add option to plot each tissue piece separately in plot_segmentation

wsi/wsi.py

@@ -1,14 +1,15 @@
 from __future__ import annotations
-import multiprocessing as mp
+
+from pathlib import Path
+import typing as tp
 import math
 import time
-import typing as tp
-from pathlib import Path
+import multiprocessing as mp
 
+from PIL import Image
 import cv2
 import numpy as np
 import openslide
-from PIL import Image
 import h5py
 import shapely
 import torch
@@ -84,7 +85,7 @@ def __init__(
         WholeSlideImage
             WholeSlideImage object.
         """
-        from .utils import is_url, download_file
+        from wsi.utils import is_url, download_file
 
         if not isinstance(path, Path):
             if is_url(path):
@@ -303,7 +304,7 @@ def vis_wsi(
         number_contours: bool = False,
         seg_display: bool = True,
         annot_display: bool = True,
-    ) -> None:
+    ) -> Image:
         """
         Visualize the whole slide image.
 
@@ -338,7 +339,7 @@ def vis_wsi(
 
         Returns
         -------
-        None
+        Image
         """
         downsample = self.level_downsamples[vis_level]
         scale = [1 / downsample[0], 1 / downsample[1]]
@@ -829,7 +830,13 @@ def segment(
             self.save_segmentation()
         self.plot_segmentation()
 
-    def plot_segmentation(self, output_file: tp.Optional[Path] = None, **kwargs) -> None:
+    def plot_segmentation(
+        self,
+        output_file: tp.Optional[Path] = None,
+        per_contour: bool = False,
+        level: int | None = None,
+        **kwargs,
+    ) -> None:
         """
         Plot the segmentation of the WSI.
 
@@ -852,8 +859,20 @@ def plot_segmentation(self, output_file: tp.Optional[Path] = None, **kwargs) ->
         if output_file is None:
             output_file = self.path.with_suffix(".segmentation.png")
 
-        level = self._get_best_level((2000, 2000))
-        self.vis_wsi(vis_level=level, **kwargs).save(output_file)
+        if level is None:
+            level = self._get_best_level((2000, 2000))
+        if not per_contour:
+            self.vis_wsi(vis_level=level, **kwargs).save(output_file)
+            return
+
+        for i, piece in enumerate(self.contours_tissue):
+            poly = shapely.geometry.Polygon(piece.squeeze())
+            top_left, bottom_right = np.asarray(poly.bounds).reshape(2, 2).astype(int)
+            img = self.vis_wsi(
+                vis_level=level, top_left=top_left, bot_right=bottom_right, **kwargs
+            )
+            n = str(i).zfill(3)
+            img.save(output_file.with_suffix(f".{n}.png"))
 
     def tile(
         self,
@@ -1716,7 +1735,7 @@ def as_tile_bag(self, **kwargs):
         kwargs : dict
             Additional keyword arguments to pass to wsi.utils.WholeSlideBag.
         """
-        from .utils import WholeSlideBag
+        from wsi.utils import WholeSlideBag
 
         # dataset = Whole_Slide_Bag(self.hdf5_file, pretrained=True)
         dataset = WholeSlideBag(
@@ -1748,7 +1767,7 @@ def as_data_loader(
         torch.utils.data.DataLoader
         """
         from functools import partial
-        from .utils import collate_features
+        from wsi.utils import collate_features
         from torch.utils.data import DataLoader
 
         collate = partial(collate_features, with_coords=with_coords)
@@ -1789,7 +1808,7 @@ def inference(
         """
         from typing import cast
         import torch
-        from tqdm import tqdm
+        from tqdm_loggable.auto import tqdm
 
         if isinstance(model, torch.nn.Module):
             assert model_name is None, "model_name must be None when model is provided"