Video v1beta2 [(#1088)](GoogleCloudPlatform/python-docs-samples#1088)

* update analyze_safe_search

* update analyze_shots

* update explicit_content_detection and test

* update fece detection

* update label detection (path)

* update label detection (file)

* flake

* safe search --> explicit content

* update faces tutorial

* update client library quickstart

* update shotchange tutorial

* update labels tutorial

* correct spelling

* correction start_time_offset

* import order

* rebased

Author: dizcology

samples/labels/labels.py

@@ -32,17 +32,15 @@
 import sys
 import time
 
-from google.cloud.gapic.videointelligence.v1beta1 import enums
-from google.cloud.gapic.videointelligence.v1beta1 import (
-    video_intelligence_service_client)
+from google.cloud import videointelligence_v1beta2
+from google.cloud.videointelligence_v1beta2 import enums
 # [END imports]
 
 
 def analyze_labels(path):
     """ Detects labels given a GCS path. """
     # [START construct_request]
-    video_client = (video_intelligence_service_client.
-                    VideoIntelligenceServiceClient())
+    video_client = videointelligence_v1beta2.VideoIntelligenceServiceClient()
     features = [enums.Feature.LABEL_DETECTION]
     operation = video_client.annotate_video(path, features)
     # [END construct_request]
@@ -60,15 +58,23 @@ def analyze_labels(path):
     # [START parse_response]
     results = operation.result().annotation_results[0]
 
-    for label in results.label_annotations:
-        print('Label description: {}'.format(label.description))
-        print('Locations:')
-
-        for l, location in enumerate(label.locations):
-            print('\t{}: {} to {}'.format(
-                l,
-                location.segment.start_time_offset,
-                location.segment.end_time_offset))
+    for i, segment_label in enumerate(results.segment_label_annotations):
+        print('Video label description: {}'.format(
+            segment_label.entity.description))
+        for category_entity in segment_label.category_entities:
+            print('\tLabel category description: {}'.format(
+                category_entity.description))
+
+        for i, segment in enumerate(segment_label.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)
+            confidence = segment.confidence
+            print('\tSegment {}: {}'.format(i, positions))
+            print('\tConfidence: {}'.format(confidence))
+        print('\n')
     # [END parse_response]
 
 

samples/labels/labels_test.py

@@ -15,10 +15,9 @@
 # limitations under the License.
 
 import os
-
+import labels
 import pytest
 
-import labels
 
 BUCKET = os.environ['CLOUD_STORAGE_BUCKET']
 LABELS_FILE_PATH = '/video/cat.mp4'
@@ -29,4 +28,4 @@ def test_feline_video_labels(capsys):
     labels.analyze_labels(
         'gs://{}{}'.format(BUCKET, LABELS_FILE_PATH))
     out, _ = capsys.readouterr()
-    assert 'Whiskers' in out
+    assert 'Video label description: cat' in out