chore(python): use black==22.3.0 (#295)

Source-Link: googleapis/synthtool@6fab84a
Post-Processor: gcr.io/cloud-devrel-public-resources/owlbot-python:latest@sha256:7cffbc10910c3ab1b852c05114a08d374c195a81cdec1d4a67a1d129331d0bfe

videointelligence/samples/analyze/analyze.py

@@ -39,7 +39,7 @@
 
 def analyze_explicit_content(path):
     # [START video_analyze_explicit_content]
-    """ Detects explicit content from the GCS path to a video. """
+    """Detects explicit content from the GCS path to a video."""
     video_client = videointelligence.VideoIntelligenceServiceClient()
     features = [videointelligence.Feature.EXPLICIT_CONTENT_DETECTION]
 
@@ -62,7 +62,7 @@ def analyze_explicit_content(path):
 
 def analyze_labels(path):
     # [START video_analyze_labels_gcs]
-    """ Detects labels given a GCS path. """
+    """Detects labels given a GCS path."""
     video_client = videointelligence.VideoIntelligenceServiceClient()
     features = [videointelligence.Feature.LABEL_DETECTION]
 
@@ -231,7 +231,7 @@ def analyze_labels_file(path):
 
 def analyze_shots(path):
     # [START video_analyze_shots]
-    """ Detects camera shot changes. """
+    """Detects camera shot changes."""
     video_client = videointelligence.VideoIntelligenceServiceClient()
     features = [videointelligence.Feature.SHOT_CHANGE_DETECTION]
     operation = video_client.annotate_video(

videointelligence/samples/analyze/beta_snippets.py

@@ -280,7 +280,7 @@ def stream_generator():
             confidence = annotation.frames[0].confidence
             # description is in Unicode
             print(
-                u"{}s: {} (confidence: {})".format(time_offset, description, confidence)
+                "{}s: {} (confidence: {})".format(time_offset, description, confidence)
             )
     # [END video_streaming_label_detection_beta]
 
@@ -420,7 +420,7 @@ def stream_generator():
 
             # description is in Unicode
             print("{}s".format(time_offset))
-            print(u"\tEntity description: {}".format(description))
+            print("\tEntity description: {}".format(description))
             print("\tTrack Id: {}".format(track_id))
             if annotation.entity.entity_id:
                 print("\tEntity id: {}".format(annotation.entity.entity_id))
@@ -728,7 +728,7 @@ def stream_generator():
 
             # description is in Unicode
             print("{}s".format(time_offset))
-            print(u"\tEntity description: {}".format(description))
+            print("\tEntity description: {}".format(description))
             print("\tTrack Id: {}".format(track_id))
             if annotation.entity.entity_id:
                 print("\tEntity id: {}".format(annotation.entity.entity_id))

videointelligence/samples/analyze/noxfile.py

@@ -29,7 +29,7 @@
 # WARNING - WARNING - WARNING - WARNING - WARNING
 # WARNING - WARNING - WARNING - WARNING - WARNING
 
-BLACK_VERSION = "black==19.10b0"
+BLACK_VERSION = "black==22.3.0"
 
 # Copy `noxfile_config.py` to your directory and modify it instead.
 
@@ -253,7 +253,7 @@ def py(session: nox.sessions.Session) -> None:
 
 
 def _get_repo_root() -> Optional[str]:
-    """ Returns the root folder of the project. """
+    """Returns the root folder of the project."""
     # Get root of this repository. Assume we don't have directories nested deeper than 10 items.
     p = Path(os.getcwd())
     for i in range(10):

videointelligence/samples/analyze/video_detect_logo.py

@@ -32,7 +32,7 @@ def detect_logo(local_file_path="path/to/your/video.mp4"):
         request={"features": features, "input_content": input_content}
     )
 
-    print(u"Waiting for operation to complete...")
+    print("Waiting for operation to complete...")
     response = operation.result()
 
     # Get the first response, since we sent only one video.
@@ -44,61 +44,61 @@ def detect_logo(local_file_path="path/to/your/video.mp4"):
 
         # Opaque entity ID. Some IDs may be available in [Google Knowledge Graph
         # Search API](https://developers.google.com/knowledge-graph/).
-        print(u"Entity Id : {}".format(entity.entity_id))
+        print("Entity Id : {}".format(entity.entity_id))
 
-        print(u"Description : {}".format(entity.description))
+        print("Description : {}".format(entity.description))
 
         # All logo tracks where the recognized logo appears. Each track corresponds
         # to one logo instance appearing in consecutive frames.
         for track in logo_recognition_annotation.tracks:
             # Video segment of a track.
             print(
-                u"\n\tStart Time Offset : {}.{}".format(
+                "\n\tStart Time Offset : {}.{}".format(
                     track.segment.start_time_offset.seconds,
                     track.segment.start_time_offset.microseconds * 1000,
                 )
             )
             print(
-                u"\tEnd Time Offset : {}.{}".format(
+                "\tEnd Time Offset : {}.{}".format(
                     track.segment.end_time_offset.seconds,
                     track.segment.end_time_offset.microseconds * 1000,
                 )
             )
-            print(u"\tConfidence : {}".format(track.confidence))
+            print("\tConfidence : {}".format(track.confidence))
 
             # The object with timestamp and attributes per frame in the track.
             for timestamped_object in track.timestamped_objects:
 
                 # Normalized Bounding box in a frame, where the object is located.
                 normalized_bounding_box = timestamped_object.normalized_bounding_box
-                print(u"\n\t\tLeft : {}".format(normalized_bounding_box.left))
-                print(u"\t\tTop : {}".format(normalized_bounding_box.top))
-                print(u"\t\tRight : {}".format(normalized_bounding_box.right))
-                print(u"\t\tBottom : {}".format(normalized_bounding_box.bottom))
+                print("\n\t\tLeft : {}".format(normalized_bounding_box.left))
+                print("\t\tTop : {}".format(normalized_bounding_box.top))
+                print("\t\tRight : {}".format(normalized_bounding_box.right))
+                print("\t\tBottom : {}".format(normalized_bounding_box.bottom))
 
                 # Optional. The attributes of the object in the bounding box.
                 for attribute in timestamped_object.attributes:
-                    print(u"\n\t\t\tName : {}".format(attribute.name))
-                    print(u"\t\t\tConfidence : {}".format(attribute.confidence))
-                    print(u"\t\t\tValue : {}".format(attribute.value))
+                    print("\n\t\t\tName : {}".format(attribute.name))
+                    print("\t\t\tConfidence : {}".format(attribute.confidence))
+                    print("\t\t\tValue : {}".format(attribute.value))
 
             # Optional. Attributes in the track level.
             for track_attribute in track.attributes:
-                print(u"\n\t\tName : {}".format(track_attribute.name))
-                print(u"\t\tConfidence : {}".format(track_attribute.confidence))
-                print(u"\t\tValue : {}".format(track_attribute.value))
+                print("\n\t\tName : {}".format(track_attribute.name))
+                print("\t\tConfidence : {}".format(track_attribute.confidence))
+                print("\t\tValue : {}".format(track_attribute.value))
 
         # All video segments where the recognized logo appears. There might be
         # multiple instances of the same logo class appearing in one VideoSegment.
         for segment in logo_recognition_annotation.segments:
             print(
-                u"\n\tStart Time Offset : {}.{}".format(
+                "\n\tStart Time Offset : {}.{}".format(
                     segment.start_time_offset.seconds,
                     segment.start_time_offset.microseconds * 1000,
                 )
             )
             print(
-                u"\tEnd Time Offset : {}.{}".format(
+                "\tEnd Time Offset : {}.{}".format(
                     segment.end_time_offset.seconds,
                     segment.end_time_offset.microseconds * 1000,
                 )

videointelligence/samples/analyze/video_detect_logo_gcs.py

@@ -27,7 +27,7 @@ def detect_logo_gcs(input_uri="gs://YOUR_BUCKET_ID/path/to/your/file.mp4"):
         request={"features": features, "input_uri": input_uri}
     )
 
-    print(u"Waiting for operation to complete...")
+    print("Waiting for operation to complete...")
     response = operation.result()
 
     # Get the first response, since we sent only one video.
@@ -39,61 +39,61 @@ def detect_logo_gcs(input_uri="gs://YOUR_BUCKET_ID/path/to/your/file.mp4"):
 
         # Opaque entity ID. Some IDs may be available in [Google Knowledge Graph
         # Search API](https://developers.google.com/knowledge-graph/).
-        print(u"Entity Id : {}".format(entity.entity_id))
+        print("Entity Id : {}".format(entity.entity_id))
 
-        print(u"Description : {}".format(entity.description))
+        print("Description : {}".format(entity.description))
 
         # All logo tracks where the recognized logo appears. Each track corresponds
         # to one logo instance appearing in consecutive frames.
         for track in logo_recognition_annotation.tracks:
 
             # Video segment of a track.
             print(
-                u"\n\tStart Time Offset : {}.{}".format(
+                "\n\tStart Time Offset : {}.{}".format(
                     track.segment.start_time_offset.seconds,
                     track.segment.start_time_offset.microseconds * 1000,
                 )
             )
             print(
-                u"\tEnd Time Offset : {}.{}".format(
+                "\tEnd Time Offset : {}.{}".format(
                     track.segment.end_time_offset.seconds,
                     track.segment.end_time_offset.microseconds * 1000,
                 )
             )
-            print(u"\tConfidence : {}".format(track.confidence))
+            print("\tConfidence : {}".format(track.confidence))
 
             # The object with timestamp and attributes per frame in the track.
             for timestamped_object in track.timestamped_objects:
                 # Normalized Bounding box in a frame, where the object is located.
                 normalized_bounding_box = timestamped_object.normalized_bounding_box
-                print(u"\n\t\tLeft : {}".format(normalized_bounding_box.left))
-                print(u"\t\tTop : {}".format(normalized_bounding_box.top))
-                print(u"\t\tRight : {}".format(normalized_bounding_box.right))
-                print(u"\t\tBottom : {}".format(normalized_bounding_box.bottom))
+                print("\n\t\tLeft : {}".format(normalized_bounding_box.left))
+                print("\t\tTop : {}".format(normalized_bounding_box.top))
+                print("\t\tRight : {}".format(normalized_bounding_box.right))
+                print("\t\tBottom : {}".format(normalized_bounding_box.bottom))
 
                 # Optional. The attributes of the object in the bounding box.
                 for attribute in timestamped_object.attributes:
-                    print(u"\n\t\t\tName : {}".format(attribute.name))
-                    print(u"\t\t\tConfidence : {}".format(attribute.confidence))
-                    print(u"\t\t\tValue : {}".format(attribute.value))
+                    print("\n\t\t\tName : {}".format(attribute.name))
+                    print("\t\t\tConfidence : {}".format(attribute.confidence))
+                    print("\t\t\tValue : {}".format(attribute.value))
 
             # Optional. Attributes in the track level.
             for track_attribute in track.attributes:
-                print(u"\n\t\tName : {}".format(track_attribute.name))
-                print(u"\t\tConfidence : {}".format(track_attribute.confidence))
-                print(u"\t\tValue : {}".format(track_attribute.value))
+                print("\n\t\tName : {}".format(track_attribute.name))
+                print("\t\tConfidence : {}".format(track_attribute.confidence))
+                print("\t\tValue : {}".format(track_attribute.value))
 
         # All video segments where the recognized logo appears. There might be
         # multiple instances of the same logo class appearing in one VideoSegment.
         for segment in logo_recognition_annotation.segments:
             print(
-                u"\n\tStart Time Offset : {}.{}".format(
+                "\n\tStart Time Offset : {}.{}".format(
                     segment.start_time_offset.seconds,
                     segment.start_time_offset.microseconds * 1000,
                 )
             )
             print(
-                u"\tEnd Time Offset : {}.{}".format(
+                "\tEnd Time Offset : {}.{}".format(
                     segment.end_time_offset.seconds,
                     segment.end_time_offset.microseconds * 1000,
                 )

videointelligence/samples/labels/labels.py

@@ -37,7 +37,7 @@
 
 
 def analyze_labels(path):
-    """ Detects labels given a GCS path. """
+    """Detects labels given a GCS path."""
     # [START video_label_tutorial_construct_request]
     video_client = videointelligence.VideoIntelligenceServiceClient()
     features = [videointelligence.Feature.LABEL_DETECTION]

videointelligence/samples/labels/noxfile.py

@@ -29,7 +29,7 @@
 # WARNING - WARNING - WARNING - WARNING - WARNING
 # WARNING - WARNING - WARNING - WARNING - WARNING
 
-BLACK_VERSION = "black==19.10b0"
+BLACK_VERSION = "black==22.3.0"
 
 # Copy `noxfile_config.py` to your directory and modify it instead.
 
@@ -253,7 +253,7 @@ def py(session: nox.sessions.Session) -> None:
 
 
 def _get_repo_root() -> Optional[str]:
-    """ Returns the root folder of the project. """
+    """Returns the root folder of the project."""
     # Get root of this repository. Assume we don't have directories nested deeper than 10 items.
     p = Path(os.getcwd())
     for i in range(10):

videointelligence/samples/quickstart/noxfile.py

@@ -29,7 +29,7 @@
 # WARNING - WARNING - WARNING - WARNING - WARNING
 # WARNING - WARNING - WARNING - WARNING - WARNING
 
-BLACK_VERSION = "black==19.10b0"
+BLACK_VERSION = "black==22.3.0"
 
 # Copy `noxfile_config.py` to your directory and modify it instead.
 
@@ -253,7 +253,7 @@ def py(session: nox.sessions.Session) -> None:
 
 
 def _get_repo_root() -> Optional[str]:
-    """ Returns the root folder of the project. """
+    """Returns the root folder of the project."""
     # Get root of this repository. Assume we don't have directories nested deeper than 10 items.
     p = Path(os.getcwd())
     for i in range(10):

videointelligence/samples/shotchange/noxfile.py

@@ -29,7 +29,7 @@
 # WARNING - WARNING - WARNING - WARNING - WARNING
 # WARNING - WARNING - WARNING - WARNING - WARNING
 
-BLACK_VERSION = "black==19.10b0"
+BLACK_VERSION = "black==22.3.0"
 
 # Copy `noxfile_config.py` to your directory and modify it instead.
 
@@ -253,7 +253,7 @@ def py(session: nox.sessions.Session) -> None:
 
 
 def _get_repo_root() -> Optional[str]:
-    """ Returns the root folder of the project. """
+    """Returns the root folder of the project."""
     # Get root of this repository. Assume we don't have directories nested deeper than 10 items.
     p = Path(os.getcwd())
     for i in range(10):

videointelligence/samples/shotchange/shotchange.py

@@ -36,7 +36,7 @@
 
 
 def analyze_shots(path):
-    """ Detects camera shot changes. """
+    """Detects camera shot changes."""
     # [START video_shot_tutorial_construct_request]
     video_client = videointelligence.VideoIntelligenceServiceClient()
     features = [videointelligence.Feature.SHOT_CHANGE_DETECTION]