AttributeError: module 'google.cloud.vision' has no attribute 'types'

I think you should follow the official documentation:

Vision Client Libraries

import io
import os

# Imports the Google Cloud client library
from google.cloud import vision

# Instantiates a client
client = vision.ImageAnnotatorClient()

# The name of the image file to annotate
file_name = os.path.abspath('resources/wakeupcat.jpg')

# Loads the image into memory
with io.open(file_name, 'rb') as image_file:
    content = image_file.read()

image = vision.Image(content=content)

# Performs label detection on the image file
response = client.label_detection(image=image)
labels = response.label_annotations

print('Labels:')
for label in labels:
    print(label.description)

It is used to create the image the vision module:

image = vision.Image(content=content) 

not 

image = types.Image(content=IMAGE_CONTENT)

This should be the answer since Google Vision changed Type and Emun methods. I also faced the issue since vision-1 and vision-2 have some differences when you need text data from pdf/img file.

import json
import re
from google.cloud import vision
from google.cloud import storage

# Supported mime_types are: 'application/pdf' and 'image/tiff'
mime_type = 'application/pdf'

# How many pages should be grouped into each json output file.
batch_size = 2

client = vision.ImageAnnotatorClient()

feature = vision.Feature(
    type_=vision.Feature.Type.DOCUMENT_TEXT_DETECTION)

gcs_source = vision.GcsSource(uri=gcs_source_uri)
input_config = vision.InputConfig(
    gcs_source=gcs_source, mime_type=mime_type)

gcs_destination = vision.GcsDestination(uri=gcs_destination_uri)
output_config = vision.OutputConfig(
    gcs_destination=gcs_destination, batch_size=batch_size)

async_request = vision.AsyncAnnotateFileRequest(
    features=[feature], input_config=input_config,
    output_config=output_config)

operation = client.async_batch_annotate_files(
    requests=[async_request])

print('Waiting for the operation to finish.')
operation.result(timeout=420)

# Once the request has completed and the output has been
# written to GCS, we can list all the output files.
storage_client = storage.Client()

match = re.match(r'gs://([^/]+)/(.+)', gcs_destination_uri)
bucket_name = match.group(1)
prefix = match.group(2)

bucket = storage_client.get_bucket(bucket_name)

# List objects with the given prefix.
blob_list = list(bucket.list_blobs(prefix=prefix))
print('Output files:')
for blob in blob_list:
    print(blob.name)

# Process the first output file from GCS.
# Since we specified batch_size=2, the first response contains
# the first two pages of the input file.
output = blob_list[0]

json_string = output.download_as_string()
response = json.loads(json_string)

# The actual response for the first page of the input file.
first_page_response = response['responses'][0]
annotation = first_page_response['fullTextAnnotation']

# Here we print the full text from the first page.
# The response contains more information:
# annotation/pages/blocks/paragraphs/words/symbols
# including confidence scores and bounding boxes
print('Full text:\n')
print(annotation['text'])