Use "colour" array constructors to give a chance to reduce memory usa…

…ge through bit-depth change.

References #42.

colour_demosaicing/bayer/demosaicing/malvar2004.py

@@ -19,7 +19,7 @@
 from scipy.ndimage.filters import convolve
 
 from colour.hints import ArrayLike, Literal, NDArray, Union
-from colour.utilities import as_float_array, tstack
+from colour.utilities import as_float_array, ones, tstack
 
 from colour_demosaicing.bayer import masks_CFA_Bayer
 
@@ -157,13 +157,13 @@ def demosaicing_CFA_Bayer_Malvar2004(
     del GR_GB, Rg_RB_Bg_BR, Rg_BR_Bg_RB, Rb_BB_Br_RR
 
     # Red rows.
-    R_r = np.transpose(np.any(R_m == 1, axis=1)[np.newaxis]) * np.ones(R.shape)
+    R_r = np.transpose(np.any(R_m == 1, axis=1)[np.newaxis]) * ones(R.shape)
     # Red columns.
-    R_c = np.any(R_m == 1, axis=0)[np.newaxis] * np.ones(R.shape)
+    R_c = np.any(R_m == 1, axis=0)[np.newaxis] * ones(R.shape)
     # Blue rows.
-    B_r = np.transpose(np.any(B_m == 1, axis=1)[np.newaxis]) * np.ones(B.shape)
+    B_r = np.transpose(np.any(B_m == 1, axis=1)[np.newaxis]) * ones(B.shape)
     # Blue columns
-    B_c = np.any(B_m == 1, axis=0)[np.newaxis] * np.ones(B.shape)
+    B_c = np.any(B_m == 1, axis=0)[np.newaxis] * ones(B.shape)
 
     del R_m, B_m
 

colour_demosaicing/bayer/demosaicing/menon2007.py

@@ -18,7 +18,7 @@
 from scipy.ndimage.filters import convolve, convolve1d
 
 from colour.hints import ArrayLike, Boolean, Literal, NDArray, Union
-from colour.utilities import as_float_array, tsplit, tstack
+from colour.utilities import as_float_array, ones, tsplit, tstack
 
 from colour_demosaicing.bayer import masks_CFA_Bayer
 
@@ -160,9 +160,9 @@ def demosaicing_CFA_Bayer_Menon2007(
     del d_H, d_V, G_H, G_V
 
     # Red rows.
-    R_r = np.transpose(np.any(R_m == 1, axis=1)[np.newaxis]) * np.ones(R.shape)
+    R_r = np.transpose(np.any(R_m == 1, axis=1)[np.newaxis]) * ones(R.shape)
     # Blue rows.
-    B_r = np.transpose(np.any(B_m == 1, axis=1)[np.newaxis]) * np.ones(B.shape)
+    B_r = np.transpose(np.any(B_m == 1, axis=1)[np.newaxis]) * ones(B.shape)
 
     k_b = as_float_array([0.5, 0, 0.5])
 
@@ -290,7 +290,7 @@ def refining_step_Menon2007(
     R_G = R - G
     B_G = B - G
 
-    FIR = np.ones(3) / 3
+    FIR = ones(3) / 3
 
     B_G_m = np.where(
         B_m == 1,
@@ -310,13 +310,13 @@ def refining_step_Menon2007(
 
     # Updating of the red and blue components in the green locations.
     # Red rows.
-    R_r = np.transpose(np.any(R_m == 1, axis=1)[np.newaxis]) * np.ones(R.shape)
+    R_r = np.transpose(np.any(R_m == 1, axis=1)[np.newaxis]) * ones(R.shape)
     # Red columns.
-    R_c = np.any(R_m == 1, axis=0)[np.newaxis] * np.ones(R.shape)
+    R_c = np.any(R_m == 1, axis=0)[np.newaxis] * ones(R.shape)
     # Blue rows.
-    B_r = np.transpose(np.any(B_m == 1, axis=1)[np.newaxis]) * np.ones(B.shape)
+    B_r = np.transpose(np.any(B_m == 1, axis=1)[np.newaxis]) * ones(B.shape)
     # Blue columns.
-    B_c = np.any(B_m == 1, axis=0)[np.newaxis] * np.ones(B.shape)
+    B_c = np.any(B_m == 1, axis=0)[np.newaxis] * ones(B.shape)
 
     R_G = R - G
     B_G = B - G

colour_demosaicing/bayer/masks.py

@@ -7,10 +7,8 @@
 
 from __future__ import annotations
 
-import numpy as np
-
 from colour.hints import Literal, NDArray, Tuple, Union
-from colour.utilities import validate_method
+from colour.utilities import validate_method, zeros
 
 __author__ = "Colour Developers"
 __copyright__ = "Copyright 2015 Colour Developers"
@@ -75,7 +73,7 @@ def masks_CFA_Bayer(
         '"{0}" CFA pattern is invalid, it must be one of {1}!',
     ).upper()
 
-    channels = {channel: np.zeros(shape) for channel in "RGB"}
+    channels = {channel: zeros(shape) for channel in "RGB"}
     for channel, (y, x) in zip(pattern, [(0, 0), (0, 1), (1, 0), (1, 1)]):
         channels[channel][y::2, x::2] = 1