.b('m_sBoxShape')]
+
         self.sizes.append((size_dict[six.b('m_dSizeX')],
                            size_dict[six.b('m_dSizeY')],
                            size_dict[six.b('m_dSizeZ')]))
-        self.shapes.append(shape)
 
-    def is_circle(self, timepoint_id=0):
-        return self.shapes[timepoint_id] == self.SHAPE_CIRCLE
+    def is_circle(self):
+        return self.shape == self.SHAPE_CIRCLE
 
-    def is_rectangle(self, timepoint_id=0):
-        return self.shapes[timepoint_id] == self.SHAPE_RECTANGLE
+    def is_rectangle(self):
+        return self.shape == self.SHAPE_RECTANGLE

nd2reader/parser/v3.py

@@ -324,7 +324,7 @@ class V3Parser(BaseParser):
         roi_objects = []
         for i in range(number_of_rois):
             current_roi = raw_roi_data[six.b('m_vectGlobal_%d' % i)]
-            roi_objects.append(Roi(current_roi))
+            roi_objects.append(Roi(current_roi, self.metadata))
 
         self.roi_metadata = roi_objects