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Add visualize codes for Q

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This commit is contained in:
D-X-Y
2021-04-11 21:45:20 +08:00
parent e777f38233
commit 0e2dd13762
16 changed files with 570 additions and 125 deletions
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36
lib/utils/affine_utils.py
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@@ -76,10 +76,12 @@ def rotate2affine(degree):
# shape is a tuple [H, W]
def normalize_points(shape, points):
assert (isinstance(shape, tuple) or isinstance(shape, list)) and len(shape) == 2, "invalid shape : {:}".format(
assert (isinstance(shape, tuple) or isinstance(shape, list)) and len(
shape
)
assert isinstance(points, torch.Tensor) and (points.shape[0] == 2), "points are wrong : {:}".format(points.shape)
) == 2, "invalid shape : {:}".format(shape)
assert isinstance(points, torch.Tensor) and (
points.shape[0] == 2
), "points are wrong : {:}".format(points.shape)
(H, W), points = shape, points.clone()
points[0, :] = normalize_L(points[0, :], W)
points[1, :] = normalize_L(points[1, :], H)
@@ -88,10 +90,12 @@ def normalize_points(shape, points):
# shape is a tuple [H, W]
def normalize_points_batch(shape, points):
assert (isinstance(shape, tuple) or isinstance(shape, list)) and len(shape) == 2, "invalid shape : {:}".format(
assert (isinstance(shape, tuple) or isinstance(shape, list)) and len(
shape
)
assert isinstance(points, torch.Tensor) and (points.size(-1) == 2), "points are wrong : {:}".format(points.shape)
) == 2, "invalid shape : {:}".format(shape)
assert isinstance(points, torch.Tensor) and (
points.size(-1) == 2
), "points are wrong : {:}".format(points.shape)
(H, W), points = shape, points.clone()
x = normalize_L(points[..., 0], W)
y = normalize_L(points[..., 1], H)
@@ -100,10 +104,12 @@ def normalize_points_batch(shape, points):
# shape is a tuple [H, W]
def denormalize_points(shape, points):
assert (isinstance(shape, tuple) or isinstance(shape, list)) and len(shape) == 2, "invalid shape : {:}".format(
assert (isinstance(shape, tuple) or isinstance(shape, list)) and len(
shape
)
assert isinstance(points, torch.Tensor) and (points.shape[0] == 2), "points are wrong : {:}".format(points.shape)
) == 2, "invalid shape : {:}".format(shape)
assert isinstance(points, torch.Tensor) and (
points.shape[0] == 2
), "points are wrong : {:}".format(points.shape)
(H, W), points = shape, points.clone()
points[0, :] = denormalize_L(points[0, :], W)
points[1, :] = denormalize_L(points[1, :], H)
@@ -112,10 +118,12 @@ def denormalize_points(shape, points):
# shape is a tuple [H, W]
def denormalize_points_batch(shape, points):
assert (isinstance(shape, tuple) or isinstance(shape, list)) and len(shape) == 2, "invalid shape : {:}".format(
assert (isinstance(shape, tuple) or isinstance(shape, list)) and len(
shape
)
assert isinstance(points, torch.Tensor) and (points.shape[-1] == 2), "points are wrong : {:}".format(points.shape)
) == 2, "invalid shape : {:}".format(shape)
assert isinstance(points, torch.Tensor) and (
points.shape[-1] == 2
), "points are wrong : {:}".format(points.shape)
(H, W), points = shape, points.clone()
x = denormalize_L(points[..., 0], W)
y = denormalize_L(points[..., 1], H)
@@ -145,5 +153,7 @@ def affine2image(image, theta, shape):
theta = theta[:2, :].unsqueeze(0)
grid_size = torch.Size([1, C, shape[0], shape[1]])
grid = F.affine_grid(theta, grid_size)
affI = F.grid_sample(image.unsqueeze(0), grid, mode="bilinear", padding_mode="border")
affI = F.grid_sample(
image.unsqueeze(0), grid, mode="bilinear", padding_mode="border"
)
return affI.squeeze(0)