def unpadding(signal, dim):
y, x = dim
sy, sx = signal.shape
y0 = int(np.trunc((sy-y) / 2))
x0 = int(np.trunc((sx-x) / 2))
yt = int(y0 + y)
xt = int(x0 + x)
return signal[y0:yt, x0:xt]
def unpadding(signal, dim):
y, x = dim
sy, sx = signal.shape
y0 = np.trunc((sy-y) / 2)
x0 = np.trunc((sx-x) / 2)
yt = y0 + y
xt = x0 + x
return signal[y0:yt,x0:xt]
def zero_padding(signal, squared=True):
"""Creates a new ndarray that """
check_dim(signal, 2)
rows, cols = signal.shape
pow_rows = int(np.ceil(np.log2(rows)))
pow_cols = int(np.ceil(np.log2(cols)))
if squared:
if pow_cols > pow_rows:
pow_rows = pow_cols
else:
pow_cols = pow_rows
padded_signal = np.zeros((2 ** pow_rows, 2 ** pow_cols),
dtype=signal.dtype)
y_0 = np.trunc((2 ** pow_rows - rows) / 2)
y_t = y_0 + signal.shape[0]
x_0 = np.trunc((2 ** pow_cols - cols) / 2)
x_t = x_0 + signal.shape[1]
padded_signal[y_0:y_t, x_0:x_t] = signal
return padded_signal