# ----------------------------------------------------------------------
option_parser = argparse.ArgumentParser(add_help=False)
option_parser.add_argument('path', help='path or url to image')
args = option_parser.parse_args()
# ----------------------------------------------------------------------
SERVICE = "Computer Vision"
KEY_FILE = os.path.join(os.getcwd(), "private.txt")
# Request subscription key and endpoint from user.
subscription_key, endpoint = azkey(KEY_FILE, SERVICE, verbose=False)
# Set credentials.
credentials = CognitiveServicesCredentials(subscription_key)
# Create client.
client = ComputerVisionClient(endpoint, credentials)
# Check the URL supplied or path exists and is an image.
# Send provided image (url or path) to azure to extract text.
# ----------------------------------------------------------------------
# URL or path
help='keep original text in output')
args = option_parser.parse_args()
lang = args.lang
fr = args.source
to = args.to if args.to != None else 'latn'
# ----------------------------------------------------------------------
# Request subscription key and endpoint from user.
# ----------------------------------------------------------------------
SERVICE = "Text Translator"
KEY_FILE = os.path.join(os.getcwd(), "private.txt")
key, endpoint = azkey(KEY_FILE, SERVICE, verbose=False, baseurl=True)
# ----------------------------------------------------------------------
# Build the REST API URLs.
# ----------------------------------------------------------------------
path = '/transliterate?api-version=3.0'
url = endpoint + path
headers = {
'Ocp-Apim-Subscription-Key': key,
'Content-type': 'application/json',
'X-ClientTraceId': str(uuid.uuid4())
}
# ------------------------------------------------------------------------
from textwrap import fill
# Constants.
SERVICE = "Anomaly Detector"
KEY_FILE = os.path.join(os.getcwd(), "private.txt")
DATA_FILE = "request.json"
# URLs for anomaly detection with the Anomaly Detector API.
BATCH_URL = "anomalydetector/v1.0/timeseries/entire/detect"
LATEST_URL = "anomalydetector/v1.0/timeseries/last/detect"
# Request subscription key and endpoint from user.
subscription_key, endpoint = azkey(KEY_FILE, SERVICE)
mlask()
# Read data from a json time series from file.
file_handler = open(DATA_FILE)
data = json.load(file_handler)
series = data['series']
sensitivity = data['sensitivity']
mlcat("Sample Data",
"""\
The dataset contains {} {} observations recording the number of requests
received for a particular service. It is quite a small dataset used to
illustrate the concepts. Below we see sample observations from the dataset.
parser = argparse.ArgumentParser(prog='detect',
parents=[option_parser],
description='Detect faces in an image.')
parser.add_argument('path',
type=str,
help='path or URL of a photo where faces will be detected')
args = parser.parse_args()
# ----------------------------------------------------------------------
# Setup
# ----------------------------------------------------------------------
img_url = args.path if is_url(args.path) else get_abspath(args.path)
face_attrs = ['age', 'gender', 'glasses', 'emotion', 'occlusion']
subscription_key, endpoint = args.key, args.endpoint
# ----------------------------------------------------------------------
# Call face API to detect and describe faces
# ----------------------------------------------------------------------
if not subscription_key or not endpoint: # Request subscription key and endpoint from user.
subscription_key, endpoint = get_face_api_key_endpoint(
*azkey(args.key_file, SERVICE, verbose=False))
credentials = CognitiveServicesCredentials(subscription_key) # Set credentials
client = FaceClient(endpoint, credentials) # Setup Azure face API client
faces = azface_detect(client, img_url, return_face_attributes=face_attrs)
print_detection_results(faces)
azface_detect,
azface_similar,
get_face_api_key_endpoint,
list_files,
show_detection_results,
show_similar_results,
)
# ----------------------------------------------------------------------
# Setup
# ----------------------------------------------------------------------
# Request subscription key and endpoint from user.
subscription_key, endpoint = get_face_api_key_endpoint(
*azkey(KEY_FILE, SERVICE))
# Set credentials.
credentials = CognitiveServicesCredentials(subscription_key)
# Create client.
client = FaceClient(endpoint, credentials) # Setup Azure face API client
# ----------------------------------------------------------------------
# Face detection
# ----------------------------------------------------------------------
# Setup
from azure.cognitiveservices.vision.computervision import ComputerVisionClient from azure.cognitiveservices.vision.computervision import VERSION as azver from azure.cognitiveservices.vision.computervision.models import VisualFeatureTypes from msrest.authentication import CognitiveServicesCredentials from azure.cognitiveservices.vision.computervision.models import TextRecognitionMode from azure.cognitiveservices.vision.computervision.models import TextOperationStatusCodes # ---------------------------------------------------------------------- # Request subscription key and endpoint from user. # ---------------------------------------------------------------------- SERVICE = "Computer Vision" KEY_FILE = os.path.join(os.getcwd(), "private.txt") key, endpoint = azkey(KEY_FILE, SERVICE) mlask() # Set credentials. credentials = CognitiveServicesCredentials(key) # Create client. client = ComputerVisionClient(endpoint, credentials) url0 = "https://upload.wikimedia.org/" url1 = "wikipedia/commons/thumb/1/12/Broadway_and_Times_Square_by_night.jpg/" url2 = "450px-Broadway_and_Times_Square_by_night.jpg" url = url0 + url1 + url2