问题
So I have a set of data which I am able to convert to form separate numpy arrays of R, G, B bands. Now I need to combine them to form an RGB image.
I tried 'Image' to do the job but it requires 'mode' to be attributed.
I tried to do a trick. I would use Image.fromarray() to take the array to image but it attains 'F' mode by default when Image.merge requires 'L' mode images to merge. If I would declare the attribute of array in fromarray() to 'L' at first place, all the R G B images become distorted.
But, if I save the images and then open them and then merge, it works fine. Image reads the image with 'L' mode.
Now I have two issues.
First, I dont think it is an elegant way of doing the work. So if anyone knows the better way of doing it, please tell
Secondly, Image.SAVE is not working properly. Following are the errors I face:
In [7]: Image.SAVE(imagefile, 'JPEG')
----------------------------------------------------------------------------------
TypeError Traceback (most recent call last)
/media/New Volume/Documents/My own works/ISAC/SAMPLES/<ipython console> in <module>()
TypeError: 'dict' object is not callable
Please suggest solutions.
And please mind that the image is around 4000x4000 size array.
回答1:
I don't really understand your question but here is an example of something similar I've done recently that seems like it might help:
# 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')
I hope that helps
回答2:
rgb = np.dstack((r,g,b)) # stacks 3 h x w arrays -> h x w x 3
To also convert floats 0 .. 1 to uint8 s,
rgb_uint8 = (np.dstack((r,g,b)) * 255.999) .astype(np.uint8) # right, Janna, not 256
回答3:
Convert the numpy arrays to uint8 before passing them to Image.fromarray
Eg. if you have floats in the range [0..1]:
r = Image.fromarray(numpy.uint8(r_array*255.999))
回答4:
Your distortion i believe is caused by the way you are splitting your original image into its individual bands and then resizing it again before putting it into merge;
`
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()
`
this works well !
回答5:
rgb = np.dstack((r,g,b)) # stacks 3 h x w arrays -> h x w x 3
This code doesnt create 3d array if you pass 3 channels. 2 channels remain.
来源：https://stackoverflow.com/questions/10443295/combine-3-separate-numpy-arrays-to-an-rgb-image-in-python