beta_snippets.py

#!/usr/bin/env python

# Copyright 2019 Google LLC. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""This application demonstrates speech transcription using the
Google Cloud API.

Usage Examples:
    python beta_snippets.py transcription \
        gs://python-docs-samples-tests/video/googlework_tiny.mp4

    python beta_snippets.py video-text-gcs \
        gs://python-docs-samples-tests/video/googlework_tiny.mp4

    python beta_snippets.py streaming-labels resources/cat.mp4

    python beta_snippets.py streaming-shot-change resources/cat.mp4

    python beta_snippets.py streaming-objects resources/cat.mp4

    python beta_snippets.py streaming-explicit-content resources/cat.mp4

    python beta_snippets.py streaming-annotation-storage resources/cat.mp4 \
    gs://mybucket/myfolder

    python beta_snippets.py streaming-automl-classification resources/cat.mp4 \
    $PROJECT_ID $MODEL_ID

    python beta_snippets.py streaming-automl-object-tracking resources/cat.mp4 \
    $PROJECT_ID $MODEL_ID

    python beta_snippets.py streaming-automl-action-recognition \
    resources/cat.mp4 $PROJECT_ID $MODEL_ID
"""

import argparse
import io


def speech_transcription(input_uri, timeout=180):
    # [START video_speech_transcription_gcs_beta]
    """Transcribe speech from a video stored on GCS."""
    from google.cloud import videointelligence_v1p1beta1 as videointelligence

    video_client = videointelligence.VideoIntelligenceServiceClient()

    features = [videointelligence.Feature.SPEECH_TRANSCRIPTION]

    config = videointelligence.SpeechTranscriptionConfig(
        language_code="en-US", enable_automatic_punctuation=True
    )
    video_context = videointelligence.VideoContext(speech_transcription_config=config)

    operation = video_client.annotate_video(
        request={
            "features": features,
            "input_uri": input_uri,
            "video_context": video_context,
        }
    )

    print("\nProcessing video for speech transcription.")

    result = operation.result(timeout)

    # There is only one annotation_result since only
    # one video is processed.
    annotation_results = result.annotation_results[0]
    for speech_transcription in annotation_results.speech_transcriptions:

        # The number of alternatives for each transcription is limited by
        # SpeechTranscriptionConfig.max_alternatives.
        # Each alternative is a different possible transcription
        # and has its own confidence score.
        for alternative in speech_transcription.alternatives:
            print("Alternative level information:")

            print("Transcript: {}".format(alternative.transcript))
            print("Confidence: {}\n".format(alternative.confidence))

            print("Word level information:")
            for word_info in alternative.words:
                word = word_info.word
                start_time = word_info.start_time
                end_time = word_info.end_time
                print(
                    "\t{}s - {}s: {}".format(
                        start_time.seconds + start_time.microseconds * 1e-6,
                        end_time.seconds + end_time.microseconds * 1e-6,
                        word,
                    )
                )
    # [END video_speech_transcription_gcs_beta]


def video_detect_text_gcs(input_uri):
    # [START video_detect_text_gcs_beta]
    """Detect text in a video stored on GCS."""
    from google.cloud import videointelligence_v1p2beta1 as videointelligence

    video_client = videointelligence.VideoIntelligenceServiceClient()
    features = [videointelligence.Feature.TEXT_DETECTION]

    operation = video_client.annotate_video(
        request={"features": features, "input_uri": input_uri}
    )

    print("\nProcessing video for text detection.")
    result = operation.result(timeout=300)

    # The first result is retrieved because a single video was processed.
    annotation_result = result.annotation_results[0]

    # Get only the first result
    text_annotation = annotation_result.text_annotations[0]
    print("\nText: {}".format(text_annotation.text))

    # Get the first text segment
    text_segment = text_annotation.segments[0]
    start_time = text_segment.segment.start_time_offset
    end_time = text_segment.segment.end_time_offset
    print(
        "start_time: {}, end_time: {}".format(
            start_time.seconds + start_time.microseconds * 1e-6,
            end_time.seconds + end_time.microseconds * 1e-6,
        )
    )

    print("Confidence: {}".format(text_segment.confidence))

    # Show the result for the first frame in this segment.
    frame = text_segment.frames[0]
    time_offset = frame.time_offset
    print(
        "Time offset for the first frame: {}".format(
            time_offset.seconds + time_offset.microseconds * 1e-6
        )
    )
    print("Rotated Bounding Box Vertices:")
    for vertex in frame.rotated_bounding_box.vertices:
        print("\tVertex.x: {}, Vertex.y: {}".format(vertex.x, vertex.y))
    # [END video_detect_text_gcs_beta]
    return annotation_result.text_annotations


def video_detect_text(path):
    # [START video_detect_text_beta]
    """Detect text in a local video."""
    from google.cloud import videointelligence_v1p2beta1 as videointelligence

    video_client = videointelligence.VideoIntelligenceServiceClient()
    features = [videointelligence.Feature.TEXT_DETECTION]
    video_context = videointelligence.VideoContext()

    with io.open(path, "rb") as file:
        input_content = file.read()

    operation = video_client.annotate_video(
        request={
            "features": features,
            "input_content": input_content,
            "video_context": video_context,
        }
    )

    print("\nProcessing video for text detection.")
    result = operation.result(timeout=300)

    # The first result is retrieved because a single video was processed.
    annotation_result = result.annotation_results[0]

    # Get only the first result
    text_annotation = annotation_result.text_annotations[0]
    print("\nText: {}".format(text_annotation.text))

    # Get the first text segment
    text_segment = text_annotation.segments[0]
    start_time = text_segment.segment.start_time_offset
    end_time = text_segment.segment.end_time_offset
    print(
        "start_time: {}, end_time: {}".format(
            start_time.seconds + start_time.microseconds * 1e-6,
            end_time.seconds + end_time.microseconds * 1e-6,
        )
    )

    print("Confidence: {}".format(text_segment.confidence))

    # Show the result for the first frame in this segment.
    frame = text_segment.frames[0]
    time_offset = frame.time_offset
    print(
        "Time offset for the first frame: {}".format(
            time_offset.seconds + time_offset.microseconds * 1e-6
        )
    )
    print("Rotated Bounding Box Vertices:")
    for vertex in frame.rotated_bounding_box.vertices:
        print("\tVertex.x: {}, Vertex.y: {}".format(vertex.x, vertex.y))
    # [END video_detect_text_beta]
    return annotation_result.text_annotations


def detect_labels_streaming(path):
    # [START video_streaming_label_detection_beta]
    from google.cloud import videointelligence_v1p3beta1 as videointelligence

    # path = 'path_to_file'

    client = videointelligence.StreamingVideoIntelligenceServiceClient()

    # Set streaming config.
    config = videointelligence.StreamingVideoConfig(
        feature=(videointelligence.StreamingFeature.STREAMING_LABEL_DETECTION)
    )

    # config_request should be the first in the stream of requests.
    config_request = videointelligence.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(path, "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.StreamingAnnotateVideoRequest(input_content=chunk)

    requests = stream_generator()

    # streaming_annotate_video returns a generator.
    # The default timeout is about 300 seconds.
    # To process longer videos it should be set to
    # larger than the length (in seconds) of the stream.
    responses = client.streaming_annotate_video(requests, timeout=600)

    # 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

        label_annotations = response.annotation_results.label_annotations

        # label_annotations could be empty
        if not label_annotations:
            continue

        for annotation in label_annotations:
            # Each annotation has one frame, which has a timeoffset.
            frame = annotation.frames[0]
            time_offset = (
                frame.time_offset.seconds + frame.time_offset.microseconds / 1e6
            )

            description = annotation.entity.description
            confidence = annotation.frames[0].confidence
            # description is in Unicode
            print(
                "{}s: {} (confidence: {})".format(time_offset, description, confidence)
            )
    # [END video_streaming_label_detection_beta]


def detect_shot_change_streaming(path):
    # [START video_streaming_shot_change_detection_beta]
    from google.cloud import videointelligence_v1p3beta1 as videointelligence

    # path = 'path_to_file'

    client = videointelligence.StreamingVideoIntelligenceServiceClient()

    # Set streaming config.
    config = videointelligence.StreamingVideoConfig(
        feature=(videointelligence.StreamingFeature.STREAMING_SHOT_CHANGE_DETECTION)
    )

    # config_request should be the first in the stream of requests.
    config_request = videointelligence.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(path, "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.StreamingAnnotateVideoRequest(input_content=chunk)

    requests = stream_generator()

    # streaming_annotate_video returns a generator.
    # The default timeout is about 300 seconds.
    # To process longer videos it should be set to
    # larger than the length (in seconds) of the stream.
    responses = client.streaming_annotate_video(requests, timeout=600)

    # 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 annotation in response.annotation_results.shot_annotations:
            start = (
                annotation.start_time_offset.seconds
                + annotation.start_time_offset.microseconds / 1e6
            )
            end = (
                annotation.end_time_offset.seconds
                + annotation.end_time_offset.microseconds / 1e6
            )

            print("Shot: {}s to {}s".format(start, end))
    # [END video_streaming_shot_change_detection_beta]


def track_objects_streaming(path):
    # [START video_streaming_object_tracking_beta]
    from google.cloud import videointelligence_v1p3beta1 as videointelligence

    # path = 'path_to_file'

    client = videointelligence.StreamingVideoIntelligenceServiceClient()

    # Set streaming config.
    config = videointelligence.StreamingVideoConfig(
        feature=(videointelligence.StreamingFeature.STREAMING_OBJECT_TRACKING)
    )

    # config_request should be the first in the stream of requests.
    config_request = videointelligence.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(path, "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.StreamingAnnotateVideoRequest(input_content=chunk)

    requests = stream_generator()

    # streaming_annotate_video returns a generator.
    # The default timeout is about 300 seconds.
    # To process longer videos it should be set to
    # larger than the length (in seconds) of the stream.
    responses = client.streaming_annotate_video(requests, timeout=900)

    # 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

        object_annotations = response.annotation_results.object_annotations

        # object_annotations could be empty
        if not object_annotations:
            continue

        for annotation in object_annotations:
            # Each annotation has one frame, which has a timeoffset.
            frame = annotation.frames[0]
            time_offset = (
                frame.time_offset.seconds + frame.time_offset.microseconds / 1e6
            )

            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("{}s".format(time_offset))
            print("\tEntity description: {}".format(description))
            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
            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))
    # [END video_streaming_object_tracking_beta]


def detect_explicit_content_streaming(path):
    # [START video_streaming_explicit_content_detection_beta]
    from google.cloud import videointelligence_v1p3beta1 as videointelligence

    # path = 'path_to_file'

    client = videointelligence.StreamingVideoIntelligenceServiceClient()

    # Set streaming config.
    config = videointelligence.StreamingVideoConfig(
        feature=(
            videointelligence.StreamingFeature.STREAMING_EXPLICIT_CONTENT_DETECTION
        )
    )

    # config_request should be the first in the stream of requests.
    config_request = videointelligence.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(path, "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.StreamingAnnotateVideoRequest(input_content=chunk)

    requests = stream_generator()

    # streaming_annotate_video returns a generator.
    # The default timeout is about 300 seconds.
    # To process longer videos it should be set to
    # larger than the length (in seconds) of the stream.
    responses = client.streaming_annotate_video(requests, timeout=900)

    # 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.microseconds / 1e6
            )
            pornography_likelihood = videointelligence.Likelihood(
                frame.pornography_likelihood
            )

            print("Time: {}s".format(time_offset))
            print("\tpornogaphy: {}".format(pornography_likelihood.name))
    # [END video_streaming_explicit_content_detection_beta]


def annotation_to_storage_streaming(path, output_uri):
    # [START video_streaming_annotation_to_storage_beta]
    from google.cloud import videointelligence_v1p3beta1 as videointelligence

    # path = 'path_to_file'
    # output_uri = 'gs://path_to_output'

    client = videointelligence.StreamingVideoIntelligenceServiceClient()

    # Set streaming config specifying the output_uri.
    # The output_uri is the prefix of the actual output files.
    storage_config = videointelligence.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.StreamingVideoConfig(
        feature=(videointelligence.StreamingFeature.STREAMING_LABEL_DETECTION),
        storage_config=storage_config,
    )

    # config_request should be the first in the stream of requests.
    config_request = videointelligence.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(path, "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.StreamingAnnotateVideoRequest(input_content=chunk)

    requests = stream_generator()

    # streaming_annotate_video returns a generator.
    # The default timeout is about 300 seconds.
    # To process longer videos it should be set to
    # larger than the length (in seconds) of the stream.
    responses = client.streaming_annotate_video(requests, timeout=600)

    for response in responses:
        # Check for errors.
        if response.error.message:
            print(response.error.message)
            break

        print("Storage URI: {}".format(response.annotation_results_uri))
    # [END video_streaming_annotation_to_storage_beta]


def streaming_automl_classification(path, project_id, model_id):
    # [START video_streaming_automl_classification_beta]
    import io

    from google.cloud import videointelligence_v1p3beta1 as videointelligence

    # path = 'path_to_file'
    # project_id = 'gcp_project_id'
    # model_id = 'automl_classification_model_id'

    client = videointelligence.StreamingVideoIntelligenceServiceClient()

    model_path = "projects/{}/locations/us-central1/models/{}".format(
        project_id, model_id
    )

    # Here we use classification as an example.
    automl_config = videointelligence.StreamingAutomlClassificationConfig(
        model_name=model_path
    )

    video_config = videointelligence.StreamingVideoConfig(
        feature=videointelligence.StreamingFeature.STREAMING_AUTOML_CLASSIFICATION,
        automl_classification_config=automl_config,
    )

    # config_request should be the first in the stream of requests.
    config_request = videointelligence.StreamingAnnotateVideoRequest(
        video_config=video_config
    )

    # Set the chunk size to 5MB (recommended less than 10MB).
    chunk_size = 5 * 1024 * 1024

    # Load file content.
    # Note: Input videos must have supported video codecs. See
    # https://cloud.google.com/video-intelligence/docs/streaming/streaming#supported_video_codecs
    # for more details.
    stream = []
    with io.open(path, "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.StreamingAnnotateVideoRequest(input_content=chunk)

    requests = stream_generator()

    # streaming_annotate_video returns a generator.
    # The default timeout is about 300 seconds.
    # To process longer videos it should be set to
    # larger than the length (in seconds) of the stream.
    responses = client.streaming_annotate_video(requests, timeout=600)

    for response in responses:
        # Check for errors.
        if response.error.message:
            print(response.error.message)
            break

        for label in response.annotation_results.label_annotations:
            for frame in label.frames:
                print(
                    "At {:3d}s segment, {:5.1%} {}".format(
                        frame.time_offset.seconds,
                        frame.confidence,
                        label.entity.entity_id,
                    )
                )
    # [END video_streaming_automl_classification_beta]


def streaming_automl_object_tracking(path, project_id, model_id):
    # [START video_streaming_automl_object_tracking_beta]
    import io

    from google.cloud import videointelligence_v1p3beta1 as videointelligence

    # path = 'path_to_file'
    # project_id = 'project_id'
    # model_id = 'automl_object_tracking_model_id'

    client = videointelligence.StreamingVideoIntelligenceServiceClient()

    model_path = "projects/{}/locations/us-central1/models/{}".format(
        project_id, model_id
    )

    automl_config = videointelligence.StreamingAutomlObjectTrackingConfig(
        model_name=model_path
    )

    video_config = videointelligence.StreamingVideoConfig(
        feature=videointelligence.StreamingFeature.STREAMING_AUTOML_OBJECT_TRACKING,
        automl_object_tracking_config=automl_config,
    )

    # config_request should be the first in the stream of requests.
    config_request = videointelligence.StreamingAnnotateVideoRequest(
        video_config=video_config
    )

    # Set the chunk size to 5MB (recommended less than 10MB).
    chunk_size = 5 * 1024 * 1024

    # Load file content.
    # Note: Input videos must have supported video codecs. See
    # https://cloud.google.com/video-intelligence/docs/streaming/streaming#supported_video_codecs
    # for more details.
    stream = []
    with io.open(path, "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.StreamingAnnotateVideoRequest(input_content=chunk)

    requests = stream_generator()

    # streaming_annotate_video returns a generator.
    # The default timeout is about 300 seconds.
    # To process longer videos it should be set to
    # larger than the length (in seconds) of the stream.
    responses = client.streaming_annotate_video(requests, timeout=900)

    # 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

        object_annotations = response.annotation_results.object_annotations

        # object_annotations could be empty
        if not object_annotations:
            continue

        for annotation in object_annotations:
            # Each annotation has one frame, which has a timeoffset.
            frame = annotation.frames[0]
            time_offset = (
                frame.time_offset.seconds + frame.time_offset.microseconds / 1e6
            )

            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("{}s".format(time_offset))
            print("\tEntity description: {}".format(description))
            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
            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))
    # [END video_streaming_automl_object_tracking_beta]


def streaming_automl_action_recognition(path, project_id, model_id):
    # [START video_streaming_automl_action_recognition_beta]
    import io

    from google.cloud import videointelligence_v1p3beta1 as videointelligence

    # path = 'path_to_file'
    # project_id = 'project_id'
    # model_id = 'automl_action_recognition_model_id'

    client = videointelligence.StreamingVideoIntelligenceServiceClient()

    model_path = "projects/{}/locations/us-central1/models/{}".format(
        project_id, model_id
    )

    automl_config = videointelligence.StreamingAutomlActionRecognitionConfig(
        model_name=model_path
    )

    video_config = videointelligence.StreamingVideoConfig(
        feature=videointelligence.StreamingFeature.STREAMING_AUTOML_ACTION_RECOGNITION,
        automl_action_recognition_config=automl_config,
    )

    # config_request should be the first in the stream of requests.
    config_request = videointelligence.StreamingAnnotateVideoRequest(
        video_config=video_config
    )

    # Set the chunk size to 5MB (recommended less than 10MB).
    chunk_size = 5 * 1024 * 1024

    def stream_generator():
        yield config_request
        # Load file content.
        # Note: Input videos must have supported video codecs. See
        # https://cloud.google.com/video-intelligence/docs/streaming/streaming#supported_video_codecs
        # for more details.
        with io.open(path, "rb") as video_file:
            while True:
                data = video_file.read(chunk_size)
                if not data:
                    break
                yield videointelligence.StreamingAnnotateVideoRequest(
                    input_content=data
                )

    requests = stream_generator()

    # streaming_annotate_video returns a generator.
    # The default timeout is about 300 seconds.
    # To process longer videos it should be set to
    # larger than the length (in seconds) of the video.
    responses = client.streaming_annotate_video(requests, timeout=900)

    # 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 label in response.annotation_results.label_annotations:
            for frame in label.frames:
                print(
                    "At {:3d}s segment, {:5.1%} {}".format(
                        frame.time_offset.seconds,
                        frame.confidence,
                        label.entity.entity_id,
                    )
                )
    # [END video_streaming_automl_action_recognition_beta]


if __name__ == "__main__":
    parser = argparse.ArgumentParser(
        description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter
    )
    subparsers = parser.add_subparsers(dest="command")

    speech_transcription_parser = subparsers.add_parser(
        "transcription", help=speech_transcription.__doc__
    )
    speech_transcription_parser.add_argument("gcs_uri")

    video_text_gcs_parser = subparsers.add_parser(
        "video-text-gcs", help=video_detect_text_gcs.__doc__
    )
    video_text_gcs_parser.add_argument("gcs_uri")

    video_text_parser = subparsers.add_parser(
        "video-text", help=video_detect_text.__doc__
    )
    video_text_parser.add_argument("path")

    video_streaming_labels_parser = subparsers.add_parser(
        "streaming-labels", help=detect_labels_streaming.__doc__
    )
    video_streaming_labels_parser.add_argument("path")

    video_streaming_shot_change_parser = subparsers.add_parser(
        "streaming-shot-change", help=detect_shot_change_streaming.__doc__
    )
    video_streaming_shot_change_parser.add_argument("path")

    video_streaming_objects_parser = subparsers.add_parser(
        "streaming-objects", help=track_objects_streaming.__doc__
    )
    video_streaming_objects_parser.add_argument("path")

    video_streaming_explicit_content_parser = subparsers.add_parser(
        "streaming-explicit-content", help=detect_explicit_content_streaming.__doc__
    )
    video_streaming_explicit_content_parser.add_argument("path")

    video_streaming_annotation_to_storage_parser = subparsers.add_parser(
        "streaming-annotation-storage", help=annotation_to_storage_streaming.__doc__
    )
    video_streaming_annotation_to_storage_parser.add_argument("path")
    video_streaming_annotation_to_storage_parser.add_argument("output_uri")

    video_streaming_automl_classification_parser = subparsers.add_parser(
        "streaming-automl-classification", help=streaming_automl_classification.__doc__
    )
    video_streaming_automl_classification_parser.add_argument("path")
    video_streaming_automl_classification_parser.add_argument("project_id")
    video_streaming_automl_classification_parser.add_argument("model_id")

    video_streaming_automl_object_tracking_parser = subparsers.add_parser(
        "streaming-automl-object-tracking",
        help=streaming_automl_object_tracking.__doc__,
    )
    video_streaming_automl_object_tracking_parser.add_argument("path")
    video_streaming_automl_object_tracking_parser.add_argument("project_id")
    video_streaming_automl_object_tracking_parser.add_argument("model_id")

    video_streaming_automl_action_recognition_parser = subparsers.add_parser(
        "streaming-automl-action-recognition",
        help=streaming_automl_action_recognition.__doc__,
    )
    video_streaming_automl_action_recognition_parser.add_argument("path")
    video_streaming_automl_action_recognition_parser.add_argument("project_id")
    video_streaming_automl_action_recognition_parser.add_argument("model_id")

    args = parser.parse_args()

    if args.command == "transcription":
        speech_transcription(args.gcs_uri)
    elif args.command == "video-text-gcs":
        video_detect_text_gcs(args.gcs_uri)
    elif args.command == "video-text":
        video_detect_text(args.path)
    elif args.command == "streaming-labels":
        detect_labels_streaming(args.path)
    elif args.command == "streaming-shot-change":
        detect_shot_change_streaming(args.path)
    elif args.command == "streaming-objects":
        track_objects_streaming(args.path)
    elif args.command == "streaming-explicit-content":
        detect_explicit_content_streaming(args.path)
    elif args.command == "streaming-annotation-storage":
        annotation_to_storage_streaming(args.path, args.output_uri)
    elif args.command == "streaming-automl-classification":
        streaming_automl_classification(args.path, args.project_id, args.model_id)
    elif args.command == "streaming-automl-object-tracking":
        streaming_automl_object_tracking(args.path, args.project_id, args.model_id)
    elif args.command == "streaming-automl-action-recognition":
        streaming_automl_action_recognition(args.path, args.project_id, args.model_id)