def rekFunction()

in rekognitionlambda/index.py [0:0]

• 28 lines of code
• 7 McCabe index (conditional complexity)

def rekFunction(ourBucket, ourKey):
    
    # Clean the string to add the colon back into requested name which was substitued by Amplify Library.
    safeKey = replaceSubstringWithColon(ourKey)
    
    print('Currently processing the following image')
    print('Bucket: ' + ourBucket + ' key name: ' + safeKey)

    detectLabelsResults = {}

    # Try and retrieve labels from Amazon Rekognition, using the confidence level we set in minConfidence var
    try:
        detectLabelsResults = rekognition_client.detect_labels(Image={'S3Object': {'Bucket':ourBucket, 'Name':safeKey}},
        MaxLabels=10,
        MinConfidence=minConfidence)

    except ClientError as e:
        logging.error(e)

    # Create our array and dict for our label construction

    objectsDetected = []

    imageLabels = {
        'image': safeKey
    }

    # Add all of our labels into imageLabels by iterating over response['Labels']

    for label in detectLabelsResults['Labels']:
        newItem = label['Name']
        objectsDetected.append(newItem)
        objectNum = len(objectsDetected)
        itemAtt = f"object{objectNum}"

        # We now have our shiny new item ready to put into DynamoDB
        imageLabels[itemAtt] = newItem


    # Instantiate a table resource object of our environment variable
    imageLabelsTable = os.environ['TABLE']
    table = dynamodb.Table(imageLabelsTable)

    # Put item into table
    try:
        table.put_item(Item=imageLabels)
    except ClientError as e:
        logging.error(e)

    return