Add Vision API usage documentation.

daspecster · daspecster · commit 95a55c145bd2 · 2016-08-19T15:33:11.000-04:00
diff --git a/docs/vision-usage.rst b/docs/vision-usage.rst
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+Using the Vision API
+====================
+
+Authentication and Configuration
+--------------------------------
+
+- For an overview of authentication in ``gcloud-python``,
+  see :doc:`gcloud-auth`.
+
+- In addition to any authentication configuration, you should also set the
+  :envvar:`GCLOUD_PROJECT` environment variable for the project you'd like
+  to interact with. If the GCLOUD_PROJECT environment variable is not present,
+  the project ID from JSON file credentials is used.
+
+  If you are using Google App Engine or Google Compute Engine
+  this will be detected automatically.
+
+- After configuring your environment, create a
+  :class:`Client <gcloud.vision.client.Client>`
+
+.. code-block:: python
+
+     >>> from gcloud import vision
+     >>> client = vision.Client()
+
+or pass in ``credentials`` and ``project`` explicitly
+
+.. code-block:: python
+
+     >>> from gcloud import vision
+     >>> client = vision.Client(project='my-project', credentials=creds)
+
+Annotating an Image
+-------------------
+
+Annotate a single image
+~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+    >>> from gcloud import vision
+    >>> client = vision.Client()
+    >>> image = client.image('./image.png')
+    >>> faces = image.detect_faces(limit=10)
+
+Annotate multiple images
+~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+     >>> first_image = client.image('./image.jpg')
+     >>> second_image = client.image('gs://my-storage-bucket/image2.jpg')
+     >>> with client.batch():
+     ...     labels = first_image.detect_labels()
+     ...     faces = second_image.detect_faces(limit=10)
+
+or
+
+.. code-block:: python
+
+     >>> images = []
+     >>> images.append(client.image('./image.jpg'))
+     >>> images.append(client.image('gs://my-storage-bucket/image2.jpg'))
+     >>> faces = client.detect_faces_multi(images, limit=10)
+
+No results returned
+~~~~~~~~~~~~~~~~~~~
+
+Failing annotations return no results for the feature type requested.
+
+.. code-block:: python
+
+    >>> from gcloud import vision
+    >>> client = vision.Client()
+    >>> image = client.image('./image.jpg')
+    >>> logos = image.detect_logos(limit=10)
+    >>> logos
+    []
+
+
+Manual Detection
+~~~~~~~~~~~~~~~~
+
+You can call the detection method manually.
+
+.. code-block:: python
+
+    >>> from gcloud import vision
+    >>> client = vision.Client()
+    >>> image = client.image('gs://my-test-bucket/image.jpg')
+    >>> faces = image.detect(type=vision.FACE_DETECTION, limit=10)
+
+Face Detection
+~~~~~~~~~~~~~~
+
+Detecting a face or faces in an image.
+For a list of the possible facial landmarks
+see: https://cloud.google.com/vision/reference/rest/v1/images/annotate#type_1
+
+
+.. code-block:: python
+
+    >>> from gcloud import vision
+    >>> client = vision.Client()
+    >>> image = client.image('./image.jpg')
+    >>> faces = image.detect_faces(limit=10)
+    >>> faces[0].landmarks[0].type
+    'LEFT_EYE'
+    >>> faces[0].landmarks[0].position.x
+    1301.2404
+    >>> faces[0].detection_confidence
+    0.9863683
+    >>> faces[0].joy_likelihood
+    0.54453093
+    >>> faces[0].anger_likelihood
+    0.02545464
+
+
+
+Label Detection
+~~~~~~~~~~~~~~~
+
+Image labels are a way to help categorize the contents of an image.
+If you have an image with a car, person and a dog it, label detection will
+attempt to identify those objects.
+
+.. code-block:: python
+
+    >>> from gcloud import vision
+    >>> client = vision.Client()
+    >>> image = client.image('./image.jpg')
+    >>> labels = image.detect_labels(limit=3)
+    >>> labels[0].description
+    'automobile'
+    >>> labels[0].score
+    0.9863683
+
+
+Landmark Detection
+~~~~~~~~~~~~~~~~~~
+
+The API will attemtp to detect landmarks such as Mount Rushmore and
+the Sydney Opera House. The API will also provide their known geographical
+locations if available.
+
+.. code-block:: python
+
+    >>> from gcloud import vision
+    >>> client = vision.Client()
+    >>> image = client.image('./image.jpg')
+    >>> landmarks = image.detect_landmarks()
+    >>> landmarks[0].description
+    'Sydney Opera House'
+    >>> landmarks[0].locations[0].latitude
+    -33.857123
+    >>> landmarks[0].locations[0].longitude
+    151.213921
+    >>> landmarks[0].bounding_poly.vertices[0].x
+    78
+    >>> landmarks[0].bounding_poly.vertices[0].y
+    162
+
+Logo Detection
+~~~~~~~~~~~~~~
+
+Google Vision can also attempt to detect company and brand logos in images.
+
+.. code-block:: python
+
+    >>> from gcloud import vision
+    >>> client = vision.Client()
+    >>> image = client.image('./image.jpg')
+    >>> logos = image.detect_logos(limit=1)
+    >>> results.logos[0].description
+    'Google'
+    >>> logos[0].score
+    0.9795432
+    >>> logos[0].bounding_poly.vertices[0].x
+    78
+    >>> logos[0].bounding_poly.vertices[0].y
+    62
+
+Safe Search Detection
+~~~~~~~~~~~~~~~~~~~~~
+
+Detecting safe search properties of an image.
+
+.. code-block:: python
+
+    >>> from gcloud import vision
+    >>> client = vision.Client()
+    >>> image = client.image('./image.jpg')
+    >>> safe_search = image.detect_safe_search()
+    >>> safe_search.adult
+    'VERY_UNLIKELY'
+    >>> safe_search.medical
+    'UNLIKELY'
+
+Text Detection
+~~~~~~~~~~~~~~
+
+Detecting text with ORC from an image.
+
+.. code-block:: python
+
+    >>> from gcloud import vision
+    >>> client = vision.Client()
+    >>> image = client.image('./image.jpg')
+    >>> text = image.detect_text()
+    >>> text.locale
+    'en'
+    >>> text.description
+    'the full text of the image.'
+
+Image Properties
+~~~~~~~~~~~~~~~~
+
+Detecting image color properties.
+
+.. code-block:: python
+
+    >>> from gcloud import vision
+    >>> client = vision.Client()
+    >>> image = client.image('./image.jpg')
+    >>> colors = image.detect_properties()
+    >>> colors[0].red
+    244
+    >>> colors[0].blue
+    134
+    >>> colors[0].score
+    0.65519291
+    >>> colors[0].pixel_fraction
+    0.758658
