import numpy as np

import cv2

import cv

import azure

from azure.storage import QueueService

from azure.storage import BlobService

from azure.storage import TableService

from base64 import b64decode

import itertools

from time import sleep

from math import ceil, floor

from pickle import dumps

def blobToOpenCV(blob):

arr = np.asarray(bytearray(blob), dtype=np.uint8)

img = cv2.imdecode(arr, -1)

return img

# Analyse the overall colour of image

def getCharacteristics( image, region, resultsHolder ):

return resultsHolder

# Constants

blob_container = 'imagecontainer'

blob_analysis = 'analysis'

imagesQueue = 'imagesqueue'

tableName = 'photos'

tablePartitionKey = 'allphotos'

# Get queue credentials

# accountName = environ["AZURE_STORAGE_ACCOUNT"]

with open ("ASA.key", "r") as myfile:

accountName=myfile.read().replace('\n', '')

# accountKey = environ["AZURE_STORAGE_ACCESS_KEY"]

with open ("ASK.key", "r") as myfile:

accountKey=myfile.read().replace('\n', '')

# Create blob service

blob_service = BlobService( account_name=accountName, account_key=accountKey )

blob_service.create_container( blob_container )

blob_service.create_container( blob_analysis )

# Open queue with given credentials

queue_service = QueueService( account_name=accountName, account_key=accountKey )

# Open table service

table_service = TableService( account_name=accountName, account_key=accountKey )

# Analysis results

results = None

# Regions for analysis

region = 4

# Repeat

while(True):

# get images form *imagesQueue* - it is invoked by CRON

messages = queue_service.get_messages( imagesQueue )

if len(messages) == 0:

sleep(15)

for message in messages:

# get image: image ID

imgBlobName = b64decode( message.message_text )

print( imgBlobName )

tableRowKey = imgBlobName

try:

blob = blob_service.get_blob(blob_container, imgBlobName)

except azure.WindowsAzureMissingResourceError:

queue_service.delete_message( imagesQueue, message.message_id, message.pop_receipt )

continue

image = blobToOpenCV(blob)

# ADDED#2 ########################################

if image is None:

print "GIF attempt in analyse"

queue_service.delete_message( imagesQueue, message.message_id, message.pop_receipt )

blob_service.delete_blob( blob_container, imgBlobName)

table_service.delete_entity( tableName, tablePartitionKey, tableRowKey)

continue

# ADDED ####################################################################

## Decrease main image size

# we need to keep in mind aspect ratio

if image.shape[1] >= image.shape[0]:

width = 800

r = (width * 1.0) / image.shape[1]

dim = ( width, int(image.shape[0] * r) )

else:

height = 800

r = (height * 1.0) / image.shape[0]

dim = ( int(image.shape[1] * r), height )

# perform the actual resizing of the image

image = cv2.resize(image, dim, interpolation = cv2.INTER_AREA)

# put to blob smaller version

lol ,image_tn = cv2.imencode( '.jpg', image )

blob_service.put_block_blob_from_bytes( blob_container, imgBlobName, str(bytearray(image_tn.flatten().tolist())) )

############################################################################

# process image

colourStructure = getCharacteristics( image, region, results )

blob_service.put_block_blob_from_bytes( blob_analysis, imgBlobName, dumps(colourStructure) )

# {'PartitionKey': 'allPhotos', 'RowKey': 'imageName', 'thumbnail' : 'thumbnailName',

# 'userId' : ?, 'local' : ?, 'hue' : 200, 'saturation' : 200, 'value' : 200}

## query for image in table to ensure existence

currentTask = table_service.get_entity( tableName, tablePartitionKey, tableRowKey)

## send the quantities to table: save thumbnail ID & save image characteristics

# currentTask.thumbnail = tnID

currentTask.analysed = True

table_service.update_entity( tableName, tablePartitionKey, tableRowKey, currentTask)

# dequeue image

queue_service.delete_message( imagesQueue, message.message_id, message.pop_receipt )

# Get the mosaic image from some queue

## Divide on quadrants and analyse each quadrant ? concurrently

## Wait until table is filled with values - no NILLs - thumbnails done

## Send request for image with given statistics

## Receive thumbnail and paste it

## REPEAT

# Save the file to blob

# Send DONE notification