我不太了解您的问题，但以下是我最近做过的类似事例，似乎对您有所帮助：

# r, g, and b are 512x512 float arrays with values >= 0 and < 1.
from PIL import Image
import numpy as np
rgbArray = np.zeros((512,512,3), 'uint8')
rgbArray[..., 0] = r*256
rgbArray[..., 1] = g*256
rgbArray[..., 2] = b*256
img = Image.fromarray(rgbArray)
img.save('myimg.jpeg')

希望对您有所帮助

我认为您的失真是由您将原始图像分割成各个频段，然后在将其合并之前再次调整其大小引起的；

`
image=Image.open("your image")

print(image.size) #size is inverted i.e columns first rows second eg: 500,250

#convert to array
li_r=list(image.getdata(band=0))
arr_r=np.array(li_r,dtype="uint8")
li_g=list(image.getdata(band=1))
arr_g=np.array(li_g,dtype="uint8")
li_b=list(image.getdata(band=2))
arr_b=np.array(li_b,dtype="uint8")

# reshape 
reshaper=arr_r.reshape(250,500) #size flipped so it reshapes correctly
reshapeb=arr_b.reshape(250,500)
reshapeg=arr_g.reshape(250,500)

imr=Image.fromarray(reshaper,mode=None) # mode I
imb=Image.fromarray(reshapeb,mode=None)
img=Image.fromarray(reshapeg,mode=None)

#merge
merged=Image.merge("RGB",(imr,img,imb))
merged.show()
`

这很好用！

rgb = np.dstack((r,g,b))  # stacks 3 h x w arrays -> h x w x 3

还要将浮点数0 .. 1转换为uint8 s，

rgb_uint8 = (np.dstack((r,g,b)) * 255.999) .astype(np.uint8)  # right, Janna, not 256

rgb = np.dstack((r,g,b))  # stacks 3 h x w arrays -> h x w x 3

如果您传递3个通道，则此代码不会创建3d数组。剩下2个频道。

在将numpy数组传递给Image.fromarray之前，将其转换为uint8

例如。如果浮点数在[0..1]范围内：

r = Image.fromarray(numpy.uint8(r_array*255.999))