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import tkinter as tknt
from typing import Union
import numpy as nmpy
from PIL import Image as image_t, ImageTk as tk_image_t
class soma_validation_window_t:
__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):
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: tknt.EventType.ButtonPress) -> None:
""""""
row = event.y
col = event.x
value = self.mip[row, col]
if value > 0:
self.lmap[self.lmap == value] = 0
soma_bmap = self.mip == value
self.mip[soma_bmap] = 0
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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