chore(deps): update dependency google-cloud-vision to v2 (GoogleCloudPlatform#4775)

This PR contains the following updates:

| Package | Update | Change |
|---|---|---|
| [google-cloud-vision](https://togithub.com/googleapis/python-vision) | major | `==1.0.0` -> `==2.0.0` |

---

### Release Notes


googleapis/python-vision

### [`v2.0.0`](https://togithub.com/googleapis/python-vision/blob/master/CHANGELOG.md#​200-httpswwwgithubcomgoogleapispython-visioncomparev100v200-2020-09-29)

[Compare Source](https://togithub.com/googleapis/python-vision/compare/v1.0.0...v2.0.0)

##### ⚠ BREAKING CHANGES

-   migrate to use microgen ([#&GoogleCloudPlatform#8203;52](https://togithub.com/googleapis/python-vision/issues/52))

##### Features

-   migrate to use microgen ([#&GoogleCloudPlatform#8203;52](https://www.github.com/googleapis/python-vision/issues/52)) ([cf3d353](https://www.github.com/googleapis/python-vision/commit/cf3d35306c3a8f6d32cc7ce1eb436c965acc30fe))

##### Bug Fixes

-   update retry configs ([#&GoogleCloudPlatform#8203;29](https://www.github.com/googleapis/python-vision/issues/29)) ([39c1652](https://www.github.com/googleapis/python-vision/commit/39c16522f7bc97544c361f8e14dbc9a2a5d4c0e4))

##### Documentation

-   added note about not supported device ([#&GoogleCloudPlatform#8203;24](https://www.github.com/googleapis/python-vision/issues/24)) ([b33fa88](https://www.github.com/googleapis/python-vision/commit/b33fa88e4f1e9cb2f6e029e6a34364fb6cdc1a96))



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codelabs/flex_and_vision/main.py

@@ -23,29 +23,29 @@
 from google.cloud import vision
 
 
-CLOUD_STORAGE_BUCKET = os.environ.get('CLOUD_STORAGE_BUCKET')
+CLOUD_STORAGE_BUCKET = os.environ.get("CLOUD_STORAGE_BUCKET")
 
 
 app = Flask(__name__)
 
 
-@app.route('/')
+@app.route("/")
 def homepage():
     # Create a Cloud Datastore client.
     datastore_client = datastore.Client()
 
     # Use the Cloud Datastore client to fetch information from Datastore about
     # each photo.
-    query = datastore_client.query(kind='Faces')
+    query = datastore_client.query(kind="Faces")
     image_entities = list(query.fetch())
 
     # Return a Jinja2 HTML template and pass in image_entities as a parameter.
-    return render_template('homepage.html', image_entities=image_entities)
+    return render_template("homepage.html", image_entities=image_entities)
 
 
-@app.route('/upload_photo', methods=['GET', 'POST'])
+@app.route("/upload_photo", methods=["GET", "POST"])
 def upload_photo():
-    photo = request.files['file']
+    photo = request.files["file"]
 
     # Create a Cloud Storage client.
     storage_client = storage.Client()
@@ -55,8 +55,7 @@ def upload_photo():
 
     # Create a new blob and upload the file's content.
     blob = bucket.blob(photo.filename)
-    blob.upload_from_string(
-            photo.read(), content_type=photo.content_type)
+    blob.upload_from_string(photo.read(), content_type=photo.content_type)
 
     # Make the blob publicly viewable.
     blob.make_public()
@@ -65,10 +64,9 @@ def upload_photo():
     vision_client = vision.ImageAnnotatorClient()
 
     # Use the Cloud Vision client to detect a face for our image.
-    source_uri = 'gs://{}/{}'.format(CLOUD_STORAGE_BUCKET, blob.name)
-    image = vision.types.Image(
-        source=vision.types.ImageSource(gcs_image_uri=source_uri))
-    faces = vision_client.face_detection(image).face_annotations
+    source_uri = "gs://{}/{}".format(CLOUD_STORAGE_BUCKET, blob.name)
+    image = vision.Image(source=vision.ImageSource(gcs_image_uri=source_uri))
+    faces = vision_client.face_detection(image=image).face_annotations
 
     # If a face is detected, save to Datastore the likelihood that the face
     # displays 'joy,' as determined by Google's Machine Learning algorithm.
@@ -77,11 +75,16 @@ def upload_photo():
 
         # Convert the likelihood string.
         likelihoods = [
-            'Unknown', 'Very Unlikely', 'Unlikely', 'Possible', 'Likely',
-            'Very Likely']
+            "Unknown",
+            "Very Unlikely",
+            "Unlikely",
+            "Possible",
+            "Likely",
+            "Very Likely",
+        ]
         face_joy = likelihoods[face.joy_likelihood]
     else:
-        face_joy = 'Unknown'
+        face_joy = "Unknown"
 
     # Create a Cloud Datastore client.
     datastore_client = datastore.Client()
@@ -90,7 +93,7 @@ def upload_photo():
     current_datetime = datetime.now()
 
     # The kind for the new entity.
-    kind = 'Faces'
+    kind = "Faces"
 
     # The name/ID for the new entity.
     name = blob.name
@@ -101,28 +104,33 @@ def upload_photo():
     # Construct the new entity using the key. Set dictionary values for entity
     # keys blob_name, storage_public_url, timestamp, and joy.
     entity = datastore.Entity(key)
-    entity['blob_name'] = blob.name
-    entity['image_public_url'] = blob.public_url
-    entity['timestamp'] = current_datetime
-    entity['joy'] = face_joy
+    entity["blob_name"] = blob.name
+    entity["image_public_url"] = blob.public_url
+    entity["timestamp"] = current_datetime
+    entity["joy"] = face_joy
 
     # Save the new entity to Datastore.
     datastore_client.put(entity)
 
     # Redirect to the home page.
-    return redirect('/')
+    return redirect("/")
 
 
 @app.errorhandler(500)
 def server_error(e):
-    logging.exception('An error occurred during a request.')
-    return """
+    logging.exception("An error occurred during a request.")
+    return (
+        """
     An internal error occurred: {}
     See logs for full stacktrace.
-    """.format(e), 500
+    """.format(
+            e
+        ),
+        500,
+    )
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     # This is used when running locally. Gunicorn is used to run the
     # application on Google App Engine. See entrypoint in app.yaml.
-    app.run(host='127.0.0.1', port=8080, debug=True)
+    app.run(host="127.0.0.1", port=8080, debug=True)

codelabs/flex_and_vision/requirements.txt

@@ -1,6 +1,6 @@
 Flask==1.1.2
 gunicorn==20.0.4; python_version > '3.0'
 gunicorn==19.10.0; python_version < '3.0'
-google-cloud-vision==1.0.0
+google-cloud-vision==2.0.0
 google-cloud-storage==1.31.2
 google-cloud-datastore==1.15.1

functions/imagemagick/main.py

@@ -30,29 +30,31 @@
 def blur_offensive_images(data, context):
     file_data = data
 
-    file_name = file_data['name']
-    bucket_name = file_data['bucket']
+    file_name = file_data["name"]
+    bucket_name = file_data["bucket"]
 
     blob = storage_client.bucket(bucket_name).get_blob(file_name)
-    blob_uri = f'gs://{bucket_name}/{file_name}'
-    blob_source = {'source': {'image_uri': blob_uri}}
+    blob_uri = f"gs://{bucket_name}/{file_name}"
+    blob_source = vision.Image(source=vision.ImageSource(gcs_image_uri=blob_uri))
 
     # Ignore already-blurred files
-    if file_name.startswith('blurred-'):
-        print(f'The image {file_name} is already blurred.')
+    if file_name.startswith("blurred-"):
+        print(f"The image {file_name} is already blurred.")
         return
 
-    print(f'Analyzing {file_name}.')
+    print(f"Analyzing {file_name}.")
 
-    result = vision_client.safe_search_detection(blob_source)
+    result = vision_client.safe_search_detection(image=blob_source)
     detected = result.safe_search_annotation
 
     # Process image
     if detected.adult == 5 or detected.violence == 5:
-        print(f'The image {file_name} was detected as inappropriate.')
+        print(f"The image {file_name} was detected as inappropriate.")
         return __blur_image(blob)
     else:
-        print(f'The image {file_name} was detected as OK.')
+        print(f"The image {file_name} was detected as OK.")
+
+
 # [END functions_imagemagick_analyze]
 
 
@@ -64,24 +66,26 @@ def __blur_image(current_blob):
 
     # Download file from bucket.
     current_blob.download_to_filename(temp_local_filename)
-    print(f'Image {file_name} was downloaded to {temp_local_filename}.')
+    print(f"Image {file_name} was downloaded to {temp_local_filename}.")
 
     # Blur the image using ImageMagick.
     with Image(filename=temp_local_filename) as image:
-        image.resize(*image.size, blur=16, filter='hamming')
+        image.resize(*image.size, blur=16, filter="hamming")
         image.save(filename=temp_local_filename)
 
-    print(f'Image {file_name} was blurred.')
+    print(f"Image {file_name} was blurred.")
 
     # Upload result to a second bucket, to avoid re-triggering the function.
     # You could instead re-upload it to the same bucket + tell your function
     # to ignore files marked as blurred (e.g. those with a "blurred" prefix)
-    blur_bucket_name = os.getenv('BLURRED_BUCKET_NAME')
+    blur_bucket_name = os.getenv("BLURRED_BUCKET_NAME")
     blur_bucket = storage_client.bucket(blur_bucket_name)
     new_blob = blur_bucket.blob(file_name)
     new_blob.upload_from_filename(temp_local_filename)
-    print(f'Blurred image uploaded to: gs://{blur_bucket_name}/{file_name}')
+    print(f"Blurred image uploaded to: gs://{blur_bucket_name}/{file_name}")
 
     # Delete the temporary file.
     os.remove(temp_local_filename)
+
+
 # [END functions_imagemagick_blur]

functions/imagemagick/requirements.txt

@@ -1,3 +1,3 @@
-google-cloud-vision==1.0.0
+google-cloud-vision==2.0.0
 google-cloud-storage==1.31.2
 Wand==0.6.3