ML 映像分类连接器

import logging
from threading import Timer

import numpy as np

import greengrass_machine_learning_sdk as ml

# We assume the inference input image is provided as a local file
# to this inference client Lambda function.
with open('/test_img/test.jpg', 'rb') as f:
    content = bytearray(f.read())

client = ml.client('inference')

def infer():
    logging.info('invoking Greengrass ML Inference service')

    try:
        resp = client.invoke_inference_service(
            AlgoType='image-classification',
            ServiceName='imageClassification',
            ContentType='image/jpeg',
            Body=content
        )
    except ml.GreengrassInferenceException as e:
        logging.info('inference exception {}("{}")'.format(e.__class__.__name__, e))
        return
    except ml.GreengrassDependencyException as e:
        logging.info('dependency exception {}("{}")'.format(e.__class__.__name__, e))
        return

    logging.info('resp: {}'.format(resp))
    predictions = resp['Body'].read().decode("utf-8")
    logging.info('predictions: {}'.format(predictions))
    
    # The connector output is in the format: [0.3,0.1,0.04,...]
    # Remove the '[' and ']' at the beginning and end.
    predictions = predictions[1:-1]
    count = len(predictions.split(','))
    predictions_arr = np.fromstring(predictions, count=count, sep=',')

    # Perform business logic that relies on the predictions_arr, which is an array
    # of probabilities.
    
    # Schedule the infer() function to run again in one second.
    Timer(1, infer).start()
    return

infer()

def function_handler(event, context):
    return

import logging
from threading import Timer

import numpy

import greengrass_machine_learning_sdk as gg_ml

# The inference input image.
with open('/test_img/test.jpg', 'rb') as f:
    content = f.read()

client = gg_ml.client('inference')


def infer():
    logging.info('Invoking Greengrass ML Inference service')

    try:
        resp = client.invoke_inference_service(
            AlgoType='image-classification',
            ServiceName='imageClassification',
            ContentType='image/jpeg',
            Body=content)
    except gg_ml.GreengrassInferenceException as e:
        logging.info('Inference exception %s("%s")', e.__class__.__name__, e)
        return
    except gg_ml.GreengrassDependencyException as e:
        logging.info('Dependency exception %s("%s")', e.__class__.__name__, e)
        return

    logging.info('Response: %s', resp)
    predictions = resp['Body'].read()
    logging.info('Predictions: %s', predictions)

    # The connector output is in the format: [0.3,0.1,0.04,...]
    # Remove the '[' and ']' at the beginning and end.
    predictions = predictions[1:-1]
    predictions_arr = numpy.fromstring(predictions, sep=',')
    logging.info("Split into %s predictions.", len(predictions_arr))

    # Perform business logic that relies on predictions_arr, which is an array
    # of probabilities.

    # Schedule the infer() function to run again in one second.
    Timer(1, infer).start()


infer()


# In this example, the required AWS Lambda handler is never called.
def function_handler(event, context):
    return