Google vision has orientation on it's own Cloud Storage when we talk about BigData.
API is extensive. Although SDK doesn't have all features available.
Advantages of GCP is a wide variety of pre-defined models and ability to upload your own. Existance of human labeled model can give better results, although the price is higher and there is no free tier.
I think the killer feature is AutoML (might take some fun from Pedro) it can simplify custom learning AutoML
Landmark Detection Logos Detection can be useful for business aggregators? Syntax Analysis Content Classification Automative description? Labeling
As a test scenario used Python SDK for Google Vision and Google NLP
Running PDF - failed with bad data. There are ways, but they look more like CURL requests
and write data back Google storage
https://cloud.google.com/vision/docs/pdf
Images work fine - Text detection
super intuitive. not that safisticated but simplicity has it's own value The API returns a set of bounding boxes to split lines as well as a set for each character
with io.open(text_img, 'rb') as image_file:
content = image_file.read()
image = types.Image(content=content)
# Performs text detection on the image file
resp = client.text_detection(image=image)
print('\n'.join([d.description for d in resp.text_annotations]))
# Response is line by line a char by char bounding boxes in format like:
# text_annotations {
# description: "curious"
# bounding_poly {
# vertices {
# x: 179
# y: 13
# }
# .........
# vertices {
# x: 179
# y: 54
# }
# }
# }
# I am curious about
# area-filling text
# rendering options
Image labeling - I'd say imppressive. Haven't seen labeling from AWS but the model returns a wider set than I can :)
just_img = os.path.join(os.path.dirname(__file__), 'jaguar.jpg')
with io.open(just_img, 'rb') as image_file:
content = image_file.read()
image = types.Image(content=content)
# Performs text detection on the image file
resp = client.label_detection(image=image)
print(resp)
print('Labels')
print('\n'.join([d.description for d in resp.label_annotations]))
# Response is a set of objects like:
# label_annotations {
# mid: "/m/01280g"
# description: "Wildlife"
# score: 0.98497146368
# topicality: 0.98497146368
# }
# ...........
# Haven't seen AWS response but this one is quite impressive.
# Wildlife
# Terrestrial animal
# Whiskers
# Mammal
# Felidae
# Jaguar
# Facial expression
# Leopard
# Snout
# Roar
Has a set of pre-defined models including sentiment analysis
nlp_client = language.LanguageServiceClient()
document = nlp_types.Document(
content=text,
type=enums.Document.Type.PLAIN_TEXT)
# Detects the sentiment of the text
sentiment = nlp_client.analyze_sentiment(document=document).document_sentiment
print('Sentiment: {}'.format(sentiment))
# Sentiment: magnitude: 0.300000011921
# score: 0.300000011921