I have a numpy array which has the following shape: (1, 128, 160, 1).
Now, I have an image which has the shape: (200, 200).
So, I do the following:
orig = np.random.rand(1, 128, 160, 1)
orig = np.squeeze(orig)
Now, what I want to do is take my original array and interpolate it to be of the same size as the input image i.e. (200, 200) using linear interpolation. I think I have to specify the grid on which the numpy array should be evaluated but I am unable to figure out how to do it.
You can do it with scipy.interpolate.interp2d like this:
from scipy import interpolate
# Make a fake image - you can use yours.
image = np.ones((200,200))
# Make your orig array (skipping the extra dimensions).
orig = np.random.rand(128, 160)
# Make its coordinates; x is horizontal.
x = np.linspace(0, image.shape[1], orig.shape[1])
y = np.linspace(0, image.shape[0], orig.shape[0])
# Make the interpolator function.
f = interpolate.interp2d(x, y, orig, kind='linear')
# Construct the new coordinate arrays.
x_new = np.arange(0, image.shape[1])
y_new = np.arange(0, image.shape[0])
# Do the interpolation.
new_orig = f(x_new, y_new)
Note the -1 adjustment to the coordinate range when forming x and y. This ensures that the image coordinates go from 0 to 199 inclusive.
scipy.interpolate.interp2d