将 3 个单独的 numpy 数组组合成 Python 中的 RGB 图像

Question

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 RGB 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.

Answer 1

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

Answer 2

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

Answer 3

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.

Answer 4

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))

Answer 5

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 !

Answer 6

If using PIL Image convert it to array and then proceed with the below, else using matplotlib or cv2 perform directly.

image = cv2.imread('')[:,:,::-1]
image_2 = image[10:150,10:100]
print(image_2.shape)

img_r = image_2[:,:,0]
img_g = image_2[:,:,1]
img_b = image_2[:,:,2]

image_2 = img_r*0.2989 + 0.587*img_g + 0.114*img_b 

image[10:150,10:100,0] = image_2
image[10:150,10:100,1] = image_2
image[10:150,10:100,2] = image_2

plt.imshow(image,cmap='gray')