Face detection beta features

video/cloud-client/analyze/beta_snippets.py

@@ -0,0 +1,203 @@
+#!/usr/bin/env python
+
+# Copyright 2017 Google Inc. 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 face detection, face emotions
+and 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
+"""
+
+import argparse
+
+from google.cloud import videointelligence_v1p1beta1 as videointelligence
+
+
+# [START video_face_bounding_boxes]
+def face_bounding_boxes(path):
+    """ 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(
+        path, 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 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(path):
+    """ 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(
+        path, 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):
+        print('Face {}'.format(i))
+
+        frame_emotions = []
+        for frame in face.frames:
+            time_offset = (frame.time_offset.seconds +
+                           frame.time_offset.nanos / 1e9)
+            emotions = frame.attributes[0].emotions
+
+            # from videointelligence.enums
+            emotion_labels = (
+                'EMOTION_UNSPECIFIED', 'AMUSEMENT', 'ANGER',
+                'CONCENTRATION', 'CONTENTMENT', 'DESIRE',
+                'DISAPPOINTMENT', 'DISGUST', 'ELATION',
+                'EMBARRASSMENT', 'INTEREST', 'PRIDE', 'SADNESS',
+                'SURPRISE')
+
+            emotion, score = sorted(
+                [(em.emotion, em.score) for em in emotions],
+                key=lambda p: p[1])[-1]
+            emotion_label = emotion_labels[emotion]
+
+            frame_emotions.append((time_offset, emotion_label, score))
+
+        for time_offset, emotion_label, score in frame_emotions:
+            print('\t{:04.2f}s: {:14}({:4.3f})'.format(
+                time_offset, emotion_label, score))
+        print('\n')
+# [END video_face_emotions]
+
+
+# [START video_speech_transcription]
+def speech_transcription(input_uri):
+    """Transcribe speech from a video stored on GCS."""
+    video_client = videointelligence.VideoIntelligenceServiceClient()
+
+    features = [videointelligence.enums.Feature.SPEECH_TRANSCRIPTION]
+
+    config = videointelligence.types.SpeechTranscriptionConfig(
+        language_code='en-US')
+    video_context = videointelligence.types.VideoContext(
+        speech_transcription_config=config)
+
+    operation = video_client.annotate_video(
+        input_uri, features=features,
+        video_context=video_context)
+
+    print('\nProcessing video for speech transcription.')
+
+    result = operation.result(timeout=180)
+
+    # There is only one annotation_result since only
+    # one video is processed.
+    annotation_results = result.annotation_results[0]
+    speech_transcription = annotation_results.speech_transcriptions[0]
+    alternative = speech_transcription.alternatives[0]
+
+    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.nanos * 1e-9,
+            end_time.seconds + end_time.nanos * 1e-9,
+            word))
+# [END video_speech_transcription]
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(
+        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('path')
+
+    analyze_emotions_parser = subparsers.add_parser(
+        'emotions', help=face_emotions.__doc__)
+    analyze_emotions_parser.add_argument('path')
+
+    speech_transcription_parser = subparsers.add_parser(
+        'transcription', help=speech_transcription.__doc__)
+    speech_transcription_parser.add_argument('path')
+
+    args = parser.parse_args()
+
+    if args.command == 'boxes':
+        face_bounding_boxes(args.path)
+    elif args.command == 'emotions':
+        face_emotions(args.path)
+    elif args.command == 'transcription':
+        speech_transcription(args.path)

video/cloud-client/analyze/beta_snippets_test.py

@@ -0,0 +1,49 @@
+#!/usr/bin/env python
+
+# Copyright 2017 Google, Inc
+#
+# 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.
+
+import os
+
+import pytest
+
+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
+
+
+@pytest.mark.slow
+def test_speech_transcription(capsys):
+    beta_snippets.speech_transcription(
+        'gs://{}/{}'.format(BUCKET, FACES_SHORT_FILE_PATH))
+    out, _ = capsys.readouterr()
+    assert 'cultural' in out

video/cloud-client/analyze/requirements.txt

@@ -1 +1 @@
-google-cloud-videointelligence==1.0.1
+google-cloud-videointelligence==1.1.0