PIL (Python Imaging Library) and Numpy work well together.

I use the following functions.

from PIL import Image
import numpy as np

def load_image( infilename ) :
    img = Image.open( infilename )
    img.load()
    data = np.asarray( img, dtype="int32" )
    return data

def save_image( npdata, outfilename ) :
    img = Image.fromarray( np.asarray( np.clip(npdata,0,255), dtype="uint8"), "L" )
    img.save( outfilename )

The 'Image.fromarray' is a little ugly because I clip incoming data to [0,255], convert to bytes, then create a grayscale image. I mostly work in gray.

An RGB image would be something like:

out_img = Image.fromarray( ycc_uint8, "RGB" )
out_img.save( "ycc.tif" )

This is what worked for me...

import cv2
import numpy as np

#Created an image (really an ndarray) with three channels 
new_image = np.ndarray((3, num_rows, num_cols), dtype=int)

#Did manipulations for my project where my array values went way over 255
#Eventually returned numbers to between 0 and 255

#Converted the datatype to np.uint8
new_image = new_image.astype(np.uint8)

#Separated the channels in my new image
new_image_red, new_image_green, new_image_blue = new_image

#Stacked the channels
new_rgb = np.dstack([new_image_red, new_image_green, new_image_blue])

#Displayed the image
cv2.imshow("WindowNameHere", new_rgbrgb)
cv2.waitKey(0)

I searched all over the internet finally I solved:

NumPy array (cv2 image) - Convert

NumPy to bytes

and

bytes to NumPy

:.

#data = cv2 image array
def encodeImage(data):
    #resize inserted image
    data= cv2.resize(data, (480,270))
    # run a color convert:
    data= cv2.cvtColor(data, cv2.COLOR_BGR2RGB)
    return bytes(data) #encode Numpay to Bytes string



def decodeImage(data):
    #Gives us 1d array
    decoded = np.fromstring(data, dtype=np.uint8)
    #We have to convert it into (270, 480,3) in order to see as an image
    decoded = decoded.reshape((270, 480,3))
    return decoded;

# Load an color image
image= cv2.imread('messi5.jpg',1)

img_code = encodeImage(image) #Output: b'\xff\xd8\xff\xe0\x00\x10...';
img = decodeImage(img_code) #Output: normal array
cv2.imshow('image_deirvlon',img);
print(decoded.shape)

You can get full code from here

If the file is in read-byte mode (which means mode='rb') in your case, then numpy.frombuffer() also works, for example :

file = request.files['fileName']
frame = cv2.imdecode(np.frombuffer(file.read(), numpy.uint8), cv2.IMREAD_COLOR)