def test_draw_keypoints_vanilla():
# Keypoints is declared on top as global variable
keypoints_cp = keypoints.clone()
img = torch.full((3, 100, 100), 0, dtype=torch.uint8)
img_cp = img.clone()
result = utils.draw_keypoints(
img,
keypoints,
colors="red",
connectivity=[
(0, 1),
],
)
path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "assets",
"fakedata", "draw_keypoint_vanilla.png")
if not os.path.exists(path):
res = Image.fromarray(result.permute(1, 2, 0).contiguous().numpy())
res.save(path)
expected = torch.as_tensor(np.array(Image.open(path))).permute(2, 0, 1)
assert_equal(result, expected)
# Check that keypoints are not modified inplace
assert_equal(keypoints, keypoints_cp)
# Check that image is not modified in place
assert_equal(img, img_cp)
def test_draw_keypoints_errors():
h, w = 10, 10
img = torch.full((3, 100, 100), 0, dtype=torch.uint8)
with pytest.raises(TypeError, match="The image must be a tensor"):
utils.draw_keypoints(image="Not A Tensor Image", keypoints=keypoints)
with pytest.raises(ValueError, match="The image dtype must be"):
img_bad_dtype = torch.full((3, h, w), 0, dtype=torch.int64)
utils.draw_keypoints(image=img_bad_dtype, keypoints=keypoints)
with pytest.raises(ValueError,
match="Pass individual images, not batches"):
batch = torch.randint(0, 256, size=(10, 3, h, w), dtype=torch.uint8)
utils.draw_keypoints(image=batch, keypoints=keypoints)
with pytest.raises(ValueError, match="Pass an RGB image"):
one_channel = torch.randint(0, 256, size=(1, h, w), dtype=torch.uint8)
utils.draw_keypoints(image=one_channel, keypoints=keypoints)
with pytest.raises(ValueError, match="keypoints must be of shape"):
invalid_keypoints = torch.tensor([[10, 10, 10, 10], [5, 6, 7, 8]],
dtype=torch.float)
utils.draw_keypoints(image=img, keypoints=invalid_keypoints)
def test_draw_keypoints_colored(colors):
# Keypoints is declared on top as global variable
keypoints_cp = keypoints.clone()
img = torch.full((3, 100, 100), 0, dtype=torch.uint8)
img_cp = img.clone()
result = utils.draw_keypoints(
img,
keypoints,
colors=colors,
connectivity=[
(0, 1),
],
)
assert result.size(0) == 3
assert_equal(keypoints, keypoints_cp)
assert_equal(img, img_cp)
detect_threshold = 0.75
idx = torch.where(scores > detect_threshold)
keypoints = kpts[idx]
print(keypoints)
#####################################
# Great, now we have the keypoints corresponding to the person.
# Each keypoint is represented by x, y coordinates and the visibility.
# We can now use the :func:`~torchvision.utils.draw_keypoints` function to draw keypoints.
# Note that the utility expects uint8 images.
from torchvision.utils import draw_keypoints
res = draw_keypoints(person_int, keypoints, colors="blue", radius=3)
show(res)
#####################################
# As we see the keypoints appear as colored circles over the image.
# The coco keypoints for a person are ordered and represent the following list.\
coco_keypoints = [
"nose", "left_eye", "right_eye", "left_ear", "right_ear",
"left_shoulder", "right_shoulder", "left_elbow", "right_elbow",
"left_wrist", "right_wrist", "left_hip", "right_hip",
"left_knee", "right_knee", "left_ankle", "right_ankle",
]
#####################################
# What if we are interested in joining the keypoints?