24bits画像データを開きBGRに分離して重ねて24bitsに戻す

(256, 512, 3)

from PIL import Image
import numpy as np
import matplotlib.pyplot as plt

# ------------------------------------------
# 任意のデータだけをゼロにする関数.
def data_set_zero( arr_data_b, arr_data_g, arr_data_r, width, height, flag_set_zero_B, flag_set_zero_G, flag_set_zero_R ):

    arr_data_b = arr_data_b.reshape( -1 )
    arr_data_g = arr_data_g.reshape( -1 )
    arr_data_r = arr_data_r.reshape( -1 )
    
    w = width
    h = height
    numpix = w * h
    
    if ( flag_set_zero_B == True ):
        for n in range( numpix ):
            arr_data_b[n] = 0

    if ( flag_set_zero_G == True ):
        for n in range( numpix ):
            arr_data_g[n] = 0
    
    if ( flag_set_zero_R == True ):
        for n in range( numpix ):
            arr_data_r[n] = 0

    arr_data_b.reshape( h, w )
    arr_data_g.reshape( h, w )
    arr_data_r.reshape( h, w )

    return
# ------------------------------------------

im = Image.open( "c:/tmp/grad_bpp24.bmp" )
print( im.mode )

w = im.width;
h = im.height;

# Pillow のイメージから ndarray に変換する.
data_src = np.array( im )

# BGR のプレーンとして切り出す.
data_b0 = np.copy( data_src[:,:,2] )
data_g0 = np.copy( data_src[:,:,1] )
data_r0 = np.copy( data_src[:,:,0] )

# 同じものをコピーする.
data_b1 = np.copy( data_b0 )
data_g1 = np.copy( data_g0 )
data_r1 = np.copy( data_r0 )

# 同じものをコピーする.
data_b2 = np.copy( data_b0 )
data_g2 = np.copy( data_g0 )
data_r2 = np.copy( data_r0 )

# 同じものをコピーする.
data_b3 = np.copy( data_b0 )
data_g3 = np.copy( data_g0 )
data_r3 = np.copy( data_r0 )

# 同じものをコピーする.
data_b4 = np.copy( data_b0 )
data_g4 = np.copy( data_g0 )
data_r4 = np.copy( data_r0 )

# 同じものをコピーする.
data_b5 = np.copy( data_b0 )
data_g5 = np.copy( data_g0 )
data_r5 = np.copy( data_r0 )

# 任意のカラープレーンだけをゼロにする関数をコールする.
data_set_zero( data_b0, data_g0, data_r0, w, h, flag_set_zero_B = False,  flag_set_zero_G =  True, flag_set_zero_R =  True )
data_set_zero( data_b1, data_g1, data_r1, w, h, flag_set_zero_B =  True,  flag_set_zero_G = False, flag_set_zero_R =  True )
data_set_zero( data_b2, data_g2, data_r2, w, h, flag_set_zero_B =  True,  flag_set_zero_G =  True, flag_set_zero_R = False )
data_set_zero( data_b3, data_g3, data_r3, w, h, flag_set_zero_B = False,  flag_set_zero_G = False, flag_set_zero_R =  True )
data_set_zero( data_b4, data_g4, data_r4, w, h, flag_set_zero_B = False,  flag_set_zero_G =  True, flag_set_zero_R = False )
data_set_zero( data_b5, data_g5, data_r5, w, h, flag_set_zero_B =  True,  flag_set_zero_G = False, flag_set_zero_R = False )

# 2次元配列を奥行方向にスタックして3次元配列にする.
data_bgr0 = np.stack(( data_r0, data_g0, data_b0 ), axis = 2 ) # blue.
data_bgr1 = np.stack(( data_r1, data_g1, data_b1 ), axis = 2 ) # green.
data_bgr2 = np.stack(( data_r2, data_g2, data_b2 ), axis = 2 ) # red.
data_bgr3 = np.stack(( data_r3, data_g3, data_b3 ), axis = 2 ) # cyan.
data_bgr4 = np.stack(( data_r4, data_g4, data_b4 ), axis = 2 ) # magenta.
data_bgr5 = np.stack(( data_r5, data_g5, data_b5 ), axis = 2 ) # yellow.

# 3次元配列の行列と奥行きを表示する.
print( "src  shape {0}".format( data_src.shape  ) )
print( "bgr0 shape {0}".format( data_bgr0.shape ) )
print( "bgr1 shape {0}".format( data_bgr1.shape ) )
print( "bgr2 shape {0}".format( data_bgr2.shape ) )
print( "bgr3 shape {0}".format( data_bgr3.shape ) )
print( "bgr4 shape {0}".format( data_bgr4.shape ) )
print( "bgr5 shape {0}".format( data_bgr5.shape ) )

# ndarray から Pillow のイメージに変換する.
im_bgr0 = Image.fromarray( data_bgr0 )
im_bgr1 = Image.fromarray( data_bgr1 )
im_bgr2 = Image.fromarray( data_bgr2 )
im_bgr3 = Image.fromarray( data_bgr3 )
im_bgr4 = Image.fromarray( data_bgr4 )
im_bgr5 = Image.fromarray( data_bgr5 )

# 画像を保存したい場合はこれらをコメントインする.
# im_bgr0.save( "c:/tmp/grad_bpp24_0_b.bmp" )
# im_bgr1.save( "c:/tmp/grad_bpp24_1_g.bmp" )
# im_bgr2.save( "c:/tmp/grad_bpp24_2_r.bmp" )
# im_bgr3.save( "c:/tmp/grad_bpp24_3_c.bmp" )
# im_bgr4.save( "c:/tmp/grad_bpp24_4_m.bmp" )
# im_bgr5.save( "c:/tmp/grad_bpp24_5_y.bmp" )

# 書き込み要素を取得する.
fig = plt.figure()
ax = fig.add_subplot( 1, 1, 1 )
ax.set_title( "source" )
ax.axis( "off" )
plt.imshow( data_src, cmap="gray" )
plt.show()

# 書き込み要素を取得する.
fig = plt.figure()

# タイルの縦横の個数.
TILE_NUM_ROW = 2
TILE_NUM_COL = 3

list_img = []
list_img.append( im_bgr0 )
list_img.append( im_bgr1 )
list_img.append( im_bgr2 )
list_img.append( im_bgr3 )
list_img.append( im_bgr4 )
list_img.append( im_bgr5 )

plot_counter = 1
for n in range( len( list_img )):
    ax = fig.add_subplot( TILE_NUM_ROW, TILE_NUM_COL, plot_counter )
    ax.set_title( "{0}".format( plot_counter ))
    ax.axis( "off" )
    plt.imshow( list_img[n], cmap="gray" )
    plot_counter += 1

plt.show()

print( "finish." )

PS C:\tmp> python test.py
RGB
src  shape (256, 512, 3)
bgr0 shape (256, 512, 3)
bgr1 shape (256, 512, 3)
bgr2 shape (256, 512, 3)
bgr3 shape (256, 512, 3)
bgr4 shape (256, 512, 3)
bgr5 shape (256, 512, 3)
finish.