CODEWITHSUNDEEP
pythonpython-3.xnumpyopencv
Martin Brisiak
Martin Brisiak

Reputation: 4061

Python - byte image to NumPy array using OpenCV

I have an image in bytes:

print(image_bytes)

b'\xff\xd8\xff\xfe\x00\x10Lavc57.64.101\x00\xff\xdb\x00C\x00\x08\x04\x04\x04\x04\x04\x05\x05\x05\x05\x05\x05\x06\x06\x06\x06\x06\x06\x06\x06\x06\x06\x06\x06\x06\x07\x07\x07\x08\x08\x08\x07\x07\x07\x06\x06\x07\x07\x08\x08\x08\x08\t\t\t\x08\x08\x08\x08\t\t\n\n\n\x0c\x0c\x0b\x0b\x0e\x0e\x0e\x11\x11\x14\xff\xc4\x01\xa2\x00\x00\x01\x05\x01\x01\x01\x01\x01\x01\x00\x00\x00\x00\x00\x00\x00\x00\x01\x02\x03\x04\x05\x06\x07\x08\t\n\x0b\x01\x00\x03\x01\x01\x01\x01\x01\x01\x01\x01\x01\x00\x00\ ... some other stuff

I am able to convert it to a NumPy array using Pillow:

image = numpy.array(Image.open(io.BytesIO(image_bytes)))

But I don't really like using Pillow. Is there a way to use clear OpenCV, or directly NumPy even better, or some other faster library?

Upvotes: 28

Views: 49552

Answers (2)

Kamran Gasimov
Kamran Gasimov

Reputation: 1793

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

Upvotes: 1

Norrius
Norrius

Reputation: 7930

I created a 2x2 JPEG image to test this. The image has white, red, green and purple pixels. I used cv2.imdecode and numpy.frombuffer

import cv2
import numpy as np

f = open('image.jpg', 'rb')
image_bytes = f.read()  # b'\xff\xd8\xff\xe0\x00\x10...'

decoded = cv2.imdecode(np.frombuffer(image_bytes, np.uint8), -1)

print('OpenCV:\n', decoded)

# your Pillow code
import io
from PIL import Image
image = np.array(Image.open(io.BytesIO(image_bytes))) 
print('PIL:\n', image)

This seems to work, although the channel order is BGR and not RGB as in PIL.Image. There are probably some flags you might use to tune this. Test results:

OpenCV:
 [[[255 254 255]
  [  0   0 254]]

 [[  1 255   0]
  [254   0 255]]]
PIL:
 [[[255 254 255]
  [254   0   0]]

 [[  0 255   1]
  [255   0 254]]]

Upvotes: 47

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