def generate_q_u_matrix(x_coordinate: cp.array,
y_coordinate: cp.array) -> tuple:
flatten_flag = x_coordinate.ndim > 1
if flatten_flag:
x_coordinate = x_coordinate.flatten()
y_coordinate = y_coordinate.flatten()
t, u = cp.modf(y_coordinate)
u = u.astype(int)
uy = cp.vstack([
cp.minimum(cp.maximum(u - 1, 0), h - 1),
cp.minimum(cp.maximum(u, 0), h - 1),
cp.minimum(cp.maximum(u + 1, 0), h - 1),
cp.minimum(cp.maximum(u + 2, 0), h - 1),
]).astype(int)
Qy = cp.dot(
coeff,
cp.vstack([
cp.ones_like(t, dtype=cp.float32), t,
cp.power(t, 2),
cp.power(t, 3)
]))
t, u = cp.modf(x_coordinate)
u = u.astype(int)
ux = cp.vstack([
cp.minimum(cp.maximum(u - 1, 0), w - 1),
cp.minimum(cp.maximum(u, 0), w - 1),
cp.minimum(cp.maximum(u + 1, 0), w - 1),
cp.minimum(cp.maximum(u + 2, 0), w - 1),
])
Qx = cp.dot(
coeff,
cp.vstack([
cp.ones_like(t, dtype=cp.float32), t,
cp.power(t, 2),
cp.power(t, 3)
]))
if flatten_flag:
Qx = Qx.reshape(4, frame_n, int(w * mag)).transpose(1, 0, 2).copy()
Qy = Qy.reshape(4, frame_n, int(h * mag)).transpose(1, 0, 2).copy()
ux = ux.reshape(4, frame_n, int(w * mag)).transpose(1, 0, 2).copy()
uy = uy.reshape(4, frame_n, int(h * mag)).transpose(1, 0, 2).copy()
return Qx, Qy, ux, uy
def g(x):
return cupy.modf(x)
def lanczos4_zoom_wrapper(images: cp.array, mag: float):
"""closure. It will return a function to zoom the images, but the parameters will not be calculated again"""
# init the parameters for lanczos image resize afterwards
h, w = images.shape[1:3]
lanczos4_core_lut = generate_lanczos4_weights_lut()
yCoordinate = cp.linspace(0, h - 1 / mag, int(h * mag), dtype=cp.float32)
t, u = cp.modf(yCoordinate)
u = u.astype(int) # select 8 sampling points
uy = [
cp.maximum(u - 3, 0),
cp.maximum(u - 2, 0),
cp.maximum(u - 1, 0),
cp.minimum(u, h - 1),
cp.minimum(u + 1, h - 1),
cp.minimum(u + 2, h - 1),
cp.minimum(u + 3, h - 1),
cp.minimum(u + 4, h - 1),
]
Q = cp.take(lanczos4_core_lut, (t * 1024).astype(int), axis=0)
Qy = [cp.take(Q, i, axis=1) for i in range(8)]
xCoordinate = cp.linspace(0, w - 1 / mag, int(w * mag), dtype=cp.float32)
del t, u, xCoordinate
t, u = cp.modf(xCoordinate)
u = u.astype(int) # select 8 sampling points
ux = [
cp.maximum(u - 3, 0),
cp.maximum(u - 2, 0),
cp.maximum(u - 1, 0),
cp.minimum(u, w - 1),
cp.minimum(u + 1, w - 1),
cp.minimum(u + 2, w - 1),
cp.minimum(u + 3, w - 1),
cp.minimum(u + 4, w - 1),
]
Q = cp.take(lanczos4_core_lut, (t * 1024).astype(int), axis=0)
Qx = [cp.take(Q, i, axis=1) for i in range(8)]
del t, u, yCoordinate, Q, lanczos4_core_lut
def lanczos4_zoom(image_mat: cp.array) -> cp.array:
"""the function to zoom image matrix"""
number_of_files = image_mat.shape[0]
# First interpolate in Y direction
mat_temp = cp.zeros((number_of_files, w, int(h * mag)),
dtype=cp.float32)
for Qi, ui in zip(Qy, uy):
cp.add(mat_temp,
cp.transpose(cp.take(image_mat, ui, axis=1),
(0, 2, 1)) * Qi,
out=mat_temp)
del image_mat
# Then interpolate in X direction
mat_zoomed = cp.zeros((number_of_files, int(h * mag), int(w * mag)),
dtype=cp.float32)
for Qi, ui in zip(Qx, ux):
cp.add(mat_zoomed,
cp.transpose(cp.take(mat_temp, ui, axis=1), (0, 2, 1)) * Qi,
out=mat_zoomed)
del mat_temp
return mat_zoomed
return lanczos4_zoom
def g(x):
return cupy.modf(x)
