#AzureML – Sample 🐍 Python app to consume a image classification AzureML HTTP REST Endpoint

Hi !

In my previous post I wrote

• How to create a custom dataset with images to be used on a Azure Machine Learning Designer project.
• How to use the custom data set and how to train an image classification model.
• How to publish the model to be used as a WebService / HTTP REST endpoint.

Each endpoint provides a set of code samples in C#, Python and R. The code is autogenerated, and because we are working with binay images, the generated code will be huge !!! This is because it will include a Base64().toString() serialization of the file.

Based on the original code, I created a sample code in Python that will read a local file, encode the file and then make the http request.

	import urllib.request
	import json
	import os
	import ssl
	import base64
	def allowSelfSignedHttps(allowed):
	# bypass the server certificate verification on client side
	if allowed and not os.environ.get('PYTHONHTTPSVERIFY', '') and getattr(ssl, '_create_unverified_context', None):
	ssl._create_default_https_context = ssl._create_unverified_context
	allowSelfSignedHttps(True) # this line is needed if you use self-signed certificate in your scoring service.
	# Request data goes here
	data = {
	"Inputs": {
	"WebServiceInput0":
	[
	{
	'image': "data:image/png;base64,",
	'id': "0",
	'category': "space_wolf",
	},
	],
	},
	"GlobalParameters": {
	}
	}
	# Save string of image file path below
	img_filepath = "./test_images/squirrel_01.jpg"
	# Create base64 encoded string
	with open(img_filepath, "rb") as f:
	image_string = base64.b64encode(f.read()).decode("utf-8")
	image_data = str(f"""data:image/png;base64,{image_string}""")
	data['Inputs']['WebServiceInput0'][0]['image'] = image_data
	body = str.encode(json.dumps(data))
	url = 'http://<ENDPOINT IP ADDRESS>:80/api/v1/service/sq06densenetendpoint/score'
	api_key = '<API KEY>' # Replace this with the API key for the web service
	headers = {'Content-Type':'application/json', 'Authorization'😦'Bearer '+ api_key)}
	req = urllib.request.Request(url, body, headers)
	try:
	response = urllib.request.urlopen(req)
	result = response.read()
	print(result)
	except urllib.error.HTTPError as error:
	print("The request failed with status code: " + str(error.code))
	# Print the headers – they include the requert ID and the timestamp, which are useful for debugging the failure
	print(error.info())
	print(json.loads(error.read().decode("utf8", 'ignore')))

view raw azuremlendpointsampleclient.py hosted with ❤ by GitHub

It works almost without a change, and here is a sample output that also includes a call to the next sample, which process the JSON response, this sample may also help!

	import urllib.request
	import json
	import os
	import ssl
	import base64
	def displayPredictions(jsonPrediction):
	global frame_Width, frame_Heigth, labelColors
	jsonObj = json.loads(jsonPrediction)
	scored_label = jsonObj['Results']['WebServiceOutput0'][0]['Scored Labels']
	scored_prob_space_wolf = jsonObj['Results']['WebServiceOutput0'][0]['Scored Probabilities_space_wolf']
	scored_prob_squirrel = jsonObj['Results']['WebServiceOutput0'][0]['Scored Probabilities_squirrel']
	print(f" > scored label: {scored_label} – squirrel: {scored_prob_squirrel} – space_wolf: {scored_prob_space_wolf}")
	color = (255,255,255)
	# # display labels
	# start_point_label = (10, 20)
	# text = "{}: {:.4f}".format(f"Scored Label: {scored_label}")
	# cv2.putText(frame, text, start_point_label, cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
	# return frame
	def allowSelfSignedHttps(allowed):
	# bypass the server certificate verification on client side
	if allowed and not os.environ.get('PYTHONHTTPSVERIFY', '') and getattr(ssl, '_create_unverified_context', None):
	ssl._create_default_https_context = ssl._create_unverified_context
	def processFile(img_filepath):
	# Request data goes here
	data = {
	"Inputs": {
	"WebServiceInput0":
	[
	{
	'image': "data:image/png;base64,",
	'id': "0",
	'category': "space_wolf",
	},
	],
	},
	"GlobalParameters": {
	}
	}
	# Create base64 encoded string
	with open(img_filepath, "rb") as f:
	image_string = base64.b64encode(f.read()).decode("utf-8")
	image_data = str(f"""data:image/png;base64,{image_string}""")
	data['Inputs']['WebServiceInput0'][0]['image'] = image_data
	body = str.encode(json.dumps(data))
	url = 'http://< API SERVER >:80/api/v1/service/squirrelimageclassification/score'
	api_key = '< API KEY >' # Replace this with the API key for the web service
	headers = {'Content-Type':'application/json', 'Authorization'😦'Bearer '+ api_key)}
	req = urllib.request.Request(url, body, headers)
	json_result = {}
	try:
	response = urllib.request.urlopen(req)
	result = response.read()
	data = json.loads(result)
	json_result = json.dumps(data)
	except urllib.error.HTTPError as error:
	print("The request failed with status code: " + str(error.code))
	print(error.info())
	print(json.loads(error.read().decode("utf8", 'ignore')))
	return json_result
	allowSelfSignedHttps(True) # this line is needed if you use self-signed certificate in your scoring service.
	# Save string of image file path below
	img_filepath = "./test_images/squirrel_01.jpg"
	res = processFile(img_filepath)
	print(f"processing {img_filepath}")
	displayPredictions(res)
	img_filepath = "./test_images/squirrel_02.jpg"
	res = processFile(img_filepath)
	print(f"processing {img_filepath}")
	#print(res)
	displayPredictions(res)
	img_filepath = "./test_images/space_wolf.jpg"
	res = processFile(img_filepath)
	print(f"processing {img_filepath}")
	#print(res)
	displayPredictions(res)

view raw AzureMLEndpointSampleProcessJson.py hosted with ❤ by GitHub

Tomorrow I’ll show a live webcam real-time analysis using the Azure ML Endpoint to detect Squirrels or Space Wolves!

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