Image successfully processed once retrieved from Redis cluster

python/redis_cli.py

 import os
+import pickle
 import skimage
-import matplotlib.pyplot as plt
+import numpy as np
 from skimage import io, filters
 from skimage.color import rgb2gray
 from rediscluster import RedisCluster #https://pypi.org/project/redis-py-cluster/#files
@@ -11,12 +12,13 @@ from rediscluster import RedisCluster #https://pypi.org/project/redis-py-cluster
 ###########################################################################################
 
 #Nodes conforming the cluster.
-startup_nodes = [{"host":"compute-11-5","port":"8000"},{"host":"compute-11-8","port":"8000"},{"host":"compute-11-6","port":"8000"}]
+startup_nodes = [{"host":"compute-11-5","port":"8000"},
+		{"host":"compute-11-8","port":"8000"},
+		{"host":"compute-11-6","port":"8000"}]
 
 #DEPLOY the cluster:
 #note: decode_responses must be set to True when used with python3.
-#rc = RedisCluster(startup_nodes = startup_nodes, decode_responses = True)
-
+rc = RedisCluster(startup_nodes = startup_nodes, decode_responses = False)
 #WRITE a key into the cluster:	rc.set("foo", "bar")
 #READ a key from the cluster:	rc.get("foo")
 
@@ -30,12 +32,12 @@ filename = os.path.join('.', 'clouds.jpg')
 clouds = io.imread(filename)
 #Turn it to grayscale.
 clouds = rgb2gray(clouds)
+#Store the image in the Redis Cluster as bytes.
+rc.set("clouds", pickle.dumps(clouds))
+#Retrieve the image from the redis cluster and reconvert it to ndarray.
+clouds_from_redis = pickle.loads(rc.get("clouds"))
 #Apply a simple threshold.
-threshold = filters.threshold_otsu(clouds)
-image_thesholded = clouds > threshold
-#Show.
-#fig, ax = plt.subplots(1,2)
-#ax[0].imshow(clouds, 'gray')
-#ax[1].imshow(image_thesholded, 'gray')
+threshold = filters.threshold_otsu(clouds_from_redis)
+image_thesholded = clouds_from_redis > threshold
 #Save the files.
 io.imsave("clouds_test.png", image_thesholded)