chore(video): delete samples in beta_snippets.py (#13193)

* chore(video): skip tests without required permissions

- Find details in b/330632499

* chore(video): delete sample 'video_detect_text_beta'

* chore(video): delete sample 'video_detect_text_gcs_beta'

* chore(video): delete sample 'video_speech_transcription_gcs_beta'

Author: eapl-gemugami

videointelligence/samples/analyze/beta_snippets.py

@@ -18,12 +18,6 @@
 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
@@ -49,169 +43,6 @@
 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
@@ -826,21 +657,6 @@ def stream_generator():
     )
     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__
     )
@@ -892,13 +708,7 @@ def stream_generator():
 
     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":
+    if args.command == "streaming-labels":
         detect_labels_streaming(args.path)
     elif args.command == "streaming-shot-change":
         detect_shot_change_streaming(args.path)

videointelligence/samples/analyze/beta_snippets_test.py

@@ -69,14 +69,7 @@ def delete_bucket():
     delete_bucket()
 
 
-def test_speech_transcription(capsys):
-    beta_snippets.speech_transcription(
-        "gs://python-docs-samples-tests/video/googlework_short.mp4", timeout=240
-    )
-    out, _ = capsys.readouterr()
-    assert "cultural" in out
-
-
+@pytest.mark.skip(reason="b/330632499")
 @pytest.mark.flaky(max_runs=3, min_passes=1)
 def test_detect_labels_streaming(capsys, video_path):
     beta_snippets.detect_labels_streaming(video_path)
@@ -85,13 +78,15 @@ def test_detect_labels_streaming(capsys, video_path):
     assert "cat" in out
 
 
+@pytest.mark.skip(reason="b/330632499")
 def test_detect_shot_change_streaming(capsys, video_path):
     beta_snippets.detect_shot_change_streaming(video_path)
 
     out, _ = capsys.readouterr()
     assert "Shot" in out
 
 
+@pytest.mark.skip(reason="b/330632499")
 # Flaky ServiceUnavailable
 @pytest.mark.flaky(max_runs=3, min_passes=1)
 def test_track_objects_streaming(capsys, video_path):
@@ -101,6 +96,7 @@ def test_track_objects_streaming(capsys, video_path):
     assert "cat" in out
 
 
+@pytest.mark.skip(reason="b/330632499")
 @pytest.mark.flaky(max_runs=3, min_passes=1)
 def test_detect_explicit_content_streaming(capsys, video_path):
     beta_snippets.detect_explicit_content_streaming(video_path)
@@ -109,6 +105,7 @@ def test_detect_explicit_content_streaming(capsys, video_path):
     assert "Time" in out
 
 
+@pytest.mark.skip(reason="b/330632499")
 @pytest.mark.flaky(max_runs=3, min_passes=1)
 def test_annotation_to_storage_streaming(capsys, video_path, bucket):
     output_uri = "gs://{}".format(bucket.name)
@@ -118,24 +115,7 @@ def test_annotation_to_storage_streaming(capsys, video_path, bucket):
     assert "Storage" in out
 
 
-# Flaky timeout
-@pytest.mark.flaky(max_runs=3, min_passes=1)
-def test_detect_text(capsys):
-    in_file = "./resources/googlework_tiny.mp4"
-    beta_snippets.video_detect_text(in_file)
-    out, _ = capsys.readouterr()
-    assert "Text" in out
-
-
-# Flaky timeout
-@pytest.mark.flaky(max_runs=3, min_passes=1)
-def test_detect_text_gcs(capsys):
-    in_file = "gs://python-docs-samples-tests/video/googlework_tiny.mp4"
-    beta_snippets.video_detect_text_gcs(in_file)
-    out, _ = capsys.readouterr()
-    assert "Text" in out
-
-
+@pytest.mark.skip(reason="b/330632499")
 # Flaky Gateway
 @pytest.mark.flaky(max_runs=3, min_passes=1)
 def test_streaming_automl_classification(capsys, video_path):
@@ -146,6 +126,7 @@ def test_streaming_automl_classification(capsys, video_path):
     assert "brush_hair" in out
 
 
+@pytest.mark.skip(reason="b/330632499")
 # Flaky Gateway
 @pytest.mark.flaky(max_runs=3, min_passes=1)
 def test_streaming_automl_object_tracking(capsys, video_path):
@@ -156,6 +137,7 @@ def test_streaming_automl_object_tracking(capsys, video_path):
     assert "Track Id" in out
 
 
+@pytest.mark.skip(reason="b/330632499")
 # Flaky Gateway
 @pytest.mark.flaky(max_runs=3, min_passes=1)
 def test_streaming_automl_action_recognition(capsys, video_path):