1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
|
"""
===============================================================
Transforms on KeyPoints
===============================================================
This example illustrates how to define and use keypoints.
For this tutorial, we use this picture of a ceramic figure from the pre-columbian period.
The image is specified "public domain" (https://www.metmuseum.org/art/collection/search/502727).
.. note::
Support for keypoints 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 transformed output
# still make sense
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)
# %%
# 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)
# %%
# 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)
# %%
# 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)
# %%
# 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)
|
