Remove video face [(#1348)](#1348)

Author: dizcology

videointelligence/samples/analyze/README.rst

@@ -12,7 +12,7 @@ This directory contains samples for Google Cloud Video Intelligence API. `Google
 
 
 
-.. _Google Cloud Video Intelligence API: https://cloud.google.com/video-intelligence/docs 
+.. _Google Cloud Video Intelligence API: https://cloud.google.com/video-intelligence/docs
 
 Setup
 -------------------------------------------------------------------------------
@@ -70,22 +70,20 @@ To run this sample:
 
     $ python analyze.py
 
-    usage: analyze.py [-h] {faces,labels,labels_file,explicit_content,shots} ...
+    usage: analyze.py [-h] {labels,labels_file,explicit_content,shots} ...
 
-    This application demonstrates face detection, label detection,
+    This application demonstrates label detection,
     explicit content, and shot change detection using the Google Cloud API.
 
     Usage Examples:
 
-        python analyze.py faces gs://demomaker/google_gmail.mp4
         python analyze.py labels gs://cloud-ml-sandbox/video/chicago.mp4
         python analyze.py labels_file resources/cat.mp4
         python analyze.py shots gs://demomaker/gbikes_dinosaur.mp4
         python analyze.py explicit_content gs://demomaker/gbikes_dinosaur.mp4
 
     positional arguments:
-      {faces,labels,labels_file,explicit_content,shots}
-        faces               Detects faces given a GCS path.
+      {labels,labels_file,explicit_content,shots}
         labels              Detects labels given a GCS path.
         labels_file         Detect labels given a file path.
         explicit_content    Detects explicit content from the GCS path to a video.

videointelligence/samples/analyze/analyze.py

@@ -14,12 +14,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-"""This application demonstrates face detection, label detection,
+"""This application demonstrates label detection,
 explicit content, and shot change detection using the Google Cloud API.
 
 Usage Examples:
 
-    python analyze.py faces gs://demomaker/google_gmail.mp4
     python analyze.py labels gs://cloud-ml-sandbox/video/chicago.mp4
     python analyze.py labels_file resources/cat.mp4
     python analyze.py shots gs://demomaker/gbikes_dinosaur.mp4
@@ -55,52 +54,6 @@ def analyze_explicit_content(path):
             likely_string[frame.pornography_likelihood]))
 
 
-def analyze_faces(path):
-    """ Detects faces given a GCS path. """
-    video_client = videointelligence.VideoIntelligenceServiceClient()
-    features = [videointelligence.enums.Feature.FACE_DETECTION]
-
-    config = videointelligence.types.FaceDetectionConfig(
-        include_bounding_boxes=True)
-    context = videointelligence.types.VideoContext(
-        face_detection_config=config)
-
-    operation = video_client.annotate_video(
-        path, features=features, video_context=context)
-    print('\nProcessing video for face annotations:')
-
-    result = operation.result(timeout=600)
-    print('\nFinished processing.')
-
-    # first result is retrieved because a single video was processed
-    faces = result.annotation_results[0].face_annotations
-    for face_id, face in enumerate(faces):
-        print('Face {}'.format(face_id))
-        print('Thumbnail size: {}'.format(len(face.thumbnail)))
-
-        for segment_id, segment in enumerate(face.segments):
-            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 {}: {}'.format(segment_id, positions))
-
-        # There are typically many frames for each face,
-        # here we print information on only the first frame.
-        frame = face.frames[0]
-        time_offset = (frame.time_offset.seconds +
-                       frame.time_offset.nanos / 1e9)
-        box = frame.normalized_bounding_boxes[0]
-        print('First frame time offset: {}s'.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')
-
-
 def analyze_labels(path):
     """ Detects labels given a GCS path. """
     video_client = videointelligence.VideoIntelligenceServiceClient()
@@ -275,9 +228,6 @@ def analyze_shots(path):
         description=__doc__,
         formatter_class=argparse.RawDescriptionHelpFormatter)
     subparsers = parser.add_subparsers(dest='command')
-    analyze_faces_parser = subparsers.add_parser(
-        'faces', help=analyze_faces.__doc__)
-    analyze_faces_parser.add_argument('path')
     analyze_labels_parser = subparsers.add_parser(
         'labels', help=analyze_labels.__doc__)
     analyze_labels_parser.add_argument('path')
@@ -293,8 +243,6 @@ def analyze_shots(path):
 
     args = parser.parse_args()
 
-    if args.command == 'faces':
-        analyze_faces(args.path)
     if args.command == 'labels':
         analyze_labels(args.path)
     if args.command == 'labels_file':

videointelligence/samples/analyze/analyze_test.py

@@ -23,7 +23,6 @@
 
 BUCKET = os.environ['CLOUD_STORAGE_BUCKET']
 LABELS_FILE_PATH = '/video/cat.mp4'
-FACES_FILE_PATH = '/video/googlework.mp4'
 EXPLICIT_CONTENT_FILE_PATH = '/video/cat.mp4'
 SHOTS_FILE_PATH = '/video/gbikes_dinosaur.mp4'
 
@@ -36,16 +35,6 @@ def test_analyze_shots(capsys):
     assert 'Shot 1:' in out
 
 
-@pytest.mark.xfail(reason='This feature is currently \
-    not visible to all projects.')
-@pytest.mark.slow
-def test_analyze_faces(capsys):
-    analyze.analyze_faces(
-        'gs://{}{}'.format(BUCKET, FACES_FILE_PATH))
-    out, _ = capsys.readouterr()
-    assert 'Thumbnail' in out
-
-
 @pytest.mark.slow
 def test_analyze_labels(capsys):
     analyze.analyze_labels(