Hi, thank you always for your help.
I am trying to convert an image set stored in torch.Tensor of like [number_of_images, width, height],
to a set of color images of like [number_of_images, 3, width, height].
I would like to use the color scheme ‘magma’ that can be used in matplotlib.
Is there any good way to realize it?
You could convert the tensors to numpy arrays and apply the colormap on them:
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib import cm
x = np.random.randn(100, 100).clip(0, 1)
magma = cm.get_cmap('magma')
x_transformed = magma(x)
Note that the colormap expects normalized inputs, so I clipped the data to [0, 1].
Once you have these transformed arrays, you could convert them back to tensors via torch.from_numpy.
PS: What is your use case that you want to artificially inflate your channels?
Add one note, that might not be the case when you use a cmap in matplotlib.
If you have a manual colormap that is torch.Tensor with shape [256, 3], you could try this cool method:
color_map = #Tensor of shape(256,3)
gray_image = (gray_image * 255).long() # Tensor values between 0 and 255 and LongTensor and shape of (512,512)
output = color_map[gray_image] #Tensor of shape (512,512,3)