def _build_spread_kernel(how, is_image):
"""Build a spreading kernel for a given composite operator"""
validate_operator(how, is_image=True)
op = composite_op_lookup[how + ("" if is_image else "_arr")]
@ngjit
def kernel(arr, mask, out):
M, N = arr.shape
w = mask.shape[0]
for y in range(M):
for x in range(N):
el = arr[y, x]
# Skip if data is transparent
process_image = is_image and (
(int(el) >> 24) & 255) # Transparent pixel
process_array = (not is_image) and (not np.isnan(el))
if process_image or process_array:
for i in range(w):
for j in range(w):
# Skip if mask is False at this value
if mask[i, j]:
if el == 0:
result = out[i + y, j + x]
if out[i + y, j + x] == 0:
result = el
else:
result = op(el, out[i + y, j + x])
out[i + y, j + x] = result
return kernel
def _build_float_kernel(how, mask_size):
"""Build a spreading kernel for a given composite operator"""
validate_operator(how, is_image=False)
op = composite_op_lookup[how + "_arr"]
@ngjit
def stencilled(arr, mask, out):
M, N = arr.shape
for y in range(M):
for x in range(N):
el = arr[y, x]
for i in range(mask_size):
for j in range(mask_size):
if mask[i, j]:
if np.isnan(el):
result = out[i + y, j + x]
elif np.isnan(out[i + y, j + x]):
result = el
else:
result = op(el, out[i + y, j + x])
out[i + y, j + x] = result
return stencilled