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 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