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Commit e59e15b

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dizcologychenyumic
dizcology
authored and
chenyumic
committed
[DO NOT MERGE] remove face detection samples (GoogleCloudPlatform#1435)
* remove face detection samples * update docstring * linter * linter
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‎video/cloud-client/analyze/beta_snippets.py

Copy file name to clipboardExpand all lines: video/cloud-client/analyze/beta_snippets.py
+3-122Lines changed: 3 additions & 122 deletions
Original file line numberDiff line numberDiff line change
@@ -14,16 +14,10 @@
1414
# See the License for the specific language governing permissions and
1515
# limitations under the License.
1616

17-
"""This application demonstrates face detection, face emotions
18-
and speech transcription using the Google Cloud API.
17+
"""This application demonstrates speech transcription using the
18+
Google Cloud API.
1919
2020
Usage Examples:
21-
python beta_snippets.py boxes \
22-
gs://python-docs-samples-tests/video/googlework_short.mp4
23-
24-
python beta_snippets.py \
25-
emotions gs://python-docs-samples-tests/video/googlework_short.mp4
26-
2721
python beta_snippets.py \
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transcription gs://python-docs-samples-tests/video/googlework_short.mp4
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"""
@@ -33,108 +27,6 @@
3327
from google.cloud import videointelligence_v1p1beta1 as videointelligence
3428

3529

36-
# [START video_face_bounding_boxes]
37-
def face_bounding_boxes(gcs_uri):
38-
""" Detects faces' bounding boxes. """
39-
video_client = videointelligence.VideoIntelligenceServiceClient()
40-
features = [videointelligence.enums.Feature.FACE_DETECTION]
41-
42-
config = videointelligence.types.FaceConfig(
43-
include_bounding_boxes=True)
44-
context = videointelligence.types.VideoContext(
45-
face_detection_config=config)
46-
47-
operation = video_client.annotate_video(
48-
gcs_uri, features=features, video_context=context)
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print('\nProcessing video for face annotations:')
50-
51-
result = operation.result(timeout=900)
52-
print('\nFinished processing.')
53-
54-
# There is only one result because a single video was processed.
55-
faces = result.annotation_results[0].face_detection_annotations
56-
for i, face in enumerate(faces):
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print('Face {}'.format(i))
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59-
# Each face_detection_annotation has only one segment.
60-
segment = face.segments[0]
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start_time = (segment.segment.start_time_offset.seconds +
62-
segment.segment.start_time_offset.nanos / 1e9)
63-
end_time = (segment.segment.end_time_offset.seconds +
64-
segment.segment.end_time_offset.nanos / 1e9)
65-
positions = '{}s to {}s'.format(start_time, end_time)
66-
print('\tSegment: {}\n'.format(positions))
67-
68-
# Each detected face may appear in many frames of the video.
69-
# Here we process only the first frame.
70-
frame = face.frames[0]
71-
72-
time_offset = (frame.time_offset.seconds +
73-
frame.time_offset.nanos / 1e9)
74-
box = frame.attributes[0].normalized_bounding_box
75-
76-
print('First frame time offset: {}s\n'.format(time_offset))
77-
78-
print('First frame normalized bounding box:')
79-
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: {}'.format(box.bottom))
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print('\n')
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# [END video_face_bounding_boxes]
85-
86-
87-
# [START video_face_emotions]
88-
def face_emotions(gcs_uri):
89-
""" Analyze faces' emotions over frames. """
90-
video_client = videointelligence.VideoIntelligenceServiceClient()
91-
features = [videointelligence.enums.Feature.FACE_DETECTION]
92-
93-
config = videointelligence.types.FaceConfig(
94-
include_emotions=True)
95-
context = videointelligence.types.VideoContext(
96-
face_detection_config=config)
97-
98-
operation = video_client.annotate_video(
99-
gcs_uri, features=features, video_context=context)
100-
print('\nProcessing video for face annotations:')
101-
102-
result = operation.result(timeout=600)
103-
print('\nFinished processing.')
104-
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# There is only one result because a single video was processed.
106-
faces = result.annotation_results[0].face_detection_annotations
107-
for i, face in enumerate(faces):
108-
for j, frame in enumerate(face.frames):
109-
time_offset = (frame.time_offset.seconds +
110-
frame.time_offset.nanos / 1e9)
111-
emotions = frame.attributes[0].emotions
112-
113-
print('Face {}, frame {}, time_offset {}\n'.format(
114-
i, j, time_offset))
115-
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# from videointelligence.enums
117-
emotion_labels = (
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'EMOTION_UNSPECIFIED', 'AMUSEMENT', 'ANGER',
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'CONCENTRATION', 'CONTENTMENT', 'DESIRE',
120-
'DISAPPOINTMENT', 'DISGUST', 'ELATION',
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'EMBARRASSMENT', 'INTEREST', 'PRIDE', 'SADNESS',
122-
'SURPRISE')
123-
124-
for emotion in emotions:
125-
emotion_index = emotion.emotion
126-
emotion_label = emotion_labels[emotion_index]
127-
emotion_score = emotion.score
128-
129-
print('emotion: {} (confidence score: {})'.format(
130-
emotion_label, emotion_score))
131-
132-
print('\n')
133-
134-
print('\n')
135-
# [END video_face_emotions]
136-
137-
13830
# [START video_speech_transcription]
13931
def speech_transcription(input_uri):
14032
"""Transcribe speech from a video stored on GCS."""
@@ -181,23 +73,12 @@ def speech_transcription(input_uri):
18173
description=__doc__,
18274
formatter_class=argparse.RawDescriptionHelpFormatter)
18375
subparsers = parser.add_subparsers(dest='command')
184-
analyze_faces_parser = subparsers.add_parser(
185-
'boxes', help=face_bounding_boxes.__doc__)
186-
analyze_faces_parser.add_argument('gcs_uri')
187-
188-
analyze_emotions_parser = subparsers.add_parser(
189-
'emotions', help=face_emotions.__doc__)
190-
analyze_emotions_parser.add_argument('gcs_uri')
19176

19277
speech_transcription_parser = subparsers.add_parser(
19378
'transcription', help=speech_transcription.__doc__)
19479
speech_transcription_parser.add_argument('gcs_uri')
19580

19681
args = parser.parse_args()
19782

198-
if args.command == 'boxes':
199-
face_bounding_boxes(args.gcs_uri)
200-
elif args.command == 'emotions':
201-
face_emotions(args.gcs_uri)
202-
elif args.command == 'transcription':
83+
if args.command == 'transcription':
20384
speech_transcription(args.gcs_uri)

‎video/cloud-client/analyze/beta_snippets_test.py

Copy file name to clipboardExpand all lines: video/cloud-client/analyze/beta_snippets_test.py
+2-19Lines changed: 2 additions & 19 deletions
Original file line numberDiff line numberDiff line change
@@ -20,30 +20,13 @@
2020

2121
import beta_snippets
2222

23-
2423
BUCKET = os.environ['CLOUD_STORAGE_BUCKET']
25-
FACES_SHORT_FILE_PATH = 'video/googlework_short.mp4'
26-
27-
28-
@pytest.mark.slow
29-
def test_face_bounding_boxes(capsys):
30-
beta_snippets.face_bounding_boxes(
31-
'gs://{}/{}'.format(BUCKET, FACES_SHORT_FILE_PATH))
32-
out, _ = capsys.readouterr()
33-
assert 'top :' in out
34-
35-
36-
@pytest.mark.slow
37-
def test_face_emotions(capsys):
38-
beta_snippets.face_emotions(
39-
'gs://{}/{}'.format(BUCKET, FACES_SHORT_FILE_PATH))
40-
out, _ = capsys.readouterr()
41-
assert 'CONCENTRATION' in out
24+
FILE_PATH = 'video/googlework_short.mp4'
4225

4326

4427
@pytest.mark.slow
4528
def test_speech_transcription(capsys):
4629
beta_snippets.speech_transcription(
47-
'gs://{}/{}'.format(BUCKET, FACES_SHORT_FILE_PATH))
30+
'gs://{}/{}'.format(BUCKET, FILE_PATH))
4831
out, _ = capsys.readouterr()
4932
assert 'cultural' in out

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