Vision cloud client snippets

translate/cloud-client/snippets.py

@@ -63,7 +63,7 @@ def list_languages_with_target(target):
         print(u'{name} ({language})'.format(**language))
 
 
-def translate_text_with_model(target, text, model=translate.BASE):
+def translate_text_with_model(target, text, model=translate.NMT):
     """Translates text into the target language.
 
     Make sure your project is whitelisted.

vision/cloud-client/snippets.py

@@ -0,0 +1,339 @@
+#!/usr/bin/env python
+
+# 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.
+
+import io
+import os
+
+# Imports the Google Cloud client library
+from google.cloud import vision
+
+
+def detect_faces(path):
+    # Instantiates a client
+    vision_client = vision.Client()
+
+    # Loads the image into memory
+    with io.open(path, 'rb') as image_file:
+        content = image_file.read()
+        image = vision_client.image(
+            content=content)
+
+    # Performs face detection on the image file
+    faces = image.detect_faces()
+
+    print 'Faces:'
+    for face in faces:
+        print 'anger', face.emotions.anger
+        print 'joy', face.emotions.joy
+        print 'surprise', face.emotions.surprise
+    print
+
+
+def detect_faces_gcs(path):
+    # Instantiates a client
+    vision_client = vision.Client()
+    image = vision_client.image(source_uri=path)
+
+    # Performs face detection on the image file
+    faces = image.detect_faces()
+
+    print 'Faces:'
+    for face in faces:
+        print 'anger', face.emotions.anger
+        print 'joy', face.emotions.joy
+        print 'surprise', face.emotions.surprise
+    print
+
+
+def detect_labels(path):
+    # Instantiates a client
+    vision_client = vision.Client()
+
+    # Loads the image into memory
+    with io.open(path, 'rb') as image_file:
+        content = image_file.read()
+        image = vision_client.image(
+            content=content)
+
+    # Performs label detection on the image file
+    labels = image.detect_labels()
+
+    print 'Labels:'
+    for label in labels:
+        print label.description
+    print
+
+
+def detect_labels_gcs(path):
+    # Instantiates a client
+    vision_client = vision.Client()
+    image = vision_client.image(source_uri=path)
+
+    # Performs label detection on the image file
+    labels = image.detect_labels()
+
+    print 'Labels:'
+    for label in labels:
+        print label.description
+    print
+
+
+def detect_landmarks(path):
+    # Instantiates a client
+    vision_client = vision.Client()
+
+    # Loads the image into memory
+    with io.open(path, 'rb') as image_file:
+        content = image_file.read()
+        image = vision_client.image(
+            content=content)
+
+    # Performs label detection on the image file
+    landmarks = image.detect_landmarks()
+
+    print 'Landmarks:'
+    for landmark in landmarks:
+        print landmark.description
+    print
+
+
+def detect_landmarks_gcs(path):
+    # Instantiates a client
+    vision_client = vision.Client()
+    image = vision_client.image(source_uri=path)
+
+    # Performs label detection on the image file
+    landmarks = image.detect_landmarks()
+
+    print 'Landmarks:'
+    for landmark in landmarks:
+        print landmark.description
+    print
+
+
+def detect_logos(path):
+    # Instantiates a client
+    vision_client = vision.Client()
+
+    # Loads the image into memory
+    with io.open(path, 'rb') as image_file:
+        content = image_file.read()
+        image = vision_client.image(content=content)
+
+    # Performs label detection on the image file
+    logos = image.detect_logos()
+
+    print 'Logos:'
+    for logo in logos:
+        print logo.description
+    print
+
+
+def detect_logos_gcs(path):
+    # Instantiates a client
+    vision_client = vision.Client()
+    image = vision_client.image(source_uri=path)
+
+    # Performs label detection on the image file
+    logos = image.detect_logos()
+
+    print 'Logos:'
+    for logo in logos:
+        print logo.description
+    print
+
+
+def detect_safe_search(path):
+    # Instantiates a client
+    vision_client = vision.Client()
+
+    # Loads the image into memory
+    with io.open(path, 'rb') as image_file:
+        content = image_file.read()
+        image = vision_client.image(
+            content=content)
+
+    # Performs label detection on the image file
+    safe_searches = image.detect_safe_search()
+    print 'Safe search:'
+    for safe in safe_searches:
+        print 'adult', safe.adult
+        print 'medical', safe.medical
+        print 'spoofed', safe.spoof
+        print 'violence', safe.violence
+    print
+
+
+def detect_safe_search_gcs(path):
+    # Instantiates a client
+    vision_client = vision.Client()
+    image = vision_client.image(source_uri=path)
+
+    # Performs label detection on the image file
+    safe_searches = image.detect_safe_search()
+    print 'Safe search:'
+    for safe in safe_searches:
+        print 'adult', safe.adult
+        print 'medical', safe.medical
+        print 'spoofed', safe.spoof
+        print 'violence', safe.violence
+    print
+
+
+def detect_text(path):
+    # Instantiates a client
+    vision_client = vision.Client()
+
+    # Loads the image into memory
+    with io.open(path, 'rb') as image_file:
+        content = image_file.read()
+        image = vision_client.image(
+            content=content)
+
+    # Performs label detection on the image file
+    texts = image.detect_text()
+    print 'Texts:'
+    for text in texts:
+        print text.description
+    print
+
+
+def detect_text_gcs(path):
+    # Instantiates a client
+    vision_client = vision.Client()
+    image = vision_client.image(source_uri=path)
+
+    # Performs label detection on the image file
+    texts = image.detect_text()
+    print 'Texts:'
+    for text in texts:
+        print text.description
+    print
+
+
+def detect_properties(path):
+    # Instantiates a client
+    vision_client = vision.Client()
+
+    # Loads the image into memory
+    with io.open(path, 'rb') as image_file:
+        content = image_file.read()
+        image = vision_client.image(
+            content=content)
+
+    # Performs label detection on the image file
+    properties = image.detect_properties()
+    print 'Properties:'
+    for prop in properties:
+        color = prop.colors[0]
+        print 'fraction: ', color.pixel_fraction
+        print 'r: ', color.color.red
+        print 'g: ', color.color.green
+        print 'b: ', color.color.blue
+    print
+
+
+def detect_properties_gcs(path):
+    # Instantiates a client
+    vision_client = vision.Client()
+    image = vision_client.image(source_uri=path)
+
+    # Performs label detection on the image file
+    properties = image.detect_properties()
+    print 'Properties:'
+    for prop in properties:
+        color = prop.colors[0]
+        print 'fraction: ', color.pixel_fraction
+        print 'r: ', color.color.red
+        print 'g: ', color.color.green
+        print 'b: ', color.color.blue
+    print
+
+
+def run_snippets():
+    # Detect labels from local file
+    file_name = os.path.join(
+        os.path.dirname(__file__),
+        'resources/wakeupcat.jpg')
+    detect_labels(file_name)
+
+    # Detect labels from Google Storage bucket
+    file_name = 'gs://cloud-samples-tests/vision/wakeupcat.jpg'
+    detect_labels_gcs(file_name)
+
+    # Detect a landmark in local file
+    file_name = os.path.join(
+        os.path.dirname(__file__),
+        'resources/landmark.jpg')
+    detect_landmarks(file_name)
+
+    # Detect a landmark in GS bucket
+    file_name = 'gs://cloud-samples-tests/vision/landmark.jpg'
+    detect_landmarks_gcs(file_name)
+
+    # Detect a face in local file
+    file_name = os.path.join(
+        os.path.dirname(__file__),
+        'resources/face_no_surprise.jpg')
+    detect_faces(file_name)
+
+    # Detect a face in GS bucket
+    file_name = 'gs://cloud-samples-tests/vision/face_no_surprise.jpg'
+    detect_faces_gcs(file_name)
+
+    # Detect a logo in local file
+    file_name = os.path.join(
+        os.path.dirname(__file__),
+        'resources/logos.png')
+    detect_logos(file_name)
+
+    # Detect a logo in GS bucket
+    file_name = 'gs://cloud-samples-tests/vision/logos.png'
+    detect_logos_gcs(file_name)
+
+    # Detect safe search in local file
+    file_name = os.path.join(
+        os.path.dirname(__file__),
+        'resources/wakeupcat.jpg')
+    detect_safe_search(file_name)
+
+    # Detect safe search in GS bucket
+    file_name = 'gs://cloud-samples-tests/vision/wakeupcat.jpg'
+    detect_safe_search_gcs(file_name)
+
+    # Detect text in local file
+    file_name = os.path.join(
+        os.path.dirname(__file__),
+        'resources/text.jpg')
+    detect_text(file_name)
+
+    # Detect text in GS bucket
+    file_name = 'gs://cloud-samples-tests/vision/text.jpg'
+    detect_text_gcs(file_name)
+
+    # Detect properties in local file
+    file_name = os.path.join(
+        os.path.dirname(__file__),
+        'resources/landmark.jpg')
+    detect_properties(file_name)
+
+    # Detect properties in GS bucket
+    file_name = 'gs://cloud-samples-tests/vision/landmark.jpg'
+    detect_properties_gcs(file_name)
+
+
+if __name__ == '__main__':
+    run_snippets()