Integrate with Machine Learning APIs: Challenge Lab

Run in Cloud Shell

export SANAME=challenge
gcloud iam service-accounts create $SANAME
gcloud projects add-iam-policy-binding $DEVSHELL_PROJECT_ID --member=serviceAccount:$SANAME@$DEVSHELL_PROJECT_ID.iam.gserviceaccount.com --role=roles/bigquery.admin
gcloud projects add-iam-policy-binding $DEVSHELL_PROJECT_ID --member=serviceAccount:$SANAME@$DEVSHELL_PROJECT_ID.iam.gserviceaccount.com --role=roles/storage.admin
gcloud iam service-accounts keys create sa-key.json --iam-account $SANAME@$DEVSHELL_PROJECT_ID.iam.gserviceaccount.com
export GOOGLE_APPLICATION_CREDENTIALS=${PWD}/sa-key.json
gsutil cp gs://$DEVSHELL_PROJECT_ID/analyze-images.py .

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Open Editor and replace the content of "analyze-images.py" file with


# Dataset: image_classification_dataset

# Table name: image_text_detail

import os

import sys



# Import Google Cloud Library modules

from google.cloud import storage, bigquery, language, vision, translate_v2



if ('GOOGLE_APPLICATION_CREDENTIALS' in os.environ):

    if (not os.path.exists(os.environ['GOOGLE_APPLICATION_CREDENTIALS'])):

        print ("The GOOGLE_APPLICATION_CREDENTIALS file does not exist.\n")

        exit()

else:

    print ("The GOOGLE_APPLICATION_CREDENTIALS environment variable is not defined.\n")

    exit()



if len(sys.argv)<3:

    print('You must provide parameters for the Google Cloud project ID and Storage bucket')

    print ('python3 '+sys.argv[0]+ '[PROJECT_NAME] [BUCKET_NAME]')

    exit()



project_name = sys.argv[1]

bucket_name = sys.argv[2]

# Set up our GCS, BigQuery, and Natural Language clients

storage_client = storage.Client()

bq_client = bigquery.Client(project=project_name)

nl_client = language.LanguageServiceClient()



# Set up client objects for the vision and translate_v2 API Libraries

vision_client = vision.ImageAnnotatorClient()

translate_client = translate_v2.Client()



# Setup the BigQuery dataset and table objects

dataset_ref = bq_client.dataset('image_classification_dataset')

dataset = bigquery.Dataset(dataset_ref)

table_ref = dataset.table('image_text_detail')

table = bq_client.get_table(table_ref)



# Create an array to store results data to be inserted into the BigQuery table

rows_for_bq = []



# Get a list of the files in the Cloud Storage Bucket

files = storage_client.bucket(bucket_name).list_blobs()

bucket = storage_client.bucket(bucket_name)



print('Processing image files from GCS. This will take a few minutes..')



# Process files from Cloud Storage and save the result to send to BigQuery

for file in files:

    if file.name.endswith('jpg') or  file.name.endswith('png'):

        file_content = file.download_as_string()



        # TBD: Create a Vision API image object called image_object

        # Ref: https://googleapis.dev/python/vision/latest/gapic/v1/types.html#google.cloud.vision_v1.types.Image

        from google.cloud import vision_v1

        import io

        client = vision.ImageAnnotatorClient()





        # TBD: Detect text in the image and save the response data into an object called response

        # Ref: https://googleapis.dev/python/vision/latest/gapic/v1/api.html#google.cloud.vision_v1.ImageAnnotatorClient.document_text_detection

        image = vision_v1.types.Image(content=file_content)

        response = client.text_detection(image=image)



        # Save the text content found by the vision API into a variable called text_data

        text_data = response.text_annotations[0].description



        # Save the text detection response data in <filename>.txt to cloud storage

        file_name = file.name.split('.')[0] + '.txt'

        blob = bucket.blob(file_name)

        # Upload the contents of the text_data string variable to the Cloud Storage file

        blob.upload_from_string(text_data, content_type='text/plain')



        # Extract the description and locale data from the response file

        # into variables called desc and locale

        # using response object properties e.g. response.text_annotations[0].description

        desc = response.text_annotations[0].description

        locale = response.text_annotations[0].locale



        # if the locale is English (en) save the description as the translated_txt

        if locale == 'en':

            translated_text = desc

        else:

            # TBD: For non EN locales pass the description data to the translation API

            # ref: https://googleapis.dev/python/translation/latest/client.html#google.cloud.translate_v2.client.Client.translate

            # Set the target_language locale to 'en')

            from google.cloud import translate_v2 as translate



            client = translate.Client()

            translation = translate_client.translate(text_data, target_language='en')

            translated_text = translation['translatedText']

        print(translated_text)



        # if there is response data save the original text read from the image,

        # the locale, translated text, and filename

        if len(response.text_annotations) > 0:

            rows_for_bq.append((desc, locale, translated_text, file.name))



print('Writing Vision API image data to BigQuery...')

# Write original text, locale and translated text to BQ

# TBD: When the script is working uncomment the next line to upload results to BigQuery

errors = bq_client.insert_rows(table, rows_for_bq)



assert errors == []


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In Cloud Shell run


python3 analyze-images.py $DEVSHELL_PROJECT_ID $DEVSHELL_PROJECT_ID

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Navigation Menu -> BigQuery, Run


SELECT locale,COUNT(locale) as lcount FROM image_classification_dataset.image_text_detail GROUP BY locale ORDER BY lcount DESC