def get_document_bounds(image_file, feature):
client = vision_v1.ImageAnnotatorClient()
bounds = []
with io.open(image_file, 'rb') as image_file:
content = image_file.read()
image = types.Image(content=content)
response = client.document_text_detection(image=image)
document = response.full_text_annotation
for page in document.pages:
for block in page.blocks:
for paragraph in block.paragraphs:
for word in paragraph.words:
for symbol in word.symbols:
if (feature == FeatureType.SYMBOL):
bounds.append(symbol.bounding_box)
if (feature == FeatureType.WORD):
bounds.append(word.bounding_box)
if (feature == FeatureType.PARA):
bounds.append(paragraph.bounding_box)
if (feature == FeatureType.BLOCK):
bounds.append(block.bounding_box)
if (feature == FeatureType.PAGE):
bounds.append(block.bounding_box)
return bounds
def sample_batch_annotate_files(file_path="input2.pdf"):
"""Perform batch file annotation."""
client = vision_v1.ImageAnnotatorClient()
# Supported mime_type: application/pdf, image/tiff, image/gif
mime_type = "application/pdf"
with io.open(file_path, "rb") as f:
content = f.read()
input_config = {"mime_type": mime_type, "content": content}
features = [{"type_": vision_v1.Feature.Type.DOCUMENT_TEXT_DETECTION}]
# The service can process up to 5 pages per document file. Here we specify
# the first, second, and last page of the document to be processed.
pages = [1, 2, -1]
requests = [{"input_config": input_config, "features": features, "pages": pages}]
response = client.batch_annotate_files(requests=requests)
file1 = open("message.txt", "w") # write mode
file1.write("")
file1.close()
for image_response in response.responses[0].responses:
print(u"Full text:\n{}".format(image_response.full_text_annotation.text))
file1 = open("message.txt", "a", encoding="utf8") # append mode
file1.write(image_response.full_text_annotation.text)
file1.close()
def sample_batch_annotate_files(storage_uri, gcp_main_pdf_file_path):
client = vision_v1.ImageAnnotatorClient()
if isinstance(storage_uri, six.binary_type):
storage_uri = storage_uri.decode("utf-8")
gcs_source = {"uri": storage_uri}
mime_type = "application/pdf"
input_config = {"gcs_source": gcs_source, "mime_type": mime_type}
type_ = enums.Feature.Type.DOCUMENT_TEXT_DETECTION
features_element = {"type": type_}
features = [features_element]
pages_element = 1
pages = [pages_element]
requests_element = {
"input_config": input_config,
"features": features,
"pages": pages,
}
requests = [requests_element]
response = client.batch_annotate_files(requests)
for image_response in response.responses[0].responses:
text_data = image_response.full_text_annotation.text
save_to_elasticsearch(gcp_main_pdf_file_path, storage_uri, text_data)
def sample_async_batch_annotate_images(input_image_uri, output_uri):
"""Perform async batch image annotation"""
client = vision_v1.ImageAnnotatorClient()
# input_image_uri = 'gs://cloud-samples-data/vision/label/wakeupcat.jpg'
# output_uri = 'gs://your-bucket/prefix/'
if isinstance(input_image_uri, six.binary_type):
input_image_uri = input_image_uri.decode('utf-8')
if isinstance(output_uri, six.binary_type):
output_uri = output_uri.decode('utf-8')
source = {'image_uri': input_image_uri}
image = {'source': source}
type_ = enums.Feature.Type.LABEL_DETECTION
features_element = {'type': type_}
type_2 = enums.Feature.Type.IMAGE_PROPERTIES
features_element_2 = {'type': type_2}
features = [features_element, features_element_2]
requests_element = {'image': image, 'features': features}
requests = [requests_element]
gcs_destination = {'uri': output_uri}
# The max number of responses to output in each JSON file
batch_size = 2
output_config = {
'gcs_destination': gcs_destination,
'batch_size': batch_size
}
operation = client.async_batch_annotate_images(requests, output_config)
print('Waiting for operation to complete...')
response = operation.result()
print(response)
def hello():
value = request.files['image']
client = vision.ImageAnnotatorClient()
img = Image.open(value)
content = image_to_byte_array(img)
image = vision.types.Image(content=content)
price_candidate = []
card_numbera_candidate = []
date_candidate = []
response = client.text_detection(image=image)
texts = response.text_annotations
print('Texts:')
for text in texts:
content = text.description
content = content.replace(',', '')
print('\n"{}"'.format(content))
if response.error.message:
raise Exception('{}\nFor more info on error messages, check: '
'https://cloud.google.com/apis/design/errors'.format(
response.error.message))
return str(texts)
def sample_batch_annotate_files(file_path="path/to/your/document.pdf"):
"""Perform batch file annotation."""
client = vision_v1.ImageAnnotatorClient()
# Supported mime_type: application/pdf, image/tiff, image/gif
mime_type = "application/pdf"
with io.open(file_path, "rb") as f:
content = f.read()
input_config = {"mime_type": mime_type, "content": content}
features = [{"type": enums.Feature.Type.DOCUMENT_TEXT_DETECTION}]
# The service can process up to 5 pages per document file. Here we specify
# the first, second, and last page of the document to be processed.
pages = [1, 2, -1]
requests = [{"input_config": input_config, "features": features, "pages": pages}]
response = client.batch_annotate_files(requests)
for image_response in response.responses[0].responses:
print(u"Full text: {}".format(image_response.full_text_annotation.text))
for page in image_response.full_text_annotation.pages:
for block in page.blocks:
print(u"\nBlock confidence: {}".format(block.confidence))
for par in block.paragraphs:
print(u"\tParagraph confidence: {}".format(par.confidence))
for word in par.words:
print(u"\t\tWord confidence: {}".format(word.confidence))
for symbol in word.symbols:
print(
u"\t\t\tSymbol: {}, (confidence: {})".format(
symbol.text, symbol.confidence
)
)
def detect_faces(path):
"""Detects faces in an image."""
from google.cloud import vision
import io
client = vision_v1.ImageAnnotatorClient()
with io.open(path, 'rb') as image_file:
content = image_file.read()
image = vision_v1.types.Image(content=content)
response = client.face_detection(image=image)
faces = response.face_annotations
# Names of likelihood from google.cloud.vision_v1.enums
likelihood_name = ('UNKNOWN', 'VERY_UNLIKELY', 'UNLIKELY', 'POSSIBLE',
'LIKELY', 'VERY_LIKELY')
print('Faces:')
for face in faces:
print('anger: {}'.format(likelihood_name[face.anger_likelihood]))
print('joy: {}'.format(likelihood_name[face.joy_likelihood]))
print('surprise: {}'.format(likelihood_name[face.surprise_likelihood]))
vertices = ([
'({},{})'.format(vertex.x, vertex.y)
for vertex in face.bounding_poly.vertices
])
print('face bounds: {}'.format(','.join(vertices)))
def __init__(self):
super().__init__()
# don't have better ocr, so no threshold
self.correct_word_threshold = 0.0
os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = config.paths[
"gcloud_credentials"]
#for annotations
try:
self.client = vision.ImageAnnotatorClient()
except DefaultCredentialsError:
print(
"Google Cloud OCR is not configured correctly. Using TesseractOCR only."
)
return
#for accessing bucket
self.storage_client = storage.Client()
self.bucket = self.storage_client.bucket("meme_bucket")
self.bucket_root = "gs://meme_bucket/"
self.image_dir = "to_annotate/"
self.annotations_dir = "annotations/"
self.bucket_annotations_path = "gs://meme_bucket/annotations/"
self.valid = True
def test_batch_annotate_images(self):
# Setup Expected Response
expected_response = {}
expected_response = image_annotator_pb2.BatchAnnotateImagesResponse(
**expected_response
)
# Mock the API response
channel = ChannelStub(responses=[expected_response])
patch = mock.patch("google.api_core.grpc_helpers.create_channel")
with patch as create_channel:
create_channel.return_value = channel
client = vision_v1.ImageAnnotatorClient()
# Setup Request
requests = []
response = client.batch_annotate_images(requests)
assert expected_response == response
assert len(channel.requests) == 1
expected_request = image_annotator_pb2.BatchAnnotateImagesRequest(
requests=requests
)
actual_request = channel.requests[0][1]
assert expected_request == actual_request
def test_batch_annotate_images(self, mock_create_stub):
# Mock gRPC layer
grpc_stub = mock.Mock()
mock_create_stub.return_value = grpc_stub
client = vision_v1.ImageAnnotatorClient()
# Mock request
requests = []
# Mock response
expected_response = {}
expected_response = image_annotator_pb2.BatchAnnotateImagesResponse(
**expected_response)
grpc_stub.BatchAnnotateImages.return_value = expected_response
response = client.batch_annotate_images(requests)
self.assertEqual(expected_response, response)
grpc_stub.BatchAnnotateImages.assert_called_once()
args, kwargs = grpc_stub.BatchAnnotateImages.call_args
self.assertEqual(len(args), 2)
self.assertEqual(len(kwargs), 1)
self.assertIn('metadata', kwargs)
actual_request = args[0]
expected_request = image_annotator_pb2.BatchAnnotateImagesRequest(
requests=requests)
self.assertEqual(expected_request, actual_request)
def test_async_batch_annotate_files(self):
# Setup Expected Response
expected_response = {}
expected_response = image_annotator_pb2.AsyncBatchAnnotateFilesResponse(
**expected_response)
operation = operations_pb2.Operation(
name="operations/test_async_batch_annotate_files", done=True)
operation.response.Pack(expected_response)
# Mock the API response
channel = ChannelStub(responses=[operation])
patch = mock.patch("google.api_core.grpc_helpers.create_channel")
with patch as create_channel:
create_channel.return_value = channel
client = vision_v1.ImageAnnotatorClient()
# Setup Request
requests = []
response = client.async_batch_annotate_files(requests)
result = response.result()
assert expected_response == result
assert len(channel.requests) == 1
expected_request = image_annotator_pb2.AsyncBatchAnnotateFilesRequest(
requests=requests)
actual_request = channel.requests[0][1]
assert expected_request == actual_request
def sample_async_batch_annotate_images(input_image_uris, output_uri):
"""Perform async batch image annotation"""
client = vision_v1.ImageAnnotatorClient()
# set up output configuration
gcs_destination = {'uri': output_uri}
# The max number of responses to output in each JSON file
batch_size = min(len(input_image_uris), 100)
output_config = {
'gcs_destination': gcs_destination,
'batch_size': batch_size
}
# set up input configuration
type_ = enums.Feature.Type.OBJECT_LOCALIZATION
features_element = {'type': type_}
features = [features_element]
requests = []
for input_image_uri in input_image_uris:
source = {'image_uri': input_image_uri}
image = {'source': source}
requests.append({"image": image, "features": features})
operation = client.async_batch_annotate_images(requests, output_config)
print("Operation submitted with output file prefix", output_uri)
def detect_document(path):
"""Detects document features in an image."""
client = vision_v1.ImageAnnotatorClient()
with io.open(path, 'rb') as image_file:
content = image_file.read()
image = vision_v1.Image(content=content)
response = client.document_text_detection(image=image)
"""
for page in response.full_text_annotation.pages:
for block in page.blocks:
print('\nBlock confidence: {}\n'.format(block.confidence))
for paragraph in block.paragraphs:
print('Paragraph confidence: {}'.format(
paragraph.confidence))
for word in paragraph.words:
word_text = ''.join([
symbol.text for symbol in word.symbols
])
print('Word text: {} (confidence: {})'.format(
word_text, word.confidence))
for symbol in word.symbols:
print('\tSymbol: {} (confidence: {})'.format(
symbol.text, symbol.confidence))
"""
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_labels(image):
client = vision_v1.ImageAnnotatorClient()
content = image.read()
image = vision_v1.types.Image(content = content)
response = client.label_detection(image = image, max_results = 40)
labels = response.label_annotations
label_data = pd.DataFrame(columns = ['description'])
for label in labels:
print(type(label))
label_data = label_data.append(
dict(
description = label.description
), ignore_index = True
)
desc_column = label_data.loc[:,'description']
label_ar = desc_column.values
label_ar = label_ar[5:]
label_list = label_ar.tolist()
return label_list
def sample_async_batch_annotate_images(
input_image_uri="gs://cloud-samples-data/vision/label/wakeupcat.jpg",
output_uri="gs://your-bucket/prefix/",
):
"""Perform async batch image annotation."""
client = vision_v1.ImageAnnotatorClient()
source = {"image_uri": input_image_uri}
image = {"source": source}
features = [
{
"type": enums.Feature.Type.LABEL_DETECTION
},
{
"type": enums.Feature.Type.IMAGE_PROPERTIES
},
]
requests = [{"image": image, "features": features}]
gcs_destination = {"uri": output_uri}
# The max number of responses to output in each JSON file
batch_size = 2
output_config = {
"gcs_destination": gcs_destination,
"batch_size": batch_size
}
operation = client.async_batch_annotate_images(requests, output_config)
print("Waiting for operation to complete...")
response = operation.result()
# The output is written to GCS with the provided output_uri as prefix
gcs_output_uri = response.output_config.gcs_destination.uri
print("Output written to GCS with prefix: {}".format(gcs_output_uri))
def post(self, resource_id):
# Instantiates a client
client = vision_v1.ImageAnnotatorClient()
# The name of the image file to annotate
file_name = os.path.abspath('files/taj.jpg')
# Loads the image into memory
with io.open(file_name, 'rb') as image_file:
content = image_file.read()
image = types.Image(content=content)
#Performs label detection on the image file
response = client.label_detection(image=image)
labels = response.label_annotations
data = {}
predictions = []
for label in labels:
# data.update({
# 'label' : label.description,
# 'score' : label.score
# })
predictions.append({
label.description, label.score
}) #make a list as print would only show up in terminal
# predictions.append(data)
print(predictions)
# data.update({tuple(predictions)})
# prediction_text = ", ".join(predictions) #make it a little more pretty with ','
return {'prediction': 'Success'}
def sample_batch_annotate_files(
storage_uri="gs://cloud-samples-data/vision/document_understanding/kafka.pdf",
):
"""Perform batch file annotation."""
mime_type = "application/pdf"
client = vision_v1.ImageAnnotatorClient()
gcs_source = {"uri": storage_uri}
input_config = {"gcs_source": gcs_source, "mime_type": mime_type}
features = [{"type_": vision_v1.Feature.Type.DOCUMENT_TEXT_DETECTION}]
# The service can process up to 5 pages per document file.
# Here we specify the first, second, and last page of the document to be
# processed.
pages = [1, 2, -1]
requests = [{"input_config": input_config, "features": features, "pages": pages}]
response = client.batch_annotate_files(requests=requests)
for image_response in response.responses[0].responses:
print(u"Full text: {}".format(image_response.full_text_annotation.text))
for page in image_response.full_text_annotation.pages:
for block in page.blocks:
print(u"\nBlock confidence: {}".format(block.confidence))
for par in block.paragraphs:
print(u"\tParagraph confidence: {}".format(par.confidence))
for word in par.words:
print(u"\t\tWord confidence: {}".format(word.confidence))
for symbol in word.symbols:
print(
u"\t\t\tSymbol: {}, (confidence: {})".format(
symbol.text, symbol.confidence
)
)
def test_async_batch_annotate_files(self):
# Setup Expected Response
expected_response = {}
expected_response = image_annotator_pb2.AsyncBatchAnnotateFilesResponse(
**expected_response)
operation = operations_pb2.Operation(
name='operations/test_async_batch_annotate_files', done=True)
operation.response.Pack(expected_response)
# Mock the API response
channel = ChannelStub(responses=[operation])
client = vision_v1.ImageAnnotatorClient(channel=channel)
# Setup Request
requests = []
response = client.async_batch_annotate_files(requests)
result = response.result()
assert expected_response == result
assert len(channel.requests) == 1
expected_request = image_annotator_pb2.AsyncBatchAnnotateFilesRequest(
requests=requests)
actual_request = channel.requests[0][1]
assert expected_request == actual_request
def text_detection_uri(uri):
credentials = service_account.Credentials.from_service_account_file(settings.GOOGLE_VISION_API_CREDENTIAL_PATH)
client = vision_v1.ImageAnnotatorClient(credentials=credentials)
image = vision_v1.types.Image()
image.source.image_uri = uri
response = client.document_text_detection(image=image)
data_dict = MessageToDict(response)
return data_dict
def __init__(self, debug=False, min_length=5, max_height=1):
self.debug = debug
self.min_length = min_length
self.max_height = max_height
self.client = vision_v1.ImageAnnotatorClient()
self.totals = []
self.market = None
self.largets_number = 0
self.bounding_box = None
def ocr_reading(result):
client = vision.ImageAnnotatorClient()
print('client', client, result, vision)
# image = vision.types.Image()
image = vision.Image()
image.source.image_uri = result['secure_url']
response = client.text_detection(image=image)
texts = response.text_annotations
return texts and texts[0].description
def visionapi(self):
print('3. visionapi')
vision_client = vision_v1.ImageAnnotatorClient()
files = os.listdir(self.img_dir)
txt = ""
for item in files:
txt += self.worker_visionapi(vision_client, (self.img_dir + item))
return txt
def test_batch_annotate_images_exception(self):
# Mock the API response
channel = ChannelStub(responses=[CustomException()])
client = vision_v1.ImageAnnotatorClient(channel=channel)
# Setup request
requests = []
with pytest.raises(CustomException):
client.batch_annotate_images(requests)
def handwriting_solve():
with io.open('../python/test.png', 'rb') as image_file:
content = image_file.read()
image = types.Image(content=imageData.read())
client = vision_v1.ImageAnnotatorClient()
response = client.text_detection(image=image)
raw_data = response.text_annotations[0].description
return raw_data
def get_ggl_ocr_data(self, image_bytes):
credentials = (service_account.Credentials.from_service_account_file(
self.json_path))
client = vision.ImageAnnotatorClient(credentials=credentials)
image = vision.types.Image(content=image_bytes)
response = client.document_text_detection(image=image)
document = response.full_text_annotation
anno_ls = self.get_word_representation(document)
anno_df = pd.DataFrame.from_dict(anno_ls)
anno_gm = (anno_df.apply(self.fit_to_poly,
axis=1).reset_index(drop=True))
return anno_gm
def sample_batch_annotate_files():
# Create a client
client = vision_v1.ImageAnnotatorClient()
# Initialize request argument(s)
request = vision_v1.BatchAnnotateFilesRequest()
# Make the request
response = client.batch_annotate_files(request=request)
# Handle the response
print(response)
def test_batch_annotate_images_exception(self):
# Mock the API response
channel = ChannelStub(responses=[CustomException()])
patch = mock.patch("google.api_core.grpc_helpers.create_channel")
with patch as create_channel:
create_channel.return_value = channel
client = vision_v1.ImageAnnotatorClient()
# Setup request
requests = []
with pytest.raises(CustomException):
client.batch_annotate_images(requests)
def localize_objects(path):
from google.cloud import vision_v1 as vision
client = vision.ImageAnnotatorClient()
with open(path, 'rb') as image_file:
content = image_file.read()
image = vision.types.Image(content=content)
objects = client.object_localization(
image=image).localized_object_annotations
print('Number of objects found: {}'.format(len(objects)))
for object_ in objects:
print('\n{} (confidence: {})'.format(object_.name, object_.score))
def test_batch_annotate_images_exception(self, mock_create_stub):
# Mock gRPC layer
grpc_stub = mock.Mock()
mock_create_stub.return_value = grpc_stub
client = vision_v1.ImageAnnotatorClient()
# Mock request
requests = []
# Mock exception response
grpc_stub.BatchAnnotateImages.side_effect = CustomException()
self.assertRaises(errors.GaxError, client.batch_annotate_images,
requests)
def detect_landmarks(path):
"""Detects landmarks in the file."""
from google.cloud import vision
import io
client = vision_v1.ImageAnnotatorClient()
with io.open(path, 'rb') as image_file:
content = image_file.read()
image = vision_v1.types.Image(content=content)
response = client.landmark_detection(image=image)
landmarks = response.landmark_annotations
print('Landmarks:')
return landmarks
