def hsv_to_rgb(image: torch.Tensor) -> torch.Tensor:
r"""Convert an HSV image to RGB
The image data is assumed to be in the range of (0, 1).
Args:
input (torch.Tensor): HSV Image to be converted to RGB.
Returns:
torch.Tensor: RGB version of the image.
"""
if not torch.is_tensor(image):
raise TypeError("Input type is not a torch.Tensor. Got {}".format(
type(image)))
if len(image.shape) < 3 or image.shape[-3] != 3:
raise ValueError(
"Input size must have a shape of (*, 3, H, W). Got {}".format(
image.shape))
h: torch.Tensor = image[..., 0, :, :] / (2 * pi.to(image.device))
s: torch.Tensor = image[..., 1, :, :]
v: torch.Tensor = image[..., 2, :, :]
hi: torch.Tensor = torch.floor(h * 6) % 6
f: torch.Tensor = ((h * 6) % 6) - hi
one: torch.Tensor = torch.tensor(1.).to(image.device)
p: torch.Tensor = v * (one - s)
q: torch.Tensor = v * (one - f * s)
t: torch.Tensor = v * (one - (one - f) * s)
hi = hi.long()
indices: torch.Tensor = torch.stack([hi, hi + 6, hi + 12], dim=-3)
out = torch.stack((
v,
q,
p,
p,
t,
v,
t,
v,
v,
q,
p,
p,
p,
p,
t,
v,
v,
q,
),
dim=-3)
out = torch.gather(out, -3, indices)
return out
def rgb_to_hsv(image: torch.Tensor) -> torch.Tensor:
r"""Convert an RGB image to HSV.
Args:
input (torch.Tensor): RGB Image to be converted to HSV.
Returns:
torch.Tensor: HSV version of the image.
"""
if not torch.is_tensor(image):
raise TypeError("Input type is not a torch.Tensor. Got {}".format(
type(image)))
if len(image.shape) < 3 or image.shape[-3] != 3:
raise ValueError(
"Input size must have a shape of (*, 3, H, W). Got {}".format(
image.shape))
r: torch.Tensor = image[..., 0, :, :]
g: torch.Tensor = image[..., 1, :, :]
b: torch.Tensor = image[..., 2, :, :]
maxc: torch.Tensor = image.max(-3)[0]
minc: torch.Tensor = image.min(-3)[0]
v: torch.Tensor = maxc # brightness
deltac: torch.Tensor = maxc - minc
s: torch.Tensor = deltac / v
s[torch.isnan(s)] = 0.
# avoid division by zero
deltac = torch.where(deltac == 0, torch.ones_like(deltac), deltac)
rc: torch.Tensor = (maxc - r) / deltac
gc: torch.Tensor = (maxc - g) / deltac
bc: torch.Tensor = (maxc - b) / deltac
maxg: torch.Tensor = g == maxc
maxr: torch.Tensor = r == maxc
h: torch.Tensor = 4.0 + gc - rc
h[maxg] = 2.0 + rc[maxg] - bc[maxg]
h[maxr] = bc[maxr] - gc[maxr]
h[minc == maxc] = 0.0
h = (h / 6.0) % 1.0
h = 2 * pi.to(image.device) * h
return torch.stack([h, s, v], dim=-3)
def rgb_to_hls(image: torch.Tensor) -> torch.Tensor:
r"""Convert an RGB image to HLS
The image data is assumed to be in the range of (0, 1).
Args:
input (torch.Tensor): RGB Image to be converted to HLS.
Returns:
torch.Tensor: HLS version of the image.
"""
if not torch.is_tensor(image):
raise TypeError("Input type is not a torch.Tensor. Got {}".format(
type(image)))
if len(image.shape) < 3 or image.shape[-3] != 3:
raise ValueError(
"Input size must have a shape of (*, 3, H, W). Got {}".format(
image.shape))
r: torch.Tensor = image[..., 0, :, :]
g: torch.Tensor = image[..., 1, :, :]
b: torch.Tensor = image[..., 2, :, :]
maxc: torch.Tensor = image.max(-3)[0]
minc: torch.Tensor = image.min(-3)[0]
imax: torch.Tensor = image.max(-3)[1]
l: torch.Tensor = (maxc + minc) / 2 # luminance
deltac: torch.Tensor = maxc - minc
s: torch.Tensor = torch.where(l < 0.5, deltac / (maxc + minc),
deltac / (torch.tensor(2.) -
(maxc + minc))) # saturation
hi: torch.Tensor = torch.zeros_like(deltac)
hi[imax == 0] = (((g - b) / deltac) % 6)[imax == 0]
hi[imax == 1] = (((b - r) / deltac) + 2)[imax == 1]
hi[imax == 2] = (((r - g) / deltac) + 4)[imax == 2]
h: torch.Tensor = 2. * pi.to(
image.device) * (60. * hi) / 360. # hue [0, 2*pi]
image_hls: torch.Tensor = torch.stack([h, l, s], dim=-3)
image_hls[torch.isnan(image_hls)] = 0.
return image_hls
def rgb_to_hsv(image: torch.Tensor) -> torch.Tensor:
r"""Convert an RGB image to HSV.
Args:
input (torch.Tensor): RGB Image to be converted to HSV.
Returns:
torch.Tensor: HSV version of the image.
"""
if not torch.is_tensor(image):
raise TypeError("Input type is not a torch.Tensor. Got {}".format(
type(image)))
if len(image.shape) < 3 or image.shape[-3] != 3:
raise ValueError(
"Input size must have a shape of (*, 3, H, W). Got {}".format(
image.shape))
maxc, max_indices = image.max(-3)
minc: torch.Tensor = image.min(-3)[0]
v: torch.Tensor = maxc # brightness
deltac: torch.Tensor = maxc - minc
s: torch.Tensor = deltac / (v + 1e-31)
# avoid division by zero
deltac = torch.where(deltac == 0, torch.ones_like(deltac), deltac)
rc, gc, bc = torch.unbind(maxc.unsqueeze(-3) - image, dim=-3)
h = torch.stack([
bc - gc,
2.0 * deltac + rc - bc,
4.0 * deltac + gc - rc,
],
dim=-3)
h = torch.gather(h, dim=-3, index=max_indices[..., None, :, :])
h = h.squeeze(-3)
h = h / deltac
h = (h / 6.0) % 1.0
h = 2 * pi.to(image.device) * h
return torch.stack([h, s, v], dim=-3)
def rad2deg(tensor: torch.Tensor) -> torch.Tensor:
r"""Function that converts angles from radians to degrees.
Args:
tensor (torch.Tensor): Tensor of arbitrary shape.
Returns:
torch.Tensor: Tensor with same shape as input.
Example:
>>> input = torch.tensor(3.1415926535) * torch.rand(1, 3, 3)
>>> output = rad2deg(input)
"""
if not isinstance(tensor, torch.Tensor):
raise TypeError("Input type is not a torch.Tensor. Got {}".format(type(tensor)))
return 180.0 * tensor / pi.to(tensor.device).type(tensor.dtype)
def hls_to_rgb(image: torch.Tensor) -> torch.Tensor:
r"""Convert an HLS image to RGB
The image data is assumed to be in the range of (0, 1).
Args:
input (torch.Tensor): HLS Image to be converted to RGB.
Returns:
torch.Tensor: RGB version of the image.
"""
if not torch.is_tensor(image):
raise TypeError("Input type is not a torch.Tensor. Got {}".format(
type(image)))
if len(image.shape) < 3 or image.shape[-3] != 3:
raise ValueError(
"Input size must have a shape of (*, 3, H, W). Got {}".format(
image.shape))
h: torch.Tensor = image[..., 0, :, :] * 360 / (2 * pi.to(image.device))
l: torch.Tensor = image[..., 1, :, :]
s: torch.Tensor = image[..., 2, :, :]
kr = (0 + h / 30) % 12
kg = (8 + h / 30) % 12
kb = (4 + h / 30) % 12
a = s * torch.min(l, torch.tensor(1.) - l)
ones_k = torch.ones_like(kr)
fr: torch.Tensor = l - a * torch.max(
torch.min(torch.min(kr - torch.tensor(3.),
torch.tensor(9.) - kr), ones_k), -1 * ones_k)
fg: torch.Tensor = l - a * torch.max(
torch.min(torch.min(kg - torch.tensor(3.),
torch.tensor(9.) - kg), ones_k), -1 * ones_k)
fb: torch.Tensor = l - a * torch.max(
torch.min(torch.min(kb - torch.tensor(3.),
torch.tensor(9.) - kb), ones_k), -1 * ones_k)
out: torch.Tensor = torch.stack([fr, fg, fb], dim=-3)
return out
def deg2rad(tensor: torch.Tensor) -> torch.Tensor:
r"""Function that converts angles from degrees to radians.
Args:
tensor: Tensor of arbitrary shape.
Returns:
tensor with same shape as input.
Examples:
>>> input = 360. * torch.rand(1, 3, 3)
>>> output = deg2rad(input)
"""
if not isinstance(tensor, torch.Tensor):
raise TypeError("Input type is not a torch.Tensor. Got {}".format(
type(tensor)))
return tensor * pi.to(tensor.device).type(tensor.dtype) / 180.0
def deg2rad(tensor: torch.Tensor) -> torch.Tensor:
r"""Function that converts angles from degrees to radians.
Args:
tensor: Tensor of arbitrary shape.
Returns:
tensor with same shape as input.
Examples:
>>> input = torch.tensor(180.)
>>> deg2rad(input)
tensor(3.1416)
"""
if not isinstance(tensor, torch.Tensor):
raise TypeError(f"Input type is not a torch.Tensor. Got {type(tensor)}")
return tensor * pi.to(tensor.device).type(tensor.dtype) / 180.0
def hsv_to_rgb(image: torch.Tensor) -> torch.Tensor:
r"""Convert an HSV image to RGB
The image data is assumed to be in the range of (0, 1).
Args:
input (torch.Tensor): HSV Image to be converted to RGB.
Returns:
torch.Tensor: RGB version of the image.
"""
if not torch.is_tensor(image):
raise TypeError("Input type is not a torch.Tensor. Got {}".format(
type(image)))
if len(image.shape) < 3 or image.shape[-3] != 3:
raise ValueError(
"Input size must have a shape of (*, 3, H, W). Got {}".format(
image.shape))
h: torch.Tensor = image[..., 0, :, :] / (2 * pi.to(image.device))
s: torch.Tensor = image[..., 1, :, :]
v: torch.Tensor = image[..., 2, :, :]
hi: torch.Tensor = torch.floor(h * 6) % 6
f: torch.Tensor = ((h * 6) % 6) - hi
one: torch.Tensor = torch.tensor(1.).to(image.device)
p: torch.Tensor = v * (one - s)
q: torch.Tensor = v * (one - f * s)
t: torch.Tensor = v * (one - (one - f) * s)
out: torch.Tensor = torch.stack([hi, hi, hi], dim=-3)
out[out == 0] = torch.stack((v, t, p), dim=-3)[out == 0]
out[out == 1] = torch.stack((q, v, p), dim=-3)[out == 1]
out[out == 2] = torch.stack((p, v, t), dim=-3)[out == 2]
out[out == 3] = torch.stack((p, q, v), dim=-3)[out == 3]
out[out == 4] = torch.stack((t, p, v), dim=-3)[out == 4]
out[out == 5] = torch.stack((v, p, q), dim=-3)[out == 5]
return out
