Update README.md (#9)

* Update README.md

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

.pre-commit-config.yaml

@@ -12,4 +12,4 @@ repos:
 -   repo: https://github.com/PyCQA/isort
     rev: 5.12.0
     hooks:
-    -   id: isort
+    -   id: isort

README.md

@@ -1,3 +1,7 @@
+⚠️ The functionality provided by this repository can now be found in [PyTorch Geometric](https://pytorch-geometric.readthedocs.io/en/latest/) in the [to_superpixels()](https://pytorch-geometric.readthedocs.io/en/latest/_modules/torch_geometric/transforms/to_superpixels.html) function.
+
+---
+
 # PyTorch Superpixels
 
 - [Why use superpixels?](#why-use-superpixels)

example.py

@@ -1,21 +1,22 @@
+from functools import partial
+from multiprocessing import Pool
+from os import cpu_count
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+import numpy as np
+import torch
+import torchvision.transforms.functional as F
 from numpy.core.fromnumeric import product
+from skimage.segmentation import find_boundaries, mark_boundaries, slic
 from skimage.segmentation.boundaries import find_boundaries
-import torch
-import numpy as np
 from torchvision.io import read_image
 from torchvision.models.segmentation import fcn_resnet50
-import matplotlib.pyplot as plt
 from torchvision.transforms.functional import convert_image_dtype
-from torchvision.utils import draw_segmentation_masks
-from torchvision.utils import make_grid
+from torchvision.utils import draw_segmentation_masks, make_grid
+
 from pytorch_superpixels.runtime import superpixelise
-from skimage.segmentation import slic, mark_boundaries, find_boundaries
-from pathlib import Path
-from multiprocessing import Pool
-from os import cpu_count
-from functools import partial
 
-import torchvision.transforms.functional as F
 
 def show(imgs):
     if not isinstance(imgs, list):
@@ -32,9 +33,27 @@ def show(imgs):
 
 if __name__ == "__main__":
     sem_classes = [
-        '__background__', 'aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus',
-        'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike',
-        'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor'
+        "__background__",
+        "aeroplane",
+        "bicycle",
+        "bird",
+        "boat",
+        "bottle",
+        "bus",
+        "car",
+        "cat",
+        "chair",
+        "cow",
+        "diningtable",
+        "dog",
+        "horse",
+        "motorbike",
+        "person",
+        "pottedplant",
+        "sheep",
+        "sofa",
+        "train",
+        "tvmonitor",
     ]
     sem_class_to_idx = {cls: idx for (idx, cls) in enumerate(sem_classes)}
     image_dims = [420, 640]
@@ -46,24 +65,25 @@ def show(imgs):
     batch = convert_image_dtype(batch_int, dtype=torch.float)
 
     # permute because slic expects the last dimension to be channel
-    with Pool(processes = cpu_count()-1) as pool:
+    with Pool(processes=cpu_count() - 1) as pool:
         # re-order axes for skimage
-        args = [x.permute(1,2,0) for x in batch]
+        args = [x.permute(1, 2, 0) for x in batch]
         # 100 segments
-        kwargs = {"n_segments":100, "start_label":0, "slic_zero":True}
+        kwargs = {"n_segments": 100, "start_label": 0, "slic_zero": True}
         func = partial(slic, **kwargs)
         masks_100sp = pool.map(func, args)
         # 1000 segments
         kwargs["n_segments"] = 1000
         func = partial(slic, **kwargs)
         masks_1000sp = pool.map(func, args)
 
-
     model = fcn_resnet50(pretrained=True, progress=False)
     model = model.eval()
 
-    normalized_batch = F.normalize(batch, mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225))
-    outputs = model(batch)['out']
+    normalized_batch = F.normalize(
+        batch, mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)
+    )
+    outputs = model(batch)["out"]
 
     normalized_masks = torch.nn.functional.softmax(outputs, dim=1)
     num_classes = normalized_masks.shape[1]
@@ -73,23 +93,33 @@ def generate_all_class_masks(outputs, masks):
         masks = torch.from_numpy(masks)
         outputs_sp = superpixelise(outputs, masks)
         normalized_masks_sp = torch.nn.functional.softmax(outputs_sp, dim=1)
-        return normalized_masks_sp[i].argmax(0) == torch.arange(num_classes)[:, None, None]
+        return (
+            normalized_masks_sp[i].argmax(0) == torch.arange(num_classes)[:, None, None]
+        )
 
     to_show = []
     for i, image in enumerate(images):
         # before
-        all_classes_masks = normalized_masks[i].argmax(0) == torch.arange(num_classes)[:, None, None]
-        to_show.append(draw_segmentation_masks(image, masks=all_classes_masks, alpha=.6))
+        all_classes_masks = (
+            normalized_masks[i].argmax(0) == torch.arange(num_classes)[:, None, None]
+        )
+        to_show.append(
+            draw_segmentation_masks(image, masks=all_classes_masks, alpha=0.6)
+        )
         # after 100
         all_classes_masks_sp = generate_all_class_masks(outputs, masks_100sp)
-        to_show.append(draw_segmentation_masks(image, masks=all_classes_masks_sp, alpha=.6))
+        to_show.append(
+            draw_segmentation_masks(image, masks=all_classes_masks_sp, alpha=0.6)
+        )
         # show superpixel boundaries
         boundaries = find_boundaries(masks_100sp[i])
         to_show[-1][0:2, boundaries] = 255
         to_show[-1][2, boundaries] = 0
         # after 1000
         all_classes_masks_sp = generate_all_class_masks(outputs, masks_1000sp)
-        to_show.append(draw_segmentation_masks(image, masks=all_classes_masks_sp, alpha=.6))
+        to_show.append(
+            draw_segmentation_masks(image, masks=all_classes_masks_sp, alpha=0.6)
+        )
         # show superpixel boundaries
         boundaries = find_boundaries(masks_1000sp[i])
         to_show[-1][0:2, boundaries] = 255