PIS图像如何使用非直觉和非阳性值保存Numpy阵列？

Question

我有一个尺寸28 x 280的数量阵列，其中包含实际的数字值（均为正值和负值）。我正在使用以下代码来保存此数组以通过pil图像归档 -

img = Image.fromarray(img)
img.save(save_path, "PNG")

现在，当我使用以下代码加载此保存的图像时 -

img = Image.open(save_path)
img = np.array(img)
print(img[:,:,0]) # since the image is saved in RGB by default, and the channels are simply all the same, I am printing out only one of the channels
print(img.shape) # printing shape of loaded image for sanity check

上面给我以下输出 -

array([[ 0,  0,  0],
       [ 0,  0,  0],
       [ 0,  0,  0],
       [14, 14, 14],
       [ 0,  0,  0],
       [14, 14, 14],
       [ 0,  0,  0],
       [ 2,  2,  2],
       [10, 10, 10],
       [ 0,  0,  0],
       [ 0,  0,  0],
       [ 0,  0,  0],
       [ 6,  6,  6],
       [ 0,  0,  0],
       [19, 19, 19],
       [16, 16, 16],
       [ 0,  0,  0],
       [ 9,  9,  9],
       [14, 14, 14],
       [ 5,  5,  5],
-- omitting the remaining matrix for spatial reasons

原始矩阵看起来像以下内容，如下所示。帮助 -

   1.54009545e+00  1.14122391e-01 -5.44282794e-01 -1.66106954e-01]
 [-2.70073628e+00 -6.25280142e+00  1.77814519e+00 -8.72797012e+00
   9.91206944e-01  6.63580036e+00  6.84081888e+00 -1.18705761e+00
  -4.54479456e+00 -5.26672935e+00  4.91975927e+00 -5.48409176e+00
  -3.93164325e+00  5.19110155e+00  1.26516495e+01  9.93665600e+00
  -5.70824432e+00  5.72582603e-01 -4.31831169e+00 -9.31297874e+00
   2.13714447e-02 -9.82507896e+00 -2.47176766e+00 -1.94778728e+00
  -1.85507727e+00 -8.01630592e+00 -4.42644596e+00  5.74180269e+00]
 [ 3.32923412e+00  1.50732050e+01 -1.01800518e+01  1.85193479e-01
  -1.77801073e+00 -4.91134501e+00 -4.94232035e+00  5.52533197e+00
  -3.84771490e+00 -5.61370182e+00 -2.91945863e+00 -9.53506768e-01
   7.03971624e-01  1.26758552e+00 -1.29794350e+01 -1.08105397e+00
  -5.57984650e-01 -1.50801647e+00 -3.45247960e+00 -6.14299655e-01
  -4.83907032e+00  5.44770575e+00  2.50088573e+00 -2.45785332e+00
  -3.94766003e-01  7.80810177e-01 -1.66951954e+00 -5.23118067e+00]
 [ 1.24226892e+00 -4.30912447e+00  1.14384556e+00 -5.38896322e+00
  -5.95073175e+00  5.03882837e+00  4.15563917e+00 -7.99412632e+00
  -1.68129158e+00 -2.23124218e+00  2.24080634e+00 -5.57195246e-01
  -2.29391623e+00 -2.70431495e+00  9.87635612e+00 -2.90223390e-01
   3.25407982e+00  3.67051101e+00 -2.86848998e+00 -4.53229618e+00
  -3.80941963e+00  3.66697168e+00  3.98574305e+00 -1.50027335e-01
  -8.77485275e+00  2.20300531e+00  4.97666216e+00  2.27730870e+00]]

-- again, omitting large chunks for spatial reasons

问题 -

1. 在这里，从我的理解中，PIL隐含将真实物转换为uint8格式（0到255像素值），但是我想知道如何确切地将reals转换为uint88发生？真实的像素值是否将其四舍五入或截断为最接近整数，如果是的，负像素值会发生什么？
2. 另外，当我尝试通过简单地打开图像来可视化pil映像时，它只是向我展示了一个黑屏，就像这样 -

但是，奇怪的是，当我将NP数组乘以255时，就像So -so -IMG = IMG * 255，然后保存它，它显示了一些值，就像这样-So -

只是像素值最初太轻而无法被我的眼睛感知吗？我认为是这样，但我只想确认。

Answer 1

如果要将负面和浮点数据作为图像保存，则可能应使用 tiff 格式。

png 只能以最多16位/通道存储未签名的整数数据，即范围为0..65535。

---

这是在TIFF中保存正浮点数和负面浮点数，然后检索它们的演示：

import numpy as np
from PIL import Image

# Set height and width
h, w = 5, 4

# Create image from Numpy array of float32 and save as TIFF
naA = np.linspace(-1000, 1000, h*w, dtype=np.float32).reshape((h,w))
Image.fromarray(naA).save('floats.tif')

# Read back image and compare
imB = Image.open('floats.tif')
naB = np.array(imB)

现在打印并检查相同：

In [101]: naA
Out[101]: 
array([[-1000.     ,  -894.7368 ,  -789.4737 ,  -684.2105 ],
       [ -578.9474 ,  -473.6842 ,  -368.42105,  -263.1579 ],
       [ -157.89473,   -52.63158,    52.63158,   157.89473],
       [  263.1579 ,   368.42105,   473.6842 ,   578.9474 ],
       [  684.2105 ,   789.4737 ,   894.7368 ,  1000.     ]],
      dtype=float32)

In [102]: naB
Out[102]: 
array([[-1000.     ,  -894.7368 ,  -789.4737 ,  -684.2105 ],
       [ -578.9474 ,  -473.6842 ,  -368.42105,  -263.1579 ],
       [ -157.89473,   -52.63158,    52.63158,   157.89473],
       [  263.1579 ,   368.42105,   473.6842 ,   578.9474 ],
       [  684.2105 ,   789.4737 ,   894.7368 ,  1000.     ]],
      dtype=float32)