def detect_crop_hints(path):
"""Detects crop hints in an image."""
from google.cloud import vision
import io
client = vision.ImageAnnotatorClient()
# [START vision_python_migration_crop_hints]
with io.open(path, 'rb') as image_file:
content = image_file.read()
image = vision.Image(content=content)
crop_hints_params = vision.CropHintsParams(aspect_ratios=[1.77])
image_context = vision.ImageContext(crop_hints_params=crop_hints_params)
response = client.crop_hints(image=image, image_context=image_context)
hints = response.crop_hints_annotation.crop_hints
for n, hint in enumerate(hints):
print('\nCrop Hint: {}'.format(n))
vertices = ([
'({},{})'.format(vertex.x, vertex.y)
for vertex in hint.bounding_poly.vertices
])
print('bounds: {}'.format(','.join(vertices)))
if response.error.message:
raise Exception('{}\nFor more info on error messages, check: '
'https://cloud.google.com/apis/design/errors'.format(
response.error.message))
def detect_crop_hints_uri(uri):
"""Detects crop hints in the file located in Google Cloud Storage."""
from google.cloud import vision
client = vision.ImageAnnotatorClient()
image = vision.Image()
image.source.image_uri = uri
crop_hints_params = vision.CropHintsParams(aspect_ratios=[1.77])
image_context = vision.ImageContext(crop_hints_params=crop_hints_params)
response = client.crop_hints(image=image, image_context=image_context)
hints = response.crop_hints_annotation.crop_hints
for n, hint in enumerate(hints):
print('\nCrop Hint: {}'.format(n))
vertices = ([
'({},{})'.format(vertex.x, vertex.y)
for vertex in hint.bounding_poly.vertices
])
print('bounds: {}'.format(','.join(vertices)))
if response.error.message:
raise Exception('{}\nFor more info on error messages, check: '
'https://cloud.google.com/apis/design/errors'.format(
response.error.message))
def web_entities_include_geo_results(path):
"""Detects web annotations given an image, using the geotag metadata
in the image to detect web entities."""
from google.cloud import vision
import io
client = vision.ImageAnnotatorClient()
with io.open(path, 'rb') as image_file:
content = image_file.read()
image = vision.Image(content=content)
web_detection_params = vision.WebDetectionParams(include_geo_results=True)
image_context = vision.ImageContext(
web_detection_params=web_detection_params)
response = client.web_detection(image=image, image_context=image_context)
for entity in response.web_detection.web_entities:
print('\n\tScore : {}'.format(entity.score))
print(u'\tDescription: {}'.format(entity.description))
if response.error.message:
raise Exception('{}\nFor more info on error messages, check: '
'https://cloud.google.com/apis/design/errors'.format(
response.error.message))
def get_similar_products_file(
project_id, location, product_set_id, product_category,
file_path, filter):
"""Search similar products to image.
Args:
project_id: Id of the project.
location: A compute region name.
product_set_id: Id of the product set.
product_category: Category of the product.
file_path: Local file path of the image to be searched.
filter: Condition to be applied on the labels.
Example for filter: (color = red OR color = blue) AND style = kids
It will search on all products with the following labels:
color:red AND style:kids
color:blue AND style:kids
"""
# product_search_client is needed only for its helper methods.
product_search_client = vision.ProductSearchClient()
image_annotator_client = vision.ImageAnnotatorClient()
# Read the image as a stream of bytes.
with open(file_path, 'rb') as image_file:
content = image_file.read()
# Create annotate image request along with product search feature.
image = vision.Image(content=content)
# product search specific parameters
product_set_path = product_search_client.product_set_path(
project=project_id, location=location,
product_set=product_set_id)
product_search_params = vision.ProductSearchParams(
product_set=product_set_path,
product_categories=[product_category],
filter=filter)
image_context = vision.ImageContext(
product_search_params=product_search_params)
# Search products similar to the image.
response = image_annotator_client.product_search(
image, image_context=image_context)
index_time = response.product_search_results.index_time
print('Product set index time: ')
print(index_time)
results = response.product_search_results.results
print('Search results:')
for result in results:
product = result.product
print('Score(Confidence): {}'.format(result.score))
print('Image name: {}'.format(result.image))
print('Product name: {}'.format(product.name))
print('Product display name: {}'.format(
product.display_name))
print('Product description: {}\n'.format(product.description))
print('Product labels: {}\n'.format(product.product_labels))
def get_similar_products_uri(project_id, location, product_set_id,
product_category, image_uri, filter):
"""Search similar products to image.
Args:
project_id: Id of the project.
location: A compute region name.
product_set_id: Id of the product set.
product_category: Category of the product.
image_uri: Cloud Storage location of image to be searched.
filter: Condition to be applied on the labels.
Example for filter: (color = red OR color = blue) AND style = kids
It will search on all products with the following labels:
color:red AND style:kids
color:blue AND style:kids
"""
# product_search_client is needed only for its helper methods.
product_search_client = vision.ProductSearchClient()
image_annotator_client = vision.ImageAnnotatorClient()
# Create annotate image request along with product search feature.
image_source = vision.ImageSource(image_uri=image_uri)
image = vision.Image(source=image_source)
# product search specific parameters
product_set_path = product_search_client.product_set_path(
project=project_id, location=location, product_set=product_set_id)
product_search_params = vision.ProductSearchParams(
product_set=product_set_path,
product_categories=[product_category],
filter=filter)
image_context = vision.ImageContext(
product_search_params=product_search_params)
# Search products similar to the image.
response = image_annotator_client.product_search(
image, image_context=image_context)
index_time = response.product_search_results.index_time
print('Product set index time: ')
print(index_time)
results = response.product_search_results.results
print('Search results:')
output = []
for result in results:
product = result.product
print('Score(Confidence): {}'.format(result.score))
product_id = product.name.split('/')[-1]
image_uri = get_reference_image_uri(PROJECT_ID, LOCATION_ID,
product_id)
blob_name = image_uri.split('/')[-1]
meta = get_image_metadata(IMAGE_BUCKET, blob_name)
print("Product Info: ", meta)
output.append(meta)
return output
def detect_document_text(vision_image, language_hints=[], full=False):
client = vision.ImageAnnotatorClient(credentials=credentials)
image_context = vision.ImageContext(language_hints=language_hints)
response = client.document_text_detection(image=vision_image,
image_context=image_context)
text = response.text_annotations[0]
print(f"Language: {text.locale}\n")
print(text.description.strip())
if full:
paragraphs, lines = extract_paragraphs(response.full_text_annotation)
print('\nSINGLE LINE\n')
print(' '.join(map(str.strip, lines)))
print('\nBLOCKS & PARAGRAPHS\n\n--')
print('\n\n'.join(paragraphs) + '\n--')
else:
print()
for page in response.full_text_annotation.pages:
for block in page.blocks:
if full:
print(f'\nBlock confidence: {f(block.confidence)}')
for paragraph in block.paragraphs:
if full:
print('\n' + paragraphs.popleft())
print(f'\nParagraph confidence: {f(paragraph.confidence)}')
for word in paragraph.words:
word_text = ''.join(
[symbol.text for symbol in word.symbols])
if full:
print(f'({f(word.confidence)}) {word_text}')
for symbol in word.symbols:
#print(f'\tSymbol: {symbol.text} (confidence: {symbol.confidence})')
if symbol.confidence < 0.8:
print(
f"Possible mistake: symbol '{symbol.text}' in word '{word_text}' (confidence: {f(symbol.confidence)})"
)
def get_crop_hint(path):
# [START vision_crop_hints_tutorial_get_crop_hints]
"""Detect crop hints on a single image and return the first result."""
client = vision.ImageAnnotatorClient()
with io.open(path, 'rb') as image_file:
content = image_file.read()
image = vision.Image(content=content)
crop_hints_params = vision.CropHintsParams(aspect_ratios=[1.77])
image_context = vision.ImageContext(crop_hints_params=crop_hints_params)
response = client.crop_hints(image=image, image_context=image_context)
hints = response.crop_hints_annotation.crop_hints
# Get bounds for the first crop hint using an aspect ratio of 1.77.
vertices = hints[0].bounding_poly.vertices
# [END vision_crop_hints_tutorial_get_crop_hints]
return vertices
def detect_text(vision_image, language_hints=[], full=False):
client = vision.ImageAnnotatorClient(credentials=credentials)
image_context = vision.ImageContext(language_hints=language_hints)
response = client.text_detection(image=vision_image,
image_context=image_context)
texts = response.text_annotations
print(f"Language: {texts[0].locale}")
if full:
print('Texts:')
for text in texts:
print('\n' + text.description)
vertices = ([
f'({vertex.x},{vertex.y})'
for vertex in text.bounding_poly.vertices
])
boundaries = ','.join(vertices)
print(f'bounds: {boundaries}')
else:
print()
print(texts[0].description.strip())
def web_entities_include_geo_results_uri(uri):
"""Detects web annotations given an image in the file located in
Google Cloud Storage., using the geotag metadata in the image to
detect web entities."""
from google.cloud import vision
client = vision.ImageAnnotatorClient()
image = vision.Image()
image.source.image_uri = uri
web_detection_params = vision.WebDetectionParams(include_geo_results=True)
image_context = vision.ImageContext(
web_detection_params=web_detection_params)
response = client.web_detection(image=image, image_context=image_context)
for entity in response.web_detection.web_entities:
print('\n\tScore : {}'.format(entity.score))
print(u'\tDescription: {}'.format(entity.description))
if response.error.message:
raise Exception('{}\nFor more info on error messages, check: '
'https://cloud.google.com/apis/design/errors'.format(
response.error.message))
parser.add_argument('input_dir', type=Path)
parser.add_argument('output_dir', type=Path)
parser.add_argument('--ext', default='png')
args = parser.parse_args()
client = vision.ImageAnnotatorClient()
image_paths = sorted(list(args.input_dir.glob(f'*.{args.ext}')))
for i, image_path in enumerate(image_paths, 1):
with io.open(image_path, 'rb') as image_file:
content = image_file.read()
image = vision.Image(content=content)
request = AnnotateImageRequest(
image=image,
image_context=vision.ImageContext(language_hints='vi'))
response: AnnotateImageResponse = client.annotate_image(
request=request)
# = client.document_text_detection(image=image)
if response.error.message:
print(image_path, response.error.message)
continue
text = response.full_text_annotation.text
output_path = args.output_dir.joinpath(
image_path.with_suffix('.txt').name)
output_path.parent.mkdir(exist_ok=True, parents=True)
with open(output_path, 'wt', encoding='utf-8') as f:
f.write(text)
def get_similar_products_file(project_id, location, product_set_id,
product_category, base_64, filter):
"""Search similar products to image.
Args:
project_id: Id of the project.
location: A compute region name.
product_set_id: Id of the product set.
product_category: Category of the product.
file_path: Local file path of the image to be searched.
filter: Condition to be applied on the labels.
Example for filter: (color = red OR color = blue) AND style = kids
It will search on all products with the following labels:
color:red AND style:kids
color:blue AND style:kids
"""
# product_search_client is needed only for its helper methods.
product_search_client = vision.ProductSearchClient()
image_annotator_client = vision.ImageAnnotatorClient()
# Read the image as a stream of bytes.
# with open(file_path, 'rb') as image_file:
# content = image_file.read()
content = base64.b64decode(base_64)
# Create annotate image request along with product search feature.
image = vision.Image(content=content)
# product search specific parameters
product_set_path = product_search_client.product_set_path(
project=project_id, location=location, product_set=product_set_id)
product_search_params = vision.ProductSearchParams(
product_set=product_set_path,
product_categories=[product_category],
filter=filter)
image_context = vision.ImageContext(
product_search_params=product_search_params)
# Search products similar to the image.
features = {"features": {"type": "LABEL_DETECTION", "maxResults": 1}}
print('hello')
response = image_annotator_client.product_search(
image, image_context=image_context)
index_time = response.product_search_results.index_time
print('Product set index time: ')
print(index_time)
results = response.product_search_results.results
jsonible = []
# print('Search results:')
for result in results:
product = result.product
info_dict = {}
info_dict['score'] = str(result.score)
info_dict['imageName'] = str(result.image)
print(result)
info_dict['productName'] = str(product.name)
info_dict['productDisplayName'] = str(product.display_name)
info_dict['productDescription'] = str(product.description)
product_labels_dict = {}
for key_value in str(product.product_labels).split(','):
key_values = key_value.replace('"', '').replace('[', '').replace(
']', '').replace('\n', ',').split(',')
keys = key_values[0]
values = key_values[1]
key = keys.split(':')[1].strip()
product_labels_dict[key] = values.split(':')[1].strip()
info_dict['productLabels'] = product_labels_dict
jsonible.append(info_dict)
# # print('Score(Confidence): {}'.format(result.score))
# # print('Image name: {}'.format(result.image))
# # print('Product name: {}'.format(product.name))
# # print('Product display name: {}'.format(
# # product.display_name))
# # print('Product description: {}\n'.format(product.description))
# # print('Product labels: {}\n'.format(product.product_labels))
return jsonible
