how to convert return binary data from azure blob storage back to image using python

Question

so this is my code for downloading the image from azure blob storage container

and here's the return data that i got

can anyone help me in converting the return data back to image format?

Answer 1

Hi @Joshua Cezar C. Claracay

Do you just want to display or save it somewhere?

To display you need something like matplotlib:

from azure.storage.blob import BlobServiceClient  
from matplotlib import pyplot as plt  
from matplotlib import image as mpimg  
import io  
  
  
STORAGEACCOUNTURL = "[your blob url]"  
STORAGEACCOUNTKEY = "[storage key]"  
CONTAINERNAME = "[your container name]"  
BLOBNAME = "[image file name]"  
  
blob_service_client_instance = BlobServiceClient(  
    account_url=STORAGEACCOUNTURL, credential=STORAGEACCOUNTKEY)  
  
blob_client_instance = blob_service_client_instance.get_blob_client(  
    CONTAINERNAME, BLOBNAME, snapshot=None)  
  
blob_data = blob_client_instance.download_blob()  
data = blob_data.readall()  
  
fp = io.BytesIO(data)  
  
with fp:  
    img = mpimg.imread(fp, format='jpeg')  
plt.imshow(img)  
plt.show()  

Answer 2

Hi @Joshua Cezar C. Claracay

See the following similar topic at https://stackoverflow.com/questions/4004710/how-to-decode-this-yuv-colorspace-string-and-save-it-as-an-image

A suggested solution (by Scott Griffiths) was:

def clip(v):  
    # Clip to 0-255  
    v = max(v, 0)  
    v = min(v, 255)  
    return v  
  
def yuvToRgb(y, u, v):  
    c = y - 16  
    d = u - 128  
    e = v - 128  
    R = (298 * c) + (409 * e) + 128  
    G = (298 * c) - (100 * d) - (208 * e) + 128  
    B = (298 * c) + (516 * d) + 128  
    R >>= 8  
    G >>= 8  
    B >>= 8  
    return (clip(R), clip(G), clip(B))  
  
b = bytearray('\x84K\x7f\x86K\x7f\x86G\x7f~K\x7f~I}\x85K}\x85') # etc...  
RGB = []  
for i in xrange(0, len(b), 3):  
    RGB.append(yuvToRgb(b[3*i], b[3*i+1], b[3*i+2]))  

More information on YUV: https://en.wikipedia.org/wiki/YUV

A way to convert YUV (NV12) files to RGB (BMP). Requires PIL (Python Image Library)

https://gist.github.com/fzakaria/2472889

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