[DO NOT MERGE] remove face detection samples [(#1435)](#1435)

* remove face detection samples

* update docstring

* linter

* linter

Author: dizcology

videointelligence/samples/analyze/beta_snippets.py

@@ -14,16 +14,10 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-"""This application demonstrates face detection, face emotions
-and speech transcription using the Google Cloud API.
+"""This application demonstrates speech transcription using the
+Google Cloud API.
 
 Usage Examples:
-    python beta_snippets.py boxes \
-    gs://python-docs-samples-tests/video/googlework_short.mp4
-
-    python beta_snippets.py \
-    emotions gs://python-docs-samples-tests/video/googlework_short.mp4
-
     python beta_snippets.py \
     transcription gs://python-docs-samples-tests/video/googlework_short.mp4
 """
@@ -33,108 +27,6 @@
 from google.cloud import videointelligence_v1p1beta1 as videointelligence
 
 
-# [START video_face_bounding_boxes]
-def face_bounding_boxes(gcs_uri):
-    """ Detects faces' bounding boxes. """
-    video_client = videointelligence.VideoIntelligenceServiceClient()
-    features = [videointelligence.enums.Feature.FACE_DETECTION]
-
-    config = videointelligence.types.FaceConfig(
-        include_bounding_boxes=True)
-    context = videointelligence.types.VideoContext(
-        face_detection_config=config)
-
-    operation = video_client.annotate_video(
-        gcs_uri, features=features, video_context=context)
-    print('\nProcessing video for face annotations:')
-
-    result = operation.result(timeout=900)
-    print('\nFinished processing.')
-
-    # There is only one result because a single video was processed.
-    faces = result.annotation_results[0].face_detection_annotations
-    for i, face in enumerate(faces):
-        print('Face {}'.format(i))
-
-        # Each face_detection_annotation has only one segment.
-        segment = face.segments[0]
-        start_time = (segment.segment.start_time_offset.seconds +
-                      segment.segment.start_time_offset.nanos / 1e9)
-        end_time = (segment.segment.end_time_offset.seconds +
-                    segment.segment.end_time_offset.nanos / 1e9)
-        positions = '{}s to {}s'.format(start_time, end_time)
-        print('\tSegment: {}\n'.format(positions))
-
-        # Each detected face may appear in many frames of the video.
-        # Here we process only the first frame.
-        frame = face.frames[0]
-
-        time_offset = (frame.time_offset.seconds +
-                       frame.time_offset.nanos / 1e9)
-        box = frame.attributes[0].normalized_bounding_box
-
-        print('First frame time offset: {}s\n'.format(time_offset))
-
-        print('First frame normalized bounding box:')
-        print('\tleft  : {}'.format(box.left))
-        print('\ttop   : {}'.format(box.top))
-        print('\tright : {}'.format(box.right))
-        print('\tbottom: {}'.format(box.bottom))
-        print('\n')
-# [END video_face_bounding_boxes]
-
-
-# [START video_face_emotions]
-def face_emotions(gcs_uri):
-    """ Analyze faces' emotions over frames. """
-    video_client = videointelligence.VideoIntelligenceServiceClient()
-    features = [videointelligence.enums.Feature.FACE_DETECTION]
-
-    config = videointelligence.types.FaceConfig(
-        include_emotions=True)
-    context = videointelligence.types.VideoContext(
-        face_detection_config=config)
-
-    operation = video_client.annotate_video(
-        gcs_uri, features=features, video_context=context)
-    print('\nProcessing video for face annotations:')
-
-    result = operation.result(timeout=600)
-    print('\nFinished processing.')
-
-    # There is only one result because a single video was processed.
-    faces = result.annotation_results[0].face_detection_annotations
-    for i, face in enumerate(faces):
-        for j, frame in enumerate(face.frames):
-            time_offset = (frame.time_offset.seconds +
-                           frame.time_offset.nanos / 1e9)
-            emotions = frame.attributes[0].emotions
-
-            print('Face {}, frame {}, time_offset {}\n'.format(
-                i, j, time_offset))
-
-            # from videointelligence.enums
-            emotion_labels = (
-                'EMOTION_UNSPECIFIED', 'AMUSEMENT', 'ANGER',
-                'CONCENTRATION', 'CONTENTMENT', 'DESIRE',
-                'DISAPPOINTMENT', 'DISGUST', 'ELATION',
-                'EMBARRASSMENT', 'INTEREST', 'PRIDE', 'SADNESS',
-                'SURPRISE')
-
-            for emotion in emotions:
-                emotion_index = emotion.emotion
-                emotion_label = emotion_labels[emotion_index]
-                emotion_score = emotion.score
-
-                print('emotion: {} (confidence score: {})'.format(
-                    emotion_label, emotion_score))
-
-            print('\n')
-
-        print('\n')
-# [END video_face_emotions]
-
-
 # [START video_speech_transcription]
 def speech_transcription(input_uri):
     """Transcribe speech from a video stored on GCS."""
@@ -181,23 +73,12 @@ def speech_transcription(input_uri):
         description=__doc__,
         formatter_class=argparse.RawDescriptionHelpFormatter)
     subparsers = parser.add_subparsers(dest='command')
-    analyze_faces_parser = subparsers.add_parser(
-        'boxes', help=face_bounding_boxes.__doc__)
-    analyze_faces_parser.add_argument('gcs_uri')
-
-    analyze_emotions_parser = subparsers.add_parser(
-        'emotions', help=face_emotions.__doc__)
-    analyze_emotions_parser.add_argument('gcs_uri')
 
     speech_transcription_parser = subparsers.add_parser(
         'transcription', help=speech_transcription.__doc__)
     speech_transcription_parser.add_argument('gcs_uri')
 
     args = parser.parse_args()
 
-    if args.command == 'boxes':
-        face_bounding_boxes(args.gcs_uri)
-    elif args.command == 'emotions':
-        face_emotions(args.gcs_uri)
-    elif args.command == 'transcription':
+    if args.command == 'transcription':
         speech_transcription(args.gcs_uri)

videointelligence/samples/analyze/beta_snippets_test.py

@@ -20,30 +20,13 @@
 
 import beta_snippets
 
-
 BUCKET = os.environ['CLOUD_STORAGE_BUCKET']
-FACES_SHORT_FILE_PATH = 'video/googlework_short.mp4'
-
-
-@pytest.mark.slow
-def test_face_bounding_boxes(capsys):
-    beta_snippets.face_bounding_boxes(
-        'gs://{}/{}'.format(BUCKET, FACES_SHORT_FILE_PATH))
-    out, _ = capsys.readouterr()
-    assert 'top   :' in out
-
-
-@pytest.mark.slow
-def test_face_emotions(capsys):
-    beta_snippets.face_emotions(
-        'gs://{}/{}'.format(BUCKET, FACES_SHORT_FILE_PATH))
-    out, _ = capsys.readouterr()
-    assert 'CONCENTRATION' in out
+FILE_PATH = 'video/googlework_short.mp4'
 
 
 @pytest.mark.slow
 def test_speech_transcription(capsys):
     beta_snippets.speech_transcription(
-        'gs://{}/{}'.format(BUCKET, FACES_SHORT_FILE_PATH))
+        'gs://{}/{}'.format(BUCKET, FILE_PATH))
     out, _ = capsys.readouterr()
     assert 'cultural' in out