vision: resolve lint issues

vision/cloud-client/detect/vision_async_batch_annotate_images.py

@@ -14,72 +14,86 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-# DO NOT EDIT! This is a generated sample ("LongRunningPromise",  "vision_async_batch_annotate_images")
+# DO NOT EDIT! This is a generated sample
+#   ("LongRunningPromise",  "vision_async_batch_annotate_images")
 
 # To install the latest published package dependency, execute the following:
 #   pip install google-cloud-vision
 
 # sample-metadata
 #   title: Async Batch Image Annotation
 #   description: Perform async batch image annotation
-#   usage: python3 samples/v1/vision_async_batch_annotate_images.py [--input_image_uri "gs://cloud-samples-data/vision/label/wakeupcat.jpg"] [--output_uri "gs://your-bucket/prefix/"]
-import sys
+#   usage: python3 samples/v1/vision_async_batch_annotate_images.py \
+#     [--input_image_uri \
+#     "gs://cloud-samples-data/vision/label/wakeupcat.jpg"] \
+#     [--output_uri "gs://your-bucket/prefix/"]
 
 # [START vision_async_batch_annotate_images]
 
 from google.cloud import vision_v1
 from google.cloud.vision_v1 import enums
 import six
 
+
 def sample_async_batch_annotate_images(input_image_uri, output_uri):
-  """Perform async batch image annotation"""
-  # [START vision_async_batch_annotate_images_core]
-
-  client = vision_v1.ImageAnnotatorClient()
-
-  # input_image_uri = 'gs://cloud-samples-data/vision/label/wakeupcat.jpg'
-  # output_uri = 'gs://your-bucket/prefix/'
-
-  if isinstance(input_image_uri, six.binary_type):
-    input_image_uri = input_image_uri.decode('utf-8')
-  if isinstance(output_uri, six.binary_type):
-    output_uri = output_uri.decode('utf-8')
-  source = {'image_uri': input_image_uri}
-  image = {'source': source}
-  type_ = enums.Feature.Type.LABEL_DETECTION
-  features_element = {'type': type_}
-  type_2 = enums.Feature.Type.IMAGE_PROPERTIES
-  features_element_2 = {'type': type_2}
-  features = [features_element, features_element_2]
-  requests_element = {'image': image, 'features': features}
-  requests = [requests_element]
-  gcs_destination = {'uri': output_uri}
-
-  # The max number of responses to output in each JSON file
-  batch_size = 2
-  output_config = {'gcs_destination': gcs_destination, 'batch_size': batch_size}
-
-  operation = client.async_batch_annotate_images(requests, output_config)
-
-  print('Waiting for operation to complete...')
-  response = operation.result()
-
-  # The output is written to GCS with the provided output_uri as prefix
-  gcs_output_uri = response.output_config.gcs_destination.uri
-  print('Output written to GCS with prefix: {}'.format(gcs_output_uri))
-
-  # [END vision_async_batch_annotate_images_core]
+    """Perform async batch image annotation"""
+    # [START vision_async_batch_annotate_images_core]
+
+    client = vision_v1.ImageAnnotatorClient()
+
+    # input_image_uri = 'gs://cloud-samples-data/vision/label/wakeupcat.jpg'
+    # output_uri = 'gs://your-bucket/prefix/'
+
+    if isinstance(input_image_uri, six.binary_type):
+        input_image_uri = input_image_uri.decode("utf-8")
+    if isinstance(output_uri, six.binary_type):
+        output_uri = output_uri.decode("utf-8")
+    source = {"image_uri": input_image_uri}
+    image = {"source": source}
+    type_ = enums.Feature.Type.LABEL_DETECTION
+    features_element = {"type": type_}
+    type_2 = enums.Feature.Type.IMAGE_PROPERTIES
+    features_element_2 = {"type": type_2}
+    features = [features_element, features_element_2]
+    requests_element = {"image": image, "features": features}
+    requests = [requests_element]
+    gcs_destination = {"uri": output_uri}
+
+    # The max number of responses to output in each JSON file
+    batch_size = 2
+    output_config = {
+        "gcs_destination": gcs_destination, "batch_size": batch_size}
+
+    operation = client.async_batch_annotate_images(requests, output_config)
+
+    print("Waiting for operation to complete...")
+    response = operation.result()
+
+    # The output is written to GCS with the provided output_uri as prefix
+    gcs_output_uri = response.output_config.gcs_destination.uri
+    print("Output written to GCS with prefix: {}".format(gcs_output_uri))
+
+    # [END vision_async_batch_annotate_images_core]
+
+
 # [END vision_async_batch_annotate_images]
 
+
 def main():
-  import argparse
+    import argparse
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--input_image_uri",
+        type=str,
+        default="gs://cloud-samples-data/vision/label/wakeupcat.jpg",
+    )
+    parser.add_argument(
+        "--output_uri", type=str, default="gs://your-bucket/prefix/")
+    args = parser.parse_args()
 
-  parser = argparse.ArgumentParser()
-  parser.add_argument('--input_image_uri', type=str, default='gs://cloud-samples-data/vision/label/wakeupcat.jpg')
-  parser.add_argument('--output_uri', type=str, default='gs://your-bucket/prefix/')
-  args = parser.parse_args()
+    sample_async_batch_annotate_images(args.input_image_uri, args.output_uri)
 
-  sample_async_batch_annotate_images(args.input_image_uri, args.output_uri)
 
-if __name__ == '__main__':
-  main()
+if __name__ == "__main__":
+    main()

vision/cloud-client/detect/vision_batch_annotate_files.py

@@ -14,16 +14,17 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-# DO NOT EDIT! This is a generated sample ("Request",  "vision_batch_annotate_files")
+# DO NOT EDIT! This is a generated sample
+#   ("Request",  "vision_batch_annotate_files")
 
 # To install the latest published package dependency, execute the following:
 #   pip install google-cloud-vision
 
 # sample-metadata
 #   title:
 #   description: Perform batch file annotation
-#   usage: python3 samples/v1/vision_batch_annotate_files.py [--file_path "resources/kafka.pdf"]
-import sys
+#   usage: python3 samples/v1/vision_batch_annotate_files.py \
+#     [--file_path "resources/kafka.pdf"]
 
 # [START vision_batch_annotate_files]
 
@@ -32,64 +33,78 @@
 import io
 import six
 
+
 def sample_batch_annotate_files(file_path):
-  """
+    """
     Perform batch file annotation
 
     Args:
       file_path Path to local pdf file, e.g. /path/document.pdf
     """
-  # [START vision_batch_annotate_files_core]
-
-  client = vision_v1.ImageAnnotatorClient()
-
-  # file_path = 'resources/kafka.pdf'
-
-  if isinstance(file_path, six.binary_type):
-    file_path = file_path.decode('utf-8')
-
-  # Supported mime_type: application/pdf, image/tiff, image/gif
-  mime_type = 'application/pdf'
-  with io.open(file_path, 'rb') as f:
-    content = f.read()
-  input_config = {'mime_type': mime_type, 'content': content}
-  type_ = enums.Feature.Type.DOCUMENT_TEXT_DETECTION
-  features_element = {'type': type_}
-  features = [features_element]
-
-  # The service can process up to 5 pages per document file. Here we specify the
-  # first, second, and last page of the document to be processed.
-  pages_element = 1
-  pages_element_2 = 2
-  pages_element_3 = -1
-  pages = [pages_element, pages_element_2, pages_element_3]
-  requests_element = {'input_config': input_config, 'features': features, 'pages': pages}
-  requests = [requests_element]
-
-  response = client.batch_annotate_files(requests)
-  for image_response in response.responses[0].responses:
-    print('Full text: {}'.format(image_response.full_text_annotation.text))
-    for page in image_response.full_text_annotation.pages:
-      for block in page.blocks:
-        print('\nBlock confidence: {}'.format(block.confidence))
-        for par in block.paragraphs:
-          print('\tParagraph confidence: {}'.format(par.confidence))
-          for word in par.words:
-            print('\t\tWord confidence: {}'.format(word.confidence))
-            for symbol in word.symbols:
-              print('\t\t\tSymbol: {}, (confidence: {})'.format(symbol.text, symbol.confidence))
-
-  # [END vision_batch_annotate_files_core]
+    # [START vision_batch_annotate_files_core]
+
+    client = vision_v1.ImageAnnotatorClient()
+
+    # file_path = 'resources/kafka.pdf'
+
+    if isinstance(file_path, six.binary_type):
+        file_path = file_path.decode("utf-8")
+
+    # Supported mime_type: application/pdf, image/tiff, image/gif
+    mime_type = "application/pdf"
+    with io.open(file_path, "rb") as f:
+        content = f.read()
+    input_config = {"mime_type": mime_type, "content": content}
+    type_ = enums.Feature.Type.DOCUMENT_TEXT_DETECTION
+    features_element = {"type": type_}
+    features = [features_element]
+
+    # The service can process up to 5 pages per document file. Here we specify
+    # the first, second, and last page of the document to be processed.
+    pages_element = 1
+    pages_element_2 = 2
+    pages_element_3 = -1
+    pages = [pages_element, pages_element_2, pages_element_3]
+    requests_element = {
+        "input_config": input_config,
+        "features": features,
+        "pages": pages,
+    }
+    requests = [requests_element]
+
+    response = client.batch_annotate_files(requests)
+    for image_response in response.responses[0].responses:
+        print("Full text: {}".format(image_response.full_text_annotation.text))
+        for page in image_response.full_text_annotation.pages:
+            for block in page.blocks:
+                print("\nBlock confidence: {}".format(block.confidence))
+                for par in block.paragraphs:
+                    print("\tParagraph confidence: {}".format(par.confidence))
+                    for word in par.words:
+                        print(
+                            "\t\tWord confidence: {}".format(word.confidence))
+                        for symbol in word.symbols:
+                            print(
+                                "\t\t\tSymbol: {}, (confidence: {})".format(
+                                    symbol.text, symbol.confidence
+                                )
+                            )
+
+    # [END vision_batch_annotate_files_core]
+
+
 # [END vision_batch_annotate_files]
 
+
 def main():
-  import argparse
+    import argparse
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--file_path", type=str, default="resources/kafka.pdf")
+    args = parser.parse_args()
 
-  parser = argparse.ArgumentParser()
-  parser.add_argument('--file_path', type=str, default='resources/kafka.pdf')
-  args = parser.parse_args()
+    sample_batch_annotate_files(args.file_path)
 
-  sample_batch_annotate_files(args.file_path)
 
-if __name__ == '__main__':
-  main()
+if __name__ == "__main__":
+    main()