Skeleton for keypoints tutorial

docs/source/conf.py

@@ -88,6 +88,7 @@ def __init__(self, src_dir):
         "plot_transforms_e2e.py",
         "plot_cutmix_mixup.py",
         "plot_rotated_box_transforms.py",
+        "plot_keypoints_transforms.py",
         "plot_custom_transforms.py",
         "plot_tv_tensors.py",
         "plot_custom_tv_tensors.py",

gallery/transforms/helpers.py

@@ -1,6 +1,6 @@
 import matplotlib.pyplot as plt
 import torch
-from torchvision.utils import draw_bounding_boxes, draw_segmentation_masks
+from torchvision.utils import draw_bounding_boxes, draw_keypoints, draw_segmentation_masks
 from torchvision import tv_tensors
 from torchvision.transforms import v2
 from torchvision.transforms.v2 import functional as F
@@ -18,6 +18,7 @@ def plot(imgs, row_title=None, bbox_width=3, **imshow_kwargs):
         for col_idx, img in enumerate(row):
             boxes = None
             masks = None
+            points = None
             if isinstance(img, tuple):
                 img, target = img
                 if isinstance(target, dict):
@@ -30,6 +31,8 @@ def plot(imgs, row_title=None, bbox_width=3, **imshow_kwargs):
                     # work with this specific format.
                     if tv_tensors.is_rotated_bounding_format(boxes.format):
                         boxes = v2.ConvertBoundingBoxFormat("xyxyxyxy")(boxes)
+                elif isinstance(target, tv_tensors.KeyPoints):
+                    points = target
                 else:
                     raise ValueError(f"Unexpected target type: {type(target)}")
             img = F.to_image(img)
@@ -44,6 +47,8 @@ def plot(imgs, row_title=None, bbox_width=3, **imshow_kwargs):
                 img = draw_bounding_boxes(img, boxes, colors="yellow", width=bbox_width)
             if masks is not None:
                 img = draw_segmentation_masks(img, masks.to(torch.bool), colors=["green"] * masks.shape[0], alpha=.65)
+            if points is not None:
+                img = draw_keypoints(img, points, colors="red", radius=10)
 
             ax = axs[row_idx, col_idx]
             ax.imshow(img.permute(1, 2, 0).numpy(), **imshow_kwargs)

gallery/transforms/plot_keypoints_transforms.py

@@ -0,0 +1,114 @@
+"""
+===============================================================
+Transforms on Rotated Bounding Boxes
+===============================================================
+
+This example illustrates how to define and use rotated bounding boxes.
+
+.. note::
+    Support for rotated bounding boxes was released in TorchVision 0.23 and is
+    currently a BETA feature. We don't expect the API to change, but there may
+    be some rare edge-cases. If you find any issues, please report them on
+    our bug tracker: https://github.com/pytorch/vision/issues?q=is:open+is:issue
+
+First, a bit of setup code:
+"""
+
+# %%
+from PIL import Image
+from pathlib import Path
+import matplotlib.pyplot as plt
+
+
+import torch
+from torchvision.tv_tensors import KeyPoints
+from torchvision.transforms import v2
+from helpers import plot
+
+plt.rcParams["figure.figsize"] = [10, 5]
+plt.rcParams["savefig.bbox"] = "tight"
+
+# if you change the seed, make sure that the randomly-applied transforms
+# properly show that the image can be both transformed and *not* transformed!
+torch.manual_seed(0)
+
+# If you're trying to run that on Colab, you can download the assets and the
+# helpers from https://github.com/pytorch/vision/tree/main/gallery/
+orig_img = Image.open(Path('../assets') / 'pottery.jpg')
+
+# %%
+# Creating KeyPoints
+# -------------------------------
+# Key points are created by instantiating the
+# :class:`~torchvision.tv_tensors.KeyPoints` class.
+
+
+orig_pts = KeyPoints(
+    [
+        [
+            [445, 700],  # nose
+            [320, 660],
+            [370, 660],
+            [420, 660],  # left eye
+            [300, 620],
+            [420, 620],  # left eyebrow
+            [475, 665],
+            [515, 665],
+            [555, 655],  # right eye
+            [460, 625],
+            [560, 600],  # right eyebrow
+            [370, 780],
+            [450, 760],
+            [540, 780],
+            [450, 820],  # mouth
+        ],
+    ],
+    canvas_size=(orig_img.size[1], orig_img.size[0]),
+)
+
+plot([(orig_img, orig_pts)])
+
+# %%
+# Transforms illustrations
+# ------------------------
+#
+# Using :class:`~torchvision.transforms.RandomRotation`:
+rotater = v2.RandomRotation(degrees=(0, 180), expand=True)
+rotated_imgs = [rotater((orig_img, orig_pts)) for _ in range(4)]
+plot([(orig_img, orig_pts)] + rotated_imgs, bbox_width=10)
+
+# %%
+# Using :class:`~torchvision.transforms.Pad`:
+padded_imgs_and_points = [
+    v2.Pad(padding=padding)(orig_img, orig_pts)
+    for padding in (30, 50, 100, 200)
+]
+plot([(orig_img, orig_pts)] + padded_imgs_and_points, bbox_width=10)
+
+# %%
+# Using :class:`~torchvision.transforms.Resize`:
+resized_imgs = [
+    v2.Resize(size=size)(orig_img, orig_pts)
+    for size in (300, 500, 1000, orig_img.size)
+]
+plot([(orig_img, orig_pts)] + resized_imgs, bbox_width=5)
+
+# %%
+# Using :class:`~torchvision.transforms.RandomPerspective`:
+perspective_transformer = v2.RandomPerspective(distortion_scale=0.6, p=1.0)
+perspective_imgs = [perspective_transformer(orig_img, orig_pts) for _ in range(4)]
+plot([(orig_img, orig_pts)] + perspective_imgs, bbox_width=10)
+
+# %%
+# Using :class:`~torchvision.transforms.CenterCrop`:
+center_crops_and_points = [
+    v2.CenterCrop(size=size)(orig_img, orig_pts)
+    for size in (300, 500, 1000, orig_img.size)
+]
+plot([(orig_img, orig_pts)] + center_crops_and_points)
+
+# %%
+# Using :class:`~torchvision.transforms.RandomRotation`:
+rotater = v2.RandomRotation(degrees=(0, 180))
+rotated_imgs = [rotater((orig_img, orig_pts)) for _ in range(4)]
+plot([(orig_img, orig_pts)] + rotated_imgs)