refactored soma validation interface + color

brick/interface/soma_validation.py

-import tkinter as tk
+import tkinter as tknt
+from typing import Union
 
-import numpy as np
-from PIL import Image, ImageTk
+import numpy as nmpy
+from PIL import Image as image_t, ImageTk as tk_image_t
 
 
-array_t = np.ndarray
+array_t = nmpy.ndarray
 
 
 class soma_validation_window_t:
-    def __init__(self, lmap: array_t, mip_axis: int = -1):
+    __slots__ = (
+        "lmap",
+        "mip",
+        "mip_4_display",
+        "root",
+        "canvas",
+        "tk_image",
+        "canvas_image",
+    )
+    lmap: array_t
+    mip: array_t
+    mip_4_display: array_t
+    root: tknt.Tk
+    canvas: tknt.Canvas
+    tk_image: tk_image_t
+    canvas_image: int
+
+    def __init__(self, lmap: array_t, mip_axis: int = -1, color_version: bool = True):
         """"""
-        self.lmap = lmap
-        self.mip = np.max(lmap, axis=mip_axis)
-        mip_shape = self.mip.shape
-
-        offset = 50
-        scaling = (255.0 - offset) / np.max(self.mip)
-        self.scaled_mip = scaling * self.mip + offset
-        self.scaled_mip[self.mip == 0] = 0
-
-        self.root = tk.Tk()
-        self.canvas = tk.Canvas(
-            self.root, width=mip_shape[1], height=mip_shape[0]
-        )
-        self.tk_image = ImageTk.PhotoImage(
-            master=self.root, image=Image.fromarray(self.scaled_mip)
-        )
-        self.canvas_image = self.canvas.create_image(
-            0, 0, anchor="nw", image=self.tk_image
-        )
-
-        self.canvas.bind("<Button-1>", self._DeleteSoma)
-
-        self.canvas.pack()
-
-    def LaunchInteraction(self) -> None:
+        mip = nmpy.max(lmap, axis=mip_axis)
+        if color_version:
+            mip_4_display = _ColoredVersion(mip)
+        else:
+            mip_4_display = _ScaledVersion(mip)
+
+        root = tknt.Tk()
+        canvas = tknt.Canvas(root, width=mip.shape[1], height=mip.shape[0])
+        tk_image = _TkImageFromNumpyArray(mip_4_display, root)
+        canvas_image = canvas.create_image(0, 0, anchor="nw", image=tk_image)
+        canvas.pack()
+
+        canvas.bind("<Button-1>", self._DeleteSoma)
+
+        for field in self.__class__.__slots__:
+            setattr(self, field, eval(field))
+
+    def LaunchValidation(self) -> None:
         """"""
         self.root.mainloop()
         # scikit-image.relabel(self.lmap)
 
-    def _DeleteSoma(self, event) -> None:
+    def _DeleteSoma(self, event: tknt.EventType.ButtonPress) -> None:
         """"""
         row = event.y
         col = event.x
@@ -50,8 +61,47 @@ class soma_validation_window_t:
 
             soma_bmap = self.mip == value
             self.mip[soma_bmap] = 0
-            self.scaled_mip[soma_bmap] = 0
-            self.tk_image = ImageTk.PhotoImage(
-                master=self.root, image=Image.fromarray(self.scaled_mip)
-            )
-            self.canvas.itemconfig(self.canvas_image, image=self.tk_image)
+            if self.mip_4_display.ndim == 1:
+                self.mip_4_display[soma_bmap] = 0
+            else:
+                for channel in range(self.mip_4_display.shape[2]):
+                    self.mip_4_display[..., channel][soma_bmap] = 0
+            self.tk_image = _TkImageFromNumpyArray(self.mip_4_display, self.root)
+            self.canvas.itemconfigure(self.canvas_image, image=self.tk_image)
+
+
+def _ScaledVersion(image: array_t, offset: int = 50) -> array_t:
+    """
+    offset: Value of darkest non-background intensity
+    """
+    scaling = (255.0 - offset) / nmpy.max(image)
+    output = scaling * image + offset
+    output[image == 0] = 0
+
+    return output.astype(nmpy.uint8)
+
+
+def _ColoredVersion(image: array_t) -> array_t:
+    """"""
+    max_label = nmpy.amax(image)
+
+    labels = tuple(range(1, max_label+1))
+    half_length = int(round(0.5 * max_label))
+    shuffled_labels = labels[half_length:] + labels[:half_length]
+    shuffled_image = nmpy.zeros_like(image)
+    for label, shuffled_label in enumerate(shuffled_labels):
+        shuffled_image[image == label] = shuffled_label
+
+    output = nmpy.dstack((image, shuffled_image, max_label - image))
+    output = (255.0 / max_label) * output
+    output[image == 0] = 0
+
+    return output.astype(nmpy.uint8)
+
+
+def _TkImageFromNumpyArray(array: array_t, parent: Union[tknt.Widget, tknt.Tk]) -> tk_image_t:
+    """"""
+    pil_image = image_t.fromarray(array)
+    output = tk_image_t.PhotoImage(master=parent, image=pil_image)
+
+    return output

nutrimorph.py

@@ -277,7 +277,7 @@ def NutriMorph(data_path: str,
     elif interactive:
         # smvl.SomaValidation(som_nfo["lmp"], mip_axis=0)
         window = smvl.soma_validation_window_t(som_nfo["lmp"], mip_axis=0)
-        window.LaunchInteraction()
+        window.LaunchValidation()
 
     #
     # -- Extentions