Hackathon/Codelab vision sample

codelabs/flex_and_vision/README.md

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+# Python Google Cloud Vision sample for Google App Engine Flexible Environment
+
+This sample demonstrates how to use the [Google Cloud Vision API](https://cloud.google.com/vision/), [Google Cloud Storage](https://cloud.google.com/storage/), and [Google Cloud Datastore](https://cloud.google.com/datastore/) on [Google App Engine Flexible Environment](https://cloud.google.com/appengine).
+
+## Setup
+
+Create a new project with the [Google Cloud Platform console](https://console.cloud.google.com/).
+Make a note of your project ID, which may be different than your project name.
+
+Make sure to [Enable Billing](https://pantheon.corp.google.com/billing?debugUI=DEVELOPERS)
+for your project.
+
+Download the [Google Cloud SDK](https://cloud.google.com/sdk/docs/) to your
+local machine. Alternatively, you could use the [Cloud Shell](https://cloud.google.com/shell/docs/quickstart), which comes with the Google Cloud SDK pre-installed.
+
+Initialize the Google Cloud SDK (skip if using Cloud Shell):
+
+    gcloud init
+
+Create your App Engine application:
+
+    gcloud app create
+
+Set an environment variable for your project ID, replacing `[YOUR_PROJECT_ID]`
+with your project ID:
+
+    export PROJECT_ID=[YOUR_PROJECT_ID]
+
+## Getting the sample code
+
+Run the following command to clone the Github repository:
+
+    git clone https://github.com/GoogleCloudPlatform/python-docs-samples.git
+
+Change directory to the sample code location:
+
+    cd python-docs-samples/codelabs/flex_and_vision
+
+## Authentication
+
+Enable the APIs:
+
+    gcloud service-management enable vision.googleapis.com
+    gcloud service-management enable storage-component.googleapis.com
+    gcloud service-management enable datastore.googleapis.com
+
+Create a Service Account to access the Google Cloud APIs when testing locally:
+
+    gcloud iam service-accounts create hackathon \
+    --display-name "My Hackathon Service Account"
+
+Give your newly created Service Account appropriate permissions:
+
+    gcloud projects add-iam-policy-binding ${PROJECT_ID} \
+    --member serviceAccount:hackathon@${PROJECT_ID}.iam.gserviceaccount.com \
+    --role roles/owner
+
+After creating your Service Account, create a Service Account key:
+
+    gcloud iam service-accounts keys create ~/key.json --iam-account \
+    hackathon@${PROJECT_ID}.iam.gserviceaccount.com
+
+Set the `GOOGLE_APPLICATION_CREDENTIALS` environment variable to point to where
+you just put your Service Account key:
+
+    export GOOGLE_APPLICATION_CREDENTIALS="/home/${USER}/key.json"
+
+## Running locally
+
+Create a virtual environment and install dependencies:
+
+    virtualenv -p python3 env
+    source env/bin/activate
+    pip install -r requirements.txt
+
+Create a Cloud Storage bucket. It is recommended that you name it the same as
+your project ID:
+
+    gsutil mb gs://${PROJECT_ID}
+
+Set the environment variable `CLOUD_STORAGE_BUCKET`:
+
+    export CLOUD_STORAGE_BUCKET=${PROJECT_ID}
+
+Start your application locally:
+
+    python main.py
+
+Visit `localhost:8080` to view your application running locally. Press `Control-C`
+on your command line when you are finished.
+
+When you are ready to leave your virtual environment:
+
+    deactivate
+
+## Deploying to App Engine
+
+Open `app.yaml` and replace <your-cloud-storage-bucket> with the name of your
+Cloud Storage bucket.
+
+Deploy your application to App Engine using `gcloud`. Please note that this may
+take several minutes.
+
+    gcloud app deploy
+
+Visit `https://[YOUR_PROJECT_ID].appspot.com` to view your deployed application.

codelabs/flex_and_vision/app.yaml

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+runtime: python
+env: flex
+entrypoint: gunicorn -b :$PORT main:app
+
+runtime_config:
+  python_version: 3
+
+env_variables:
+  CLOUD_STORAGE_BUCKET: <your-cloud-storage-bucket>

codelabs/flex_and_vision/main.py

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+# Copyright 2017 Google Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# [START app]
+from datetime import datetime
+import logging
+import os
+
+from flask import Flask, redirect, render_template, request
+
+from google.cloud import datastore
+from google.cloud import storage
+from google.cloud import vision
+
+
+CLOUD_STORAGE_BUCKET = os.environ.get('CLOUD_STORAGE_BUCKET')
+
+
+app = Flask(__name__)
+
+
+@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')
+    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)
+
+
+@app.route('/upload_photo', methods=['GET', 'POST'])
+def upload_photo():
+    photo = request.files['file']
+
+    # Create a Cloud Storage client.
+    storage_client = storage.Client()
+
+    # Get the bucket that the file will be uploaded to.
+    bucket = storage_client.get_bucket(CLOUD_STORAGE_BUCKET)
+
+    # 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)
+
+    # Make the blob publicly viewable.
+    blob.make_public()
+
+    # Create a Cloud Vision client.
+    vision_client = vision.Client()
+
+    # Use the Cloud Vision client to detect a face for our image.
+    source_uri = 'gs://{}/{}'.format(CLOUD_STORAGE_BUCKET, blob.name)
+    image = vision_client.image(source_uri=source_uri)
+    faces = image.detect_faces(limit=1)
+
+    # If a face is detected, save to Datastore the likelihood that the face
+    # displays 'joy,' as determined by Google's Machine Learning algorithm.
+    if len(faces) > 0:
+        face = faces[0]
+
+        # Convert the face.emotions.joy enum type to a string, which will be
+        # something like 'Likelihood.VERY_LIKELY'. Parse that string by the
+        # period to extract only the 'VERY_LIKELY' portion.
+        face_joy = str(face.emotions.joy).split('.')[1]
+    else:
+        face_joy = 'Unknown'
+
+    # Create a Cloud Datastore client.
+    datastore_client = datastore.Client()
+
+    # Fetch the current date / time.
+    current_datetime = datetime.now()
+
+    # The kind for the new entity.
+    kind = 'Faces'
+
+    # The name/ID for the new entity.
+    name = blob.name
+
+    # Create the Cloud Datastore key for the new entity.
+    key = datastore_client.key(kind, name)
+
+    # 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
+
+    # Save the new entity to Datastore.
+    datastore_client.put(entity)
+
+    # Redirect to the home page.
+    return redirect('/')
+
+
+@app.errorhandler(500)
+def server_error(e):
+    logging.exception('An error occurred during a request.')
+    return """
+    An internal error occurred: <pre>{}</pre>
+    See logs for full stacktrace.
+    """.format(e), 500
+
+
+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)
+# [END app]

codelabs/flex_and_vision/requirements.txt

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+Flask==0.12
+gunicorn==19.6.0
+google-cloud-vision==0.22.0
+google-cloud-storage==0.22.0
+google-cloud-datastore==0.22.0

codelabs/flex_and_vision/templates/homepage.html

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+<h1>Google Cloud Platform - Face Detection Sample</h1>
+
+<p>This Python Flask application demonstrates App Engine Flexible, Google Cloud
+Storage, Datastore, and the Cloud Vision API.</p>
+
+<br>
+
+<html>
+  <body>
+    <form action="upload_photo" method="POST" enctype="multipart/form-data">
+      Upload File: <input type="file" name="file"><br>
+      <input type="submit" name="submit" value="Submit">
+    </form>
+    {% for image_entity in image_entities %}
+      <img src="{{image_entity['image_public_url']}}" width=200 height=200>
+      <p>{{image_entity['blob_name']}} was uploaded {{image_entity['timestamp']}}.</p>
+      <p>Joy Likelihood for Face: {{image_entity['joy']}}</p>
+    {% endfor %}
+  </body>
+</html>