하고 싶은 일

jpg 형식의 이미지 파일을 numpy로 읽고 싶습니다.

이미지 파일

임의의 그래픽 소프트웨어 등을 사용하여 미리 작성해 둔다.
여기에서는 다음과 같은 input_label_2.jpg를 작성했다. width:28px height:28px

구현

from PIL import Image
import numpy as np

input_file_name = 'input_label_2.jpg'
im = np.array(Image.open(input_file_name))
im_gray = np.array(Image.open(input_file_name).convert('L'))
print(im_gray)

출력

[[  0   0   0   0   0   0   0   0   0   0   0   0   1   1   0   0   0   1    1   0   0   0   1   4   0   0   0   0]
 [  0   0   0   0   0   0   0   0  14   5   0   0   1   5   3   0   0   1    4   7   7   4   0   0   0   0   0   0]
 [  0   0   0   0   0   0   0   0   0   0   4   7   5   1   0   0  12   5    0   0   0   0   2   3   0   0   0   0]
 [  0   0   0   0   0   0   0   0  14  12   7   0   0   0   3   8   0   0    0   5   8   9   6   3   0   0   0   0]
 [  0   0   0   0   0   0   0   0   2   0   0   0   1   4   2   0   4   5    4   1   0   0   0   0   0   0   0   0]
 [  0   0   0   0   0   0   0   0   0   3   8   6   0   0   0   5   0   0    0   2   7  12  16  18   0   0   0   0]
 [  0   0   0   0   0   0   0   0   3   0   0   0  13  33  55  69   9   0    0   0   0   4   0   0   0   0   0   0]
 [  0   0   0   0   0   0   0   0   5   3  33 111 205 255 255 237 242 180   94  28   0   0   5   8   0   0   0   0]
 [  0   0   0   0   0   0   0   0   7  47 241 255 231 174 141 117 238 246  250 188  15   1   1   8   0   0   0   0]
 [  0   0   0   0   0   0   0   0  24 246 243 242 120   3   0  17   0 207  254 253 162   0  20   0   0   0   0   0]
 [  0   0   0   0   0   0   0   0  72 255 250 180   0   0  15   0  15  68  255 243 230   3   7  11   0   0   0   0]
 [  0   0   0   0   0   0   0   0 155 237 252 124  12  21   0   5   0 130  255 238 209   0   0  12   0   0   0   0]
 [  0   0   0   0   0   0   0   0  64  77  69   0   0   1   8   0  16 225  245 255 107   0   5   0   0   0   0   0]
 [  0   0   0   0   0   0   0   0   0   0   4   2  10   5  11  28 213 242  246 172   3   7  14   0   0   0   0   0]
 [  0   0   0   0   0   0   0   0  20  14   0   0   0   9  48 241 255 250  174  26   1   0  10   0   0   0   0   0]
 [  0   0   0   0   0   0   0   0   0   1   1  11  11 157 251 255 249 106    4   0  12  14   0   5   0   0   0   0]
 [  0   6   3   1   5   2   0   2   2   1   2  49 216 254 255 219  52  10    0   7   0   0  12   2   0   0   0   0]
 [  3  10   1   0   0   7   7  10   2  14 106 255 239 253 161  10   4   0    0   6   2   0   1   0   0   0   0   0]
 [  0   9   5   0   3   5   0   0   9 156 255 238 237  77   8   0   6   5    0   0   0   0   5  17   0   0   0   0]
 [  0   5   4   5   7   0   0   8 185 251 246 231  57  13   0  10   0   6    0   5  15   0   0  10   0   0   0   0]
 [  8   0   0   1   4   0  16  54 252 255 254 251 237 232 252 245 215 219  208 204 179  91  14  11   0   0   0   0]
 [  5   0   0   4   6   0  11  50 123 135 129 137 131 121 121 114 133 135  127 134 125  55   0   0   0   0   0   0]
 [  0   2   3   6   8   0   0  10   0   9   0   0   7   3   3   7   0   2    0   0  13   3   0  12   0   0   0   0]
 [  0   9   4   0   1   5   2   1   6   0   7   2  13   0  15   0   0   7    4   0   1   3   0   1   0   0   0   0]
 [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0    0   0   0   0   0   0   0   0   0   0]
 [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0    0   0   0   0   0   0   0   0   0   0]
 [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0    0   0   0   0   0   0   0   0   0   0]
 [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0    0   0   0   0   0   0   0   0   0   0]
]