#AzureML – Sample ๐Ÿ Python app using a WebCam to perform Image Analysis from an 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.
  • How to consume the AzureML Webservice Endpoint from a python app using local files.

Today’s code sample is a twist on the previous sample, where we will use a WebCam to send frames to be analyzed by the AzureML Endpoint. And we will show the results online.

Just look at the code.

# Copyright (c) 2022
# Author : Bruno Capuano
# Create Time : 2022 January
# Change Log :
# – Open a camera feed from a local webcam and analyze each frame using an AzureML Endpoint
# – The camera is a USBCam defined by the index 0
# – Pres [D] to enable / disable the frame analysis process
#
# The MIT License (MIT)
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
import os
import cv2
import ssl
import json
import base64
import urllib.request
import time
def displayPredictions(jsonPrediction, frame):
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, 40)
text = str(f"Scored Label: {scored_label}")
cv2.putText(frame, text, start_point_label, cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
start_point_label = (10, 60)
text = "Squirrel: {:.4f}".format(scored_prob_squirrel)
cv2.putText(frame, text, start_point_label, cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
start_point_label = (10, 80)
text = "Space Wolf: {:.4f}".format(scored_prob_space_wolf)
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 = '< AZURE ML HTTP EndPoint Uri >'
api_key = '< API KEY >' # Replace this with the API key for the web service
headers = {'Content-Type':'application/json', 'Authorization'๐Ÿ˜ฆ'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
def main():
# instantiate flask app and push a context
app = Flask(__name__)
# Camera Settings
frame_Width = 640 # 1024 # 1280 # 640
frame_Heigth = 480 # 780 # 960 # 480
frame_Size = (frame_Width, frame_Heigth)
video_capture = cv2.VideoCapture(0)
time.sleep(1.0)
# working dirs
i = 0
directoryDet = '.\det'
detectionEnabled = False
while True:
start_time = time.time()
ret, frameOrig = video_capture.read()
frame = cv2.resize(frameOrig, frame_Size)
if (detectionEnabled):
i = i + 1
imgNumber = str(i).zfill(5)
imgName = 'frame_' + str(imgNumber) + '.jpg'
imgNameOri = imgName + "_01_Orig.jpg"
imgNameOriFullPath = os.path.join(directoryDet, imgNameOri)
imgNameDet = imgName + "_03_Detected.jpg"
imgNameDetFullPath = os.path.join(directoryDet, imgNameDet)
cv2.imwrite(imgNameOriFullPath, frame)
# analyze file using AzureML Endpoint
jsonStr = processFile(imgNameOriFullPath)
frame = displayPredictions(jsonStr, frame)
cv2.imwrite(imgNameDetFullPath, frame)
if (time.time() start_time ) > 0:
fpsInfo = "FPS: " + str(1.0 / (time.time() start_time)) # FPS = 1 / time to process loop
font = cv2.FONT_HERSHEY_DUPLEX
cv2.putText(frame, fpsInfo, (10, 20), font, 0.4, (255, 255, 255), 1)
cv2.imshow('@elbruno – Camera FPS', frame)
if (detectionEnabled):
time.sleep(2.0)
# key controller
key = cv2.waitKey(1) & 0xFF
if key == ord("d"):
if (detectionEnabled == True):
detectionEnabled = False
else:
detectionEnabled = True
if key == ord("q"):
break
video_capture.release()
cv2.destroyAllWindows()
if __name__ == '__main__':
main()

Happy coding!

Greetings

El Bruno

More posts in my blog ElBruno.com.



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