refactoring: replaced split mapper with dataclass

image_prediction/stitching/merging.py

@@ -10,7 +10,7 @@ from image_prediction.info import Info
 from image_prediction.stitching.grouping import CoordGrouper
 from image_prediction.stitching.utils import make_coord_getter, flatten_groups_once
 from image_prediction.utils.generic import until
-from image_prediction.stitching.split_mapper import HorizontalSplitMapper, VerticalMapper
+from image_prediction.stitching.split_mapper import HorizontalSplitMapper, VerticalSplitMapper
 
 
 def no_new_merges(pairs1, pairs2):
@@ -120,7 +120,7 @@ def merge_pair_horizontally(p1: ImageMetadataPair, p2: ImageMetadataPair):
 
 
 def merge_metadata_vertically(m1: dict, m2: dict):
-    m1, m2 = map(VerticalMapper, [m1, m2])
+    m1, m2 = map(VerticalSplitMapper, [m1, m2])
     return merge_metadata(m1, m2)
 
 

image_prediction/stitching/split_mapper.py

@@ -1,49 +1,41 @@
-import abc
+from copy import deepcopy
+from dataclasses import field, dataclass
+from operator import attrgetter
 
 from image_prediction.info import Info
 
 
-class SplitMapper(abc.ABC):
+@dataclass
-    def __init__(self, wrapped: dict, keymap: dict):
+class SplitMapper:
-        self.wrapped = wrapped
+    """Manages access into a coordinate encoding mapping by abstracting over x1, x2 and y1, y2 as c1, c2; as well as
-        self.keymap = keymap
+    over width and height as 'dim'."""
+    __keymap: dict
+    wrapped: dict
+    __wrapped: dict = field(init=False)
+    dim: float = field(init=False)
+    c1: float = field(init=False)
+    c2: float = field(init=False)
 
-    def __getitem__(self, key):
+    def __post_init__(self):
-        return self.wrapped[self.keymap[key]]
+        for k, v in self.__keymap.items():
-
+            setattr(self, k, self.__wrapped[v])
-    def __setitem__(self, key, value):
-        self.wrapped[self.keymap[key]] = value
 
     @property
-    def dim(self):
+    def wrapped(self):
-        return self["dim"]
+        ret = deepcopy(self.__wrapped)
+        ret.update(dict(zip(self.__keymap.values(), attrgetter(*self.__keymap.keys())(self))))
+        return ret
 
-    @dim.setter
+    @wrapped.setter
-    def dim(self, value):
+    def wrapped(self, wrapped):
-        self["dim"] = value
+        self.__wrapped = wrapped
-
-    @property
-    def c1(self):
-        return self["c1"]
-
-    @c1.setter
-    def c1(self, value):
-        self["c1"] = value
-
-    @property
-    def c2(self):
-        return self["c2"]
-
-    @c2.setter
-    def c2(self, value):
-        self["c2"] = value
 
 
 class HorizontalSplitMapper(SplitMapper):
     def __init__(self, wrapped: dict):
-        super().__init__(wrapped, {"dim": Info.WIDTH, "c1": Info.X1, "c2": Info.X2})
+        super().__init__({"dim": Info.WIDTH, "c1": Info.X1, "c2": Info.X2}, wrapped)
 
 
-class VerticalMapper(SplitMapper):
+class VerticalSplitMapper(SplitMapper):
     def __init__(self, wrapped: dict):
-        super().__init__(wrapped, {"dim": Info.HEIGHT, "c1": Info.Y1, "c2": Info.Y2})
+        super().__init__({"dim": Info.HEIGHT, "c1": Info.Y1, "c2": Info.Y2}, wrapped)

test/utils/stitching.py

@@ -4,7 +4,7 @@ from itertools import chain
 
 from funcy import rpartial, juxt
 
-from image_prediction.stitching.split_mapper import SplitMapper, HorizontalSplitMapper, VerticalMapper
+from image_prediction.stitching.split_mapper import SplitMapper, HorizontalSplitMapper, VerticalSplitMapper
 
 
 class BoxSplitter:
@@ -42,7 +42,7 @@ class BoxSplitter:
         return self.__split_if_large_enough(HorizontalSplitMapper(box))
 
     def __split_vertical(self, box):
-        return self.__split_if_large_enough(VerticalMapper(box))
+        return self.__split_if_large_enough(VerticalSplitMapper(box))
 
     def __split_if_large_enough(self, wrapped_box: SplitMapper):
         return (