Video v1beta2 (#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

video/cloud-client/analyze/README.rst

@@ -59,25 +59,25 @@ To run this sample:
 
     $ python analyze.py
 
-    usage: analyze.py [-h] {faces,labels,labels_file,safe_search,shots} ...
+    usage: analyze.py [-h] {faces,labels,labels_file,explicit_content,shots} ...
     
-    This application demonstrates face detection, label detection, safe search,
-    and shot change detection using the Google Cloud API.
+    This application demonstrates face detection, 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 safe_search gs://demomaker/gbikes_dinosaur.mp4
+        python analyze.py explicit_content gs://demomaker/gbikes_dinosaur.mp4
     
     positional arguments:
-      {faces,labels,labels_file,safe_search,shots}
+      {faces,labels,labels_file,explicit_content,shots}
         faces               Detects faces given a GCS path.
         labels              Detects labels given a GCS path.
         labels_file         Detects labels given a file path.
-        safe_search         Detects safe search features the GCS path to a video.
+        explicit_content    Detects explicit content from the GCS path to a video.
         shots               Detects camera shot changes.
     
     optional arguments:

video/cloud-client/analyze/analyze.py

@@ -15,46 +15,44 @@
 # limitations under the License.
 
 import os
-
-import pytest
-
 import analyze
+import pytest
 
 
 BUCKET = os.environ['CLOUD_STORAGE_BUCKET']
 LABELS_FILE_PATH = '/video/cat.mp4'
 FACES_FILE_PATH = '/video/googlework.mp4'
-SAFE_SEARCH_FILE_PATH = '/video/cat.mp4'
+EXPLICIT_CONTENT_FILE_PATH = '/video/cat.mp4'
 SHOTS_FILE_PATH = '/video/gbikes_dinosaur.mp4'
 
 
 @pytest.mark.slow
-def test_cat_video_shots(capsys):
+def test_analyze_shots(capsys):
     analyze.analyze_shots(
         'gs://{}{}'.format(BUCKET, SHOTS_FILE_PATH))
     out, _ = capsys.readouterr()
-    assert 'Scene 1:' in out
+    assert 'Shot 1:' in out
 
 
 @pytest.mark.slow
-def test_work_video_faces(capsys):
+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_dino_video_labels(capsys):
+def test_analyze_labels(capsys):
     analyze.analyze_labels(
         'gs://{}{}'.format(BUCKET, LABELS_FILE_PATH))
     out, _ = capsys.readouterr()
-    assert 'Whiskers' in out
+    assert 'label description: cat' in out
 
 
 @pytest.mark.slow
-def test_cat_safe_search(capsys):
-    analyze.analyze_safe_search(
-        'gs://{}{}'.format(BUCKET, SAFE_SEARCH_FILE_PATH))
+def test_analyze_explicit_content(capsys):
+    analyze.analyze_explicit_content(
+        'gs://{}{}'.format(BUCKET, EXPLICIT_CONTENT_FILE_PATH))
     out, _ = capsys.readouterr()
-    assert 'medical' in out
+    assert 'pornography' in out

video/cloud-client/analyze/analyze_test.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_faces(path):
     # [START construct_request]
     """ Detects faces given a GCS path. """
-    video_client = (video_intelligence_service_client.
-                    VideoIntelligenceServiceClient())
+    video_client = videointelligence_v1beta2.VideoIntelligenceServiceClient()
     features = [enums.Feature.FACE_DETECTION]
     operation = video_client.annotate_video(path, features)
     # [END construct_request]
@@ -66,10 +64,12 @@ def analyze_faces(path):
         print('Thumbnail size: {}'.format(len(face.thumbnail)))
 
         for segment_id, segment in enumerate(face.segments):
-            print('Track {}: {} to {}'.format(
-                segment_id,
-                segment.start_time_offset,
-                segment.end_time_offset))
+            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))
     # [END parse_response]
 
 

video/cloud-client/faces/faces.py

@@ -15,10 +15,9 @@
 # limitations under the License.
 
 import os
-
+import faces
 import pytest
 
-import faces
 
 BUCKET = os.environ['CLOUD_STORAGE_BUCKET']
 FACES_FILE_PATH = '/video/googlework.mp4'

video/cloud-client/faces/faces_test.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]
 
 

video/cloud-client/labels/labels.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

video/cloud-client/labels/labels_test.py

@@ -26,12 +26,10 @@ def run_quickstart():
     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
 
-    video_client = (video_intelligence_service_client.
-                    VideoIntelligenceServiceClient())
+    video_client = videointelligence_v1beta2.VideoIntelligenceServiceClient()
     features = [enums.Feature.LABEL_DETECTION]
     operation = video_client.annotate_video('gs://demomaker/cat.mp4', features)
     print('\nProcessing video for label annotations:')
@@ -46,19 +44,22 @@ def run_quickstart():
     # first result is retrieved because a single video was processed
     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):
-            positions = 'Entire video'
-            if (location.segment.start_time_offset != -1 or
-                    location.segment.end_time_offset != -1):
-                positions = '{} to {}'.format(
-                    location.segment.start_time_offset / 1000000.0,
-                    location.segment.end_time_offset / 1000000.0)
-            print('\t{}: {}'.format(l, positions))
+    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 videointelligence_quickstart]
 

video/cloud-client/quickstart/quickstart.py

@@ -23,4 +23,4 @@
 def test_quickstart(capsys):
     quickstart.run_quickstart()
     out, _ = capsys.readouterr()
-    assert 'Whiskers' in out
+    assert 'Video label description: cat' in out

video/cloud-client/quickstart/quickstart_test.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_shots(path):
     """ Detects camera shot changes. """
     # [START construct_request]
-    video_client = (video_intelligence_service_client.
-                    VideoIntelligenceServiceClient())
+    video_client = videointelligence_v1beta2.VideoIntelligenceServiceClient()
     features = [enums.Feature.SHOT_CHANGE_DETECTION]
     operation = video_client.annotate_video(path, features)
     # [END construct_request]
@@ -58,13 +56,14 @@ def analyze_shots(path):
     # [END check_operation]
 
     # [START parse_response]
-    shots = operation.result().annotation_results[0]
-
-    for note, shot in enumerate(shots.shot_annotations):
-        print('Scene {}: {} to {}'.format(
-            note,
-            shot.start_time_offset,
-            shot.end_time_offset))
+    shots = operation.result().annotation_results[0].shot_annotations
+
+    for i, shot in enumerate(shots):
+        start_time = (shot.start_time_offset.seconds +
+                      shot.start_time_offset.nanos / 1e9)
+        end_time = (shot.end_time_offset.seconds +
+                    shot.end_time_offset.nanos / 1e9)
+        print('\tShot {}: {} to {}'.format(i, start_time, end_time))
     # [END parse_response]