VOC2012：PIL Image.open 将 PNG 转换为二维数组
VOC2012: PIL Image.open converts PNG to 2d array
我正在使用 VOC2012 数据集。当我使用 imageio 打开图像时，输入图像是 PNG 格式，形状为 (375, 500, 4) 。当我使用 PIL 打开图像时，形状突然变成 (500, 375)。 PNG 图像在最后一个轴上应该有四个维度：r g b & alpha。
图片明显是彩色图片，所以应该有3个维度（高、宽、深）。 PIL 似乎暗示它只有两个维度：宽度和高度。
PNG图像可以用二维数组表示吗？请帮忙！此刻如此失落。谢谢！
from PIL import Image
from keras.preprocessing.image import img_to_array
import os, imageio
import numpy as np
root_path = '/Users/johnson/Downloads/'
imageio_img = imageio.imread(
os.path.join(root_path, '2009_003193.png')
)
# (375, 500, 4)
print(imageio_img.shape)
# [ 0 128 192 224 255]
print(np.unique(imageio_img))
PIL_img = Image.open(
os.path.join(root_path, '2009_003193.png')
)
# (500, 375)
print(PIL_img.size)
PIL_img_to_array = img_to_array(PIL_img)
# (375, 500, 1)
print(PIL_img_to_array.shape)
# [ 0. 2. 255.]
print(np.unique(PIL_img_to_array))
同样神奇的是PIL好像知道VOC2012是怎么标注数据的。 PIL_image_to_array 具有唯一值 [0, 2, 255]。方便的是，2 在 VOC2012 中表示自行车。 0 表示背景，255 可能表示自行车周围的黄色边界。但是从第一个代码片段开始，我从未将 pascal 类 传递给 PIL 进行转换。
def pascal_classes():
classes = {'aeroplane' : 1, 'bicycle' : 2, 'bird' : 3, 'boat' : 4,
'bottle' : 5, 'bus' : 6, 'car' : 7, 'cat' : 8,
'chair' : 9, 'cow' : 10, 'diningtable' : 11, 'dog' : 12,
'horse' : 13, 'motorbike' : 14, 'person' : 15, 'potted-plant' : 16,
'sheep' : 17, 'sofa' : 18, 'train' : 19, 'tv/monitor' : 20}
return classes
def pascal_palette():
palette = {( 0, 0, 0) : 0 ,
(128, 0, 0) : 1 ,
( 0, 128, 0) : 2 ,
(128, 128, 0) : 3 ,
( 0, 0, 128) : 4 ,
(128, 0, 128) : 5 ,
( 0, 128, 128) : 6 ,
(128, 128, 128) : 7 ,
( 64, 0, 0) : 8 ,
(192, 0, 0) : 9 ,
( 64, 128, 0) : 10,
(192, 128, 0) : 11,
( 64, 0, 128) : 12,
(192, 0, 128) : 13,
( 64, 128, 128) : 14,
(192, 128, 128) : 15,
( 0, 64, 0) : 16,
(128, 64, 0) : 17,
( 0, 192, 0) : 18,
(128, 192, 0) : 19,
( 0, 64, 128) : 20 }
您的图像是托盘化的，不是 RGB。每个像素由调色板中的 8 位索引表示。您可以通过查看显示为 P.
的 image.mode 来了解这一点
如果您想要 RGB 图像，请使用：
rgb = Image.open('bike.png').convert('RGB')
如果您想要具有透明度的 RGBA 图像，请使用：
RGBA = Image.open('bike.png').convert('RGBA')
但是，alpha 通道中没有有用的信息，所以这似乎毫无意义。
关于 pascal 调色板，您可以像这样通过 PIL 获得它：
im = Image.open('bike.png')
p = im.getpalette()
for i in range (256):
print(p[3*i:3*i+3])
[0, 0, 0]
[128, 0, 0]
[0, 128, 0]
[128, 128, 0]
[0, 0, 128]
[128, 0, 128]
[0, 128, 128]
[128, 128, 128]
[64, 0, 0]
[192, 0, 0]
[64, 128, 0]
[192, 128, 0]
[64, 0, 128]
[192, 0, 128]
[64, 128, 128]
[192, 128, 128]
[0, 64, 0]
[128, 64, 0]
[0, 192, 0]
[128, 192, 0]
[0, 64, 128]
[128, 64, 128]
[0, 192, 128]
[128, 192, 128]
[64, 64, 0]
[192, 64, 0]
[64, 192, 0]
[192, 192, 0]
[64, 64, 128]
[192, 64, 128]
[64, 192, 128]
[192, 192, 128]
[0, 0, 64]
[128, 0, 64]
[0, 128, 64]
[128, 128, 64]
[0, 0, 192]
[128, 0, 192]
[0, 128, 192]
[128, 128, 192]
[64, 0, 64]
[192, 0, 64]
[64, 128, 64]
[192, 128, 64]
[64, 0, 192]
[192, 0, 192]
[64, 128, 192]
[192, 128, 192]
[0, 64, 64]
[128, 64, 64]
[0, 192, 64]
[128, 192, 64]
[0, 64, 192]
[128, 64, 192]
[0, 192, 192]
[128, 192, 192]
[64, 64, 64]
[192, 64, 64]
[64, 192, 64]
[192, 192, 64]
[64, 64, 192]
[192, 64, 192]
[64, 192, 192]
[192, 192, 192]
[32, 0, 0]
[160, 0, 0]
[32, 128, 0]
[160, 128, 0]
[32, 0, 128]
[160, 0, 128]
[32, 128, 128]
[160, 128, 128]
[96, 0, 0]
[224, 0, 0]
[96, 128, 0]
[224, 128, 0]
[96, 0, 128]
[224, 0, 128]
[96, 128, 128]
[224, 128, 128]
[32, 64, 0]
[160, 64, 0]
[32, 192, 0]
[160, 192, 0]
[32, 64, 128]
[160, 64, 128]
[32, 192, 128]
[160, 192, 128]
[96, 64, 0]
[224, 64, 0]
[96, 192, 0]
[224, 192, 0]
[96, 64, 128]
[224, 64, 128]
[96, 192, 128]
[224, 192, 128]
[32, 0, 64]
[160, 0, 64]
[32, 128, 64]
[160, 128, 64]
[32, 0, 192]
[160, 0, 192]
[32, 128, 192]
[160, 128, 192]
[96, 0, 64]
[224, 0, 64]
[96, 128, 64]
[224, 128, 64]
[96, 0, 192]
[224, 0, 192]
[96, 128, 192]
[224, 128, 192]
[32, 64, 64]
[160, 64, 64]
[32, 192, 64]
[160, 192, 64]
[32, 64, 192]
[160, 64, 192]
[32, 192, 192]
[160, 192, 192]
[96, 64, 64]
[224, 64, 64]
[96, 192, 64]
[224, 192, 64]
[96, 64, 192]
[224, 64, 192]
[96, 192, 192]
[224, 192, 192]
[0, 32, 0]
[128, 32, 0]
[0, 160, 0]
[128, 160, 0]
[0, 32, 128]
[128, 32, 128]
[0, 160, 128]
[128, 160, 128]
[64, 32, 0]
[192, 32, 0]
[64, 160, 0]
[192, 160, 0]
[64, 32, 128]
[192, 32, 128]
[64, 160, 128]
[192, 160, 128]
[0, 96, 0]
[128, 96, 0]
[0, 224, 0]
[128, 224, 0]
[0, 96, 128]
[128, 96, 128]
[0, 224, 128]
[128, 224, 128]
[64, 96, 0]
[192, 96, 0]
[64, 224, 0]
[192, 224, 0]
[64, 96, 128]
[192, 96, 128]
[64, 224, 128]
[192, 224, 128]
[0, 32, 64]
[128, 32, 64]
[0, 160, 64]
[128, 160, 64]
[0, 32, 192]
[128, 32, 192]
[0, 160, 192]
[128, 160, 192]
[64, 32, 64]
[192, 32, 64]
[64, 160, 64]
[192, 160, 64]
[64, 32, 192]
[192, 32, 192]
[64, 160, 192]
[192, 160, 192]
[0, 96, 64]
[128, 96, 64]
[0, 224, 64]
[128, 224, 64]
[0, 96, 192]
[128, 96, 192]
[0, 224, 192]
[128, 224, 192]
[64, 96, 64]
[192, 96, 64]
[64, 224, 64]
[192, 224, 64]
[64, 96, 192]
[192, 96, 192]
[64, 224, 192]
[192, 224, 192]
[32, 32, 0]
[160, 32, 0]
[32, 160, 0]
[160, 160, 0]
[32, 32, 128]
[160, 32, 128]
[32, 160, 128]
[160, 160, 128]
[96, 32, 0]
[224, 32, 0]
[96, 160, 0]
[224, 160, 0]
[96, 32, 128]
[224, 32, 128]
[96, 160, 128]
[224, 160, 128]
[32, 96, 0]
[160, 96, 0]
[32, 224, 0]
[160, 224, 0]
[32, 96, 128]
[160, 96, 128]
[32, 224, 128]
[160, 224, 128]
[96, 96, 0]
[224, 96, 0]
[96, 224, 0]
[224, 224, 0]
[96, 96, 128]
[224, 96, 128]
[96, 224, 128]
[224, 224, 128]
[32, 32, 64]
[160, 32, 64]
[32, 160, 64]
[160, 160, 64]
[32, 32, 192]
[160, 32, 192]
[32, 160, 192]
[160, 160, 192]
[96, 32, 64]
[224, 32, 64]
[96, 160, 64]
[224, 160, 64]
[96, 32, 192]
[224, 32, 192]
[96, 160, 192]
[224, 160, 192]
[32, 96, 64]
[160, 96, 64]
[32, 224, 64]
[160, 224, 64]
[32, 96, 192]
[160, 96, 192]
[32, 224, 192]
[160, 224, 192]
[96, 96, 64]
[224, 96, 64]
[96, 224, 64]
[224, 224, 64]
[96, 96, 192]
[224, 96, 192]
[96, 224, 192]
[224, 224, 192]
那么，如果你想让自行车变成红色，你可以这样做：
# Load the image and make Numpy version
im = Image.open('bike.png')
n = np.array(im)
# Make all pixels belonging to bike (2) into red (palette index 9)
n[n==2] = 9
# Make all pixels not red (9) into grey (palette index 7)
n[n!=9] = 7
# Convert back into PIL palettised image and re-apply original palette
r = Image.fromarray(n,mode='P')
r.putpalette(im.getpalette())
r.save('result.png')
关键字：Python、PIL、Pillow、图像处理、调色板、调色板操作、遮罩图像、遮罩、提取调色板、应用调色板。
我正在使用 VOC2012 数据集。当我使用 imageio 打开图像时，输入图像是 PNG 格式，形状为 (375, 500, 4) 。当我使用 PIL 打开图像时，形状突然变成 (500, 375)。 PNG 图像在最后一个轴上应该有四个维度：r g b & alpha。
图片明显是彩色图片，所以应该有3个维度（高、宽、深）。 PIL 似乎暗示它只有两个维度：宽度和高度。
PNG图像可以用二维数组表示吗？请帮忙！此刻如此失落。谢谢！
from PIL import Image
from keras.preprocessing.image import img_to_array
import os, imageio
import numpy as np
root_path = '/Users/johnson/Downloads/'
imageio_img = imageio.imread(
os.path.join(root_path, '2009_003193.png')
)
# (375, 500, 4)
print(imageio_img.shape)
# [ 0 128 192 224 255]
print(np.unique(imageio_img))
PIL_img = Image.open(
os.path.join(root_path, '2009_003193.png')
)
# (500, 375)
print(PIL_img.size)
PIL_img_to_array = img_to_array(PIL_img)
# (375, 500, 1)
print(PIL_img_to_array.shape)
# [ 0. 2. 255.]
print(np.unique(PIL_img_to_array))
同样神奇的是PIL好像知道VOC2012是怎么标注数据的。 PIL_image_to_array 具有唯一值 [0, 2, 255]。方便的是，2 在 VOC2012 中表示自行车。 0 表示背景，255 可能表示自行车周围的黄色边界。但是从第一个代码片段开始，我从未将 pascal 类 传递给 PIL 进行转换。
def pascal_classes():
classes = {'aeroplane' : 1, 'bicycle' : 2, 'bird' : 3, 'boat' : 4,
'bottle' : 5, 'bus' : 6, 'car' : 7, 'cat' : 8,
'chair' : 9, 'cow' : 10, 'diningtable' : 11, 'dog' : 12,
'horse' : 13, 'motorbike' : 14, 'person' : 15, 'potted-plant' : 16,
'sheep' : 17, 'sofa' : 18, 'train' : 19, 'tv/monitor' : 20}
return classes
def pascal_palette():
palette = {( 0, 0, 0) : 0 ,
(128, 0, 0) : 1 ,
( 0, 128, 0) : 2 ,
(128, 128, 0) : 3 ,
( 0, 0, 128) : 4 ,
(128, 0, 128) : 5 ,
( 0, 128, 128) : 6 ,
(128, 128, 128) : 7 ,
( 64, 0, 0) : 8 ,
(192, 0, 0) : 9 ,
( 64, 128, 0) : 10,
(192, 128, 0) : 11,
( 64, 0, 128) : 12,
(192, 0, 128) : 13,
( 64, 128, 128) : 14,
(192, 128, 128) : 15,
( 0, 64, 0) : 16,
(128, 64, 0) : 17,
( 0, 192, 0) : 18,
(128, 192, 0) : 19,
( 0, 64, 128) : 20 }
您的图像是托盘化的，不是 RGB。每个像素由调色板中的 8 位索引表示。您可以通过查看显示为 P.
image.mode 来了解这一点
如果您想要 RGB 图像，请使用：
rgb = Image.open('bike.png').convert('RGB')
如果您想要具有透明度的 RGBA 图像，请使用：
RGBA = Image.open('bike.png').convert('RGBA')
但是，alpha 通道中没有有用的信息，所以这似乎毫无意义。
关于 pascal 调色板，您可以像这样通过 PIL 获得它：
im = Image.open('bike.png')
p = im.getpalette()
for i in range (256):
print(p[3*i:3*i+3])
[0, 0, 0]
[128, 0, 0]
[0, 128, 0]
[128, 128, 0]
[0, 0, 128]
[128, 0, 128]
[0, 128, 128]
[128, 128, 128]
[64, 0, 0]
[192, 0, 0]
[64, 128, 0]
[192, 128, 0]
[64, 0, 128]
[192, 0, 128]
[64, 128, 128]
[192, 128, 128]
[0, 64, 0]
[128, 64, 0]
[0, 192, 0]
[128, 192, 0]
[0, 64, 128]
[128, 64, 128]
[0, 192, 128]
[128, 192, 128]
[64, 64, 0]
[192, 64, 0]
[64, 192, 0]
[192, 192, 0]
[64, 64, 128]
[192, 64, 128]
[64, 192, 128]
[192, 192, 128]
[0, 0, 64]
[128, 0, 64]
[0, 128, 64]
[128, 128, 64]
[0, 0, 192]
[128, 0, 192]
[0, 128, 192]
[128, 128, 192]
[64, 0, 64]
[192, 0, 64]
[64, 128, 64]
[192, 128, 64]
[64, 0, 192]
[192, 0, 192]
[64, 128, 192]
[192, 128, 192]
[0, 64, 64]
[128, 64, 64]
[0, 192, 64]
[128, 192, 64]
[0, 64, 192]
[128, 64, 192]
[0, 192, 192]
[128, 192, 192]
[64, 64, 64]
[192, 64, 64]
[64, 192, 64]
[192, 192, 64]
[64, 64, 192]
[192, 64, 192]
[64, 192, 192]
[192, 192, 192]
[32, 0, 0]
[160, 0, 0]
[32, 128, 0]
[160, 128, 0]
[32, 0, 128]
[160, 0, 128]
[32, 128, 128]
[160, 128, 128]
[96, 0, 0]
[224, 0, 0]
[96, 128, 0]
[224, 128, 0]
[96, 0, 128]
[224, 0, 128]
[96, 128, 128]
[224, 128, 128]
[32, 64, 0]
[160, 64, 0]
[32, 192, 0]
[160, 192, 0]
[32, 64, 128]
[160, 64, 128]
[32, 192, 128]
[160, 192, 128]
[96, 64, 0]
[224, 64, 0]
[96, 192, 0]
[224, 192, 0]
[96, 64, 128]
[224, 64, 128]
[96, 192, 128]
[224, 192, 128]
[32, 0, 64]
[160, 0, 64]
[32, 128, 64]
[160, 128, 64]
[32, 0, 192]
[160, 0, 192]
[32, 128, 192]
[160, 128, 192]
[96, 0, 64]
[224, 0, 64]
[96, 128, 64]
[224, 128, 64]
[96, 0, 192]
[224, 0, 192]
[96, 128, 192]
[224, 128, 192]
[32, 64, 64]
[160, 64, 64]
[32, 192, 64]
[160, 192, 64]
[32, 64, 192]
[160, 64, 192]
[32, 192, 192]
[160, 192, 192]
[96, 64, 64]
[224, 64, 64]
[96, 192, 64]
[224, 192, 64]
[96, 64, 192]
[224, 64, 192]
[96, 192, 192]
[224, 192, 192]
[0, 32, 0]
[128, 32, 0]
[0, 160, 0]
[128, 160, 0]
[0, 32, 128]
[128, 32, 128]
[0, 160, 128]
[128, 160, 128]
[64, 32, 0]
[192, 32, 0]
[64, 160, 0]
[192, 160, 0]
[64, 32, 128]
[192, 32, 128]
[64, 160, 128]
[192, 160, 128]
[0, 96, 0]
[128, 96, 0]
[0, 224, 0]
[128, 224, 0]
[0, 96, 128]
[128, 96, 128]
[0, 224, 128]
[128, 224, 128]
[64, 96, 0]
[192, 96, 0]
[64, 224, 0]
[192, 224, 0]
[64, 96, 128]
[192, 96, 128]
[64, 224, 128]
[192, 224, 128]
[0, 32, 64]
[128, 32, 64]
[0, 160, 64]
[128, 160, 64]
[0, 32, 192]
[128, 32, 192]
[0, 160, 192]
[128, 160, 192]
[64, 32, 64]
[192, 32, 64]
[64, 160, 64]
[192, 160, 64]
[64, 32, 192]
[192, 32, 192]
[64, 160, 192]
[192, 160, 192]
[0, 96, 64]
[128, 96, 64]
[0, 224, 64]
[128, 224, 64]
[0, 96, 192]
[128, 96, 192]
[0, 224, 192]
[128, 224, 192]
[64, 96, 64]
[192, 96, 64]
[64, 224, 64]
[192, 224, 64]
[64, 96, 192]
[192, 96, 192]
[64, 224, 192]
[192, 224, 192]
[32, 32, 0]
[160, 32, 0]
[32, 160, 0]
[160, 160, 0]
[32, 32, 128]
[160, 32, 128]
[32, 160, 128]
[160, 160, 128]
[96, 32, 0]
[224, 32, 0]
[96, 160, 0]
[224, 160, 0]
[96, 32, 128]
[224, 32, 128]
[96, 160, 128]
[224, 160, 128]
[32, 96, 0]
[160, 96, 0]
[32, 224, 0]
[160, 224, 0]
[32, 96, 128]
[160, 96, 128]
[32, 224, 128]
[160, 224, 128]
[96, 96, 0]
[224, 96, 0]
[96, 224, 0]
[224, 224, 0]
[96, 96, 128]
[224, 96, 128]
[96, 224, 128]
[224, 224, 128]
[32, 32, 64]
[160, 32, 64]
[32, 160, 64]
[160, 160, 64]
[32, 32, 192]
[160, 32, 192]
[32, 160, 192]
[160, 160, 192]
[96, 32, 64]
[224, 32, 64]
[96, 160, 64]
[224, 160, 64]
[96, 32, 192]
[224, 32, 192]
[96, 160, 192]
[224, 160, 192]
[32, 96, 64]
[160, 96, 64]
[32, 224, 64]
[160, 224, 64]
[32, 96, 192]
[160, 96, 192]
[32, 224, 192]
[160, 224, 192]
[96, 96, 64]
[224, 96, 64]
[96, 224, 64]
[224, 224, 64]
[96, 96, 192]
[224, 96, 192]
[96, 224, 192]
[224, 224, 192]
那么，如果你想让自行车变成红色，你可以这样做：
# Load the image and make Numpy version
im = Image.open('bike.png')
n = np.array(im)
# Make all pixels belonging to bike (2) into red (palette index 9)
n[n==2] = 9
# Make all pixels not red (9) into grey (palette index 7)
n[n!=9] = 7
# Convert back into PIL palettised image and re-apply original palette
r = Image.fromarray(n,mode='P')
r.putpalette(im.getpalette())
r.save('result.png')
关键字：Python、PIL、Pillow、图像处理、调色板、调色板操作、遮罩图像、遮罩、提取调色板、应用调色板。