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add video streaming samples [(#2047)](#2047)
* add video streaming samples * address review comments * flake * flake * flake
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‎videointelligence/samples/analyze/beta_snippets.py

Lines changed: 357 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -20,9 +20,22 @@
2020
Usage Examples:
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python beta_snippets.py transcription \
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gs://python-docs-samples-tests/video/googlework_short.mp4
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python beta_snippets.py video-text-gcs \
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gs://python-docs-samples-tests/video/googlework_short.mp4
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python beta_snippets.py track-objects /resources/cat.mp4
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python beta_snippets.py track-objects resources/cat.mp4
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python beta_snippets.py streaming-labels resources/cat.mp4
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python beta_snippets.py streaming-shot-change resources/cat.mp4
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python beta_snippets.py streaming-objects resources/cat.mp4
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python beta_snippets.py streaming-explicit-content resources/cat.mp4
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python beta_snippets.py streaming-annotation-storage resources/cat.mp4 \
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gs://mybucket/myfolder
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"""
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import argparse
@@ -274,6 +287,316 @@ def track_objects(path):
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return object_annotations
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def detect_labels_streaming(path):
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# [START video_streaming_label_detection_beta]
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from google.cloud import videointelligence_v1p3beta1 as videointelligence
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# path = 'path_to_file'
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client = videointelligence.StreamingVideoIntelligenceServiceClient()
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# Set streaming config.
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config = videointelligence.types.StreamingVideoConfig(
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feature=(videointelligence.enums.
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StreamingFeature.STREAMING_LABEL_DETECTION))
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# config_request should be the first in the stream of requests.
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config_request = videointelligence.types.StreamingAnnotateVideoRequest(
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video_config=config)
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# Set the chunk size to 5MB (recommended less than 10MB).
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chunk_size = 5 * 1024 * 1024
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# Load file content.
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stream = []
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with io.open(path, 'rb') as video_file:
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while True:
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data = video_file.read(chunk_size)
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if not data:
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break
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stream.append(data)
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def stream_generator():
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yield config_request
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for chunk in stream:
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yield videointelligence.types.StreamingAnnotateVideoRequest(
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input_content=chunk)
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requests = stream_generator()
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# streaming_annotate_video returns a generator.
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responses = client.streaming_annotate_video(requests)
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# Each response corresponds to about 1 second of video.
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for response in responses:
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# Check for errors.
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if response.error.message:
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print(response.error.message)
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break
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# Get the time offset of the response.
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frame = response.annotation_results.label_annotations[0].frames[0]
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time_offset = frame.time_offset.seconds + frame.time_offset.nanos / 1e9
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print('{}s:'.format(time_offset))
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for annotation in response.annotation_results.label_annotations:
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description = annotation.entity.description
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# Every annotation has only one frame
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confidence = annotation.frames[0].confidence
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print('\t{} (confidence: {})'.format(description, confidence))
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# [END video_streaming_label_detection_beta]
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def detect_shot_change_streaming(path):
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# [START video_streaming_shot_change_detection_beta]
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from google.cloud import videointelligence_v1p3beta1 as videointelligence
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# path = 'path_to_file'
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client = videointelligence.StreamingVideoIntelligenceServiceClient()
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# Set streaming config.
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config = videointelligence.types.StreamingVideoConfig(
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feature=(videointelligence.enums.StreamingFeature.
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STREAMING_SHOT_CHANGE_DETECTION))
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# config_request should be the first in the stream of requests.
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config_request = videointelligence.types.StreamingAnnotateVideoRequest(
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video_config=config)
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# Set the chunk size to 5MB (recommended less than 10MB).
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chunk_size = 5 * 1024 * 1024
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# Load file content.
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stream = []
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with io.open(path, 'rb') as video_file:
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while True:
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data = video_file.read(chunk_size)
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if not data:
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break
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stream.append(data)
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def stream_generator():
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yield config_request
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for chunk in stream:
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yield videointelligence.types.StreamingAnnotateVideoRequest(
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input_content=chunk)
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requests = stream_generator()
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# streaming_annotate_video returns a generator.
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responses = client.streaming_annotate_video(requests)
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# Each response corresponds to about 1 second of video.
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for response in responses:
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# Check for errors.
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if response.error.message:
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print(response.error.message)
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break
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for annotation in response.annotation_results.shot_annotations:
398+
start = (annotation.start_time_offset.seconds +
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annotation.start_time_offset.nanos / 1e9)
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end = (annotation.end_time_offset.seconds +
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annotation.end_time_offset.nanos / 1e9)
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print('Shot: {}s to {}s'.format(start, end))
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# [END video_streaming_shot_change_detection_beta]
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def track_objects_streaming(path):
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# [START video_streaming_object_tracking_beta]
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from google.cloud import videointelligence_v1p3beta1 as videointelligence
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# path = 'path_to_file'
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client = videointelligence.StreamingVideoIntelligenceServiceClient()
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# Set streaming config.
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config = videointelligence.types.StreamingVideoConfig(
417+
feature=(videointelligence.enums.
418+
StreamingFeature.STREAMING_OBJECT_TRACKING))
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# config_request should be the first in the stream of requests.
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config_request = videointelligence.types.StreamingAnnotateVideoRequest(
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video_config=config)
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# Set the chunk size to 5MB (recommended less than 10MB).
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chunk_size = 5 * 1024 * 1024
426+
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# Load file content.
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stream = []
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with io.open(path, 'rb') as video_file:
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while True:
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data = video_file.read(chunk_size)
432+
if not data:
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break
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stream.append(data)
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436+
def stream_generator():
437+
yield config_request
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for chunk in stream:
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yield videointelligence.types.StreamingAnnotateVideoRequest(
440+
input_content=chunk)
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requests = stream_generator()
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# streaming_annotate_video returns a generator.
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responses = client.streaming_annotate_video(requests)
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# Each response corresponds to about 1 second of video.
448+
for response in responses:
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# Check for errors.
450+
if response.error.message:
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print(response.error.message)
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break
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# Get the time offset of the response.
455+
frame = response.annotation_results.object_annotations[0].frames[0]
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time_offset = frame.time_offset.seconds + frame.time_offset.nanos / 1e9
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print('{}s:'.format(time_offset))
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for annotation in response.annotation_results.object_annotations:
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description = annotation.entity.description
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confidence = annotation.confidence
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# track_id tracks the same object in the video.
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track_id = annotation.track_id
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print('\tEntity description: {}'.format(description))
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print('\tTrack Id: {}'.format(track_id))
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if annotation.entity.entity_id:
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print('\tEntity id: {}'.format(annotation.entity.entity_id))
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print('\tConfidence: {}'.format(confidence))
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# Every annotation has only one frame
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frame = annotation.frames[0]
475+
box = frame.normalized_bounding_box
476+
print('\tBounding box position:')
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print('\tleft : {}'.format(box.left))
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print('\ttop : {}'.format(box.top))
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print('\tright : {}'.format(box.right))
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print('\tbottom: {}\n'.format(box.bottom))
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# [END video_streaming_object_tracking_beta]
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def detect_explicit_content_streaming(path):
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# [START video_streaming_explicit_content_detection_beta]
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from google.cloud import videointelligence_v1p3beta1 as videointelligence
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# path = 'path_to_file'
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client = videointelligence.StreamingVideoIntelligenceServiceClient()
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# Set streaming config.
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config = videointelligence.types.StreamingVideoConfig(
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feature=(videointelligence.enums.StreamingFeature.
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STREAMING_EXPLICIT_CONTENT_DETECTION))
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# config_request should be the first in the stream of requests.
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config_request = videointelligence.types.StreamingAnnotateVideoRequest(
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video_config=config)
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# Set the chunk size to 5MB (recommended less than 10MB).
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chunk_size = 5 * 1024 * 1024
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# Load file content.
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stream = []
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with io.open(path, 'rb') as video_file:
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while True:
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data = video_file.read(chunk_size)
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if not data:
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break
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stream.append(data)
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def stream_generator():
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yield config_request
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for chunk in stream:
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yield videointelligence.types.StreamingAnnotateVideoRequest(
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input_content=chunk)
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requests = stream_generator()
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# streaming_annotate_video returns a generator.
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responses = client.streaming_annotate_video(requests)
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# Each response corresponds to about 1 second of video.
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for response in responses:
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# Check for errors.
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if response.error.message:
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print(response.error.message)
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break
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531+
for frame in response.annotation_results.explicit_annotation.frames:
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time_offset = (frame.time_offset.seconds +
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frame.time_offset.nanos / 1e9)
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pornography_likelihood = videointelligence.enums.Likelihood(
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frame.pornography_likelihood)
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print('Time: {}s'.format(time_offset))
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print('\tpornogaphy: {}'.format(pornography_likelihood.name))
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# [END video_streaming_explicit_content_detection_beta]
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def annotation_to_storage_streaming(path, output_uri):
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# [START video_streaming_annotation_to_storage_beta]
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from google.cloud import videointelligence_v1p3beta1 as videointelligence
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# path = 'path_to_file'
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# output_uri = 'gs://path_to_output'
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client = videointelligence.StreamingVideoIntelligenceServiceClient()
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# Set streaming config specifying the output_uri.
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# The output_uri is the prefix of the actual output files.
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storage_config = videointelligence.types.StreamingStorageConfig(
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enable_storage_annotation_result=True,
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annotation_result_storage_directory=output_uri)
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# Here we use label detection as an example.
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# All features support output to GCS.
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config = videointelligence.types.StreamingVideoConfig(
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feature=(videointelligence.enums.
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StreamingFeature.STREAMING_LABEL_DETECTION),
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storage_config=storage_config)
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# config_request should be the first in the stream of requests.
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config_request = videointelligence.types.StreamingAnnotateVideoRequest(
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video_config=config)
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# Set the chunk size to 5MB (recommended less than 10MB).
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chunk_size = 5 * 1024 * 1024
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# Load file content.
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stream = []
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with io.open(path, 'rb') as video_file:
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while True:
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data = video_file.read(chunk_size)
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if not data:
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break
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stream.append(data)
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def stream_generator():
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yield config_request
581+
for chunk in stream:
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yield videointelligence.types.StreamingAnnotateVideoRequest(
583+
input_content=chunk)
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requests = stream_generator()
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# streaming_annotate_video returns a generator.
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responses = client.streaming_annotate_video(requests)
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for response in responses:
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# Check for errors.
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if response.error.message:
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print(response.error.message)
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break
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print('Storage URI: {}'.format(response.annotation_results_uri))
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# [END video_streaming_annotation_to_storage_beta]
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277600
if __name__ == '__main__':
278601
parser = argparse.ArgumentParser(
279602
description=__doc__,
@@ -300,6 +623,29 @@ def track_objects(path):
300623
'track-objects', help=track_objects.__doc__)
301624
video_object_tracking_parser.add_argument('path')
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video_streaming_labels_parser = subparsers.add_parser(
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'streaming-labels', help=detect_labels_streaming.__doc__)
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video_streaming_labels_parser.add_argument('path')
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video_streaming_shot_change_parser = subparsers.add_parser(
631+
'streaming-shot-change', help=detect_shot_change_streaming.__doc__)
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video_streaming_shot_change_parser.add_argument('path')
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video_streaming_objects_parser = subparsers.add_parser(
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'streaming-objects', help=track_objects_streaming.__doc__)
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video_streaming_objects_parser.add_argument('path')
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video_streaming_explicit_content_parser = subparsers.add_parser(
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'streaming-explicit-content',
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help=detect_explicit_content_streaming.__doc__)
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video_streaming_explicit_content_parser.add_argument('path')
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video_streaming_annotation_to_storage_parser = subparsers.add_parser(
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'streaming-annotation-storage',
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help=annotation_to_storage_streaming.__doc__)
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video_streaming_annotation_to_storage_parser.add_argument('path')
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video_streaming_annotation_to_storage_parser.add_argument('output_uri')
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303649
args = parser.parse_args()
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305651
if args.command == 'transcription':
@@ -312,3 +658,13 @@ def track_objects(path):
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track_objects_gcs(args.gcs_uri)
313659
elif args.command == 'track-objects':
314660
track_objects(args.path)
661+
elif args.command == 'streaming-labels':
662+
detect_labels_streaming(args.path)
663+
elif args.command == 'streaming-shot-change':
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detect_shot_change_streaming(args.path)
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elif args.command == 'streaming-objects':
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track_objects_streaming(args.path)
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elif args.command == 'streaming-explicit-content':
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detect_explicit_content_streaming(args.path)
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elif args.command == 'streaming-annotation-storage':
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annotation_to_storage_streaming(args.path, args.output_uri)

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