def test_streaming_annotate_video(self):
# Setup Expected Response
annotation_results_uri = "annotationResultsUri-238075757"
expected_response = {"annotation_results_uri": annotation_results_uri}
expected_response = video_intelligence_pb2.StreamingAnnotateVideoResponse(
**expected_response
)
# Mock the API response
channel = ChannelStub(responses=[iter([expected_response])])
patch = mock.patch("google.api_core.grpc_helpers.create_channel")
with patch as create_channel:
create_channel.return_value = channel
client = (
videointelligence_v1p3beta1.StreamingVideoIntelligenceServiceClient()
)
# Setup Request
request = {}
request = video_intelligence_pb2.StreamingAnnotateVideoRequest(**request)
requests = [request]
response = client.streaming_annotate_video(requests)
resources = list(response)
assert len(resources) == 1
assert expected_response == resources[0]
assert len(channel.requests) == 1
actual_requests = channel.requests[0][1]
assert len(actual_requests) == 1
actual_request = list(actual_requests)[0]
assert request == actual_request
def streaming_annotate(stream_file, output_uri):
client = videointelligence.StreamingVideoIntelligenceServiceClient()
# Set streaming config specifying the output_uri.
# The output_uri is the prefix of the actual output files.
storage_config = videointelligence.types.StreamingStorageConfig(
enable_storage_annotation_result=True,
annotation_result_storage_directory=output_uri)
# Here we use label detection as an example.
# All features support output to GCS.
config = videointelligence.types.StreamingVideoConfig(
feature=(videointelligence.enums.
StreamingFeature.STREAMING_LABEL_DETECTION),
storage_config=storage_config)
# config_request should be the first in the stream of requests.
config_request = videointelligence.types.StreamingAnnotateVideoRequest(
video_config=config)
# Set the chunk size to 5MB (recommended less than 10MB).
chunk_size = 5 * 1024 * 1024
# Load file content.
stream = []
with io.open(stream_file, 'rb') as video_file:
while True:
data = video_file.read(chunk_size)
if not data:
break
stream.append(data)
def stream_generator():
yield config_request
for chunk in stream:
yield videointelligence.types.StreamingAnnotateVideoRequest(
input_content=chunk)
requests = stream_generator()
# streaming_annotate_video returns a generator.
# timeout argument specifies the maximum allowable time duration between
# the time that the last packet is sent to Google video intelligence API
# and the time that an annotation result is returned from the API.
# timeout argument is represented in number of seconds.
responses = client.streaming_annotate_video(requests, timeout=3600)
# Each response corresponds to about 1 second of video.
for response in responses:
# Check for errors.
if response.error.message:
print(response.error.message)
break
print('Storage URI: {}'.format(response.annotation_results_uri))
def streaming_annotate(stream_file):
client = videointelligence.StreamingVideoIntelligenceServiceClient()
# Set streaming config.
config = videointelligence.types.StreamingVideoConfig(
feature=(videointelligence.enums.StreamingFeature.
STREAMING_EXPLICIT_CONTENT_DETECTION))
# config_request should be the first in the stream of requests.
config_request = videointelligence.types.StreamingAnnotateVideoRequest(
video_config=config)
# Set the chunk size to 5MB (recommended less than 10MB).
chunk_size = 5 * 1024 * 1024
# Load file content.
stream = []
with io.open(stream_file, 'rb') as video_file:
while True:
data = video_file.read(chunk_size)
if not data:
break
stream.append(data)
def stream_generator():
yield config_request
for chunk in stream:
yield videointelligence.types.StreamingAnnotateVideoRequest(
input_content=chunk)
requests = stream_generator()
# streaming_annotate_video returns a generator.
# timeout argument specifies the maximum allowable time duration between
# the time that the last packet is sent to Google video intelligence API
# and the time that an annotation result is returned from the API.
# timeout argument is represented in number of seconds.
responses = client.streaming_annotate_video(requests, timeout=3600)
# Each response corresponds to about 1 second of video.
for response in responses:
# Check for errors.
if response.error.message:
print(response.error.message)
break
for frame in response.annotation_results.explicit_annotation.frames:
time_offset = (frame.time_offset.seconds +
frame.time_offset.nanos / 1e9)
pornography_likelihood = videointelligence.enums.Likelihood(
frame.pornography_likelihood)
print('Time: {}s'.format(time_offset))
print('\tpornogaphy: {}'.format(pornography_likelihood.name))
def test_streaming_annotate_video_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 = (
videointelligence_v1p3beta1.StreamingVideoIntelligenceServiceClient()
)
# Setup request
request = {}
request = video_intelligence_pb2.StreamingAnnotateVideoRequest(**request)
requests = [request]
with pytest.raises(CustomException):
client.streaming_annotate_video(requests)
def streaming_annotate(stream_file):
client = videointelligence.StreamingVideoIntelligenceServiceClient()
# Set streaming config.
config = videointelligence.types.StreamingVideoConfig(feature=(
videointelligence.enums.StreamingFeature.STREAMING_OBJECT_TRACKING))
# config_request should be the first in the stream of requests.
config_request = videointelligence.types.StreamingAnnotateVideoRequest(
video_config=config)
# Set the chunk size to 5MB (recommended less than 10MB).
chunk_size = 5 * 1024 * 1024
def stream_generator(chunk):
yield config_request
yield videointelligence.types.StreamingAnnotateVideoRequest(
input_content=chunk)
def focus_camera(top, bottom, left, right):
# focus camera to coordinates
print("person found moving camera")
def response_filter(requests):
# streaming_annotate_video returns a generator.
# timeout argument specifies the maximum allowable time duration between
# the time that the last packet is sent to Google video intelligence API
# and the time that an annotation result is returned from the API.
# timeout argument is represented in number of seconds.
responses = client.streaming_annotate_video(requests, timeout=3600)
# Each response corresponds to about 1 second of video.
for response in responses:
# Check for errors.
if response.error.message:
print(response.error.message)
# handle more gracefully later
continue
if response.annotation_results.object_annotations:
for annotation in response.annotation_results.object_annotations:
description = annotation.entity.description
confidence = annotation.confidence
# track_id tracks the same object in the video.
track_id = annotation.track_id
# description is in Unicode
print('\tEntity description: {}'.format(
description.encode('utf-8').strip()))
print('\tTrack Id: {}'.format(track_id))
if annotation.entity.entity_id:
print('\tEntity id: {}'.format(
annotation.entity.entity_id))
print('\tConfidence: {}'.format(confidence))
# Every annotation has only one frame
frame = annotation.frames[0]
box = frame.normalized_bounding_box
time_offset = frame.time_offset.seconds + frame.time_offset.nanos / 1e9
print('\tTimestamp: {}'.format(time_offset))
print('\tbounding box position:')
print('\tleft : {}'.format(box.left))
print('\ttop : {}'.format(box.top))
print('\tright : {}'.format(box.right))
print('\tbottom: {}\n'.format(box.bottom))
if (description.encode('utf-8').strip() == "person"
and confidence > 0.70):
# send coordinates to camera
focus_camera(box.top, box.bottom, box.left, box.right)
# Load file content.
with io.open(stream_file, 'rb') as video_file:
counter = 0
while True:
data = video_file.read(chunk_size)
if not data:
break
counter += 1
print('chuncks of data sent : {} data size : {}'.format(
counter, len(data)))
requests = stream_generator(data)
response_filter(requests)
def streaming_annotate(stream_file):
client = videointelligence.StreamingVideoIntelligenceServiceClient()
# Set streaming config with stationary_camera option.
# stationary_camera flag can be set to False (default option) or True.
label_config = videointelligence.types.StreamingLabelDetectionConfig(
stationary_camera=False)
config = videointelligence.types.StreamingVideoConfig(
feature=(videointelligence.enums.StreamingFeature.
STREAMING_LABEL_DETECTION),
label_detection_config=label_config)
# config_request should be the first in the stream of requests.
config_request = videointelligence.types.StreamingAnnotateVideoRequest(
video_config=config)
# Set the chunk size to 5MB (recommended less than 10MB).
chunk_size = 5 * 1024 * 1024
# Load file content.
stream = []
with io.open(stream_file, 'rb') as video_file:
while True:
data = video_file.read(chunk_size)
if not data:
break
stream.append(data)
def stream_generator():
yield config_request
for chunk in stream:
yield videointelligence.types.StreamingAnnotateVideoRequest(
input_content=chunk)
requests = stream_generator()
# streaming_annotate_video returns a generator.
# timeout argument specifies the maximum allowable time duration between
# the time that the last packet is sent to Google video intelligence API
# and the time that an annotation result is returned from the API.
# timeout argument is represented in number of seconds.
responses = client.streaming_annotate_video(requests, timeout=3600)
# Each response corresponds to about 1 second of video.
for response in responses:
# Check for errors.
if response.error.message:
print(response.error.message)
break
# Get the time offset of the response.
frame = response.annotation_results.label_annotations[0].frames[0]
time_offset = frame.time_offset.seconds + frame.time_offset.nanos / 1e9
print('{}s:'.format(time_offset))
for annotation in response.annotation_results.label_annotations:
description = annotation.entity.description
# Every annotation has only one frame
confidence = annotation.frames[0].confidence
print('\t{} (confidence: {})'.format(
description.encode('utf-8').strip(), confidence))